VIS and NIR land surface albedo sensitivity of the Ent Terrestrial Biosphere Model to forcing leaf area index

NASA Astrophysics Data System (ADS)

Montes, C.; Kiang, N. Y.; Ni-Meister, W.; Yang, W.; Schaaf, C.; Aleinov, I. D.; Jonas, J.; Zhao, F. A.; Yao, T.; Wang, Z.; Sun, Q.; Carrer, D.

2016-12-01

Land surface albedo is a major controlling factor in vegetation-atmosphere transfers, modifying the components of the energy budget, the ecosystem productivity and patterns of regional and global climate. General Circulation Models (GCMs) are coupled to Dynamic Global Vegetation Models (DGVMs) to solve vegetation albedo by using simple schemes prescribing albedo based on vegetation classification, and approximations of canopy radiation transport for multiple plant functional types (PFTs). In this work, we aim at evaluating the sensitivity of the NASA Ent Terrestrial Biosphere Model (TBM), a demographic DGVM coupled to the NASA Goddard Institute for Space Studies (GISS) GCM, in estimating VIS and NIR surface albedo by using variable forcing leaf area index (LAI). The Ent TBM utilizes a new Global Vegetation Structure Dataset (GVSD) to account for geographically varying vegetation tree heights and densities, as boundary conditions to the gap-probability based Analytical Clumped Two-Stream (ACTS) canopy radiative transfer scheme (Ni-Meister et al., 2010). Land surface and vegetation characteristics for the Ent GVSD are obtained from a number of earth observation platforms and algorithms, including the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover and plant functional types (PFTs) (Friedl et al., 2010), soil albedo derived from MODIS (Carrer et al., 2014), and vegetation height from the Geoscience Laser Altimeter System (GLAS) on board ICESat (Ice, Cloud, and land Elevation Satellite) (Simard et al., 2011; Tang et al., 2014). Three LAI products are used as input to ACTS/Ent TBM: MODIS MOD15A2H product (Yang et al., 2006), Beijing Normal University LAI (Yuan et al., 2011), and Global Data Sets of Vegetation (LAI3g) (Zhu et al. 2013). The sensitivity of the Ent TBM VIS and NIR albedo to the three LAI products is assessed, compared against the previous GISS GCM vegetation classification and prescribed Lambertian albedoes (Matthews, 1984), and against MODIS snow-free black-sky and white-sky albedo estimates. In addition, we test the sensitivity of the Ent/ACTS albedo to different sets of leaf spectral albedos derived from the literature.

Constraints on High Northern Photosynthesis Increase Using Earth System Models and a Set of Independent Observations

NASA Astrophysics Data System (ADS)

Winkler, A. J.; Brovkin, V.; Myneni, R.; Alexandrov, G.

2017-12-01

Plant growth in the northern high latitudes benefits from increasing temperature (radiative effect) and CO2 fertilization as a consequence of rising atmospheric CO2 concentration. This enhanced gross primary production (GPP) is evident in large scale increase in summer time greening over the 36-year record of satellite observations. In this time period also various global ecosystem models simulate a greening trend in terms of increasing leaf area index (LAI). We also found a persistent greening trend analyzing historical simulations of Earth system models (ESM) participating in Phase 5 of the Coupled Model Intercomparison Project (CMIP5). However, these models span a large range in strength of the LAI trend, expressed as sensitivity to both key environmental factors, temperature and CO2 concentration. There is also a wide spread in magnitude of the associated increase of terrestrial GPP among the ESMs, which contributes to pronounced uncertainties in projections of future climate change. Here we demonstrate that there is a linear relationship across the CMIP5 model ensemble between projected GPP changes and historical LAI sensitivity, which allows using the observed LAI sensitivity as an "emerging constraint" on GPP estimation at future CO2 concentration. This constrained estimate of future GPP is substantially higher than the traditional multi-model mean suggesting that the majority of current ESMs may be significantly underestimating carbon fixation by vegetation in NHL. We provide three independent lines of evidence in analyzing observed and simulated CO2 amplitude as well as atmospheric CO2 inversion products to arrive at the same conclusion.

Climate mitigation from vegetation biophysical feedbacks during the past three decades

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zeng, Zhenzhong; Piao, Shilong; Li, Laurent Z. X.

The surface air temperature response to vegetation changes has been studied for the extreme case of land-cover change; yet, it has never been quantified for the slow but persistent increase in leaf area index (LAI) observed over the past 30 years (Earth greening). We isolate the fingerprint of increasing LAI on surface air temperature using a coupled land–atmosphere global climate model prescribed with satellite LAI observations. Furthermore, we found that the global greening has slowed down the rise in global land-surface air temperature by 0.09 ± 0.02 °C since 1982. This net cooling effect is the sum of cooling frommore » increased evapotranspiration (70%), changed atmospheric circulation (44%), decreased shortwave transmissivity (21%), and warming from increased longwave air emissivity (-29%) and decreased albedo (-6%). The global cooling originated from the regions where LAI has increased, including boreal Eurasia, Europe, India, northwest Amazonia, and the Sahel. Increasing LAI did not, but, significantly change surface air temperature in eastern North America and East Asia, where the effects of large-scale atmospheric circulation changes mask local vegetation feedbacks. Overall, the sum of biophysical feedbacks related to the greening of the Earth mitigated 12% of global land-surface warming for the past 30 years.« less

The Change of Climate and Terrestrial Carbon Cycle over Tibetan Plateau in CMIP5 Models

NASA Astrophysics Data System (ADS)

Li, S.

2015-12-01

Six earth system models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are evaluated over Tibetan Plateau (TP) by comparing the modeled temperature (Tas), precipitation (Pr), net primary production (NPP) and leaf area index (LAI) with the observed Tas, Pr, IGBP NPP and MPIM LAI in the historical, and then we analyzed the change of climate and carbon cycle and explored the relationship between the carbon cycle and main climatic drivers in the historical and representative concentration pathway 4.5 (RCP4.5) simulation over TP. While model results differ, their region spatial distributions from 1971 to 2000 agree reasonably with observed Tas, Pr and proxy LAI and NPP. The climatic variables, LAI and carbon flux vary between two simulations, the ration of NPP to gross primary production (GPP) does not change much in the historical and RCP4.5 scenarios. The linear trends of LAI and carbon flux show an obvious continuous increase from historical climatic period (1971-2000) to the first two climatic periods (2011-2040; 2041-2700) of RCP4.5, then the trends decrease in the third climatic period (2071-2100) of RCP4.5. The cumulative multi model ensemble (MME) net biome production (NBP) is 0.32 kgCm-2yr-1 during 1850 to 2005 and 1.43 kgCm-2yr-1 during 2006 to 2100, the Tibetan Plateau is a carbon sink during the historical scenario, and TP will uptake more carbon from atmosphere during 2006 to 2100 than 1850 to 2005 under RCP4.5 scenario. LAI, GPP, NPP, Ra and Rh appear more related to the Tas than Pr and Rsds, and the Tas is the primary climatic driver for the plant growth and carbon cycle. With the climate change in twenty-first century under RCP4.5 scenario, Tas still is the primary climate driver for the plant growth and carbon cycle, but the effect of temperature on plant growth and carbon cycle gets weaker.

On the Performance of Alternate Conceptual Ecohydrological Models for Streamflow Prediction

NASA Astrophysics Data System (ADS)

Naseem, Bushra; Ajami, Hoori; Cordery, Ian; Sharma, Ashish

2016-04-01

A merging of a lumped conceptual hydrological model with two conceptual dynamic vegetation models is presented to assess the performance of these models for simultaneous simulations of streamflow and leaf area index (LAI). Two conceptual dynamic vegetation models with differing representation of ecological processes are merged with a lumped conceptual hydrological model (HYMOD) to predict catchment scale streamflow and LAI. The merged RR-LAI-I model computes relative leaf biomass based on transpiration rates while the RR-LAI-II model computes above ground green and dead biomass based on net primary productivity and water use efficiency in response to soil moisture dynamics. To assess the performance of these models, daily discharge and 8-day MODIS LAI product for 27 catchments of 90 - 1600km2 in size located in the Murray - Darling Basin in Australia are used. Our results illustrate that when single-objective optimisation was focussed on maximizing the objective function for streamflow or LAI, the other un-calibrated predicted outcome (LAI if streamflow is the focus) was consistently compromised. Thus, single-objective optimization cannot take into account the essence of all processes in the conceptual ecohydrological models. However, multi-objective optimisation showed great strength for streamflow and LAI predictions. Both response outputs were better simulated by RR-LAI-II than RR-LAI-I due to better representation of physical processes such as net primary productivity (NPP) in RR-LAI-II. Our results highlight that simultaneous calibration of streamflow and LAI using a multi-objective algorithm proves to be an attractive tool for improved streamflow predictions.

Linking in situ LAI and fine resolution remote sensing data to map reference LAI over cropland and grassland using geostatistical regression method

NASA Astrophysics Data System (ADS)

He, Yaqian; Bo, Yanchen; Chai, Leilei; Liu, Xiaolong; Li, Aihua

2016-08-01

Leaf Area Index (LAI) is an important parameter of vegetation structure. A number of moderate resolution LAI products have been produced in urgent need of large scale vegetation monitoring. High resolution LAI reference maps are necessary to validate these LAI products. This study used a geostatistical regression (GR) method to estimate LAI reference maps by linking in situ LAI and Landsat TM/ETM+ and SPOT-HRV data over two cropland and two grassland sites. To explore the discrepancies of employing different vegetation indices (VIs) on estimating LAI reference maps, this study established the GR models for different VIs, including difference vegetation index (DVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI). To further assess the performance of the GR model, the results from the GR and Reduced Major Axis (RMA) models were compared. The results show that the performance of the GR model varies between the cropland and grassland sites. At the cropland sites, the GR model based on DVI provides the best estimation, while at the grassland sites, the GR model based on DVI performs poorly. Compared to the RMA model, the GR model improves the accuracy of reference LAI maps in terms of root mean square errors (RMSE) and bias.

Modeling the effect of photosynthetic vegetation properties on the NDVI--LAI relationship.

PubMed

Steltzer, Heidi; Welker, Jeffrey M

2006-11-01

Developing a relationship between the normalized difference vegetation index (NDVI) and the leaf area index (LAI) is essential to describe the pattern of spatial or temporal variation in LAI that controls carbon, water, and energy exchange in many ecosystem process models. Photosynthetic vegetation (PV) properties can affect the estimation of LAI, but no models integrate the effects of multiple species. We developed four alternative NDVI-LAI models, three of which integrate PV effects: no PV effects, leaf-level effects, canopy-level effects, and effects at both levels. The models were fit to data across the natural range of variation in NDVI for a widespread High Arctic ecosystem. The weight of evidence supported the canopy-level model (Akaike weight, wr = 0.98), which includes species-specific canopy coefficients that primarily scale fractional PV cover to LAI by accounting for the area of unexposed PV. Modeling the canopy-level effects improved prediction of LAI (R2 = 0.82) over the model with no PV effect (R2 = 0.71) across the natural range of variation in NDVI but did not affect the site-level estimate of LAI. Satellite-based methods to estimate species composition, a variable in the model, will need to be developed. We expect that including the effects of PV properties in NDVI-LAI models will improve prediction of LAI where species composition varies across space or changes over time.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Guiling; Yu, Miao; Pal, Jeremy

This paper presents a regional climate system model RCM-CLM-CN-DV and its validation over Tropical Africa. The model development involves the initial coupling between the ICTP regional climate model RegCM4.3.4 (RCM) and the Community Land Model version 4 (CLM4) including models of carbon-nitrogen dynamics (CN) and vegetation dynamics (DV), and further improvements of the models. Model improvements derive from the new parameterization from CLM4.5 that addresses the well documented overestimation of gross primary production (GPP), a refinement of stress deciduous phenology scheme in CN that addresses a spurious LAI fluctuation for drought-deciduous plants, and the incorporation of a survival rule intomore » the DV model to prevent tropical broadleaf evergreens trees from growing in areas with a prolonged drought season. The impact of the modifications on model results is documented based on numerical experiments using various subcomponents of the model. The performance of the coupled model is then validated against observational data based on three configurations with increasing capacity: RCM-CLM with prescribed leaf area index and fractional coverage of different plant functional types (PFTs); RCM-CLM-CN with prescribed PFTs coverage but prognostic plant phenology; RCM-CLM-CN-DV in which both the plant phenology and PFTs coverage are simulated by the model. Results from these three models are compared against the FLUXNET up-scaled GPP and ET data, LAI and PFT coverages from remote sensing data including MODIS and GIMMS, University of Delaware precipitation and temperature data, and surface radiation data from MVIRI and SRB. Our results indicate that the models perform well in reproducing the physical climate and surface radiative budgets in the domain of interest. However, PFTs coverage is significantly underestimated by the model over arid and semi-arid regions of Tropical Africa, caused by an underestimation of LAI in these regions by the CN model that gets exacerbated through vegetation dynamics in RCM-CLM-CN-DV.« less

A photosynthesis-based two-leaf canopy stomatal ...

EPA Pesticide Factsheets

A coupled photosynthesis-stomatal conductance model with single-layer sunlit and shaded leaf canopy scaling is implemented and evaluated in a diagnostic box model with the Pleim-Xiu land surface model (PX LSM) and ozone deposition model components taken directly from the meteorology and air quality modeling system—WRF/CMAQ (Weather Research and Forecast model and Community Multiscale Air Quality model). The photosynthesis-based model for PX LSM (PX PSN) is evaluated at a FLUXNET site for implementation against different parameterizations and the current PX LSM approach with a simple Jarvis function (PX Jarvis). Latent heat flux (LH) from PX PSN is further evaluated at five FLUXNET sites with different vegetation types and landscape characteristics. Simulated ozone deposition and flux from PX PSN are evaluated at one of the sites with ozone flux measurements. Overall, the PX PSN simulates LH as well as the PX Jarvis approach. The PX PSN, however, shows distinct advantages over the PX Jarvis approach for grassland that likely result from its treatment of C3 and C4 plants for CO2 assimilation. Simulations using Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) rather than LAI measured at each site assess how the model would perform with grid averaged data used in WRF/CMAQ. MODIS LAI estimates degrade model performance at all sites but one site having exceptionally old and tall trees. Ozone deposition velocity and ozone flux along with LH

Projections of leaf area index in earth system models

NASA Astrophysics Data System (ADS)

Mahowald, Natalie; Lo, Fiona; Zheng, Yun; Harrison, Laura; Funk, Chris; Lombardozzi, Danica; Goodale, Christine

2016-03-01

The area of leaves in the plant canopy, measured as leaf area index (LAI), modulates key land-atmosphere interactions, including the exchange of energy, moisture, carbon dioxide (CO2), and other trace gases and aerosols, and is therefore an essential variable in predicting terrestrial carbon, water, and energy fluxes. Here our goal is to characterize the LAI projections from the latest generation of earth system models (ESMs) for the Representative Concentration Pathway (RCP) 8.5 and RCP4.5 scenarios. On average, the models project increases in LAI in both RCP8.5 and RCP4.5 over most of the globe, but also show decreases in some parts of the tropics. Because of projected increases in variability, there are also more frequent periods of low LAI across broad regions of the tropics. Projections of LAI changes varied greatly among models: some models project very modest changes, while others project large changes, usually increases. Modeled LAI typically increases with modeled warming in the high latitudes, but often decreases with increasing local warming in the tropics. The models with the most skill in simulating current LAI in the tropics relative to satellite observations tend to project smaller increases in LAI in the tropics in the future compared to the average of all the models. Using LAI projections to identify regions that may be vulnerable to climate change presents a slightly different picture than using precipitation projections, suggesting LAI may be an additional useful tool for understanding climate change impacts. Going forward, users of LAI projections from the CMIP5 ESMs evaluated here should be aware that model outputs do not exhibit clear-cut relationships to vegetation carbon and precipitation. Our findings underscore the need for more attention to LAI projections, in terms of understanding the drivers of projected changes and improvements to model skill.

Projections of leaf area index in earth system models

DOE PAGES

Mahowald, Natalie; Lo, Fiona; Zheng, Yun; ...

2016-03-09

The area of leaves in the plant canopy, measured as leaf area index (LAI), modulates key land–atmosphere interactions, including the exchange of energy, moisture, carbon dioxide (CO 2), and other trace gases and aerosols, and is therefore an essential variable in predicting terrestrial carbon, water, and energy fluxes. Here our goal is to characterize the LAI projections from the latest generation of earth system models (ESMs) for the Representative Concentration Pathway (RCP) 8.5 and RCP4.5 scenarios. On average, the models project increases in LAI in both RCP8.5 and RCP4.5 over most of the globe, but also show decreases in somemore » parts of the tropics. Because of projected increases in variability, there are also more frequent periods of low LAI across broad regions of the tropics. Projections of LAI changes varied greatly among models: some models project very modest changes, while others project large changes, usually increases. Modeled LAI typically increases with modeled warming in the high latitudes, but often decreases with increasing local warming in the tropics. The models with the most skill in simulating current LAI in the tropics relative to satellite observations tend to project smaller increases in LAI in the tropics in the future compared to the average of all the models. Using LAI projections to identify regions that may be vulnerable to climate change presents a slightly different picture than using precipitation projections, suggesting LAI may be an additional useful tool for understanding climate change impacts. Going forward, users of LAI projections from the CMIP5 ESMs evaluated here should be aware that model outputs do not exhibit clear-cut relationships to vegetation carbon and precipitation. Lastly, our findings underscore the need for more attention to LAI projections, in terms of understanding the drivers of projected changes and improvements to model skill.« less

Leaf and fine root carbon stocks and turnover are coupled across Arctic ecosystems.

PubMed

Sloan, Victoria L; Fletcher, Benjamin J; Press, Malcolm C; Williams, Mathew; Phoenix, Gareth K

2013-12-01

Estimates of vegetation carbon pools and their turnover rates are central to understanding and modelling ecosystem responses to climate change and their feedbacks to climate. In the Arctic, a region containing globally important stores of soil carbon, and where the most rapid climate change is expected over the coming century, plant communities have on average sixfold more biomass below ground than above ground, but knowledge of the root carbon pool sizes and turnover rates is limited. Here, we show that across eight plant communities, there is a significant positive relationship between leaf and fine root turnover rates (r(2) = 0.68, P < 0.05), and that the turnover rates of both leaf (r(2) = 0.63, P < 0.05) and fine root (r(2) = 0.55, P < 0.05) pools are strongly correlated with leaf area index (LAI, leaf area per unit ground area). This coupling of root and leaf dynamics supports the theory of a whole-plant economics spectrum. We also show that the size of the fine root carbon pool initially increases linearly with increasing LAI, and then levels off at LAI = 1 m(2) m(-2), suggesting a functional balance between investment in leaves and fine roots at the whole community scale. These ecological relationships not only demonstrate close links between above and below-ground plant carbon dynamics but also allow plant carbon pool sizes and their turnover rates to be predicted from the single readily quantifiable (and remotely sensed) parameter of LAI, including the possibility of estimating root data from satellites. © 2013 John Wiley & Sons Ltd.

Land-atmosphere coupling and climate prediction over the U.S. Southern Great Plains

NASA Astrophysics Data System (ADS)

Williams, I. N.; Lu, Y.; Kueppers, L. M.; Riley, W. J.; Biraud, S.; Bagley, J. E.; Torn, M. S.

2016-12-01

Biases in land-atmosphere coupling in climate models can contribute to climate prediction biases, but land models are rarely evaluated in the context of this coupling. We tested land-atmosphere coupling and explored effects of land surface parameterizations on climate prediction in a single-column version of the NCAR Community Earth System Model (CESM1.2.2) and an offline Community Land Model (CLM4.5). The correlation between leaf area index (LAI) and surface evaporative fraction (ratio of latent to total turbulent heat flux) was substantially underpredicted compared to observations in the U.S. Southern Great Plains, while the correlation between soil moisture and evaporative fraction was overpredicted by CLM4.5. These correlations were improved by prescribing observed LAI, increasing soil resistance to evaporation, increasing minimum stomatal conductance, and increasing leaf reflectance. The modifications reduced the root mean squared error (RMSE) in daytime 2 m air temperature from 3.6 C to 2 C in summer (JJA), and reduced RMSE in total JJA precipitation from 133 to 84 mm. The modifications had the largest effect on prediction of summer drought in 2006, when a warm bias in daytime 2 m air temperature was reduced from +6 C to a smaller cold bias of -1.3 C, and a corresponding dry bias in total JJA precipitation was reduced from -111 mm to -23 mm. Thus, the role of vegetation in droughts and heat waves is likely underpredicted in CESM1.2.2, and improvements in land surface models can improve prediction of climate extremes.

Applying the concept of ecohydrological equilibrium to predict steady-state leaf area index for Australian ecosystems

NASA Astrophysics Data System (ADS)

Yang, J.; Medlyn, B.; De Kauwe, M. G.; Duursma, R.

2017-12-01

Leaf Area Index (LAI) is a key variable in modelling terrestrial vegetation, because it has a major impact on carbon, water and energy fluxes. However, LAI is difficult to predict: several recent intercomparisons have shown that modelled LAI differs significantly among models, and between models and satellite-derived estimates. Empirical studies show that long-term mean LAI is strongly related to mean annual precipitation. This observation is predicted by the theory of ecohydrological equilibrium, which provides a promising alternative means to predict steady-state LAI. We implemented this theory in a simple optimisation model. We hypothesized that, when water availability is limited, plants should adjust long-term LAI and stomatal behavior (g1) to maximize net canopy carbon export, under the constraint that canopy transpiration is a fixed fraction of total precipitation. We evaluated the predicted LAI (Lopt) for Australia against ground-based observations of LAI at 135 sites, and continental-scale satellite-derived estimates. For the site-level data, the RMSE of predicted Lopt was 0.14 m2 m-2, which was similar to the RMSE of a comparison of the data against nine-year mean satellite-derived LAI at those sites. Continentally, Lopt had a R2 of over 70% when compared to satellite-derived LAI, which is comparable to the R2 obtained when different satellite products are compared against each other. The predicted response of Lopt to the increase in atmospheric CO2 over the last 30 years also agreed with the estimate based on satellite-derivatives. Our results indicate that long-term equilibrium LAI can be successfully predicted from a simple application of ecohydrological theory. We suggest that this theory could be usefully incorporated into terrestrial vegetation models to improve their predictions of LAI.

EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) ...

EPA Pesticide Factsheets

Leaf Area Index (LAI) is an important parameter in assessing vegetation structure for characterizing forest canopies over large areas at broad spatial scales using satellite remote sensing data. However, satellite-derived LAI products can be limited by obstructed atmospheric conditions yielding sub-optimal values, or complete non-returns. The United States Environmental Protection Agency’s Exposure Methods and Measurements and Computational Exposure Divisions are investigating the viability of supplemental modelled LAI inputs into satellite-derived data streams to support various regional and local scale air quality models for retrospective and future climate assessments. In this present study, one-year (2002) of plot level stand characteristics at four study sites located in Virginia and North Carolina are used to calibrate species-specific plant parameters in a semi-empirical biogeochemical model. The Environmental Policy Integrated Climate (EPIC) model was designed primarily for managed agricultural field crop ecosystems, but also includes managed woody species that span both xeric and mesic sites (e.g., mesquite, pine, oak, etc.). LAI was simulated using EPIC at a 4 km2 and 12 km2 grid coincident with the regional Community Multiscale Air Quality Model (CMAQ) grid. LAI comparisons were made between model-simulated and MODIS-derived LAI. Field/satellite-upscaled LAI was also compared to the corresponding MODIS LAI value. Preliminary results show field/satel

Forest canopy growth dynamic modeling based on remote sensing prodcuts and meteorological data in Daxing'anling of Northeast China

NASA Astrophysics Data System (ADS)

Wu, Qiaoli; Song, Jinling; Wang, Jindi; Xiao, Zhiqiang

2014-11-01

Leaf Area Index (LAI) is an important biophysical variable for vegetation. Compared with vegetation indexes like NDVI and EVI, LAI is more capable of monitoring forest canopy growth quantitatively. GLASS LAI is a spatially complete and temporally continuous product derived from AVHRR and MODIS reflectance data. In this paper, we present the approach to build dynamic LAI growth models for young and mature Larix gmelinii forest in north Daxing'anling in Inner Mongolia of China using the Dynamic Harmonic Regression (DHR) model and Double Logistic (D-L) model respectively, based on the time series extracted from multi-temporal GLASS LAI data. Meanwhile we used the dynamic threshold method to attract the key phenological phases of Larix gmelinii forest from the simulated time series. Then, through the relationship analysis between phenological phases and the meteorological factors, we found that the annual peak LAI and the annual maximum temperature have a good correlation coefficient. The results indicate this forest canopy growth dynamic model to be very effective in predicting forest canopy LAI growth and extracting forest canopy LAI growth dynamic.

[Simulating of carbon fluxes in bamboo forest ecosystem using BEPS model based on the LAI assimilated with Dual Ensemble Kalman Filter].

PubMed

Li, Xue Jian; Mao, Fang Jie; Du, Hua Qiang; Zhou, Guo Mo; Xu, Xiao Jun; Li, Ping Heng; Liu, Yu Li; Cui, Lu

2016-12-01

LAI is one of the most important observation data in the research of carbon cycle of forest ecosystem, and it is also an important parameter to drive process-based ecosystem model. The Moso bamboo forest (MBF) and Lei bamboo forest (LBF) were selected as the study targets. Firstly, the MODIS LAI time series data during 2014-2015 was assimilated with Dual Ensemble Kalman Filter method. Secondly, the high quality assimilated MBF LAI and LBF LAI were used as input dataset to drive BEPS model for simulating the gross primary productivity (GPP), net ecosystem exchange (NEE) and total ecosystem respiration (TER) of the two types of bamboo forest ecosystem, respectively. The modeled carbon fluxes were evaluated by the observed carbon fluxes data, and the effects of different quality LAI inputs on carbon cycle simulation were also studied. The LAI assimilated using Dual Ensemble Kalman Filter of MBF and LBF were significantly correlated with the observed LAI, with high R 2 of 0.81 and 0.91 respectively, and lower RMSE and absolute bias, which represented the great improvement of the accuracy of MODIS LAI products. With the driving of assimilated LAI, the modeled GPP, NEE, and TER were also highly correlated with the flux observation data, with the R 2 of 0.66, 0.47, and 0.64 for MBF, respectively, and 0.66, 0.45, and 0.73 for LBF, respectively. The accuracy of carbon fluxes modeled with assimilated LAI was higher than that acquired by the locally adjusted cubic-spline capping method, in which, the accuracy of mo-deled NEE for MBF and LBF increased by 11.2% and 11.8% at the most degrees, respectively.

Determining the K coefficient to leaf area index estimations in a tropical dry forest

NASA Astrophysics Data System (ADS)

Magalhães, Sarah Freitas; Calvo-Rodriguez, Sofia; do Espírito Santo, Mário Marcos; Sánchez Azofeifa, Gerardo Arturo

2018-03-01

Vegetation indices are useful tools to remotely estimate several important parameters related to ecosystem functioning. However, improving and validating estimations for a wide range of vegetation types are necessary. In this study, we provide a methodology for the estimation of the leaf area index (LAI) in a tropical dry forest (TDF) using the light diffusion through the canopy as a function of the successional stage. For this purpose, we estimated the K coefficient, a parameter that relates the normalized difference vegetation index (NDVI) to LAI, based on photosynthetically active radiation (PAR) and solar radiation. The study was conducted in the Mata Seca State Park, in southeastern Brazil, from 2012 to 2013. We defined four successional stages (very early, early, intermediate, and late) and established one optical phenology tower at one plot of 20 × 20 m per stage. Towers measured the incoming and reflected solar radiation and PAR for NDVI calculation. For each plot, we established 24 points for LAI sampling through hemispherical photographs. Because leaf cover is highly seasonal in TDFs, we determined ΔK (leaf growth phase) and K max (leaf maturity phase). We detected a strong correlation between NDVI and LAI, which is necessary for a reliable determination of the K coefficient. Both NDVI and LAI varied significantly between successional stages, indicating sensitivity to structural changes in forest regeneration. Furthermore, the K values differed between successional stages and correlated significantly with other environmental variables such as air temperature and humidity, fraction of absorbed PAR, and soil moisture. Thus, we established a model based on spectral properties of the vegetation coupled with biophysical characteristics in a TDF that makes possible to estimate LAI from NDVI values. The application of the K coefficient can improve remote estimations of forest primary productivity and gases and energy exchanges between vegetation and atmosphere. This model can be applied to distinguish different successional stages of TDFs, supporting environmental monitoring and conservation policies towards this biome.

A LAI inversion algorithm based on the unified model of canopy bidirectional reflectance distribution function for the Heihe River Basin

NASA Astrophysics Data System (ADS)

Ma, B.; Li, J.; Fan, W.; Ren, H.; Xu, X.

2017-12-01

Leaf area index (LAI) is one of the important parameters of vegetation canopy structure, which can represent the growth condition of vegetation effectively. The accuracy, availability and timeliness of LAI data can be improved greatly, which is of great importance to vegetation-related research, such as the study of atmospheric, land surface and hydrological processes to obtain LAI by remote sensing method. Heihe River Basin is the inland river basin in northwest China. There are various types of vegetation and all kinds of terrain conditions in the basin, so it is helpful for testing the accuracy of the model under the complex surface and evaluating the correctness of the model to study LAI in this area. On the other hand, located in west arid area of China, the ecological environment of Heihe Basin is fragile, LAI is an important parameter to represent the vegetation growth condition, and can help us understand the status of vegetation in the Heihe River Basin. Different from the previous LAI inversion models, the BRDF (bidirectional reflectance distribution function) unified model can be applied for both continuous vegetation and discrete vegetation, it is appropriate to the complex vegetation distribution. LAI is the key input parameter of the model. We establish the inversion algorithm that can exactly retrieve LAI using remote sensing image based on the unified model. First, we determine the vegetation type through the vegetation classification map to obtain the corresponding G function, leaf and surface reflectivity. Then, we need to determine the leaf area index (LAI), the aggregation index (ζ) and the sky scattered light ratio (β) range and the value of the interval, entering all the parameters into the model to calculate the corresponding reflectivity ρ and establish the lookup table of different vegetation. Finally, we can invert LAI on the basis of the established lookup table. The principle of inversion is least squares method. We have produced 1 km LAI products from 2000 to 2014, once every 8 days. The results show that the algorithm owns good stability and can effectively invert LAI in areas with very complex vegetation and terrain conditions.

Effect of year-to-year variability of leaf area index on variable infiltration capacity model performance and simulation of streamflow during drought

NASA Astrophysics Data System (ADS)

Tesemma, Z. K.; Wei, Y.; Peel, M. C.; Western, A. W.

2014-09-01

This study assessed the effect of using observed monthly leaf area index (LAI) on hydrologic model performance and the simulation of streamflow during drought using the variable infiltration capacity (VIC) hydrological model in the Goulburn-Broken catchment of Australia, which has heterogeneous vegetation, soil and climate zones. VIC was calibrated with both observed monthly LAI and long-term mean monthly LAI, which were derived from the Global Land Surface Satellite (GLASS) observed monthly LAI dataset covering the period from 1982 to 2012. The model performance under wet and dry climates for the two different LAI inputs was assessed using three criteria, the classical Nash-Sutcliffe efficiency, the logarithm transformed flow Nash-Sutcliffe efficiency and the percentage bias. Finally, the percentage deviation of the simulated monthly streamflow using the observed monthly LAI from simulated streamflow using long-term mean monthly LAI was computed. The VIC model predicted monthly streamflow in the selected sub-catchments with model efficiencies ranging from 61.5 to 95.9% during calibration (1982-1997) and 59 to 92.4% during validation (1998-2012). Our results suggest systematic improvements from 4 to 25% in the Nash-Sutcliffe efficiency in pasture dominated catchments when the VIC model was calibrated with the observed monthly LAI instead of the long-term mean monthly LAI. There was limited systematic improvement in tree dominated catchments. The results also suggest that the model overestimation or underestimation of streamflow during wet and dry periods can be reduced to some extent by including the year-to-year variability of LAI in the model, thus reflecting the responses of vegetation to fluctuations in climate and other factors. Hence, the year-to-year variability in LAI should not be neglected; rather it should be included in model calibration as well as simulation of monthly water balance.

Combined effects of climate and land management on watershed vegetation dynamics in an arid environment

Treesearch

Peilong Liu; Lu Hao; Cen Pan; Decheng Zhou; Yongqiang Liu; Ge Sun

2017-01-01

Leaf area index (LAI) is a key parameter to characterize vegetation dynamics and ecosystemstructure that determines the ecosystem functions and services such as cleanwater supply and carbon sequestration in awatershed. However, linking LAI dynamics and environmental controls (i.e., coupling biosphere, atmosphere, and anthroposphere) remains challenging and such type of...

Model-simulated and Satellite-derived Leaf Area Index (LAI) Comparisons Across Multiple Spatial Scales

NASA Astrophysics Data System (ADS)

Iiames, J. S., Jr.; Cooter, E. J.

2016-12-01

Leaf Area Index (LAI) is an important parameter in assessing vegetation structure for characterizing forest canopies over large areas at broad spatial scales using satellite remote sensing data. However, satellite-derived LAI products can be limited by obstructed atmospheric conditions yielding sub-optimal values, or complete non-returns. The United States Environmental Protection Agency's Exposure Methods and Measurements and Computational Exposure Divisions are investigating the viability of supplemental modelled LAI inputs into satellite-derived data streams to support various regional and local scale air quality models for retrospective and future climate assessments. In this present study, one-year (2002) of plot level stand characteristics at four study sites located in Virginia and North Carolina (USA) are used to calibrate species-specific plant parameters in a semi-empirical biogeochemical model. The Environmental Policy Integrated Climate (EPIC) model was designed primarily for managed agricultural field crop ecosystems, but also includes managed woody species that span both xeric and mesic sites (e.g., mesquite, pine, oak, etc.). LAI was simulated using EPIC at a 4 km2 and 12 km2 grid coincident with the regional Community Multiscale Air Quality Model (CMAQ) grid. LAI comparisons were made between model-simulated and MODIS-derived LAI. Field/satellite-upscaled LAI was also compared to the corresponding MODIS LAI value. Preliminary results show field/satellite-upscaled LAI (1 km2) was 1.5 to 3 times smaller than that with the corresponding 1 km2 MODIS LAI for all four sites across all dates, with the largest discrepancies occurring at leaf-out and leaf senescence periods. Simulated LAI/MODIS LAI comparison results will be presented at the conference. Disclaimer: This work is done in support of EPA's Sustainable Healthy Communities Research Program. The U.S. Environmental Protection Agency funded and conducted the research described in this paper. Although this work was reviewed by the EPA and has been approved for publication, it may not necessarily reflect official Agency policy. Mention of any trade names or commercial products does not constitute endorsement or recommendation for use. * Primary author and presenter (Iiames.john@epa.gov)

A numerical study of the effect of irrigation on land-atmosphere interactions in a spring wheat cropland in India using a coupled atmosphere-crop growth dynamics model

NASA Astrophysics Data System (ADS)

Kumari, S.; Sharma, P.; Srivastava, A.; Rastogi, D.; Sehgal, V. K.; Dhakar, R.; Roy, S. B.

2017-12-01

Vegetation dynamics and surface meteorology are tightly coupled through the exchange of momentum, moisture and heat between the land surface and the atmosphere. In this study, we use a recently developed coupled atmosphere-crop growth dynamics model to study these exchanges and their effects in a spring wheat cropland in northern India. In particular, we investigate the role of irrigation in controlling crop growth rates, surface meteorology, and sensible and latent heat fluxes. The model is developed by implementing a crop growth module based on the Simple and Universal Crop growth Simulator (SUCROS) model in the Weather Research Forecasting (WRF) mesoscale atmospheric model. The crop module calculates photosynthesis rates, carbon assimilation, and biomass partitioning as a function of environmental factors and crop development stage. The leaf area index (LAI) and root depth calculated by the crop module is then fed to the Noah-MP land module of WRF to calculate land-atmosphere fluxes. The crop model is calibrated using data from an experimental spring wheat crop site in the Indian Agriculture Research Institute. The coupled model is capable of simulating the observed spring wheat phenology. Irrigation is simulated by changing the soil moisture levels from 50% - 100% of field capacity. Results show that the yield first increases with increasing soil moisture and then starts decreasing as we further increase the soil moisture. Yield attains its maximum value with soil moisture at the level of 60% water of FC. At this level, high LAI values lead to a decrease in the Bowen Ratio because more energy is transferred to the atmosphere as latent heat rather than sensible heat resulting in a cooling effect on near-surface air temperatures. Apart from improving simulation of land-atmosphere interactions, this coupled modeling approach can form the basis for the seamless crop yield and seasonal scale weather outlook prediction system.

The effect of year-to-year variability of leaf area index on Variable Infiltration Capacity model performance and simulation of runoff

NASA Astrophysics Data System (ADS)

Tesemma, Z. K.; Wei, Y.; Peel, M. C.; Western, A. W.

2015-09-01

This study assessed the effect of using observed monthly leaf area index (LAI) on hydrological model performance and the simulation of runoff using the Variable Infiltration Capacity (VIC) hydrological model in the Goulburn-Broken catchment of Australia, which has heterogeneous vegetation, soil and climate zones. VIC was calibrated with both observed monthly LAI and long-term mean monthly LAI, which were derived from the Global Land Surface Satellite (GLASS) leaf area index dataset covering the period from 1982 to 2012. The model performance under wet and dry climates for the two different LAI inputs was assessed using three criteria, the classical Nash-Sutcliffe efficiency, the logarithm transformed flow Nash-Sutcliffe efficiency and the percentage bias. Finally, the deviation of the simulated monthly runoff using the observed monthly LAI from simulated runoff using long-term mean monthly LAI was computed. The VIC model predicted monthly runoff in the selected sub-catchments with model efficiencies ranging from 61.5% to 95.9% during calibration (1982-1997) and 59% to 92.4% during validation (1998-2012). Our results suggest systematic improvements, from 4% to 25% in Nash-Sutcliffe efficiency, in sparsely forested sub-catchments when the VIC model was calibrated with observed monthly LAI instead of long-term mean monthly LAI. There was limited systematic improvement in tree dominated sub-catchments. The results also suggest that the model overestimation or underestimation of runoff during wet and dry periods can be reduced to 25 mm and 35 mm respectively by including the year-to-year variability of LAI in the model, thus reflecting the responses of vegetation to fluctuations in climate and other factors. Hence, the year-to-year variability in LAI should not be neglected; rather it should be included in model calibration as well as simulation of monthly water balance.

Assimilation of Remotely Sensed Leaf Area Index into the Community Land Model with Explicit Carbon and Nitrogen Components using Data Assimilation Research Testbed

NASA Astrophysics Data System (ADS)

Ling, X.; Fu, C.; Yang, Z. L.; Guo, W.

2017-12-01

Information of the spatial and temporal patterns of leaf area index (LAI) is crucial to understand the exchanges of momentum, carbon, energy, and water between the terrestrial ecosystem and the atmosphere, while both in-situ observation and model simulation usually show distinct deficiency in terms of LAI coverage and value. Land data assimilation, combined with observation and simulation together, is a promising way to provide variable estimation. The Data Assimilation Research Testbed (DART) developed and maintained by the National Centre for Atmospheric Research (NCAR) provides a powerful tool to facilitate the combination of assimilation algorithms, models, and real (as well as synthetic) observations to better understanding of all three. Here we systematically investigated the effects of data assimilation on improving LAI simulation based on NCAR Community Land Model with the prognostic carbon-nitrogen option (CLM4CN) linked with DART using the deterministic Ensemble Adjustment Kalman Filter (EAKF). Random 40-member atmospheric forcing was used to drive the CLM4CN with or without LAI assimilation. The Global Land Surface Satellite LAI data (GLASS LAI) LAI is assimilated into the CLM4CN at a frequency of 8 days, and LAI (and leaf carbon / nitrogen) are adjusted at each time step. The results show that assimilating remotely sensed LAI into the CLM4CN is an effective method for improving model performance. In detail, the CLM4-CN simulated LAI systematically overestimates global LAI, especially in low latitude with the largest bias of 5 m2/m2. While if updating both LAI and leaf carbon and leaf nitrogen simultaneously during assimilation, the analyzed LAI can be corrected, especially in low latitude regions with the bias controlled around ±1 m2/m2. Analyzed LAI could also represent the seasonal variation except for the Southern Temperate (23°S-90°S). The obviously improved regions located in the center of Africa, Amazon, the South of Eurasia, the northeast of China, and the west of Europe, where were mainly covered by evergreen/deciduous forests and mixed forests. In addition, the best method for LAI assimilation should include the EAKF method, the accepted percentage of all observation, as well as the carbon-nitrogen control.

A photosynthesis-based two-leaf canopy stomatal conductance model for meteorology and air quality modeling with WRF/CMAQ PX LSM

NASA Astrophysics Data System (ADS)

Ran, Limei; Pleim, Jonathan; Song, Conghe; Band, Larry; Walker, John T.; Binkowski, Francis S.

2017-02-01

A coupled photosynthesis-stomatal conductance model with single-layer sunlit and shaded leaf canopy scaling is implemented and evaluated in a diagnostic box model with the Pleim-Xiu land surface model (PX LSM) and ozone deposition model components taken directly from the meteorology and air quality modeling system - WRF/CMAQ (Weather Research and Forecast model and Community Multiscale Air Quality model). The photosynthesis-based model for PX LSM (PX PSN) is evaluated at a FLUXNET site for implementation against different parameterizations and the current PX LSM approach with a simple Jarvis function (PX Jarvis). Latent heat flux (LH) from PX PSN is further evaluated at five FLUXNET sites with different vegetation types and landscape characteristics. Simulated ozone deposition and flux from PX PSN are evaluated at one of the sites with ozone flux measurements. Overall, the PX PSN simulates LH as well as the PX Jarvis approach. The PX PSN, however, shows distinct advantages over the PX Jarvis approach for grassland that likely result from its treatment of C3 and C4 plants for CO2 assimilation. Simulations using Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) rather than LAI measured at each site assess how the model would perform with grid averaged data used in WRF/CMAQ. MODIS LAI estimates degrade model performance at all sites but one site having exceptionally old and tall trees. Ozone deposition velocity and ozone flux along with LH are simulated especially well by the PX PSN compared to significant overestimation by the PX Jarvis for a grassland site.

Development of a coupled model of a distributed hydrological model and a rice growth model for optimizing irrigation schedule

NASA Astrophysics Data System (ADS)

Tsujimoto, Kumiko; Homma, Koki; Koike, Toshio; Ohta, Tetsu

2013-04-01

A coupled model of a distributed hydrological model and a rice growth model was developed in this study. The distributed hydrological model used in this study is the Water and Energy Budget-based Distributed Hydrological Model (WEB-DHM) developed by Wang et al. (2009). This model includes a modified SiB2 (Simple Biosphere Model, Sellers et al., 1996) and the Geomorphology-Based Hydrological Model (GBHM) and thus it can physically calculate both water and energy fluxes. The rice growth model used in this study is the Simulation Model for Rice-Weather relations (SIMRIW) - rainfed developed by Homma et al. (2009). This is an updated version of the original SIMRIW (Horie et al., 1987) and can calculate rice growth by considering the yield reduction due to water stress. The purpose of the coupling is the integration of hydrology and crop science to develop a tool to support decision making 1) for determining the necessary agricultural water resources and 2) for allocating limited water resources to various sectors. The efficient water use and optimal water allocation in the agricultural sector are necessary to balance supply and demand of limited water resources. In addition, variations in available soil moisture are the main reasons of variations in rice yield. In our model, soil moisture and the Leaf Area Index (LAI) are calculated inside SIMRIW-rainfed so that these variables can be simulated dynamically and more precisely based on the rice than the more general calculations is the original WEB-DHM. At the same time by coupling SIMRIW-rainfed with WEB-DHM, lateral flow of soil water, increases in soil moisture and reduction of river discharge due to the irrigation, and its effects on the rice growth can be calculated. Agricultural information such as planting date, rice cultivar, fertilization amount are given in a fully distributed manner. The coupled model was validated using LAI and soil moisture in a small basin in western Cambodia (Sangker River Basin). This basin is mostly rainfed paddy so that irrigation scheme was firstly switched off. Several simulations with varying irrigation scheme were performed to determine the optimal irrigation schedule in this basin.

A sensitivity analysis of a surface energy balance model to LAI (Leaf Area Index)

NASA Astrophysics Data System (ADS)

Maltese, A.; Cannarozzo, M.; Capodici, F.; La Loggia, G.; Santangelo, T.

2008-10-01

The LAI is a key parameter in hydrological processes, especially in the physically based distribution models. It is a critical ecosystem attribute since physiological processes such as photosynthesis, transpiration and evaporation depend on it. The diffusion of water vapor, momentum, heat and light through the canopy is regulated by the distribution and density of the leaves, branches, twigs and stems. The LAI influences the sensible heat flux H in the surface energy balance single source models through the calculation of the roughness length and of the displacement height. The aerodynamic resistance between the soil and within-canopy source height is a function of the LAI through the roughness length. This research carried out a sensitivity analysis of some of the most important parameters of surface energy balance models to the LAI time variation, in order to take into account the effects of the LAI variation with the phenological period. Finally empirical retrieved relationships between field spectroradiometric data and the field LAI measured via a light-sensitive instrument are presented for a cereal field.

Retrieval of Vertical LAI Profiles Over Tropical Rain Forests using Waveform Lidar at La Selva, Costa Rica

NASA Technical Reports Server (NTRS)

Tang, Hao; Dubayah, Ralph; Swatantra, Anu; Hofton, Michelle; Sheldon, Sage; Clark, David B.; Blair, Bryan

2012-01-01

This study explores the potential of waveform lidar in mapping the vertical and spatial distributions of leaf area index (LAI) over the tropical rain forest of La Selva Biological Station in Costa Rica. Vertical profiles of LAI were derived at 0.3 m height intervals from the Laser Vegetation Imaging Sensor (LVIS) data using the Geometric Optical and Radiative Transfer (GORT) model. Cumulative LAI profiles obtained from LVIS were validated with data from 55 ground to canopy vertical transects using a modular field tower to destructively sample all vegetation. Our results showed moderate agreement between lidar and field derived LAI (r2=0.42, RMSE=1.91, bias=-0.32), which further improved when differences between lidar and tower footprint scales (r2=0.50, RMSE=1.79, bias=0.27) and distance of field tower from lidar footprint center (r2=0.63, RMSE=1.36, bias=0.0) were accounted for. Next, we mapped the spatial distribution of total LAI across the landscape and analyzed LAI variations over different land cover types. Mean values of total LAI were 1.74, 5.20, 5.41 and 5.62 over open pasture, secondary forests, regeneration forests after selective-logging and old-growth forests respectively. Lastly, we evaluated the sensitivities of our LAI retrieval model to variations in canopy/ground reflectance ratio and to waveform noise such as induced by topographic slopes. We found for both, that the effects were not significant for moderate LAI values (about 4). However model derivations of LAI might be inaccurate in areas of high-slope and high LAI (about 8) if ground return energies are low. This research suggests that large footprint waveform lidar can provide accurate vertical LAI profile estimates that do not saturate even at the high LAI levels in tropical rain forests and may be a useful tool for understanding the light transmittance within these canopies.

Improving winter leaf area index estimation in evergreen coniferous forests and its significance in carbon and water fluxes modeling

NASA Astrophysics Data System (ADS)

Wang, R.; Chen, J. M.; Luo, X.

2016-12-01

Modeling of carbon and water fluxes at the continental and global scales requires remotely sensed LAI as inputs. For evergreen coniferous forests (ENF), severely underestimated winter LAI has been one of the issues for mostly available remote sensing products, which could cause negative bias in the modeling of Gross Primary Productivity (GPP) and evapotranspiration (ET). Unlike deciduous trees which shed all the leaves in winter, conifers retains part of their needles and the proportion of the retained needles depends on the needle longevity. In this work, the Boreal Ecosystem Productivity Simulator (BEPS) was used to model GPP and ET at eight FLUXNET Canada ENF sites. Two sets of LAI were used as the model inputs: the 250m 10-day University of Toronto (U of T) LAI product Version 2 and the corrected LAI based on the U of T LAI product and the needle longevity of the corresponding tree species at individual sites. Validating model daily GPP (gC/m2) against site measurements, the mean RMSE over eight sites decreases from 1.85 to 1.15, and the bias changes from -0.99 to -0.19. For daily ET (mm), mean RMSE decreases from 0.63 to 0.33, and the bias changes from -0.31 to -0.16. Most of the improvements occur in the beginning and at the end of the growing season when there is large correction of LAI and meanwhile temperature is still suitable for photosynthesis and transpiration. For the dormant season, the improvement in ET simulation mostly comes from the increased interception of precipitation brought by the elevated LAI during that time. The results indicate that model performance can be improved by the application the corrected LAI. Improving the winter RS LAI can make a large impact on land surface carbon and energy budget.

Predicting Phenologic Response to Water Stress and Implications for Carbon Uptake across the Southeast U.S.

NASA Astrophysics Data System (ADS)

Lowman, L.; Barros, A. P.

2016-12-01

Representation of plant photosynthesis in modeling studies requires phenologic indicators to scale carbon assimilation by plants. These indicators are typically the fraction of photosynthetically active radiation (FPAR) and leaf area index (LAI) which represent plant responses to light and water availability, as well as temperature constraints. In this study, a prognostic phenology model based on the growing season index is adapted to determine the phenologic indicators of LAI and FPAR at the sub-daily scale based on meteorological and soil conditions. Specifically, we directly model vegetation green-up and die-off responses to temperature, vapor pressure deficit, soil water potential, and incoming solar radiation. The indices are based on the properties of individual plant functional types, driven by observational data and prior modeling applications. First, we describe and test the sensitivity of the carbon uptake response to predicted phenology for different vegetation types. Second, the prognostic phenology model is incorporated into a land-surface hydrology model, the Duke Coupled Hydrology Model with Prognostic Vegetation (DCHM-PV), to demonstrate the impact of dynamic phenology on modeled carbon assimilation rates and hydrologic feedbacks. Preliminary results show reduced carbon uptake rates when incorporating a prognostic phenology model that match well against the eddy-covariance flux tower observations. Additionally, grassland vegetation shows the most variability in LAI and FPAR tied to meteorological and soil conditions. These results highlight the need to incorporate vegetation-specific responses to water limitation in order to accurately estimate the terrestrial carbon storage component of the global carbon budget.

Influence of Leaf Area Index Prescriptions on Simulations of Heat, Moisture, and Carbon Fluxes

NASA Technical Reports Server (NTRS)

Kala, Jatin; Decker, Mark; Exbrayat, Jean-Francois; Pitman, Andy J.; Carouge, Claire; Evans, Jason P.; Abramowitz, Gab; Mocko, David

2013-01-01

Leaf-area index (LAI), the total one-sided surface area of leaf per ground surface area, is a key component of land surface models. We investigate the influence of differing, plausible LAI prescriptions on heat, moisture, and carbon fluxes simulated by the Community Atmosphere Biosphere Land Exchange (CABLEv1.4b) model over the Australian continent. A 15-member ensemble monthly LAI data-set is generated using the MODIS LAI product and gridded observations of temperature and precipitation. Offline simulations lasting 29 years (1980-2008) are carried out at 25 km resolution with the composite monthly means from the MODIS LAI product (control simulation) and compared with simulations using each of the 15-member ensemble monthly-varying LAI data-sets generated. The imposed changes in LAI did not strongly influence the sensible and latent fluxes but the carbon fluxes were more strongly affected. Croplands showed the largest sensitivity in gross primary production with differences ranging from -90 to 60 %. PFTs with high absolute LAI and low inter-annual variability, such as evergreen broadleaf trees, showed the least response to the different LAI prescriptions, whilst those with lower absolute LAI and higher inter-annual variability, such as croplands, were more sensitive. We show that reliance on a single LAI prescription may not accurately reflect the uncertainty in the simulation of the terrestrial carbon fluxes, especially for PFTs with high inter-annual variability. Our study highlights that the accurate representation of LAI in land surface models is key to the simulation of the terrestrial carbon cycle. Hence this will become critical in quantifying the uncertainty in future changes in primary production.

Directional effects on NDVI and LAI retrievals from MODIS: A case study in Brazil with soybean

NASA Astrophysics Data System (ADS)

Breunig, Fábio Marcelo; Galvão, Lênio Soares; Formaggio, Antônio Roberto; Epiphanio, José Carlos Neves

2011-02-01

The Moderate Resolution Imaging Spectroradiometer (MODIS) is largely used to estimate Leaf Area Index (LAI) using radiative transfer modeling (the "main" algorithm). When this algorithm fails for a pixel, which frequently occurs over Brazilian soybean areas, an empirical model (the "backup" algorithm) based on the relationship between the Normalized Difference Vegetation Index (NDVI) and LAI is utilized. The objective of this study is to evaluate directional effects on NDVI and subsequent LAI estimates using global (biome 3) and local empirical models, as a function of the soybean development in two growing seasons (2004-2005 and 2005-2006). The local model was derived from the pixels that had LAI values retrieved from the main algorithm. In order to keep the reproductive stage for a given cultivar as a constant factor while varying the viewing geometry, pairs of MODIS images acquired in close dates from opposite directions (backscattering and forward scattering) were selected. Linear regression relationships between the NDVI values calculated from these two directions were evaluated for different view angles (0-25°; 25-45°; 45-60°) and development stages (<45; 45-90; >90 days after planting). Impacts on LAI retrievals were analyzed. Results showed higher reflectance values in backscattering direction due to the predominance of sunlit soybean canopy components towards the sensor and higher NDVI values in forward scattering direction due to stronger shadow effects in the red waveband. NDVI differences between the two directions were statistically significant for view angles larger than 25°. The main algorithm for LAI estimation failed in the two growing seasons with gradual crop development. As a result, up to 94% of the pixels had LAI values calculated from the backup algorithm at the peak of canopy closure. Most of the pixels selected to compose the 8-day MODIS LAI product came from the forward scattering view because it displayed larger LAI values than the backscattering. Directional effects on the subsequent LAI retrievals were stronger at the peak of the soybean development (NDVI values between 0.70 and 0.85). When the global empirical model was used, LAI differences up to 3.2 for consecutive days and opposite viewing directions were observed. Such differences were reduced to values up to 1.5 with the local model. Because of the predominance of LAI retrievals from the MODIS backup algorithm during the Brazilian soybean development, care is necessary if one considers using these data in agronomic growing/yield models.

Quantifying forest LAI succession in sub-tropical forests using time-series of Landsat data, 1987 -2015

NASA Astrophysics Data System (ADS)

Wu, Q.; Song, J.; Wang, J.; Chen, S.; Yu, B.; Liao, L.

2016-12-01

Monitoring the dynamics of leaf area index (LAI) throughout the life-cycle of forests (from seeding to maturity) is vital for simulating forest growth and quantifying carbon sequestration. However, all current global LAI produts show extremely low accuracy in forests and the coarse spatial resolution(nearly 1-km) mismatch with the spatial scale of forest inventory plots (nearly 26m*26m). To date, several studies have explored the possibility of satellite data to classify forest succession or predict stand age. And a few studies have explored the potential of using long term Landsat data to monitor the growing trend of forests, but no studies have quantified the inter-annual and intra-annual LAI dynamics along with forest succession. Vegetation indexes are not perfect variables in quantifying forest foliage dynamics. Hallet (1995) suggested remote sensing of biophysical characteristics should shift away from direct inference from vegetation indices toward more physically based algorithms. This work intends to be a pioneer example for improving the accuracy of forests LAI and providing temporal-spatial matching LAI datasets for monitoring forest processes. We integrates the Geometric-Optical and Radiative Transfer (GORT) model with the Physiological Principles Predicting Growth (3-PG) model to improve the estimation of the forest canopy LAI dynamics. Reflectance time-series data from 1987 to 2015 were collected and preprocessed for forests in southern China, using all available Landsat data (with <80% cloud). Effective LAI and true LAI were field measured to validate our results using various instruments, including digital hemispheric photographs (DHP), LAI-2000 Plant Canopy Analyzer (LI-COR), and Tracing radiation and Architecture of Canopies (TRAC). Results show that the relationship between spectral metrics of satellite images and forest LAI is clear in early stages before maturity. 3-PG provide accurate inter-annual trend of forest LAI, while satellite images provide clear intra-annual LAI dynamics. We concluded that the GORT-3PG model improved the LAI estimation significantly of forest stands. Improving forest LAI estimates will help inform forest management policy and such methods may be applied in other similar forests.

Carbon economics of LAI drive photosynthesis patterns across an Amazonian precipitation gradient

NASA Astrophysics Data System (ADS)

Flack, Sophie; Williams, Mathew; Meir, Patrick; Malhi, Yadvinder

2017-04-01

The Amazon rainforest is an integral part of the terrestrial carbon cycle, yet whilst the physiological response of its plants to water availability is increasingly well quantified, constraints to photosynthesis through adaptive response to precipitation regime have received little attention. We use the Soil Plant Atmosphere model to apportion variation in photosynthesis to individual drivers for plots with detailed measurements of carbon cycling, leaf traits and canopy properties, along an Amazonian mean annual precipitation (MAP) gradient. We hypothesised that leaf area index (LAI) would be the principal driver of variation in photosynthesis. Differences in LAI are predicted to result from economic factors; plants balance the carbon cost of leaf construction and maintenance with assimilation potential, to maximise canopy carbon export. Model analysis showed that LAI was the primary driver of differences in GPP along the precipitation gradient, accounting for 49% of observed variation. Meteorology accounted for 19%, whilst plant traits accounted for only 5%. To explain the observed spatial trends in LAI we undertook model experiments. For each plot the carbon budget was quantified iteratively using the field measured LAI time-series of the other plots, keeping meteorology, soil and plant traits constant. The mean annual LAI achieving maximum photosynthesis and net canopy carbon export increased with MAP, reflecting observed LAI trends. At the driest site, alternative, higher LAI strategies were unsustainable. The carbon cost of leaf construction and maintenance was disproportional to GPP achieved. At high MAP, increased foliar carbon costs were remunerative and GPP was maximised by high LAI. Our evidence therefore suggests that observed LAI trends across the precipitation gradient are driven by carbon economics. Forests LAI response to temporal changes in precipitation reflects trends observed across spatial gradients, identifying LAI as a key mechanism for plant response to water availability. This research improves our understanding of the constraints on photosynthesis through plants' adaptive response to precipitation, which in light of precipitation projections, has implications for the future Amazon carbon balance.

Improving winter leaf area index estimation in coniferous forests and its significance in estimating the land surface albedo

NASA Astrophysics Data System (ADS)

Wang, Rong; Chen, Jing M.; Pavlic, Goran; Arain, Altaf

2016-09-01

Winter leaf area index (LAI) of evergreen coniferous forests exerts strong control on the interception of snow, snowmelt and energy balance. Simulation of winter LAI and associated winter processes in land surface models is challenging. Retrieving winter LAI from remote sensing data is difficult due to cloud contamination, poor illumination, lower solar elevation and higher radiation reflection by snow background. Underestimated winter LAI in evergreen coniferous forests is one of the major issues limiting the application of current remote sensing LAI products. It has not been fully addressed in past studies in the literature. In this study, we used needle lifespan to correct winter LAI in a remote sensing product developed by the University of Toronto. For the validation purpose, the corrected winter LAI was then used to calculate land surface albedo at five FLUXNET coniferous forests in Canada. The RMSE and bias values for estimated albedo were 0.05 and 0.011, respectively, for all sites. The albedo map over coniferous forests across Canada produced with corrected winter LAI showed much better agreement with the GLASS (Global LAnd Surface Satellites) albedo product than the one produced with uncorrected winter LAI. The results revealed that the corrected winter LAI yielded much greater accuracy in simulating land surface albedo, making the new LAI product an improvement over the original one. Our study will help to increase the usability of remote sensing LAI products in land surface energy budget modeling.

Retrieval of Winter Wheat Leaf Area Index from Chinese GF-1 Satellite Data Using the PROSAIL Model.

PubMed

Li, He; Liu, Gaohuan; Liu, Qingsheng; Chen, Zhongxin; Huang, Chong

2018-04-06

Leaf area index (LAI) is one of the key biophysical parameters in crop structure. The accurate quantitative estimation of crop LAI is essential to verify crop growth and health. The PROSAIL radiative transfer model (RTM) is one of the most established methods for estimating crop LAI. In this study, a look-up table (LUT) based on the PROSAIL RTM was first used to estimate winter wheat LAI from GF-1 data, which accounted for some available prior knowledge relating to the distribution of winter wheat characteristics. Next, the effects of 15 LAI-LUT strategies with reflectance bands and 10 LAI-LUT strategies with vegetation indexes on the accuracy of the winter wheat LAI retrieval with different phenological stages were evaluated against in situ LAI measurements. The results showed that the LUT strategies of LAI-GNDVI were optimal and had the highest accuracy with a root mean squared error (RMSE) value of 0.34, and a coefficient of determination (R²) of 0.61 during the elongation stages, and the LUT strategies of LAI-Green were optimal with a RMSE of 0.74, and R² of 0.20 during the grain-filling stages. The results demonstrated that the PROSAIL RTM had great potential in winter wheat LAI inversion with GF-1 satellite data and the performance could be improved by selecting the appropriate LUT inversion strategies in different growth periods.

Preliminary validation of leaf area index sensor in Huailai

NASA Astrophysics Data System (ADS)

Cai, Erli; Li, Xiuhong; Liu, Qiang; Dou, Baocheng; Chang, Chongyan; Niu, Hailin; Lin, Xingwen; Zhang, Jialin

2015-12-01

Leaf area index (LAI) is a key variable in many land surface models that involve energy and mass exchange between vegetation and the environment. In recent years, extracting vegetation structure parameters from digital photography becomes a widely used indirect method to estimate LAI for its simplicity and ease of use. A Leaf Area Index Sensor (LAIS) system was developed to continuously monitor the growth of crops in several sampling points in Huailai, China. The system applies 3G/WIFI communication technology to remotely collect crop photos in real-time. Then the crop photos are automatically processed and LAI is estimated based on the improved leaf area index of Lang and Xiang (LAILX) algorithm in LAIS. The objective of this study is to primarily verify the LAI estimated from LAIS (Lphoto) through comparing them with the destructive green LAI (Ldest). Ldest was measured across the growing season ntil maximum canopy development while plants are still green. The preliminary verification shows that Lphoto corresponds well with the Ldest (R2=0.975). In general, LAI could be accurately estimated with LAIS and its LAI shows high consistency compared with the destructive green LAI. The continuous LAI measurement obtained from LAIS could be used for the validation of remote sensing LAI products.

Combined effects of climate and land management on watershed vegetation dynamics in an arid environment.

PubMed

Liu, Peilong; Hao, Lu; Pan, Cen; Zhou, Decheng; Liu, Yongqiang; Sun, Ge

2017-07-01

Leaf area index (LAI) is a key parameter to characterize vegetation dynamics and ecosystem structure that determines the ecosystem functions and services such as clean water supply and carbon sequestration in a watershed. However, linking LAI dynamics and environmental controls (i.e., coupling biosphere, atmosphere, and anthroposphere) remains challenging and such type of studies have rarely been done at a watershed scale due to data availability. The present study examined the spatial and temporal variations of LAI for five ecosystem types within a watershed with a complex topography in the Upper Heihe River Basin, a major inland river in the arid and semi-arid western China. We integrated remote sensing-based GLASS (Global Land Surface Satellite) LAI products, interpolated climate data, watershed characteristics, and land management records for the period of 2001-2012. We determined the relationships among LAI, topography, air temperature and precipitation, and grazing history by five ecosystem types using several advanced statistical methods. We show that long-term mean LAI distribution had an obvious vertical pattern as controlled by precipitation and temperature in a hilly watershed. Overall, watershed-wide mean LAI had an increasing trend overtime for all ecosystem types during 2001-2012, presumably as a result of global warming and a wetting climate. However, the fluctuations of observed LAI at a pixel scale (1km) varied greatly across the watershed. We classified the vegetation changes within the watershed as 'Improved', 'Stabilized', and 'Degraded' according their respective LAI changes. We found that climate was not the only driver for temporal vegetation changes for all land cover types. Grazing partially contributed to the decline of LAI in some areas and masked the positive climate warming effects in other areas. Extreme weathers such as cold spells and droughts could substantially affect inter-annual variability of LAI dynamics. We concluded that temporal and spatial LAI dynamics were rather complex and were affected by both climate variations and human disturbances in the study basin. Future monitoring studies should focus on the functional interactions among vegetation dynamics, climate variations, land management, and human disturbances. Published by Elsevier B.V.

Canopy structural complexity predicts forest canopy light absorption at continental scales

NASA Astrophysics Data System (ADS)

Atkins, J. W.; Fahey, R. T.; Hardiman, B. S.; Gough, C. M.

2017-12-01

Understanding how the physical structure of forest canopies influence light acquisition is a long-standing area of inquiry fundamental to advancing understanding of many areas of the physical sciences, including the modeling and interpretation of biogeochemical cycles. Conventional measures of forest canopy structure employed in earth system models are often limited to leaf area index (LAI)—a measure of the quantity of leaves in the canopy. However, more novel multi-dimensional measures of canopy structural complexity (CSC) that describe the arrangement of vegetation are now possible because of technological advances, and may improve modeled estimates of canopy light absorption. During 2016 and 2017, we surveyed forests at sites from across the eastern, southern, and midwestern United States using portable canopy LiDAR (PCL). This survey included 14 National Ecological Observation Network (NEON), Long-Term Ecological Research Network (LTER,) Ameriflux, and University affiliated sites. Our findings show that a composite model including CSC parameters and LAI explains 96.8% of the variance in light acquisition, measured as the fraction of photosynthetically absorbed radiation (fPAR) at the continental scale, and improvement of 12% over an LAI only model. Under high light sky conditions, measures of CSC are more strongly coupled with light acquisition than under low light, possibly because light scattering partially decouples CSC from canopy light absorption under low, predominately diffuse light conditions. We conclude that scalable estimates of CSC metrics may improve continent-wide estimates of canopy light absorption and, therefore, carbon uptake, with implications for remote sensing and earth system modeling.

Remote Sensing of Miombo Woodland's Aboveground Biomass and LAI using RADARSAT and Landsat ETM+ Data

NASA Astrophysics Data System (ADS)

Ribeiro, N. S.; Saatchi, S. S.; Shugart, H. H.; Wshington-Allen, R. A.

2007-05-01

Estimations of biomass are critical in Miombo Woodlands because they represent a primary source of food, fiber, and fuel for 340 million rural peoples and another 15 million urban dwellers in southern Africa. The purpose of this study is to estimate woody aboveground biomass and Leaf Area Index (LAI) in Niassa Reserve, northern Mozambique. The objective of this study is to use optical and microwave satellite data with contemporaneous field data to estimate biomass and LAI. Fifty field plots were surveyed across the Niassa Reserve for biomass and LAI in July and December 2004, respectively. Remote sensing data consisting of RADARSAT backscatter (C- band, ë=5.6 cm) and a June 2004 Landsat ETM+ were acquired. Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR), and a land-cover map (72% total accuracy) were derived from the Landsat scene. Field measurements of biomass and LAI correlated with Radarsat backscatter (Rsqbiomass=0.45, RsqLAI = 0.35, P<0.0001 ), NDVI (Rsqbiomass =0.15, RsqLAI=0.14-, p <0.0001 ) and SR (Rsqbiomass=-0.14, RsqLAI= 0.17, p <0.0001). A jackknife stepwise regression technique was used to develop the best predictive models for biomass (biomass = -5.19 +0.074*radarsat+1.56*SR, Rsq=0.53) and LAI (LAI= -0.66+0.01*radarsat+0.22*SR, Rsq=0.45). The addition of NDVI did not improve the model. Forest biomass and LAI maps were then produced for Niassa Reserve with an estimated peak total biomass of 18 kg/hm2 and a mean LAI of 2.8 m2/m2. In the east both biomass and LAI are lower than the western Niassa Reserve.

Leaf Area Index Estimation Using Chinese GF-1 Wide Field View Data in an Agriculture Region.

PubMed

Wei, Xiangqin; Gu, Xingfa; Meng, Qingyan; Yu, Tao; Zhou, Xiang; Wei, Zheng; Jia, Kun; Wang, Chunmei

2017-07-08

Leaf area index (LAI) is an important vegetation parameter that characterizes leaf density and canopy structure, and plays an important role in global change study, land surface process simulation and agriculture monitoring. The wide field view (WFV) sensor on board the Chinese GF-1 satellite can acquire multi-spectral data with decametric spatial resolution, high temporal resolution and wide coverage, which are valuable data sources for dynamic monitoring of LAI. Therefore, an automatic LAI estimation algorithm for GF-1 WFV data was developed based on the radiative transfer model and LAI estimation accuracy of the developed algorithm was assessed in an agriculture region with maize as the dominated crop type. The radiative transfer model was firstly used to simulate the physical relationship between canopy reflectance and LAI under different soil and vegetation conditions, and then the training sample dataset was formed. Then, neural networks (NNs) were used to develop the LAI estimation algorithm using the training sample dataset. Green, red and near-infrared band reflectances of GF-1 WFV data were used as the input variables of the NNs, as well as the corresponding LAI was the output variable. The validation results using field LAI measurements in the agriculture region indicated that the LAI estimation algorithm could achieve satisfactory results (such as R² = 0.818, RMSE = 0.50). In addition, the developed LAI estimation algorithm had potential to operationally generate LAI datasets using GF-1 WFV land surface reflectance data, which could provide high spatial and temporal resolution LAI data for agriculture, ecosystem and environmental management researches.

Future vegetation ecosystem response to warming climate over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Bao, Y.; Gao, Y.; Wang, Y.

2017-12-01

The amplified vegetation response to climate variability has been found over the Tibetan Plateau (TP) in recent decades. In this study, the potential impacts of 21st century climate change on the vegetation ecosystem over the TP are assessed based on the dynamic vegetation outputs of models from Coupled Model Intercomparison Project Phase 5 (CMIP5), and the sensitivity of the TP vegetation in response to warming climate was investigated. Models project a continuous and accelerating greening in future, especially in the eastern TP, which closely associates with the plant type upgrade due to the pronouncing warming in growing season.Vegetation leaf area index (LAI) increase well follows the global warming, suggesting the warming climate instead of co2 fertilization controlls the future TP plant growth. The warming spring may advance the start of green-up day and extend the growing season length. More carbon accumulation in vegetation and soil will intensify the TP carbon cycle and will keep it as a carbon sink in future. Keywords: Leaf Area Index (LAI), Climate Change, Global Dynamic Vegetation Models (DGVMs), CMIP5, Tibetan Plateau (TP)

Retrieval of Winter Wheat Leaf Area Index from Chinese GF-1 Satellite Data Using the PROSAIL Model

PubMed Central

Li, He; Liu, Gaohuan; Liu, Qingsheng; Chen, Zhongxin; Huang, Chong

2018-01-01

Leaf area index (LAI) is one of the key biophysical parameters in crop structure. The accurate quantitative estimation of crop LAI is essential to verify crop growth and health. The PROSAIL radiative transfer model (RTM) is one of the most established methods for estimating crop LAI. In this study, a look-up table (LUT) based on the PROSAIL RTM was first used to estimate winter wheat LAI from GF-1 data, which accounted for some available prior knowledge relating to the distribution of winter wheat characteristics. Next, the effects of 15 LAI-LUT strategies with reflectance bands and 10 LAI-LUT strategies with vegetation indexes on the accuracy of the winter wheat LAI retrieval with different phenological stages were evaluated against in situ LAI measurements. The results showed that the LUT strategies of LAI-GNDVI were optimal and had the highest accuracy with a root mean squared error (RMSE) value of 0.34, and a coefficient of determination (R2) of 0.61 during the elongation stages, and the LUT strategies of LAI-Green were optimal with a RMSE of 0.74, and R2 of 0.20 during the grain-filling stages. The results demonstrated that the PROSAIL RTM had great potential in winter wheat LAI inversion with GF-1 satellite data and the performance could be improved by selecting the appropriate LUT inversion strategies in different growth periods. PMID:29642395

A New Global LAI Product and Its Use for Terrestrial Carbon Cycle Estimation

NASA Astrophysics Data System (ADS)

Chen, J. M.; Liu, R.; Ju, W.; Liu, Y.

2014-12-01

For improving the estimation of the spatio-temporal dynamics of the terrestrial carbon cycle, a new time series of the leaf area index (LAI) is generated for the global land surface at 8 km resolution from 1981 to 2012 by combining AVHRR and MODIS satellite data. This product differs from existing LAI products in the following two aspects: (1) the non-random spatial distribution of leaves with the canopy is considered, and (2) the seasonal variation of the vegetation background is included. The non-randomness of the leaf spatial distribution in the canopy is considered using the second vegetation structural parameter named clumping index (CI), which quantifies the deviation of the leaf spatial distribution from the random case. Using the MODIS Bidirectional Reflectance Distribution Function product, a global map of CI is produced at 500 m resolution. In our LAI algorithm, CI is used to convert the effective LAI obtained from mono-angle remote sensing into the true LAI, otherwise LAI would be considerably underestimated. The vegetation background is soil in crop, grass and shrub but includes soil, grass, moss, and litter in forests. Through processing a large volume of MISR data from 2000 to 2010, monthly red and near-infrared reflectances of the vegetation background is mapped globally at 1 km resolution. This new LAI product has been validated extensively using ground-based LAI measurements distributed globally. In carbon cycle modeling, the use of CI in addition to LAI allows for accurate separation of sunlit and shaded leaves as an important step in terrestrial photosynthesis and respiration modeling. Carbon flux measurements over 100 sites over the globe are used to validate an ecosystem model named Boreal Ecosystem Productivity Simulator (BEPS). The validated model is run globally at 8 km resolution for the period from 1981 to 2012 using the LAI product and other spatial datasets. The modeled results suggest that changes in vegetation structure as quantified by LAI do not contribute significantly to the increasing trend in carbon sink over the last 32 years. The increases in atmospheric CO2 concentration and nitrogen deposition are found to be the major causes for the increases in plant productivity and carbon sink over the last 32 years.

Application and Evaluation of MODIS LAI, fPAR, and Albedo Products in the WRFCMAQ System

EPA Science Inventory

Leaf area index (LAI), vegetation fraction (VF), and surface albedo are important parameters in the land surface model (LSM) for meteorology and air quality modeling systems such as WRF/CMAQ. LAI and VF control not only leaf to canopy level evapotranspiration flux scaling but al...

Evaluation of the MODIS LAI product using independent lidar-derived LAI: A case study in mixed conifer forest

Treesearch

Jennifer L. R. Jensen; Karen S. Humes; Andrew T. Hudak; Lee A. Vierling; Eric Delmelle

2011-01-01

This study presents an alternative assessment of the MODIS LAI product for a 58,000 ha evergreen needleleaf forest located in the western Rocky Mountain range in northern Idaho by using lidar data to model (R2=0.86, RMSE=0.76) and map LAI at higher resolution across a large number of MODIS pixels in their entirety. Moderate resolution (30 m) lidar-based LAI estimates...

Assimilation of LAI time-series in crop production models

NASA Astrophysics Data System (ADS)

Kooistra, Lammert; Rijk, Bert; Nannes, Louis

2014-05-01

Agriculture is worldwide a large consumer of freshwater, nutrients and land. Spatial explicit agricultural management activities (e.g., fertilization, irrigation) could significantly improve efficiency in resource use. In previous studies and operational applications, remote sensing has shown to be a powerful method for spatio-temporal monitoring of actual crop status. As a next step, yield forecasting by assimilating remote sensing based plant variables in crop production models would improve agricultural decision support both at the farm and field level. In this study we investigated the potential of remote sensing based Leaf Area Index (LAI) time-series assimilated in the crop production model LINTUL to improve yield forecasting at field level. The effect of assimilation method and amount of assimilated observations was evaluated. The LINTUL-3 crop production model was calibrated and validated for a potato crop on two experimental fields in the south of the Netherlands. A range of data sources (e.g., in-situ soil moisture and weather sensors, destructive crop measurements) was used for calibration of the model for the experimental field in 2010. LAI from cropscan field radiometer measurements and actual LAI measured with the LAI-2000 instrument were used as input for the LAI time-series. The LAI time-series were assimilated in the LINTUL model and validated for a second experimental field on which potatoes were grown in 2011. Yield in 2011 was simulated with an R2 of 0.82 when compared with field measured yield. Furthermore, we analysed the potential of assimilation of LAI into the LINTUL-3 model through the 'updating' assimilation technique. The deviation between measured and simulated yield decreased from 9371 kg/ha to 8729 kg/ha when assimilating weekly LAI measurements in the LINTUL model over the season of 2011. LINTUL-3 furthermore shows the main growth reducing factors, which are useful for farm decision support. The combination of crop models and sensor techniques shows promising results for precision agriculture application and thereby for reduction of the footprint agriculture has on the world's resources.

Numerical Simulation of Electromagnetic Field Variation in the Lithosphere-Atmosphere-Ionosphere Associated with Seismogenic Process in a Curvature Coordinate System

NASA Astrophysics Data System (ADS)

Liu, L.; Zhao, Z.; Wang, Y.; Huang, Q.

2013-12-01

The lithosphere-atmosphere- ionosphere (LAI) system formed an electromagnetic (EM) cavity that hosts the EM field excited by electric currents generated by lightning and other natural sources. There have also been numerous reports on variations of the EM field existing in LAI system prior to some significance earthquakes. We simulated the EM field in the lithosphere-ionosphere waveguide with a whole-earth model using a curvature coordinate by the hybrid pseudo-spectral and finite difference time domain method. Considering the seismogensis as a fully coupled seismoelectric process, we simulate the seismic wave and the EM wave in this 2D model. In the model we have observed the excitation of the Schumann Resonance (SR) as the background EM field generated by randomly placed electric-current impulses within the lowest 10 kilometers of the atmosphere. The diurnal variation and the latitude-dependence in ion concentration in the ionosphere are included in the model. After the SR reaching a steady state, an electric impulse is introduced in the shallow lithosphere to mimic the seismogenic process (pre-, co- and post-seismic) to assess the possible precursory effects on SR strength and frequency. The modeling results can explain the observed fact of why SR has a much more sensitive response to continental earthquakes, and much less response to oceanic events. The fundamental reason is simply due to the shielding effect of the conductive ocean that prevents effective radiation of the seismoelectric signals from oceanic earthquake events into the LAI waveguide.

A Better Representation of European Croplands into a Global Biosphere Model

NASA Astrophysics Data System (ADS)

Gervois, S.; de Noblet, N.; Viovy, N.; Ciais, P.; Brisson, N.; Seguin, B.

2002-12-01

Croplands cover a quarter of Europe's surface (about an hundred million hectares), their impact on carbon and water fluxes must therefore be estimated. Global biosphere models such as ORCHIDEE (http://www.ipsl.jussieu.fr/~ssipsl/) were conceived to simulate natural ecosystems only, so croplands are often described as grasslands. Not only cropland productivity depends on climate and soil conditions but also on irrigations, fertilisers impact, date of sowing... In addition crop species are usually selected genetically to shorten and accelerate their growth. Agronomic models such as STICS (Brisson et al. 1998) give a more realistic picture of croplands as they are especially designed to account for this human forcing. On the other hand they can be used at the local scale only. First we evaluate the ability of the two models to reproduce the seasonal behaviour the leaf area index (LAI), the aerial biomass, and the exchanges of water vapour and CO2 with the atmosphere. For that we compare the model outputs with measurements performed at five sites that are representative of most common European crops (wheat, corn, soybean). As expected the agronomic STICS better behaves than the generic model ORCHIDEE in representing the seasonal cycle of the above variables. In order to get a realistic representation of croplands areas at the regional scale, we decided to couple ORCHIDEE with STICS. First we present the main steps of the coupling procedure. The principle consists in forcing ORCHIDEE with five more realistic outputs of STICS: LAI, date of harvest, nitrogen stress, root profile, and vegetation height. On the other hand, ORCHIDEE computes its own carbon and water balance. The allocation scheme was also modified in ORCHIDEE in order to conserve the coherence between LAI and leaf biomass, and we added a harvest module into ORCHIDEE. The coupled model was validated against carbon and water fluxes observed respectively at two fields (wheat and corn) in the US. We also conducted at European scale two experiments where all arable lands are covered by corn for the first one, and by natural grasslands for the second one. We compared the fluxes between these two simulations. In the case of corn cover, the vegetative period is reduced and the absorption of carbon is more enhanced (until 15 per cent) during the maximum extension of vegetation. This work shows that croplands can be integrated into global biosphere models to simulate CO2 and water vapour regional fluxes, which should allow a better representation of those ecosystems for climate studies.

Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research.

PubMed

Liu, R; Chen, J M; Liu, J; Deng, F; Sun, R

2007-11-01

An operational system was developed for mapping the leaf area index (LAI) for carbon cycle models from the moderate resolution imaging spectroradiometer (MODIS) data. The LAI retrieval algorithm is based on Deng et al. [2006. Algorithm for global leaf area index retrieval using satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 44, 2219-2229], which uses the 4-scale radiative transfer model [Chen, J.M., Leblancs, 1997. A 4-scale bidirectional reflection model based on canopy architecture. IEEE Transactions on Geoscience and Remote Sensing, 35, 1316-1337] to simulate the relationship of LAI with vegetated surface reflectance measured from space for various spectral bands and solar and view angles. This algorithm has been integrated to the MODISoft platform, a software system designed for processing MODIS data, to generate 250 m, 500 m and 1 km resolution LAI products covering all of China from MODIS MOD02 or MOD09 products. The multi-temporal interpolation method was implemented to remove the residual cloud and other noise in the final LAI product so that it can be directly used in carbon models without further processing. The retrieval uncertainties from land cover data were evaluated using five different data sets available in China. The results showed that mean LAI discrepancies can reach 27%. The current product was also compared with the NASA MODIS MOD15 LAI product to determine the agreement and disagreement of two different product series. LAI values in the MODIS product were found to be 21% larger than those in the new product. These LAI products were compared against ground TRAC measurements in forests in Qilian Mountain and Changbaishan. On average, the new LAI product agrees with the field measurement in Changbaishan within 2%, but the MODIS product is positively biased by about 20%. In Qilian Mountain, where forests are sparse, the new product is lower than field measurements by about 38%, while the MODIS product is larger by about 65%.

Spatial and seasonal variations of leaf area index (LAI) in subtropical secondary forests related to floristic composition and stand characters

NASA Astrophysics Data System (ADS)

Zhu, Wenjuan; Xiang, Wenhua; Pan, Qiong; Zeng, Yelin; Ouyang, Shuai; Lei, Pifeng; Deng, Xiangwen; Fang, Xi; Peng, Changhui

2016-07-01

Leaf area index (LAI) is an important parameter related to carbon, water, and energy exchange between canopy and atmosphere and is widely applied in process models that simulate production and hydrological cycles in forest ecosystems. However, fine-scale spatial heterogeneity of LAI and its controlling factors have yet to be fully understood in Chinese subtropical forests. We used hemispherical photography to measure LAI values in three subtropical forests (Pinus massoniana-Lithocarpus glaber coniferous and evergreen broadleaved mixed forests, Choerospondias axillaris deciduous broadleaved forests, and L. glaber-Cyclobalanopsis glauca evergreen broadleaved forests) from April 2014 to January 2015. Spatial heterogeneity of LAI and its controlling factors were analysed using geostatistical methods and the generalised additive models (GAMs) respectively. Our results showed that LAI values differed greatly in the three forests and their seasonal variations were consistent with plant phenology. LAI values exhibited strong spatial autocorrelation for the three forests measured in January and for the L. glaber-C. glauca forest in April, July, and October. Obvious patch distribution pattern of LAI values occurred in three forests during the non-growing period and this pattern gradually dwindled in the growing season. Stem number, crown coverage, proportion of evergreen conifer species on basal area basis, proportion of deciduous species on basal area basis, and forest types affected the spatial variations in LAI values in January, while stem number and proportion of deciduous species on basal area basis affected the spatial variations in LAI values in July. Floristic composition, spatial heterogeneity, and seasonal variations should be considered for sampling strategy in indirect LAI measurement and application of LAI to simulate functional processes in subtropical forests.

An enhanced approach for the use of satellite-derived leaf area index values in dry deposition modeling in the Athabasca oil sands region.

PubMed

Davies, Mervyn; Cho, Sunny; Spink, David; Pauls, Ron; Desilets, Michael; Shen, Yan; Bajwa, Kanwardeep; Person, Reid

2016-12-15

In the Athabasca oil sands region (AOSR) of Northern Alberta, the dry deposition of sulphur and nitrogen compounds represents a major fraction of total (wet plus dry) deposition due to oil sands emissions. The leaf area index (LAI) is a critical parameter that affects the dry deposition of these gaseous and particulate compounds to the surrounding boreal forest canopy. For this study, LAI values based on Moderate Resolution Imaging Spectroradiometer satellite imagery were obtained and compared to ground-based measurements, and two limitations with the satellite data were identified. The satellite LAI data firstly represents one-sided LAI values that do not account for the enhanced LAI associated with needle leaf geometry, and secondly, underestimates LAI in winter-time northern latitude regions. An approach for adjusting satellite LAI values for different boreal forest cover types, as a function of time of year, was developed to produce more representative LAI values that can be used by air quality sulphur and nitrogen deposition models. The application of the approach increases the AOSR average LAI for January from 0.19 to 1.40, which represents an increase of 637%. Based on the application of the CALMET/CALPUFF model system, this increases the predicted regional average dry deposition of sulphur and nitrogen compounds for January by factors of 1.40 to 1.30, respectively. The corresponding AOSR average LAI for July increased from 2.8 to 4.0, which represents an increase of 43%. This increases the predicted regional average dry deposition of sulphur and nitrogen compounds for July by factors of 1.28 to 1.22, respectively. These findings reinforce the importance of the LAI metric for predicting the dry deposition of sulphur and nitrogen compounds. While satellite data can provide enhanced spatial and temporal resolution, adjustments are identified to overcome associated limitations. This work is considered to have application for other deposition model studies where dry deposition represents a significant fraction of total deposition. Copyright © 2016 Elsevier Ltd. All rights reserved.

Spin-Up and Tuning of the Global Carbon Cycle Model Inside the GISS ModelE2 GCM

NASA Technical Reports Server (NTRS)

Aleinov, Igor; Kiang, Nancy Y.; Romanou, Anastasia

2015-01-01

Planetary carbon cycle involves multiple phenomena, acting at variety of temporal and spacial scales. The typical times range from minutes for leaf stomata physiology to centuries for passive soil carbon pools and deep ocean layers. So, finding a satisfactory equilibrium state becomes a challenging and computationally expensive task. Here we present the spin-up processes for different configurations of the GISS Carbon Cycle model from the model forced with MODIS observed Leaf Area Index (LAI) and prescribed ocean to the prognostic LAI and to the model fully coupled to the dynamic ocean and ocean biology. We investigate the time it takes the model to reach the equilibrium and discuss the ways to speed up this process. NASA Goddard Institute for Space Studies General Circulation Model (GISS ModelE2) is currently equipped with all major algorithms necessary for the simulation of the Global Carbon Cycle. The terrestrial part is presented by Ent Terrestrial Biosphere Model (Ent TBM), which includes leaf biophysics, prognostic phenology and soil biogeochemistry module (based on Carnegie-Ames-Stanford model). The ocean part is based on the NASA Ocean Biogeochemistry Model (NOBM). The transport of atmospheric CO2 is performed by the atmospheric part of ModelE2, which employs quadratic upstream algorithm for this purpose.

Joint structural and physiological control on the interannual variation in productivity in a temperate grassland: A data-model comparison.

PubMed

Hu, Zhongmin; Shi, Hao; Cheng, Kaili; Wang, Ying-Ping; Piao, Shilong; Li, Yue; Zhang, Li; Xia, Jianyang; Zhou, Lei; Yuan, Wenping; Running, Steve; Li, Longhui; Hao, Yanbin; He, Nianpeng; Yu, Qiang; Yu, Guirui

2018-04-17

Given the important contributions of semiarid region to global land carbon cycle, accurate modeling of the interannual variability (IAV) of terrestrial gross primary productivity (GPP) is important but remains challenging. By decomposing GPP into leaf area index (LAI) and photosynthesis per leaf area (i.e., GPP_leaf), we investigated the IAV of GPP and the mechanisms responsible in a temperate grassland of northwestern China. We further assessed six ecosystem models for their capabilities in reproducing the observed IAV of GPP in a temperate grassland from 2004 to 2011 in China. We observed that the responses to LAI and GPP_leaf to soil water significantly contributed to IAV of GPP at the grassland ecosystem. Two of six models with prescribed LAI simulated of the observed IAV of GPP quite well, but still underestimated the variance of GPP_leaf, therefore the variance of GPP. In comparison, simulated pattern by the other four models with prognostic LAI differed significantly from the observed IAV of GPP. Only some models with prognostic LAI can capture the observed sharp decline of GPP in drought years. Further analysis indicated that accurately representing the responses of GPP_leaf and leaf stomatal conductance to soil moisture are critical for the models to reproduce the observed IAV of GPP_leaf. Our framework also identified that the contributions of LAI and GPP_leaf to the observed IAV of GPP were relatively independent. We conclude that our framework of decomposing GPP into LAI and GPP_leaf has a significant potential for facilitating future model intercomparison, benchmarking and optimization should be adopted for future data-model comparisons. © 2018 John Wiley & Sons Ltd.

Investigation of North American Vegetation Variability under Recent Climate: A Study Using the SSiB4/TRIFFID Biophysical/Dynamic Vegetation Model

NASA Technical Reports Server (NTRS)

Zhang, Zhengqiu; Xue, Yongkang; MacDonald, Glen; Cox, Peter M.; Collatz, George J.

2015-01-01

Recent studies have shown that current dynamic vegetation models have serious weaknesses in reproducing the observed vegetation dynamics and contribute to bias in climate simulations. This study intends to identify the major factors that underlie the connections between vegetation dynamics and climate variability and investigates vegetation spatial distribution and temporal variability at seasonal to decadal scales over North America (NA) to assess a 2-D biophysical model/dynamic vegetation model's (Simplified Simple Biosphere Model version 4, coupled with the Top-down Representation of Interactive Foliage and Flora Including Dynamics Model (SSiB4/TRIFFID)) ability to simulate these characteristics for the past 60 years (1948 through 2008). Satellite data are employed as constraints for the study and to compare the relationships between vegetation and climate from the observational and the simulation data sets. Trends in NA vegetation over this period are examined. The optimum temperature for photosynthesis, leaf drop threshold temperatures, and competition coefficients in the Lotka-Volterra equation, which describes the population dynamics of species competing for some common resource, have been identified as having major impacts on vegetation spatial distribution and obtaining proper initial vegetation conditions in SSiB4/TRIFFID. The finding that vegetation competition coefficients significantly affect vegetation distribution suggests the importance of including biotic effects in dynamical vegetation modeling. The improved SSiB4/TRIFFID can reproduce the main features of the NA distributions of dominant vegetation types, the vegetation fraction, and leaf area index (LAI), including its seasonal, interannual, and decadal variabilities. The simulated NA LAI also shows a general increasing trend after the 1970s in responding to warming. Both simulation and satellite observations reveal that LAI increased substantially in the southeastern U.S. starting from the 1980s. The effects of the severe drought during 1987-1992 and the last decade in the southwestern U.S. on vegetation are also evident from decreases in the simulated and satellite-derived LAIs. Both simulated and satellite-derived LAIs have the strongest correlations with air temperature at northern middle to high latitudes in spring reflecting the effect of these climatic variables on photosynthesis and phenological processes. Meanwhile, in southwestern dry lands, negative correlations appear due to the heat and moisture stress there during the summer. Furthermore, there are also positive correlations between soil wetness and LAI, which increases from spring to summer. The present study shows both the current improvements and remaining weaknesses in dynamical vegetation models. It also highlights large continental-scale variations that have occurred in NA vegetation over the past six decades and their potential relations to climate. With more observational data availability, more studies with differentmodels and focusing on different regions will be possible and are necessary to achieve comprehensive understanding of the vegetation dynamics and climate interactions.

Cost-effectiveness model of long-acting risperidone in schizophrenia in the US.

PubMed

Edwards, Natalie C; Rupnow, Marcia F T; Pashos, Chris L; Botteman, Marc F; Diamond, Ronald J

2005-01-01

Schizophrenia is a devastating and costly illness that affects 1% of the population in the US. Effective pharmacological therapies are available but suboptimal patient adherence to either acute or long-term therapeutic regimens reduces their effectiveness. The availability of a long-acting injection (LAI) formulation of risperidone may increase adherence and improve clinical and economic outcomes for people with schizophrenia. To assess the cost effectiveness of risperidone LAI compared with oral risperidone, oral olanzapine and haloperidol decanoate LAI over a 1-year time period in outpatients with schizophrenia who had previously suffered a relapse requiring hospitalisation. US healthcare system. Published medical literature, unpublished data from clinical trials and a consumer health database, and a clinical expert panel were used to populate a decision-analysis model comparing the four treatment alternatives. The model captured: rates of patient compliance; rates, frequency and duration of relapse; incidence of adverse events (bodyweight gain and extrapyramidal effects); and healthcare resource utilisation and associated costs. Primary outcomes were: the proportion of patients with relapse; the frequency of relapse per patient; the number of relapse days per patient; and total direct medical cost per patient per year. Costs are in year 2002 US dollars. Based on model projections, the proportions of patients experiencing a relapse requiring hospitalisation after 1 year of treatment were 66% for haloperidol decanoate LAI, 41% for oral risperidone and oral olanzapine and 26% for risperidone LAI, while the proportion of patients with a relapse not requiring hospitalisation were 60%, 37%, 37% and 24%, respectively. The mean number of days of relapse requiring hospitalisation per patient per year was 28 for haloperidol decanoate LAI, 18 for oral risperidone and oral olanzapine and 11 for risperidone LAI, while the mean number of days of relapse not requiring hospitalisation was 8, 5, 5 and 3, respectively. This would translate into direct medical cost savings with risperidone LAI compared with oral risperidone, oral olanzapine and haloperidol decanoate LAI of USD 397, USD 1742, and USD 8328, respectively. These findings were supported by sensitivity analyses. The use of risperidone LAI for treatment of outpatients with schizophrenia is predicted in this model to result in better clinical outcomes and lower total healthcare costs over 1 year than its comparators, oral risperidone, oral olanzapine and haloperidol decanoate LAI. Risperidone LAI may therefore be a cost saving therapeutic option for outpatients with schizophrenia in the US healthcare setting.

Towards an improved LAI collection protocol via simulated field-based PAR sensing

DOE PAGES

Yao, Wei; Van Leeuwen, Martin; Romanczyk, Paul; ...

2016-07-14

In support of NASA’s next-generation spectrometer—the Hyperspectral Infrared Imager (HyspIRI)—we are working towards assessing sub-pixel vegetation structure from imaging spectroscopy data. Of particular interest is Leaf Area Index (LAI), which is an informative, yet notoriously challenging parameter to efficiently measure in situ. While photosynthetically-active radiation (PAR) sensors have been validated for measuring crop LAI, there is limited literature on the efficacy of PAR-based LAI measurement in the forest environment. This study (i) validates PAR-based LAI measurement in forest environments, and (ii) proposes a suitable collection protocol, which balances efficiency with measurement variation, e.g., due to sun flecks and various-sized canopymore » gaps. A synthetic PAR sensor model was developed in the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model and used to validate LAI measurement based on first-principles and explicitly-known leaf geometry. Simulated collection parameters were adjusted to empirically identify optimal collection protocols. Furthermore, these collection protocols were then validated in the field by correlating PAR-based LAI measurement to the normalized difference vegetation index (NDVI) extracted from the “classic” Airborne Visible Infrared Imaging Spectrometer (AVIRIS-C) data (R 2 was 0.61). The results indicate that our proposed collecting protocol is suitable for measuring the LAI of sparse forest (LAI < 3–5 ( m 2/m 2)).« less

Constructing seasonal LAI trajectory by data-model fusion for global evergreen needle-leaf forests

NASA Astrophysics Data System (ADS)

Wang, R.; Chen, J.; Mo, G.

2010-12-01

For decades, advancements in optical remote sensors made it possible to produce maps of a biophysical parameter--the Leaf Area Index (LAI), which is critically necessary in regional and global modeling of exchanges of carbon, water, energy and other substances, across large areas in a fast way. Quite a few global LAI products have been generated since 2000, e.g. GLOBCARBON (Deng et al., 2006), MODIS Collection 5 (Shabanov et al., 2007), CYCLOPES (Baret et al., 2007), etc. Albeit these progresses, the basic physics behind the technology restrains it from accurate estimation of LAI in winter, especially for northern high-latitude evergreen needle-leaf forests. Underestimation of winter LAI in these regions has been reported in literature (Yang et al., 2000; Cohen et al., 2003; Tian et al., 2004; Weiss et al., 2007; Pisek et al., 2007), and the distortion is usually attributed to the variations of canopy reflectance caused by understory change (Weiss et al., 2007) as well as by the presence of ice and snow on leaves and ground (Cohen, 2003; Tian et al., 2004). Seasonal changes in leaf pigments can also be another reason for low LAI retrieved in winter. Low conifer LAI values in winter retrieved from remote sensing make them unusable for surface energy budget calculations. To avoid these drawbacks of remote sensing approaches, we attempt to reconstruct the seasonal LAI trajectory through model-data fusion. A 1-degree LAI map of global evergreen needle-leaf forests at 10-day interval is produced based on the carbon allocation principle in trees. With net primary productivity (NPP) calculated by the Boreal Ecosystems Productivity Simulator (BEPS) (Chen et al., 1999), carbon allocated to needles is quantitatively evaluated and then can be further transformed into LAI using the specific leaf area (SLA). A leaf-fall scheme is developed to mimic the carbon loss caused by falling needles throughout the year. The seasonally maximum LAI from remote sensing data for each pixel is used as an anchor point of the LAI trajectory. Ground data are used for validation. The resulting LAI does not show strong seasonality within a year, which is reasonable for evergreen needle-leaf forests with known leaf longevity.

Optimal interpolation analysis of leaf area index using MODIS data

USGS Publications Warehouse

Gu, Yingxin; Belair, Stephane; Mahfouf, Jean-Francois; Deblonde, Godelieve

2006-01-01

A simple data analysis technique for vegetation leaf area index (LAI) using Moderate Resolution Imaging Spectroradiometer (MODIS) data is presented. The objective is to generate LAI data that is appropriate for numerical weather prediction. A series of techniques and procedures which includes data quality control, time-series data smoothing, and simple data analysis is applied. The LAI analysis is an optimal combination of the MODIS observations and derived climatology, depending on their associated errors σo and σc. The “best estimate” LAI is derived from a simple three-point smoothing technique combined with a selection of maximum LAI (after data quality control) values to ensure a higher quality. The LAI climatology is a time smoothed mean value of the “best estimate” LAI during the years of 2002–2004. The observation error is obtained by comparing the MODIS observed LAI with the “best estimate” of the LAI, and the climatological error is obtained by comparing the “best estimate” of LAI with the climatological LAI value. The LAI analysis is the result of a weighting between these two errors. Demonstration of the method described in this paper is presented for the 15-km grid of Meteorological Service of Canada (MSC)'s regional version of the numerical weather prediction model. The final LAI analyses have a relatively smooth temporal evolution, which makes them more appropriate for environmental prediction than the original MODIS LAI observation data. They are also more realistic than the LAI data currently used operationally at the MSC which is based on land-cover databases.

An evaluation of MODIS 250-m data for green LAI estimation in crops

NASA Astrophysics Data System (ADS)

Gitelson, Anatoly A.; Wardlow, Brian D.; Keydan, Galina P.; Leavitt, Bryan

2007-10-01

Green leaf area index (LAI) is an important variable for climate modeling, estimates of primary production, agricultural yield forecasting, and many other diverse applications. Remotely sensed data provide considerable potential for estimating LAI at local, regional, and global scales. The goal of this study was to retrieve green LAI from MODIS 250-m vegetation index (VI) data for irrigated and rainfed maize and soybeans. The performance of both MODIS-derived NDVI and Wide Dynamic Range Vegetation Index (WDRVI) were evaluated across three growing seasons (2002 through 2004) over a wide range of LAI and also compared to the performance of NDVI and WDRVI derived from reflectance data collected at close-range across the same field locations. The NDVI vs. LAI relationship showed asymptotic behavior with a sharp decrease in the sensitivity of the NDVI to LAI exceeding 2 m2/m2 for both crops. WDRVI vs. LAI relation was linear across the entire range of LAI variation with determination coefficients above 0.93. Importantly, the coefficients of the close-range WDRVI vs. LAI equation and the MODIS-retrieved WDRVI vs. LAI equation were very close. The WDRVI was found to be capable of accurately estimating LAI across a much greater LAI range than the NDVI and can be used for assessing even slight variations in LAI, which are indicative of the early stages of plant stress. These results demonstrate the new possibilities for analyzing the spatio-temporal variation of the LAI of crops using multi-temporal MODIS 250-m imagery.

Microwave Backscatter and Attenuation Dependence of Leaf Area Index for Flooded Rice Fields

NASA Technical Reports Server (NTRS)

Durden, Stephen L.; Morrissey, Leslie A.; Livingston, Gerald P.

1995-01-01

Wetlands are important for their role in global climate as a source of methane and other reduced trace gases. As part of an effort to determine whether radar is suitable for wetland vegetation monitoring, we have studied the dependence of microwave backscatter and attenuation on leaf area index (LAI) for flooded rice fields. We find that the radar return from a flooded rice field does show dependence on LAI. In particular, the C-band VV cross section per unit area decreases with increasing LAI. A simple model for scattering from rice fields is derived and fit to the observed HH and VV data. The model fit provides insight into the relation of backscatter to LAI and is also used to calculate the canopy path attenuation as a function of LAI.

Comparison of the New LEAF Area INDEX (LAI 3G) with the Kazahstan-Wide LEAF Area INDEX DATA SET (GGRS-LAI) over Central ASIA

NASA Astrophysics Data System (ADS)

Kappas, M.; Propastin, P.; Degener, J.; Renchin, T.

2014-12-01

Long-term global data sets of Leaf Area Index (LAI) are important for monitoring global vegetation dynamics. LAI indicating phenological development of vegetation is an important state variable for modeling land surface processes. The comparison of long-term data sets is based on two recently available data sets both derived from AVHRR time series. The LAI 3g data set introduced by Zaichun Zhu et al. (2013) is developed from the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) and best-quality MODIS LAI data. The second long-term data set is based on the 8 km spatial resolution GIMMS-AVHRR data (GGRS-data set by Propastin et al. 2012). The GGRS-LAI product uses a three-dimensional physical radiative transfer model which establishes relationship between LAI, vegetation fractional cover and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation. The model incorporates a number of site/region specific parameters, including the vegetation architecture variables such as leaf angle distribution, clumping index, and light extinction coefficient. For the application of the model to Kazakhstan, the vegetation architecture variables were computed at the local (pixel) level based on extensive field surveys of the biophysical properties of vegetation in representative grassland areas of Kazakhstan. The comparison of both long-term data sets will be used to interpret their quality for scientific research in other disciplines. References:Propastin, P., Kappas, M. (2012). Retrieval of coarse-resolution leaf area index over the Republic of Kazakhstan using NOAA AVHRR satellite data and ground measurements," Remote Sensing, vol. 4, no. 1, pp. 220-246. Zaichun Zhu, Jian Bi, Yaozhong Pan, Sangram Ganguly, Alessandro Anav, Liang Xu, Arindam Samanta, Shilong Piao, Ramakrishna R. Nemani and Ranga B. Myneni (2013). Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011. Remote Sens. 2013, 5, 927-948; doi:10.3390/rs5020927

Combined Study of Snow Depth Determination and Winter Leaf Area Index Retrieval by Unmanned Aerial Vehicle Photogrammetry

NASA Astrophysics Data System (ADS)

Lendzioch, Theodora; Langhammer, Jakub; Jenicek, Michal

2017-04-01

A rapid and robust approach using Unmanned Aerial Vehicle (UAV) digital photogrammetry was performed for evaluating snow accumulation over different small localities (e.g. disturbed forest and open area) and for indirect field measurements of Leaf Area Index (LAI) of coniferous forest within the Šumava National Park, Czech Republic. The approach was used to reveal impacts related to changes in forest and snowpack and to determine winter effective LAI for monitoring the impact of forest canopy metrics on snow accumulation. Due to the advancement of the technique, snow depth and volumetric changes of snow depth over these selected study areas were estimated at high spatial resolution (1 cm) by subtracting a snow-free digital elevation model (DEM) from a snow-covered DEM. Both, downward-looking UAV images and upward-looking digital hemispherical photography (DHP), and additional widely used LAI-2200 canopy analyser measurements were applied to determine the winter LAI, controlling interception and transmitting radiation. For the performance of downward-looking UAV images the snow background instead of the sky fraction was used. The reliability of UAV-based LAI retrieval was tested by taking an independent data set during the snow cover mapping campaigns. The results showed the potential of digital photogrammetry for snow depth mapping and LAI determination by UAV techniques. The average difference obtained between ground-based and UAV-based measurements of snow depth was 7.1 cm with higher values obtained by UAV. The SD of 22 cm for the open area seemed competitive with the typical precision of point measurements. In contrast, the average difference in disturbed forest area was 25 cm with lower values obtained by UAV and a SD of 36 cm, which is in agreement with other studies. The UAV-based LAI measurements revealed the lowest effective LAI values and the plant canopy analyser LAI-2200 the highest effective LAI values. The biggest bias of effective LAI was observed between LAI-2200 and UAV-based analyses. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.

Cost Effectiveness of Paliperidone Long-Acting Injectable Versus Other Antipsychotics for the Maintenance Treatment of Schizophrenia in France.

PubMed

Druais, Sylvain; Doutriaux, Agathe; Cognet, Magali; Godet, Annabelle; Lançon, Christophe; Levy, Pierre; Samalin, Ludovic; Guillon, Pascal

2016-04-01

French clinical recommendations suggest prescribing long-acting injectable (LAI) antipsychotics to patients with a maintenance treatment indication in schizophrenia. Despite this, and due to their relatively high acquisition and administration costs, LAIs are still underused in clinical practice in France, thus highlighting the need for pharmacoeconomic evaluations. Our objective was to estimate the cost effectiveness of paliperidone LAI (or paliperidone palmitate), a once-monthly second-generation LAI antipsychotic, compared with the most common antipsychotic medications for the maintenance treatment of schizophrenia in France. A Markov model was developed to simulate the progression of a cohort of schizophrenic patients through four health states (stable treated, stable non-treated, relapse and death) and to consider up to three lines of treatment to account for changes in treatment management. Paliperidone LAI was compared with risperidone LAI, aripiprazole LAI, olanzapine LAI, haloperidol LAI (or haloperidol decanoate) and oral olanzapine. Costs, quality-adjusted life-years (QALYs) and number of relapses were assessed over 5 years based on 3-month cycles with a discount rate of 4% and from a French health insurance perspective. Patients were considered to be stabilised after a schizophrenic episode and would enter the model at an initiation phase, followed by a prevention of relapse phase if successful. Data (e.g. relapse or discontinuation rates) for the initiation phase came from randomised clinical trials, whereas relapse rates in the prevention phase were derived from hospitalisation risks based on real-life French data to capture adherence effects. Safety and utility data were derived from international publications. Additionally, costs were retrieved from French health insurance databases and publications. Finally, expert opinion was used for validation purposes or in case of gaps in data. The robustness of results was assessed through deterministic and probabilistic sensitivity analyses. All LAI antipsychotics were found to have similar costs over 5 years: approximatively €55,000, except for paliperidone LAI which had a discounted cost of €50,880. Oral olanzapine was less costly than LAIs (i.e. €50,379 after 5 years) but was associated with fewer QALYs gained and relapses avoided. Paliperidone LAI dominated aripiprazole LAI, olanzapine LAI and haloperidol LAI in terms of costs per QALY, and it was associated with slightly fewer QALYs when compared with risperidone LAI (i.e. 3.763 vs 3.764). This resulted in a high incremental cost-effectiveness ratio (ICER) (i.e. €4,770,018 per QALY gained) for risperidone LAI compared with paliperidone LAI. Paliperidone LAI was more costly than olanzapine oral but associated with more QALYs (i.e. ICER of €2411 per QALY gained for paliperidone LAI compared with oral olanzapine). Paliperidone LAI had a probability of being the optimal strategy in more than 50% of cases for a willingness-to-pay threshold of €8000 per QALY gained. This analysis, to the best of our knowledge, is the first of its kind to assess the cost effectiveness of antipsychotics based on French observational data. Paliperidone LAI appeared to be a cost-effective option in the treatment of schizophrenia from the French health insurance perspective.

Parameter optimisation for a better representation of drought by LSMs: inverse modelling vs. sequential data assimilation

NASA Astrophysics Data System (ADS)

Dewaele, Hélène; Munier, Simon; Albergel, Clément; Planque, Carole; Laanaia, Nabil; Carrer, Dominique; Calvet, Jean-Christophe

2017-09-01

Soil maximum available water content (MaxAWC) is a key parameter in land surface models (LSMs). However, being difficult to measure, this parameter is usually uncertain. This study assesses the feasibility of using a 15-year (1999-2013) time series of satellite-derived low-resolution observations of leaf area index (LAI) to estimate MaxAWC for rainfed croplands over France. LAI interannual variability is simulated using the CO2-responsive version of the Interactions between Soil, Biosphere and Atmosphere (ISBA) LSM for various values of MaxAWC. Optimal value is then selected by using (1) a simple inverse modelling technique, comparing simulated and observed LAI and (2) a more complex method consisting in integrating observed LAI in ISBA through a land data assimilation system (LDAS) and minimising LAI analysis increments. The evaluation of the MaxAWC estimates from both methods is done using simulated annual maximum above-ground biomass (Bag) and straw cereal grain yield (GY) values from the Agreste French agricultural statistics portal, for 45 administrative units presenting a high proportion of straw cereals. Significant correlations (p value < 0.01) between Bag and GY are found for up to 36 and 53 % of the administrative units for the inverse modelling and LDAS tuning methods, respectively. It is found that the LDAS tuning experiment gives more realistic values of MaxAWC and maximum Bag than the inverse modelling experiment. Using undisaggregated LAI observations leads to an underestimation of MaxAWC and maximum Bag in both experiments. Median annual maximum values of disaggregated LAI observations are found to correlate very well with MaxAWC.

Effects of foliage clumping on the estimation of global terrestrial gross primary productivity

NASA Astrophysics Data System (ADS)

Chen, Jing M.; Mo, Gang; Pisek, Jan; Liu, Jane; Deng, Feng; Ishizawa, Misa; Chan, Douglas

2012-03-01

Sunlit and shaded leaf separation proposed by Norman (1982) is an effective way to upscale from leaf to canopy in modeling vegetation photosynthesis. The Boreal Ecosystem Productivity Simulator (BEPS) makes use of this methodology, and has been shown to be reliable in modeling the gross primary productivity (GPP) derived from CO2flux and tree ring measurements. In this study, we use BEPS to investigate the effect of canopy architecture on the global distribution of GPP. For this purpose, we use not only leaf area index (LAI) but also the first ever global map of the foliage clumping index derived from the multiangle satellite sensor POLDER at 6 km resolution. The clumping index, which characterizes the degree of the deviation of 3-dimensional leaf spatial distributions from the random case, is used to separate sunlit and shaded LAI values for a given LAI. Our model results show that global GPP in 2003 was 132 ± 22 Pg C. Relative to this baseline case, our results also show: (1) global GPP is overestimated by 12% when accurate LAI is available but clumping is ignored, and (2) global GPP is underestimated by 9% when the effective LAI is available and clumping is ignored. The clumping effects in both cases are statistically significant (p < 0.001). The effective LAI is often derived from remote sensing by inverting the measured canopy gap fraction to LAI without considering the clumping. Global GPP would therefore be generally underestimated when remotely sensed LAI (actually effective LAI by our definition) is used. This is due to the underestimation of the shaded LAI and therefore the contribution of shaded leaves to GPP. We found that shaded leaves contribute 50%, 38%, 37%, 39%, 26%, 29% and 21% to the total GPP for broadleaf evergreen forest, broadleaf deciduous forest, evergreen conifer forest, deciduous conifer forest, shrub, C4 vegetation, and other vegetation, respectively. The global average of this ratio is 35%.

A Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI)

NASA Astrophysics Data System (ADS)

Houborg, Rasmus; McCabe, Matthew F.; Gao, Feng

2016-05-01

Satellite remote sensing has been used successfully to map leaf area index (LAI) across landscapes, but advances are still needed to exploit multi-scale data streams for producing LAI at both high spatial and temporal resolution. A multi-scale Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI) has been developed to generate 4-day time-series of Landsat-scale LAI from existing medium resolution LAI products. STEM-LAI has been designed to meet the demands of applications requiring frequent and spatially explicit information, such as effectively resolving rapidly evolving vegetation dynamics at sub-field (30 m) scales. In this study, STEM-LAI is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) based LAI data and utilizes a reference-based regression tree approach for producing MODIS-consistent, but Landsat-based, LAI. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is used to interpolate the downscaled LAI between Landsat acquisition dates, providing a high spatial and temporal resolution improvement over existing LAI products. STARFM predicts high resolution LAI by blending MODIS and Landsat based information from a common acquisition date, with MODIS data from a prediction date. To demonstrate its capacity to reproduce fine-scale spatial features observed in actual Landsat LAI, the STEM-LAI approach is tested over an agricultural region in Nebraska. The implementation of a 250 m resolution LAI product, derived from MODIS 1 km data and using a scale consistent approach based on the Normalized Difference Vegetation Index (NDVI), is found to significantly improve accuracies of spatial pattern prediction, with the coefficient of efficiency (E) ranging from 0.77-0.94 compared to 0.01-0.85 when using 1 km LAI inputs alone. Comparisons against an 11-year record of in-situ measured LAI over maize and soybean highlight the utility of STEM-LAI in reproducing observed LAI dynamics (both characterized by r2 = 0.86) over a range of plant development stages. Overall, STEM-LAI represents an effective downscaling and temporal enhancement mechanism that predicts in-situ measured LAI better than estimates derived through linear interpolation between Landsat acquisitions. This is particularly true when the in-situ measurement date is greater than 10 days from the nearest Landsat acquisition, with prediction errors reduced by up to 50%. With a streamlined and completely automated processing interface, STEM-LAI represents a flexible tool for LAI disaggregation in space and time that is adaptable to different land cover types, landscape heterogeneities, and cloud cover conditions.

Towards 250 m mapping of terrestrial primary productivity over Canada

NASA Astrophysics Data System (ADS)

Gonsamo, A.; Chen, J. M.

2011-12-01

Terrestrial ecosystems are an important part of the climate and global change systems. Their role in climate change and in the global carbon cycle is yet to be well understood. Dataset from satellite earth observation, coupled with numerical models provide the unique tools for monitoring the spatial and temporal dynamics of territorial carbon cycle. The Boreal Ecosystems Productivity Simulator (BEPS) is a remote sensing based approach to quantifying the terrestrial carbon cycle by that gross and net primary productivity (GPP and NPP) and terrestrial carbon sinks and sources expressed as net ecosystem productivity (NEP). We have currently implemented a scheme to map the GPP, NPP and NEP at 250 m for first time over Canada using BEPS model. This is supplemented by improved mapping of land cover and leaf area index (LAI) at 250 m over Canada from MODIS satellite dataset. The results from BEPS are compared with MODIS GPP product and further evaluated with estimated LAI from various sources to evaluate if the results capture the trend in amount of photosynthetic biomass distributions. Final evaluation will be to validate both BEPS and MODIS primary productivity estimates over the Fluxnet sites over Canada. The primary evaluation indicate that BEPS GPP estimates capture the over storey LAI variations over Canada very well compared to MODIS GPP estimates. There is a large offset of MODIS GPP, over-estimating the lower GPP value compared to BEPS GPP estimates. These variations will further be validated based on the measured values from the Fluxnet tower measurements over Canadian. The high resolution GPP (NPP) products at 250 m will further be used to scale the outputs between different ecosystem productivity models, in our case the Canadian carbon budget model of Canadian forest sector CBM-CFS) and the Integrated Terrestrial Ecosystem Carbon model (InTEC).

Shallow to Deep Convection Transition over a Heterogeneous Land Surface Using the Land Model Coupled Large-Eddy Simulation

NASA Astrophysics Data System (ADS)

Lee, J.; Zhang, Y.; Klein, S. A.

2017-12-01

The triggering of the land breeze, and hence the development of deep convection over heterogeneous land should be understood as a consequence of the complex processes involving various factors from land surface and atmosphere simultaneously. That is a sub-grid scale process that many large-scale models have difficulty incorporating it into the parameterization scheme partly due to lack of our understanding. Thus, it is imperative that we approach the problem using a high-resolution modeling framework. In this study, we use SAM-SLM (Lee and Khairoutdinov, 2015), a large-eddy simulation model coupled to a land model, to explore the cloud effect such as cold pool, the cloud shading and the soil moisture memory on the land breeze structure and the further development of cloud and precipitation over a heterogeneous land surface. The atmospheric large scale forcing and the initial sounding are taken from the new composite case study of the fair-weather, non-precipitating shallow cumuli at ARM SGP (Zhang et al., 2017). We model the land surface as a chess board pattern with alternating leaf area index (LAI). The patch contrast of the LAI is adjusted to encompass the weak to strong heterogeneity amplitude. The surface sensible- and latent heat fluxes are computed according to the given LAI representing the differential surface heating over a heterogeneous land surface. Separate from the surface forcing imposed from the originally modeled surface, the cases that transition into the moist convection can induce another layer of the surface heterogeneity from the 1) radiation shading by clouds, 2) adjusted soil moisture pattern by the rain, 3) spreading cold pool. First, we assess and quantifies the individual cloud effect on the land breeze and the moist convection under the weak wind to simplify the feedback processes. And then, the same set of experiments is repeated under sheared background wind with low level jet, a typical summer time wind pattern at ARM SGP site, to account for more realistic situations. Our goal is to assist answering the question: "Do the sub-grid scale land surface heterogeneity matter for the weather and climate modeling?" This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS- 736011.

Assimilating leaf area index of three typical types of subtropical forest in China from MODIS time series data based on the integrated ensemble Kalman filter and PROSAIL model

NASA Astrophysics Data System (ADS)

Li, Xuejian; Mao, Fangjie; Du, Huaqiang; Zhou, Guomo; Xu, Xiaojun; Han, Ning; Sun, Shaobo; Gao, Guolong; Chen, Liang

2017-04-01

Subtropical forest ecosystems play essential roles in the global carbon cycle and in carbon sequestration functions, which challenge the traditional understanding of the main functional areas of carbon sequestration in the temperate forests of Europe and America. The leaf area index (LAI) is an important biological parameter in the spatiotemporal simulation of the carbon cycle, and it has considerable significance in carbon cycle research. Dynamic retrieval based on remote sensing data is an important method with which to obtain large-scale high-accuracy assessments of LAI. This study developed an algorithm for assimilating LAI dynamics based on an integrated ensemble Kalman filter using MODIS LAI data, MODIS reflectance data, and canopy reflectance data modeled by PROSAIL, for three typical types of subtropical forest (Moso bamboo forest, Lei bamboo forest, and evergreen and deciduous broadleaf forest) in China during 2014-2015. There were some errors of assimilation in winter, because of the bad data quality of the MODIS product. Overall, the assimilated LAI well matched the observed LAI, with R2 of 0.82, 0.93, and 0.87, RMSE of 0.73, 0.49, and 0.42, and aBIAS of 0.50, 0.23, and 0.03 for Moso bamboo forest, Lei bamboo forest, and evergreen and deciduous broadleaf forest, respectively. The algorithm greatly decreased the uncertainty of the MODIS LAI in the growing season and it improved the accuracy of the MODIS LAI. The advantage of the algorithm is its use of biophysical parameters (e.g., measured LAI) in the LAI assimilation, which makes it possible to assimilate long-term MODIS LAI time series data, and to provide high-accuracy LAI data for the study of carbon cycle characteristics in subtropical forest ecosystems.

Three Different Methods of Estimating LAI in a Small Watershed

NASA Astrophysics Data System (ADS)

Speckman, H. N.; Ewers, B. E.; Beverly, D.

2015-12-01

Leaf area index (LAI) is a critical input of models that improve predictive understanding of ecology, hydrology, and climate change. Multiple techniques exist to quantify LAI, most of which are labor intensive, and all often fail to converge on similar estimates. . Recent large-scale bark beetle induced mortality greatly altered LAI, which is now dominated by younger and more metabolically active trees compared to the pre-beetle forest. Tree mortality increases error in optical LAI estimates due to the lack of differentiation between live and dead branches in dense canopy. Our study aims to quantify LAI using three different LAI methods, and then to compare the techniques to each other and topographic drivers to develop an effective predictive model of LAI. This study focuses on quantifying LAI within a small (~120 ha) beetle infested watershed in Wyoming's Snowy Range Mountains. The first technique estimated LAI using in-situ hemispherical canopy photographs that were then analyzed with Hemisfer software. The second LAI estimation technique was use of the Kaufmann 1982 allometrerics from forest inventories conducted throughout the watershed, accounting for stand basal area, species composition, and the extent of bark beetle driven mortality. The final technique used airborne light detection and ranging (LIDAR) first DMS returns, which were used to estimating canopy heights and crown area. LIDAR final returns provided topographical information and were then ground-truthed during forest inventories. Once data was collected, a fractural analysis was conducted comparing the three methods. Species composition was driven by slope position and elevation Ultimately the three different techniques provided very different estimations of LAI, but each had their advantage: estimates from hemisphere photos were well correlated with SWE and snow depth measurements, forest inventories provided insight into stand health and composition, and LIDAR were able to quickly and efficiently cover a very large area.

LAI inversion algorithm based on directional reflectance kernels.

PubMed

Tang, S; Chen, J M; Zhu, Q; Li, X; Chen, M; Sun, R; Zhou, Y; Deng, F; Xie, D

2007-11-01

Leaf area index (LAI) is an important ecological and environmental parameter. A new LAI algorithm is developed using the principles of ground LAI measurements based on canopy gap fraction. First, the relationship between LAI and gap fraction at various zenith angles is derived from the definition of LAI. Then, the directional gap fraction is acquired from a remote sensing bidirectional reflectance distribution function (BRDF) product. This acquisition is obtained by using a kernel driven model and a large-scale directional gap fraction algorithm. The algorithm has been applied to estimate a LAI distribution in China in mid-July 2002. The ground data acquired from two field experiments in Changbai Mountain and Qilian Mountain were used to validate the algorithm. To resolve the scale discrepancy between high resolution ground observations and low resolution remote sensing data, two TM images with a resolution approaching the size of ground plots were used to relate the coarse resolution LAI map to ground measurements. First, an empirical relationship between the measured LAI and a vegetation index was established. Next, a high resolution LAI map was generated using the relationship. The LAI value of a low resolution pixel was calculated from the area-weighted sum of high resolution LAIs composing the low resolution pixel. The results of this comparison showed that the inversion algorithm has an accuracy of 82%. Factors that may influence the accuracy are also discussed in this paper.

Evaluation of seasonal variations of remotely sensed leaf area index over five evergreen coniferous forests

NASA Astrophysics Data System (ADS)

Wang, Rong; Chen, Jing M.; Liu, Zhili; Arain, Altaf

2017-08-01

Seasonal variations of leaf area index (LAI) have crucial controls on the interactions between the land surface and the atmosphere. Over the past decades, a number of remote sensing (RS) LAI products have been developed at both global and regional scales for various applications. These products are so far only validated using ground LAI data acquired mostly in the middle of the growing season. The accuracy of the seasonal LAI variation in these products remains unknown and there are few ground data available for this purpose. We performed regular LAI measurements over a whole year at five coniferous sites using two methods: (1) an optical method with LAI-2000 and TRAC; (2) a direct method through needle elongation monitoring and litterfall collection. We compared seasonal trajectory of LAI from remote sensing (RS LAI) with that from a direct method (direct LAI). RS LAI agrees very well with direct LAI from the onset of needle growth to the seasonal peak (R2 = 0.94, RMSE = 0.44), whereas RS LAI declines earlier and faster than direct LAI from the seasonal peak to the completion of needle fall. To investigate the possible reasons for the discrepancy, the MERIS Terrestrial Chlorophyll Index (MTCI) was compared with RS LAI. Meanwhile, phenological metrics, i.e. the start of growing season (SOS) and the end of growing season (EOS), were extracted from direct LAI, RS LAI and MTCI time series. SOS from RS LAI is later than that from direct LAI by 9.3 ± 4.0 days but earlier than that from MTCI by 2.6 ± 1.9 days. On the contrary, for EOS, RS LAI is later than MTCI by 3.3 ± 8.4 days and much earlier than direct LAI by 30.8 ± 7.2 days. Our results suggest that the seasonal trajectory of RS LAI well captures canopy structural information from the onset of needle growth to the seasonal peak, but is greatly influenced by the decrease in leaf chlorophyll content, as indicated by MTCI, from the seasonal peak to the completion of needle fall. These findings have significant implications for improving existing RS LAI products and terrestrial productivity modeling.

LAI inversion from optical reflectance using a neural network trained with a multiple scattering model

NASA Technical Reports Server (NTRS)

Smith, James A.

1992-01-01

The inversion of the leaf area index (LAI) canopy parameter from optical spectral reflectance measurements is obtained using a backpropagation artificial neural network trained using input-output pairs generated by a multiple scattering reflectance model. The problem of LAI estimation over sparse canopies (LAI < 1.0) with varying soil reflectance backgrounds is particularly difficult. Standard multiple regression methods applied to canopies within a single homogeneous soil type yield good results but perform unacceptably when applied across soil boundaries, resulting in absolute percentage errors of >1000 percent for low LAI. Minimization methods applied to merit functions constructed from differences between measured reflectances and predicted reflectances using multiple-scattering models are unacceptably sensitive to a good initial guess for the desired parameter. In contrast, the neural network reported generally yields absolute percentage errors of <30 percent when weighting coefficients trained on one soil type were applied to predicted canopy reflectance at a different soil background.

Evaluating the potential of vegetation indices for winter wheat LAI estimation under different fertilization and water conditions

NASA Astrophysics Data System (ADS)

Xie, Qiaoyun; Huang, Wenjiang; Dash, Jadunandan; Song, Xiaoyu; Huang, Linsheng; Zhao, Jinling; Wang, Renhong

2015-12-01

Leaf area index (LAI) is an important indicator for monitoring crop growth conditions and forecasting grain yield. Many algorithms have been developed for remote estimation of the leaf area index of vegetation, such as using spectral vegetation indices, inversion of radiative transfer models, and supervised learning techniques. Spectral vegetation indices, mathematical combination of reflectance bands, are widely used for LAI estimation due to their computational simplicity and their applications ranged from the leaf scale to the entire globe. However, in many cases, their applicability is limited to specific vegetation types or local conditions due to species specific nature of the relationship used to transfer the vegetation indices to LAI. The overall objective of this study is to investigate the most suitable vegetation index for estimating winter wheat LAI under eight different types of fertilizer and irrigation conditions. Regression models were used to estimate LAI using hyperspectral reflectance data from the Pushbroom Hyperspectral Imager (PHI) and in-situ measurements. Our results showed that, among six vegetation indices investigated, the modified soil-adjusted vegetation index (MSAVI) and the normalized difference vegetation index (NDVI) exhibited strong and significant relationships with LAI, and thus were sensitive across different nitrogen and water treatments. The modified triangular vegetation index (MTVI2) confirmed its potential on crop LAI estimation, although second to MSAVI and NDVI in our study. The enhanced vegetation index (EVI) showed moderate performance. However, the ratio vegetation index (RVI) and the modified simple ratio index (MSR) predicted the least accurate estimations of LAI, exposing the simple band ratio index's weakness under different treatment conditions. The results support the use of vegetation indices for a quick and effective LAI mapping procedure that is suitable for winter wheat under different management practices.

Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza sativa L. at Diverse Phenological Stages

PubMed Central

Din, Mairaj; Zheng, Wen; Rashid, Muhammad; Wang, Shanqin; Shi, Zhihua

2017-01-01

Hyperspectral reflectance derived vegetation indices (VIs) are used for non-destructive leaf area index (LAI) monitoring for precise and efficient N nutrition management. This study tested the hypothesis that there is potential for using various hyperspectral VIs for estimating LAI at different growth stages of rice under varying N rates. Hyperspectral reflectance and crop canopy LAI measurements were carried out over 2 years (2015 and 2016) in Meichuan, Hubei, China. Different N fertilization, 0, 45, 82, 127, 165, 210, 247, and 292 kg ha-1, were applied to generate various scales of VIs and LAI values. Regression models were used to perform quantitative analyses between spectral VIs and LAI measured under different phenological stages. In addition, the coefficient of determination and RMSE were employed to evaluate these models. Among the nine VIs, the ratio vegetation index, normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index (MSAVI), modified triangular vegetation index (MTVI2) and exhibited strong and significant relationships with the LAI estimation at different phenological stages. The enhanced vegetation index performed moderately. However, the green normalized vegetation index and blue normalized vegetation index confirmed that there is potential for crop LAI estimation at early phenological stages; the soil-adjusted vegetation index and optimized soil-adjusted vegetation index were more related to the soil optical properties, which were predicted to be the least accurate for LAI estimation. The noise equivalent accounted for the sensitivity of the VIs and MSAVI, MTVI2, and NDVI for the LAI estimation at phenological stages. The results note that LAI at different crop phenological stages has a significant influence on the potential of hyperspectral derived VIs under different N management practices. PMID:28588596

Comparative Analysis of EO-1 ALI and Hyperion, and Landsat ETM+ Data for Mapping Forest Crown Closure and Leaf Area Index

PubMed Central

Pu, Ruiliang; Gong, Peng; Yu, Qian

2008-01-01

In this study, a comparative analysis of capabilities of three sensors for mapping forest crown closure (CC) and leaf area index (LAI) was conducted. The three sensors are Hyperspectral Imager (Hyperion) and Advanced Land Imager (ALI) onboard EO-1 satellite and Landsat-7 Enhanced Thematic Mapper Plus (ETM+). A total of 38 mixed coniferous forest CC and 38 LAI measurements were collected at Blodgett Forest Research Station, University of California at Berkeley, USA. The analysis method consists of (1) extracting spectral vegetation indices (VIs), spectral texture information and maximum noise fractions (MNFs), (2) establishing multivariate prediction models, (3) predicting and mapping pixel-based CC and LAI values, and (4) validating the mapped CC and LAI results with field validated photo-interpreted CC and LAI values. The experimental results indicate that the Hyperion data are the most effective for mapping forest CC and LAI (CC mapped accuracy (MA) = 76.0%, LAI MA = 74.7%), followed by ALI data (CC MA = 74.5%, LAI MA = 70.7%), with ETM+ data results being least effective (CC MA = 71.1%, LAI MA = 63.4%). This analysis demonstrates that the Hyperion sensor outperforms the other two sensors: ALI and ETM+. This is because of its high spectral resolution with rich subtle spectral information, of its short-wave infrared data for constructing optimal VIs that are slightly affected by the atmosphere, and of its more available MNFs than the other two sensors to be selected for establishing prediction models. Compared to ETM+ data, ALI data are better for mapping forest CC and LAI due to ALI data with more bands and higher signal-to-noise ratios than those of ETM+ data. PMID:27879906

Comparative Analysis of EO-1 ALI and Hyperion, and Landsat ETM+ Data for Mapping Forest Crown Closure and Leaf Area Index.

PubMed

Pu, Ruiliang; Gong, Peng; Yu, Qian

2008-06-06

In this study, a comparative analysis of capabilities of three sensors for mapping forest crown closure (CC) and leaf area index (LAI) was conducted. The three sensors are Hyperspectral Imager (Hyperion) and Advanced Land Imager (ALI) onboard EO-1 satellite and Landsat-7 Enhanced Thematic Mapper Plus (ETM+). A total of 38 mixed coniferous forest CC and 38 LAI measurements were collected at Blodgett Forest Research Station, University of California at Berkeley, USA. The analysis method consists of (1) extracting spectral vegetation indices (VIs), spectral texture information and maximum noise fractions (MNFs), (2) establishing multivariate prediction models, (3) predicting and mapping pixel-based CC and LAI values, and (4) validating the mapped CC and LAI results with field validated photo-interpreted CC and LAI values. The experimental results indicate that the Hyperion data are the most effective for mapping forest CC and LAI (CC mapped accuracy (MA) = 76.0%, LAI MA = 74.7%), followed by ALI data (CC MA = 74.5%, LAI MA = 70.7%), with ETM+ data results being least effective (CC MA = 71.1%, LAI MA = 63.4%). This analysis demonstrates that the Hyperion sensor outperforms the other two sensors: ALI and ETM+. This is because of its high spectral resolution with rich subtle spectral information, of its short-wave infrared data for constructing optimal VIs that are slightly affected by the atmosphere, and of its more available MNFs than the other two sensors to be selected for establishing prediction models. Compared to ETM+ data, ALI data are better for mapping forest CC and LAI due to ALI data with more bands and higher signal-to-noise ratios than those of ETM+ data.

Assimilation of Leaf Area Index and Soil Wetness Index into the ISBA-A-gs land surface model over France

NASA Astrophysics Data System (ADS)

Barbu, A. L.; Calvet, J.-C.; Lafont, S.

2012-04-01

The development of a Land Data Assimilation System (LDAS) dedicated to carbon and water cycles is considered as a key aspect for monitoring activities of terrestrial carbon fluxes. It allows the assimilation of biophysical products in order to reduce the bias between the model simulations and the observations and have a positive impact on carbon and water fluxes. This work shows the benefits of data assimilation of Earth observations for the monitoring of vegetation status and carbon fluxes, in the framework of the GEOLAND2 project, co-funded by the European Commission within the GMES initiative in FP7. In this study, the SURFEX modelling platform developed at Meteo-France is used for describing the continental vegetation state, surface fluxes and soil moisture. It consists of the land surface model ISBA-A-gs that simulates photosynthesis and plant growth. The vegetation biomass and Leaf Area Index (LAI) evolve dynamically in response to weather and climate conditions. The ECOCLIMAP database provides detailed information about the land cover at a resolution of 1 km. Over the France domain, the most present ecosystem types are grasslands (32%), C3 crop lands (24%), deciduous forest (20%), bare soil (11%), and C4 crop lands (8%).The model also includes a representation of the soil moisture stress with two different types of drought responses for herbaceous vegetation and forests. A version of the Extended Kalman Filter (EKF) scheme is developed for the joint assimilation of satellite-derived surface soil moisture from ASCAT-25 km product, namely Soil Wetness Index (SWI-01) developed by TU-Wien, and remote sensing LAI product provided by GEOLAND2. The GEOLAND2 LAI product is derived from CYCLOPES V3.1 and MODIS collection 5 data. It is more consistent with an effective LAI for low LAI and close to the actual LAI for high values. The assimilation experiment was conducted across France at a spatial resolution of 8 km. The study period ranges from July 2007 to December 2010. In the model simulation, the start of the growing season tends to occur later than in the observations. Similarly, the senescence phase is delayed. The assimilation is able to reduce this bias. The lack of detailed knowledge of the farming practices and other shortcomings of the model are compensated by the assimilation of the remotely sensed LAI. The analyzed seasonal LAI cycle across large cropland regions (north-eastern France) is closer to the observations. A coherent impact of LAI and soil moisture updates on the carbon fluxes is noticed. Increased LAI values in the growing season due to data assimilation corrections trigger an increased photosynthetic activity. Similarly, lower LAI values corresponding to the senescence phase cause a decrease in the carbon dioxide uptake when compared to the original model simulations.

Coupling a distributed hydrological model with detailed forest structural information for large-scale global change impact assessment

NASA Astrophysics Data System (ADS)

Eisner, Stephanie; Huang, Shaochun; Majasalmi, Titta; Bright, Ryan; Astrup, Rasmus; Beldring, Stein

2017-04-01

Forests are recognized for their decisive effect on landscape water balance with structural forest characteristics as stand density or species composition determining energy partitioning and dominant flow paths. However, spatial and temporal variability in forest structure is often poorly represented in hydrological modeling frameworks, in particular in regional to large scale hydrological modeling and impact analysis. As a common practice, prescribed land cover classes (including different generic forest types) are linked to parameter values derived from literature, or parameters are determined by calibration. While national forest inventory (NFI) data provide comprehensive, detailed information on hydrologically relevant forest characteristics, their potential to inform hydrological simulation over larger spatial domains is rarely exploited. In this study we present a modeling framework that couples the distributed hydrological model HBV with forest structural information derived from the Norwegian NFI and multi-source remote sensing data. The modeling framework, set up for the entire of continental Norway at 1 km spatial resolution, is explicitly designed to study the combined and isolated impacts of climate change, forest management and land use change on hydrological fluxes. We use a forest classification system based on forest structure rather than biomes which allows to implicitly account for impacts of forest management on forest structural attributes. In the hydrological model, different forest classes are represented by three parameters: leaf area index (LAI), mean tree height and surface albedo. Seasonal cycles of LAI and surface albedo are dynamically simulated to make the framework applicable under climate change conditions. Based on a hindcast for the pilot regions Nord-Trøndelag and Sør-Trøndelag, we show how forest management has affected regional hydrological fluxes during the second half of the 20th century as contrasted to climate variability.

[Hyperspectral Estimation of Apple Tree Canopy LAI Based on SVM and RF Regression].

PubMed

Han, Zhao-ying; Zhu, Xi-cun; Fang, Xian-yi; Wang, Zhuo-yuan; Wang, Ling; Zhao, Geng-Xing; Jiang, Yuan-mao

2016-03-01

Leaf area index (LAI) is the dynamic index of crop population size. Hyperspectral technology can be used to estimate apple canopy LAI rapidly and nondestructively. It can be provide a reference for monitoring the tree growing and yield estimation. The Red Fuji apple trees of full bearing fruit are the researching objects. Ninety apple trees canopies spectral reflectance and LAI values were measured by the ASD Fieldspec3 spectrometer and LAI-2200 in thirty orchards in constant two years in Qixia research area of Shandong Province. The optimal vegetation indices were selected by the method of correlation analysis of the original spectral reflectance and vegetation indices. The models of predicting the LAI were built with the multivariate regression analysis method of support vector machine (SVM) and random forest (RF). The new vegetation indices, GNDVI527, ND-VI676, RVI682, FD-NVI656 and GRVI517 and the previous two main vegetation indices, NDVI670 and NDVI705, are in accordance with LAI. In the RF regression model, the calibration set decision coefficient C-R2 of 0.920 and validation set decision coefficient V-R2 of 0.889 are higher than the SVM regression model by 0.045 and 0.033 respectively. The root mean square error of calibration set C-RMSE of 0.249, the root mean square error validation set V-RMSE of 0.236 are lower than that of the SVM regression model by 0.054 and 0.058 respectively. Relative analysis of calibrating error C-RPD and relative analysis of validation set V-RPD reached 3.363 and 2.520, 0.598 and 0.262, respectively, which were higher than the SVM regression model. The measured and predicted the scatterplot trend line slope of the calibration set and validation set C-S and V-S are close to 1. The estimation result of RF regression model is better than that of the SVM. RF regression model can be used to estimate the LAI of red Fuji apple trees in full fruit period.

Inconsistencies of interannual variability and trends in long-term satellite leaf area index products.

PubMed

Jiang, Chongya; Ryu, Youngryel; Fang, Hongliang; Myneni, Ranga; Claverie, Martin; Zhu, Zaichun

2017-10-01

Understanding the long-term performance of global satellite leaf area index (LAI) products is important for global change research. However, few effort has been devoted to evaluating the long-term time-series consistencies of LAI products. This study compared four long-term LAI products (GLASS, GLOBMAP, LAI3g, and TCDR) in terms of trends, interannual variabilities, and uncertainty variations from 1982 through 2011. This study also used four ancillary LAI products (GEOV1, MERIS, MODIS C5, and MODIS C6) from 2003 through 2011 to help clarify the performances of the four long-term LAI products. In general, there were marked discrepancies between the four long-term LAI products. During the pre-MODIS period (1982-1999), both linear trends and interannual variabilities of global mean LAI followed the order GLASS>LAI3g>TCDR>GLOBMAP. The GLASS linear trend and interannual variability were almost 4.5 times those of GLOBMAP. During the overlap period (2003-2011), GLASS and GLOBMAP exhibited a decreasing trend, TCDR no trend, and LAI3g an increasing trend. GEOV1, MERIS, and MODIS C6 also exhibited an increasing trend, but to a much smaller extent than that from LAI3g. During both periods, the R 2 of detrended anomalies between the four long-term LAI products was smaller than 0.4 for most regions. Interannual variabilities of the four long-term LAI products were considerably different over the two periods, and the differences followed the order GLASS>LAI3g>TCDR>GLOBMAP. Uncertainty variations quantified by a collocation error model followed the same order. Our results indicate that the four long-term LAI products were neither intraconsistent over time nor interconsistent with each other. These inconsistencies may be due to NOAA satellite orbit changes and MODIS sensor degradation. Caution should be used in the interpretation of global changes derived from the four long-term LAI products. © 2017 John Wiley & Sons Ltd.

Comprehensive ecosystem model-experiment synthesis using multiple datasets at two temperate forest free-air CO2 enrichment experiments: model performance and compensating biases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Walker, Anthony P; Hanson, Paul J; DeKauwe, Martin G

2014-01-01

Free Air CO2 Enrichment (FACE) experiments provide a remarkable wealth of data to test the sensitivities of terrestrial ecosystem models (TEMs). In this study, a broad set of 11 TEMs were compared to 22 years of data from two contrasting FACE experiments in temperate forests of the south eastern US the evergreen Duke Forest and the deciduous Oak Ridge forest. We evaluated the models' ability to reproduce observed net primary productivity (NPP), transpiration and Leaf Area index (LAI) in ambient CO2 treatments. Encouragingly, many models simulated annual NPP and transpiration within observed uncertainty. Daily transpiration model errors were often relatedmore » to errors in leaf area phenology and peak LAI. Our analysis demonstrates that the simulation of LAI often drives the simulation of transpiration and hence there is a need to adopt the most appropriate of hypothesis driven methods to simulate and predict LAI. Of the three competing hypotheses determining peak LAI (1) optimisation to maximise carbon export, (2) increasing SLA with canopy depth and (3) the pipe model the pipe model produced LAI closest to the observations. Modelled phenology was either prescribed or based on broader empirical calibrations to climate. In some cases, simulation accuracy was achieved through compensating biases in component variables. For example, NPP accuracy was sometimes achieved with counter-balancing biases in nitrogen use efficiency and nitrogen uptake. Combined analysis of parallel measurements aides the identification of offsetting biases; without which over-confidence in model abilities to predict ecosystem function may emerge, potentially leading to erroneous predictions of change under future climates.« less

Extracting leaf area index using viewing geometry effects-A new perspective on high-resolution unmanned aerial system photography

NASA Astrophysics Data System (ADS)

Roth, Lukas; Aasen, Helge; Walter, Achim; Liebisch, Frank

2018-07-01

Extraction of leaf area index (LAI) is an important prerequisite in numerous studies related to plant ecology, physiology and breeding. LAI is indicative for the performance of a plant canopy and of its potential for growth and yield. In this study, a novel method to estimate LAI based on RGB images taken by an unmanned aerial system (UAS) is introduced. Soybean was taken as the model crop of investigation. The method integrates viewing geometry information in an approach related to gap fraction theory. A 3-D simulation of virtual canopies helped developing and verifying the underlying model. In addition, the method includes techniques to extract plot based data from individual oblique images using image projection, as well as image segmentation applying an active learning approach. Data from a soybean field experiment were used to validate the method. The thereby measured LAI prediction accuracy was comparable with the one of a gap fraction-based handheld device (R2 of 0.92 , RMSE of 0.42 m 2m-2) and correlated well with destructive LAI measurements (R2 of 0.89 , RMSE of 0.41 m2 m-2). These results indicate that, if respecting the range (LAI ≤ 3) the method was tested for, extracting LAI from UAS derived RGB images using viewing geometry information represents a valid alternative to destructive and optical handheld device LAI measurements in soybean. Thereby, we open the door for automated, high-throughput assessment of LAI in plant and crop science.

Winter wheat yield estimation of remote sensing research based on WOFOST crop model and leaf area index assimilation

NASA Astrophysics Data System (ADS)

Chen, Yanling; Gong, Adu; Li, Jing; Wang, Jingmei

2017-04-01

Accurate crop growth monitoring and yield predictive information are significant to improve the sustainable development of agriculture and ensure the security of national food. Remote sensing observation and crop growth simulation models are two new technologies, which have highly potential applications in crop growth monitoring and yield forecasting in recent years. However, both of them have limitations in mechanism or regional application respectively. Remote sensing information can not reveal crop growth and development, inner mechanism of yield formation and the affection of environmental meteorological conditions. Crop growth simulation models have difficulties in obtaining data and parameterization from single-point to regional application. In order to make good use of the advantages of these two technologies, the coupling technique of remote sensing information and crop growth simulation models has been studied. Filtering and optimizing model parameters are key to yield estimation by remote sensing and crop model based on regional crop assimilation. Winter wheat of GaoCheng was selected as the experiment object in this paper. And then the essential data was collected, such as biochemical data and farmland environmental data and meteorological data about several critical growing periods. Meanwhile, the image of environmental mitigation small satellite HJ-CCD was obtained. In this paper, research work and major conclusions are as follows. (1) Seven vegetation indexes were selected to retrieve LAI, and then linear regression model was built up between each of these indexes and the measured LAI. The result shows that the accuracy of EVI model was the highest (R2=0.964 at anthesis stage and R2=0.920 at filling stage). Thus, EVI as the most optimal vegetation index to predict LAI in this paper. (2) EFAST method was adopted in this paper to conduct the sensitive analysis to the 26 initial parameters of the WOFOST model and then a sensitivity index was constructed to evaluate the influence of each parameter mentioned above on the winter wheat yield formation. Finally, six parameters that sensitivity index more than 0.1 as sensitivity factors were chose, which are TSUM1, SLATB1, SLATB2, SPAN, EFFTB3 and TMPF4. To other parameters, we confirmed them via practical measurement and calculation, available literature or WOFOST default. Eventually, we completed the regulation of WOFOST parameters. (3) Look-up table algorithm was used to realize single-point yield estimation through the assimilation of the WOFOST model and the retrieval LAI. This simulation achieved a high accuracy which perfectly meet the purpose of assimilation (R2=0.941 and RMSE=194.58kg/hm2). In this paper, the optimum value of sensitivity parameters were confirmed and the estimation of single-point yield were finished. Key words: yield estimation of winter wheat, LAI, WOFOST crop growth model, assimilation

Development of a global LAI estimation algorithm for JAXA's new earth observation satellite sensor, GCOM-C/SGLI

NASA Astrophysics Data System (ADS)

Ono, Y.; Murakami, H.; Kobayashi, H.; Nasahara, K. N.; Kajiwara, K.; Honda, Y.

2014-12-01

Leaf Area Index (LAI) is defined as the one-side green leaf area per unit ground surface area. Global LAI products, such as MOD15 (Terra&Aqua/MODIS) and CYCLOPES (SPOT/VEGETATION) are used for many global terrestrial carbon models. Japan Aerospace eXploration Agency (JAXA) is planning to launch GCOM-C (Global Change Observation Mission-Climate) which carries SGLI (Second-generation GLobal Imager) in the Japanese Fiscal Year 2017. SGLI has the features, such as 17-channel from near ultraviolet to thermal infrared, 250-m spatial resolution, polarization, and multi-angle (nadir and ±45-deg. along-track slant) observation. In the GCOM-C/SGLI land science team, LAI is scheduled to be generated from GCOM-C/SGLI observation data as a standard product (daily 250-m). In extisting algorithms, LAI is estimated by the reverse analysis of vegetation radiative transfer models (RTMs) using multi-spectral and mono-angle observation data. Here, understory layer in vegetation RTMs is assumed by plane parallel (green leaves + soil) which set up arbitrary understroy LAI. However, actual understory consists of various elements, such as green leaves, dead leaves, branches, soil, and snow. Therefore, if understory in vegetation RTMs differs from reality, it will cause an error of LAI to estimate. This report describes an algorithm which estimates LAI in consideration of the influence of understory using GCOM-C/SGLI multi-spectral and multi-angle observation data.

Investigating the Impact of Aerosol Deposition on Snow Melt over the Greenland Ice Sheet Using a New Kernel

NASA Astrophysics Data System (ADS)

Li, Y.; Flanner, M.

2017-12-01

Accelerating surface melt on the Greenland Ice Sheet (GrIS) has led to a doubling of Greenland's contribution to global sea level rise during recent decades. The darkening effect due to black carbon (BC), dust, and other light absorbing impurities (LAI) enhances snow melt by boosting its absorption of solar energy. It is therefore important for coupled aerosol-climate and ice sheet models to include snow darkening effects from LAI, and yet most do not. In this study, we develop an aerosol deposition—snow melt kernel based on the Community Earth System Model (CESM) to investigate changes in melt flux due to variations in the amount and timing of aerosol deposition on the GrIS. The Community Land Model (CLM) component of CESM is driven with a large range of aerosol deposition fluxes to determine non-linear relationships between melt perturbation and deposition amount occurring in different months and location (thereby capturing variations in base state associated with elevation and latitude). The kernel product will include climatological-mean effects and standard deviations associated with interannual variability. Finally, the kernel will allow aerosol deposition fluxes from any global or regional aerosol model to be translated into surface melt perturbations of the GrIS, thus extending the utility of state-of-the-art aerosol models.

Digital cover photography for estimating leaf area index (LAI) in apple trees using a variable light extinction coefficient.

PubMed

Poblete-Echeverría, Carlos; Fuentes, Sigfredo; Ortega-Farias, Samuel; Gonzalez-Talice, Jaime; Yuri, Jose Antonio

2015-01-28

Leaf area index (LAI) is one of the key biophysical variables required for crop modeling. Direct LAI measurements are time consuming and difficult to obtain for experimental and commercial fruit orchards. Devices used to estimate LAI have shown considerable errors when compared to ground-truth or destructive measurements, requiring tedious site-specific calibrations. The objective of this study was to test the performance of a modified digital cover photography method to estimate LAI in apple trees using conventional digital photography and instantaneous measurements of incident radiation (Io) and transmitted radiation (I) through the canopy. Leaf area of 40 single apple trees were measured destructively to obtain real leaf area index (LAI(D)), which was compared with LAI estimated by the proposed digital photography method (LAI(M)). Results showed that the LAI(M) was able to estimate LAI(D) with an error of 25% using a constant light extinction coefficient (k = 0.68). However, when k was estimated using an exponential function based on the fraction of foliage cover (f(f)) derived from images, the error was reduced to 18%. Furthermore, when measurements of light intercepted by the canopy (Ic) were used as a proxy value for k, the method presented an error of only 9%. These results have shown that by using a proxy k value, estimated by Ic, helped to increase accuracy of LAI estimates using digital cover images for apple trees with different canopy sizes and under field conditions.

Modeling variability and scale integration of LAI measurements

Treesearch

Kris Nackaerts; Pol Coppin

2000-01-01

Rapid and reliable estimation of leaf area at various scales is important for research on chance detection of leaf area index (LAI) as an indicator of ecosystem condition. It is of utmost importance to know to what extent boundary and illumination conditions, data aggregation method, and sampling scheme influence the relative accuracy of stand-level LAI measurements....

Leaf Area Index Estimation in Vineyards from Uav Hyperspectral Data, 2d Image Mosaics and 3d Canopy Surface Models

NASA Astrophysics Data System (ADS)

Kalisperakis, I.; Stentoumis, Ch.; Grammatikopoulos, L.; Karantzalos, K.

2015-08-01

The indirect estimation of leaf area index (LAI) in large spatial scales is crucial for several environmental and agricultural applications. To this end, in this paper, we compare and evaluate LAI estimation in vineyards from different UAV imaging datasets. In particular, canopy levels were estimated from i.e., (i) hyperspectral data, (ii) 2D RGB orthophotomosaics and (iii) 3D crop surface models. The computed canopy levels have been used to establish relationships with the measured LAI (ground truth) from several vines in Nemea, Greece. The overall evaluation indicated that the estimated canopy levels were correlated (r2 > 73%) with the in-situ, ground truth LAI measurements. As expected the lowest correlations were derived from the calculated greenness levels from the 2D RGB orthomosaics. The highest correlation rates were established with the hyperspectral canopy greenness and the 3D canopy surface models. For the later the accurate detection of canopy, soil and other materials in between the vine rows is required. All approaches tend to overestimate LAI in cases with sparse, weak, unhealthy plants and canopy.

Economic and clinical comparison of atypical depot antipsychotic drugs for treatment of chronic schizophrenia in the Czech Republic.

PubMed

Einarson, Thomas R; Zilbershtein, Roman; Skoupá, Jana; Veselá, Sárka; Garg, Madhur; Hemels, Michiel E H

2013-09-01

The Czech Republic is faced with making choices between pharmaceutical products, including depot injectable antipsychotics. A pharmacoeconomic analysis was conducted to determine the cost-effectiveness of atypical depots. An existing 1-year decision-analytic framework was adapted to model drug use in this healthcare system. The average direct costs to the General Insurance Company of the Czech Republic of using paliperidone palmitate (Xeplion®), risperidone (Risperdal Consta®), and olanzapine pamoate (Zypadhera®) were determined. Literature-derived clinical rates populated the model, with costs adjusted to 2012 Euros using the consumer price index. Outcomes included quality-adjusted life-years (QALYs), days in remission, and proportions hospitalized or visiting emergency rooms. One-way sensitivity analyses were calculated for all important inputs. A multivariate probability analysis was used to examine the stability of results using 10,000 iterations of simulated input over reasonable ranges of all included variables. Expected average costs/per patient treated were €5377 for PP-LAI, €6118 for RIS-LAI, and €6537 for OLZ-LAI. Respective QALYs were 0.817, 0.809, and 0.811; ER visits were 0.127, 0.134, and 0.141; hospitalizations were 0.252, 0.298, and 0.289. Results were generally robust in sensitivity analyses. PP-LAI dominated RIS-LAI and OLZ-LAI in 90.2% and 92.1% of simulations, respectively. Results were insensitive to drug prices but sensitive to adherence and hospitalization rates. PP-LAI dominated the other two drugs, as it had a lower overall cost and superior clinical outcomes, making it the preferred choice. Using PP-LAI in place of RIS-LAI for chronic relapsing schizophrenia would reduce the overall costs of care for the healthcare system.

Spatial variation and seasonal dynamics of leaf-area index in the arctic tundra-implications for linking ground observations and satellite images

NASA Astrophysics Data System (ADS)

Juutinen, Sari; Virtanen, Tarmo; Kondratyev, Vladimir; Laurila, Tuomas; Linkosalmi, Maiju; Mikola, Juha; Nyman, Johanna; Räsänen, Aleksi; Tuovinen, Juha-Pekka; Aurela, Mika

2017-09-01

Vegetation in the arctic tundra typically consists of a small-scale mosaic of plant communities, with species differing in growth forms, seasonality, and biogeochemical properties. Characterization of this variation is essential for understanding and modeling the functioning of the arctic tundra in global carbon cycling, as well as for evaluating the resolution requirements for remote sensing. Our objective was to quantify the seasonal development of the leaf-area index (LAI) and its variation among plant communities in the arctic tundra near Tiksi, coastal Siberia, consisting of graminoid, dwarf shrub, moss, and lichen vegetation. We measured the LAI in the field and used two very-high-spatial resolution multispectral satellite images (QuickBird and WorldView-2), acquired at different phenological stages, to predict landscape-scale patterns. We used the empirical relationships between the plant community-specific LAI and degree-day accumulation (0 °C threshold) and quantified the relationship between the LAI and satellite NDVI (normalized difference vegetation index). Due to the temporal difference between the field data and satellite images, the LAI was approximated for the imagery dates, using the empirical model. LAI explained variation in the NDVI values well (R 2 adj. 0.42-0.92). Of the plant functional types, the graminoid LAI showed the largest seasonal amplitudes and was the main cause of the varying spatial patterns of the NDVI and the related LAI between the two images. Our results illustrate how the short growing season, rapid development of the LAI, yearly climatic variation, and timing of the satellite data should be accounted for in matching imagery and field verification data in the Arctic region.

Developing a global mixed-canopy, height-variable vegetation structure dataset for estimating global vegetation albedo by a clumped canopy radiative transfer scheme in the NASA Ent Terrestrial Biosphere Model and GISS GCM

NASA Astrophysics Data System (ADS)

Montes, Carlo; Kiang, Nancy Y.; Ni-Meister, Wenge; Yang, Wenze; Schaaf, Crystal; Aleinov, Igor; Jonas, Jeffrey A.; Zhao, Feng; Yao, Tian; Wang, Zhuosen; Sun, Qingsong; Carrer, Dominique

2016-04-01

Processes determining biosphere-atmosphere coupling are strongly influenced by vegetation structure. Thus, ecosystem carbon sequestration and evapotranspiration affecting global carbon and water balances will depend upon the spatial extent of vegetation, its vertical structure, and its physiological variability. To represent this globally, Dynamic Global Vegetation Models (DGVMs) coupled to General Circulation Models (GCMs) make use of satellite and/or model-based vegetation classifications often composed by homogeneous communities. This work aims at developing a new Global Vegetation Structure Dataset (GVSD) by incorporating varying vegetation heights for mixed plant communities to be used as boundary conditions to the Analytical Clumped Two-Stream (ACTS) canopy radiative transfer scheme (Ni-Meister et al., 2010) incorporated into the NASA Ent Terrestrial Biosphere Model (TBM), the DGVM coupled to the NASA Goddard Institute for Space Studies (GISS) GCM. Information sources about land surface and vegetation characteristics obtained from a number of earth observation platforms and algorithms include the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover and plant functional types (PFTs) (Friedl et al., 2010), soil albedo derived from MODIS (Carrer et al., 2014), along with vegetation height from the Geoscience Laser Altimeter System (GLAS) on board ICESat (Ice, Cloud, and land Elevation Satellite) (Simard et al., 2011; Tang et al., 2014). Three widely used Leaf Area Index (LAI) products are compared as input to the GVSD and ACTS forcing in terms of vegetation albedo: Global Data Sets of Vegetation (LAI)3g (Zhu et al. 2013), Beijing Normal University LAI (Yuan et al., 2011), and MODIS MOD15A2H product (Yang et al., 2006). Further PFT partitioning is performed according to a climate classification utilizing the Climate Research Unit (CRU; Harris et al., 2013) and the NOAA Global Precipitation Climatology Centre (GPCC; Scheider et al., 2014) data. Final products are a GVSD consisting of mixed plant communities (e.g. mixed forests, savannas, mixed PFTs) following the Ecosystem Demography model (Moorcroft et al., 2001) approach represented by multi-cohort community patches at the sub-grid level of the GCM, which are ensembles of identical individuals whose differences are represented by PFTs, canopy height, density and vegetation structure sensitivity to allometric parameters. The performance of the Ent TBM in estimating VIS-NIR vegetation albedo by the new GVSD and ACTS is assessed first by comparison against the previous GISS GCM vegetation classification and prescribed Lambertian albedoes of Matthews (1984), and secondly, against MODIS global estimations and FLUXNET site-scale observations. Ultimately, this GVSD will serve as a template for community data sets, and be used as boundary conditions to the Ent TBM for prediction of biomass, carbon balances and GISS GCM climate.

Large-scale Estimates of Leaf Area Index from Active Remote Sensing Laser Altimetry

NASA Astrophysics Data System (ADS)

Hopkinson, C.; Mahoney, C.

2016-12-01

Leaf area index (LAI) is a key parameter that describes the spatial distribution of foliage within forest canopies which in turn control numerous relationships between the ground, canopy, and atmosphere. The retrieval of LAI has demonstrated success by in-situ (digital) hemispherical photography (DHP) and airborne laser scanning (ALS) data; however, field and ALS acquisitions are often spatially limited (100's km2) and costly. Large-scale (>1000's km2) retrievals have been demonstrated by optical sensors, however, accuracies remain uncertain due to the sensor's inability to penetrate the canopy. The spaceborne Geoscience Laser Altimeter System (GLAS) provides a possible solution in retrieving large-scale derivations whilst simultaneously penetrating the canopy. LAI retrieved by multiple DHP from 6 Australian sites, representing a cross-section of Australian ecosystems, were employed to model ALS LAI, which in turn were used to infer LAI from GLAS data at 5 other sites. An optimally filtered GLAS dataset was then employed in conjunction with a host of supplementary data to build a Random Forest (RF) model to infer predictions (and uncertainties) of LAI at a 250 m resolution across the forested regions of Australia. Predictions were validated against ALS-based LAI from 20 sites (R2=0.64, RMSE=1.1 m2m-2); MODIS-based LAI were also assessed against these sites (R2=0.30, RMSE=1.78 m2m-2) to demonstrate the strength of GLAS-based predictions. The large-scale nature of current predictions was also leveraged to demonstrate large-scale relationships of LAI with other environmental characteristics, such as: canopy height, elevation, and slope. The need for such wide-scale quantification of LAI is key in the assessment and modification of forest management strategies across Australia. Such work also assists Australia's Terrestrial Ecosystem Research Network, in fulfilling their government issued mandates.

Retrieval of LAI and leaf chlorophyll content from remote sensing data by agronomy mechanism knowledge to solve the ill-posed inverse problem

NASA Astrophysics Data System (ADS)

Li, Zhenhai; Nie, Chenwei; Yang, Guijun; Xu, Xingang; Jin, Xiuliang; Gu, Xiaohe

2014-10-01

Leaf area index (LAI) and LCC, as the two most important crop growth variables, are major considerations in management decisions, agricultural planning and policy making. Estimation of canopy biophysical variables from remote sensing data was investigated using a radiative transfer model. However, the ill-posed problem is unavoidable for the unique solution of the inverse problem and the uncertainty of measurements and model assumptions. This study focused on the use of agronomy mechanism knowledge to restrict and remove the ill-posed inversion results. For this purpose, the inversion results obtained using the PROSAIL model alone (NAMK) and linked with agronomic mechanism knowledge (AMK) were compared. The results showed that AMK did not significantly improve the accuracy of LAI inversion. LAI was estimated with high accuracy, and there was no significant improvement after considering AMK. The validation results of the determination coefficient (R2) and the corresponding root mean square error (RMSE) between measured LAI and estimated LAI were 0.635 and 1.022 for NAMK, and 0.637 and 0.999 for AMK, respectively. LCC estimation was significantly improved with agronomy mechanism knowledge; the R2 and RMSE values were 0.377 and 14.495 μg cm-2 for NAMK, and 0.503 and 10.661 μg cm-2 for AMK, respectively. Results of the comparison demonstrated the need for agronomy mechanism knowledge in radiative transfer model inversion.

Relationships of Leaf Area Index and NDVI for 12 Brassica Cultivars in Northeastern Montana

NASA Astrophysics Data System (ADS)

Jabro, Jay; Allen, Brett; Long, Dan; Isbell, Terry; Gesch, Russ; Brown, Jack; Hatfield, Jerry; Archer, David; Oblath, Emily; Vigil, Merle; Kiniry, Jim; Hunter, Kimberly; Shonnard, David

2017-04-01

To our knowledge, there is limited information on the relationship of the normalized difference vegetation index (NDVI) and leaf area index (LAI) in spring Brassica oilseed crops. The 2014 results of NDVI and LAI of 12 spring varieties of oilseed crops were measured in a field study conducted in Sidney, Montana, USA under dryland conditions. These 12 varieties were grouped under six species (B. napus, B. rapa, B. juncea, B. carinata, Sinapis alba, and Camelina sativa). The NDVI and LAI were measured weekly throughout the growing season. The NDVI was continually measured at one sample per second across the whole plot using a Crop Circle ACS-470 active crop canopy sensor. The LAI was measured at two locations at 12 samples per plot using an AccuPar model LP-80 Ceptometer. Treatments were replicated four times in a randomized complete block design in plots of 3 m×9 m. Temporal dynamics of NDVI and LAI in various growth stages of 12 varieties were evaluated throughout the growing season. Significant relationships and models between NDVI and LAI were obtained when 12 varieties were grouped under six species.

Aboveground biomass and leaf area index (LAI) mapping for Niassa Reserve, northern Mozambique

NASA Astrophysics Data System (ADS)

Ribeiro, Natasha S.; Saatchi, Sassan S.; Shugart, Herman H.; Washington-Allen, Robert A.

2008-09-01

Estimations of biomass are critical in miombo woodlands because they represent the primary source of goods and services for over 80% of the population in southern Africa. This study was carried out in Niassa Reserve, northern Mozambique. The main objectives were first to estimate woody biomass and Leaf Area Index (LAI) using remotely sensed data [RADARSAT (C-band, λ = 5.7-cm)] and Landsat ETM+ derived Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR) calibrated by field measurements and, second to determine, at both landscape and plot scales, the environmental controls (precipitation, woody cover density, fire and elephants) of biomass and LAI. A land-cover map (72% overall accuracy) was derived from the June 2004 ETM+ mosaic. Field biomass and LAI were correlated with RADARSAT backscatter (rbiomass = 0.65, rLAI = 0.57, p < 0.0001) from July 2004, NDVI (rbiomass = 0.30, rLAI = 0.35; p < 0.0001) and SR (rbiomass = 0.36, rLAI = 0.40, p < 0.0001). A jackknife stepwise regression technique was used to develop the best predictive models for biomass (biomass = -5.19 + 0.074 * radarsat + 1.56 * SR, r2 = 0.55) and LAI (LAI = -0.66 + 0.01 * radarsat + 0.22 * SR, r2 = 0.45). Biomass and LAI maps were produced with an estimated peak of 18 kg m-2 and 2.80 m2 m-2, respectively. On the landscape-scale, both biomass and LAI were strongly determined by mean annual precipitation (F = 13.91, p = 0.0002). On the plot spatial scale, woody biomass was significantly determined by fire frequency, and LAI by vegetation type.

Look-up-table approach for leaf area index retrieval from remotely sensed data based on scale information

NASA Astrophysics Data System (ADS)

Zhu, Xiaohua; Li, Chuanrong; Tang, Lingli

2018-03-01

Leaf area index (LAI) is a key structural characteristic of vegetation and plays a significant role in global change research. Several methods and remotely sensed data have been evaluated for LAI estimation. This study aimed to evaluate the suitability of the look-up-table (LUT) approach for crop LAI retrieval from Satellite Pour l'Observation de la Terre (SPOT)-5 data and establish an LUT approach for LAI inversion based on scale information. The LAI inversion result was validated by in situ LAI measurements, indicating that the LUT generated based on the PROSAIL (PROSPECT+SAIL: properties spectra + scattering by arbitrarily inclined leaves) model was suitable for crop LAI estimation, with a root mean square error (RMSE) of ˜0.31m2 / m2 and determination coefficient (R2) of 0.65. The scale effect of crop LAI was analyzed based on Taylor expansion theory, indicating that when the SPOT data aggregated by 200 × 200 pixel, the relative error is significant with 13.7%. Finally, an LUT method integrated with scale information was proposed in this article, improving the inversion accuracy with RMSE of 0.20 m2 / m2 and R2 of 0.83.

Evaluation and Intercomparison of MODIS and GEOV1 Global Leaf Area Index Products over Four Sites in North China

PubMed Central

Li, Zhenwang; Tang, Huan; Zhang, Baohui; Yang, Guixia; Xin, Xiaoping

2015-01-01

This study investigated the performances of the Moderate Resolution Imaging Spectroradiometer (MODIS) and GEOLAND2 Version 1 (GEOV1) Leaf Area Index (LAI) products using ground measurements and LAI reference maps over four sites in North China for 2011–2013. The Terra + Aqua MODIS and Terra MODIS LAI retrieved by the main algorithm and GEOV1 LAI within the valid range were evaluated and intercompared using LAI reference maps to assess their uncertainty and seasonal variability The results showed that GEOV1 LAI is the most similar product with the LAI reference maps (R2 = 0.78 and RMSE = 0.59). The MODIS products performed well for biomes with low LAI values, but considerable uncertainty arose when the LAI was larger than 3. Terra + Aqua MODIS (R2 = 0.72 and RMSE = 0.68) was slightly more accurate than Terra MODIS (R2 = 0.57 and RMSE = 0.90) for producing slightly more successful observations. Both MODIS and GEOV1 products effectively followed the seasonal trajectory of the reference maps, and GEOV1 exhibited a smoother seasonal trajectory than MODIS. MODIS anomalies mainly occurred during summer and likely occurred because of surface reflectance uncertainty, shorter temporal resolutions and inconsistency between simulated and MODIS surface reflectances. This study suggests that further improvements of the MODIS LAI products should focus on finer algorithm inputs and improved seasonal variation modeling of MODIS observations. Future field work considering finer biome maps and better generation of LAI reference maps is still needed. PMID:25781509

2011 spring drought in France : Evaluation of the SURFEX land surface model.

NASA Astrophysics Data System (ADS)

Lafont, S.; Barbu, A.; Szczypta, C.; Carrer, D.; Delire, C.; Calvet, J.-C.

2012-04-01

The spring of the year 2011 has been exceptionally dry in Western Europe. Over France, May 2011 has been one of the driest over the last 50 years. This event had a marked impact on vegetation development leading to very low value of the Leaf Area Index (LAI) during the growing season . In contrast, July 2011 has been in general wet and cold allowing a new vegetation development. This extreme event, followed by higher than normal rainfall is an excellent case-study to evaluate the capacity of a land surface model to simulate the drought impact on vegetation, and vegetation recovery after a drought. In this study, we used the SURFEX land surface model, in its ISBA-CC (CC stands for Carbon Cycle) configuration. The ISBA-CC version simulates the vegetation carbon cycle, interactive LAI and the carbon accumulation in wood and in the soil organic matter. This model is used by the GEOLAND2 Land Carbon Core Information Service. We performed 20-years simulations of SURFEX at high resolution (8 km) with atmospheric forcing from the SAFRAN dataset, an operational product over France. The vegetation map is provided by the ECOCLIMAP2 database. Following previous work that have confirmed a good simulation of the LAI inter-annual variability, this study investigates the ability of the model of reproducing the observed anomalies of LAI in 2011, in terms of timing and spatial patterns. We compare the simulated LAI with long time series (10 yr) of LAI derived from Earth Observation product within GEOLAND2 BIOPAR project. We quantify the anomalies of energy, water and carbon fluxes. We investigate the robustness of these results and the impact of modifying several important sub-modules of the model: soil texture, photosynthesis, and rainfall interception.

Interactions and Feedbacks Between Land Surface Processes and Water Cycle Dynamics in Africa

NASA Astrophysics Data System (ADS)

Prince, S. D.; Xue, Y.; Song, G.; Cox, P. M.

2012-12-01

In the past three decades, numerous modeling sensitivity studies have established the importance of detailed vegetation and atmosphere interactions in West African water cycle dynamics. Recently, new evidence has emerged from satellite data analyses that indicate a fully coupled process is needed to explain the relationships discovered in these analyses. In order to elucidate the processes, we have applied the off-line Simplified Simple Biosphere Model version 4/Top-down Representation of Interactive Foliage and Flora Including Dynamics Model (SSiB4/TRIFFID). SSiB4 is a biophysical model based on surface water and energy balance which interacts with TRIFFID by providing the carbon assimilation. TRIFFID is a dynamic vegetation model based on carbon balance. The offline SSiB4/TRIFFID was integrated using the observed precipitation and reanalysis-based meteorological forcing from 1948 to 2006 over West Africa. West Africa has diverse climate and ecosystem regions. It suffered the most severe and longest drought in the world during the 20th century, and has the most pronounced decadal water cycle variability in the planet. The simulation results indicate that the water cycle variability has significant effects on the spatial distributions and temporal variations of plant functional types and leaf area index (LAI), which are generally consistent with those observed from satellites since the 1980s. The simulated vegetation conditions over Sahel region exhibited seasonal, inter-annual variations, consistent with West Africa monsoon variability, and the simulated inter-decadal variability in vegetation was consistent with the Sahel drought in the 1970s and 1980s and partial recovery in the 1990s and 2000s. To further understand the cause of decadal variability of climate, water cycle and vegetation dynamics, experiments were conducted to investigate the relationship between the LAI, atmospheric carbon dioxide increase and global warming. In one experiment, the 1948 atmospheric carbon dioxide was used (310 ppmv) and in another it was increased as observed. The LAI increased linearly between the fixed and elevated carbon dioxide, suggesting carbon dioxide fertilization. This increase was related to an increase in shrubs and decrease in C4 grasses. The greatest increases in LAI in the Sahel occurred during the winter. To understand how the warming trend affected decadal variability, we compared an experiment with observed temperature (with warming trend) and another in which the warming trend was removed. The simulations showed a reduction in LAI due to the warming after 1980, although it was not as strong as the carbon fertilization effects. High temperature created stress on vegetation over the Sahel, and especially over its transition zone. However, the fertilization effect dominated the global warming effect.

Carbon cycling of European croplands: A framework for the assimilation of optical and microwave Earth observation data

NASA Astrophysics Data System (ADS)

Revill, Andrew; Sus, Oliver; Williams, Mathew

2013-04-01

Croplands are traditionally managed to maximise the production of food, feed, fibre and bioenergy. Advancements in agricultural technologies, together with land-use change, have approximately doubled World grain harvests over the past 50 years. Cropland ecosystems also play a significant role in the global carbon (C) cycle and, through changes to C storage in response to management activities, they can provide opportunities for climate change mitigation. However, quantifying and understanding the cropland C cycle is complex, due to variable environmental drivers, varied management practices and often highly heterogeneous landscapes. Efforts to upscale processes using simulation models must resolve these challenges. Here we show how data assimilation (DA) approaches can link C cycle modelling to Earth observation (EO) and reduce uncertainty in upscaling. We evaluate a framework for the assimilation of leaf area index (LAI) time series, empirically derived from EO optical and radar sensors, for state-updating a model of crop development and C fluxes. Sensors are selected with fine spatial resolutions (20-50 m) to resolve variability across field sizes typically used in European agriculture. Sequential DA is used to improve the canopy development simulation, which is validated by comparing time-series LAI and net ecosystem exchange (NEE) predictions to independent ground measurements and eddy covariance observations at multiple European cereal crop sites. Significant empirical relationships were established between the LAI ground measurements and the optical reflectance and radar backscatter, which allowed for single LAI calibrations being valid for all the cropland sites for each sensor. The DA of all EO LAI estimates results indicated clear adjustments in LAI and an enhanced representation of daily CO2 exchanges, particularly around the time of peak C uptake. Compared to the simulation without DA, the assimilation of all EO LAI estimates improved the predicted at-harvest cumulative NEE for all cropland sites by an average of 69%. The use of radar sensors, being relatively unaffected by cloud cover and sensitive to the structural properties of the crop, significantly improves the analyses when compared to the combined, and individual, use of the optical LAI estimates. When assimilating the radar derived LAI only, the estimated at-harvest cumulative NEE was improved by 79% when compared to the simulation without DA. Future developments would include the spatial upscaling of the existing model framework and the assimilation of additional state variables, such as soil moisture.

Vegetation-rainfall feedbacks across the Sahel: a combined observational and modeling study

NASA Astrophysics Data System (ADS)

Yu, Y.; Notaro, M.; Wang, F.; Mao, J.; Shi, X.; Wei, Y.

2016-12-01

The Sahel rainfall is characterized by large interannual variability. Past modeling studies have concluded that the Sahel rainfall variability is primarily driven by oceanic forcings and amplified by land-atmosphere interactions. However, the relative importance of oceanic versus terrestrial drivers has never been assessed from observations. The current understanding of vegetation's impacts on climate, i.e. positive vegetation-rainfall feedback through the albedo, moisture, and momentum mechanisms, comes from untested models. Neither the positive vegetation-rainfall feedback, nor the underlying mechanisms, has been fully resolved in observations. The current study fills the knowledge gap about the observed vegetation-rainfall feedbacks, through the application of the multivariate statistical method Generalized Equilibrium Feedback Assessment (GEFA) to observational data. According to GEFA, the observed oceanic impacts dominate over terrestrial impacts on Sahel rainfall, except in the post-monsoon period. Positive leaf area index (LAI) anomalies favor an extended, wetter monsoon across the Sahel, largely due to moisture recycling. The albedo mechanism is not responsible for this positive vegetation feedback on the seasonal-interannual time scale, which is too short for a grass-desert transition. A low-level stabilization and subsidence is observed in response to increased LAI - potentially responsible for a negative vegetation-rainfall feedback. However, the positive moisture feedback overwhelms the negative momentum feedback, resulting in an observed positive vegetation-rainfall feedback. We further applied GEFA to a fully-coupled Community Earth System Model (CESM) control run, as an example of evaluating climate models against the GEFA-based observational benchmark. In contrast to the observed positive vegetation-rainfall feedbacks, CESM simulates a negative vegetation-rainfall feedback across Sahel, peaking in the pre-monsoon season. The simulated negative feedback is largely due to the low-level stabilization caused by increased LAI. Positive moisture feedback is present in the CESM simulation, but an order weaker than the observed and weaker than the negative momentum feedback, thereby leading to the simulated negative vegetation-rainfall feedbacks.

[Simulation of water and carbon fluxes in harvard forest area based on data assimilation method].

PubMed

Zhang, Ting-Long; Sun, Rui; Zhang, Rong-Hua; Zhang, Lei

2013-10-01

Model simulation and in situ observation are the two most important means in studying the water and carbon cycles of terrestrial ecosystems, but have their own advantages and shortcomings. To combine these two means would help to reflect the dynamic changes of ecosystem water and carbon fluxes more accurately. Data assimilation provides an effective way to integrate the model simulation and in situ observation. Based on the observation data from the Harvard Forest Environmental Monitoring Site (EMS), and by using ensemble Kalman Filter algorithm, this paper assimilated the field measured LAI and remote sensing LAI into the Biome-BGC model to simulate the water and carbon fluxes in Harvard forest area. As compared with the original model simulated without data assimilation, the improved Biome-BGC model with the assimilation of the field measured LAI in 1998, 1999, and 2006 increased the coefficient of determination R2 between model simulation and flux observation for the net ecosystem exchange (NEE) and evapotranspiration by 8.4% and 10.6%, decreased the sum of absolute error (SAE) and root mean square error (RMSE) of NEE by 17.7% and 21.2%, and decreased the SAE and RMSE of the evapotranspiration by 26. 8% and 28.3%, respectively. After assimilated the MODIS LAI products of 2000-2004 into the improved Biome-BGC model, the R2 between simulated and observed results of NEE and evapotranspiration was increased by 7.8% and 4.7%, the SAE and RMSE of NEE were decreased by 21.9% and 26.3%, and the SAE and RMSE of evapotranspiration were decreased by 24.5% and 25.5%, respectively. It was suggested that the simulation accuracy of ecosystem water and carbon fluxes could be effectively improved if the field measured LAI or remote sensing LAI was integrated into the model.

Evaluation of Multispectral Based Radiative Transfer Model Inversion to Estimate Leaf Area Index in Wheat

USDA-ARS?s Scientific Manuscript database

Leaf area index (LAI) is a critical variable for predicting the growth and productivity of crops. Remote sensing estimates of LAI have relied upon empirical relationships between spectral vegetation indices and ground measurements that are costly to obtain. Radiative transfer model inversion based o...

Global meta-analysis of leaf area index in wetlands indicates uncertainties in understanding of their ecosystem function

NASA Astrophysics Data System (ADS)

Dronova, I.; Taddeo, S.; Foster, K.

2017-12-01

Projecting ecosystem responses to global change relies on the accurate understanding of properties governing their functions in different environments. An important variable in models of ecosystem function is canopy leaf area index (LAI; leaf area per unit ground area) declared as one of the Essential Climate Variables in the Global Climate Observing System and extensively measured in terrestrial landscapes. However, wetlands have been largely under-represented in these efforts, which globally limits understanding of their contribution to carbon sequestration, climate regulation and resilience to natural and anthropogenic disturbances. This study provides a global synthesis of >350 wetland-specific LAI observations from 182 studies and compares LAI among wetland ecosystem and vegetation types, biomes and measurement approaches. Results indicate that most wetland types and even individual locations show a substantial local dispersion of LAI values (average coefficient of variation 65%) due to heterogeneity of environmental properties and vegetation composition. Such variation indicates that mean LAI values may not sufficiently represent complex wetland environments, and the use of this index in ecosystem function models needs to incorporate within-site variation in canopy properties. Mean LAI did not significantly differ between direct and indirect measurement methods on a pooled global sample; however, within some of the specific biomes and wetland types significant contrasts between these approaches were detected. These contrasts highlight unique aspects of wetland vegetation physiology and canopy structure affecting measurement principles that need to be considered in generalizing canopy properties in ecosystem models. Finally, efforts to assess wetland LAI using remote sensing strongly indicate the promise of this technology for cost-effective regional-scale modeling of canopy properties similar to terrestrial systems. However, such efforts urgently require more rigorous corrections for three-dimensional contributions of non-canopy material and non-vegetated surfaces to wetland canopy reflectance.

Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model

NASA Astrophysics Data System (ADS)

Gelati, Emiliano; Decharme, Bertrand; Calvet, Jean-Christophe; Minvielle, Marie; Polcher, Jan; Fairbairn, David; Weedon, Graham P.

2018-04-01

Physically consistent descriptions of land surface hydrology are crucial for planning human activities that involve freshwater resources, especially in light of the expected climate change scenarios. We assess how atmospheric forcing data uncertainties affect land surface model (LSM) simulations by means of an extensive evaluation exercise using a number of state-of-the-art remote sensing and station-based datasets. For this purpose, we use the CO2-responsive ISBA-A-gs LSM coupled with the CNRM version of the Total Runoff Integrated Pathways (CTRIP) river routing model. We perform multi-forcing simulations over the Euro-Mediterranean area (25-75.5° N, 11.5° W-62.5° E, at 0.5° resolution) from 1979 to 2012. The model is forced using four atmospheric datasets. Three of them are based on the ERA-Interim reanalysis (ERA-I). The fourth dataset is independent from ERA-Interim: PGF, developed at Princeton University. The hydrological impacts of atmospheric forcing uncertainties are assessed by comparing simulated surface soil moisture (SSM), leaf area index (LAI) and river discharge against observation-based datasets: SSM from the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative projects (ESA-CCI), LAI of the Global Inventory Modeling and Mapping Studies (GIMMS), and Global Runoff Data Centre (GRDC) river discharge. The atmospheric forcing data are also compared to reference datasets. Precipitation is the most uncertain forcing variable across datasets, while the most consistent are air temperature and SW and LW radiation. At the monthly timescale, SSM and LAI simulations are relatively insensitive to forcing uncertainties. Some discrepancies with ESA-CCI appear to be forcing-independent and may be due to different assumptions underlying the LSM and the remote sensing retrieval algorithm. All simulations overestimate average summer and early-autumn LAI. Forcing uncertainty impacts on simulated river discharge are larger on mean values and standard deviations than on correlations with GRDC data. Anomaly correlation coefficients are not inferior to those computed from raw monthly discharge time series, indicating that the model reproduces inter-annual variability fairly well. However, simulated river discharge time series generally feature larger variability compared to measurements. They also tend to overestimate winter-spring high flows and underestimate summer-autumn low flows. Considering that several differences emerge between simulations and reference data, which may not be completely explained by forcing uncertainty, we suggest several research directions. These range from further investigating the discrepancies between LSMs and remote sensing retrievals to developing new model components to represent physical and anthropogenic processes.

Techniques for the estimation of leaf area index using spectral data

NASA Technical Reports Server (NTRS)

Badhwar, G. D.; Shen, S. S.

1984-01-01

Based on the radiative transport theory of a homogeneous canopy, a new approach for obtaining transformations of spectral data used to estimate leaf area index (LAI), is developed. The transformations which are obtained without any ground knowledge of LAI show low sensitivity to soil variability, and are linearly related to LAI with relationships which are predictable from leaf reflectance, transmittance properties, and canopy reflectance models. Evaluation of the SAIL (scattering by arbitrarily inclined leaves) model is considered. Using only nadir view data, results obtained on winter and spring wheat and corn crops are presented.

Agro-hydrology and multi-temporal high-resolution remote sensing: toward an explicit spatial processes calibration

NASA Astrophysics Data System (ADS)

Ferrant, S.; Gascoin, S.; Veloso, A.; Salmon-Monviola, J.; Claverie, M.; Rivalland, V.; Dedieu, G.; Demarez, V.; Ceschia, E.; Probst, J.-L.; Durand, P.; Bustillo, V.

2014-12-01

The growing availability of high-resolution satellite image series offers new opportunities in agro-hydrological research and modeling. We investigated the possibilities offered for improving crop-growth dynamic simulation with the distributed agro-hydrological model: topography-based nitrogen transfer and transformation (TNT2). We used a leaf area index (LAI) map series derived from 105 Formosat-2 (F2) images covering the period 2006-2010. The TNT2 model (Beaujouan et al., 2002), calibrated against discharge and in-stream nitrate fluxes for the period 1985-2001, was tested on the 2005-2010 data set (climate, land use, agricultural practices, and discharge and nitrate fluxes at the outlet). Data from the first year (2005) were used to initialize the hydrological model. A priori agricultural practices obtained from an extensive field survey, such as seeding date, crop cultivar, and amount of fertilizer, were used as input variables. Continuous values of LAI as a function of cumulative daily temperature were obtained at the crop-field level by fitting a double logistic equation against discrete satellite-derived LAI. Model predictions of LAI dynamics using the a priori input parameters displayed temporal shifts from those observed LAI profiles that are irregularly distributed in space (between field crops) and time (between years). By resetting the seeding date at the crop-field level, we have developed an optimization method designed to efficiently minimize this temporal shift and better fit the crop growth against both the spatial observations and crop production. This optimization of simulated LAI has a negligible impact on water budgets at the catchment scale (1 mm yr-1 on average) but a noticeable impact on in-stream nitrogen fluxes (around 12%), which is of interest when considering nitrate stream contamination issues and the objectives of TNT2 modeling. This study demonstrates the potential contribution of the forthcoming high spatial and temporal resolution products from the Sentinel-2 satellite mission for improving agro-hydrological modeling by constraining the spatial representation of crop productivity.

Impact of new land boundary conditions from Moderate Resolution Imaging Spectroradiometer (MODIS) data on the climatology of land surface variables

NASA Astrophysics Data System (ADS)

Tian, Y.; Dickinson, R. E.; Zhou, L.; Shaikh, M.

2004-10-01

This paper uses the Community Land Model (CLM2) to investigate the improvements of a new land surface data set, created from multiple high-quality collection 4 Moderate Resolution Imaging Spectroradiometer data of leaf area index (LAI), plant functional type, and vegetation continuous fields, for modeled land surface variables. The previous land surface data in CLM2 underestimate LAI and overestimate the percent cover of grass/crop over most of the global area. For snow-covered regions with abundant solar energy the increased LAI and percent cover of tree/shrub in the new data set decreases the percent cover of surface snow and increases net radiation and thus increases ground and surface (2-m) air temperature, which reduces most of the model cold bias. For snow-free regions the increased LAI and changes in the percent cover from grass/crop to tree or shrub decrease ground and surface air temperature by converting most of the increased net radiation to latent heat flux, which decreases the model warm bias. Furthermore, the new data set greatly decreases ground evaporation and increases canopy evapotranspiration over tropical forests, especially during the wet season, owing to the higher LAI and more trees in the new data set. It makes the simulated ground evaporation and canopy evapotranspiration closer to reality and also reduces the warm biases over tropical regions.

Estimation of Multiple Parameters over Vegetated Surfaces by Integrating Optical-Thermal Remote Sensing Observations

NASA Astrophysics Data System (ADS)

Ma, H.

2016-12-01

Land surface parameters from remote sensing observations are critical in monitoring and modeling of global climate change and biogeochemical cycles. Current methods for estimating land surface parameters are generally parameter-specific algorithms and are based on instantaneous physical models, which result in spatial, temporal and physical inconsistencies in current global products. Besides, optical and Thermal Infrared (TIR) remote sensing observations are usually separated to use based on different models , and the Middle InfraRed (MIR) observations have received little attention due to the complexity of the radiometric signal that mixes both reflected and emitted fluxes. In this paper, we proposed a unified algorithm for simultaneously retrieving a total of seven land surface parameters, including Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), land surface albedo, Land Surface Temperature (LST), surface emissivity, downward and upward longwave radiation, by exploiting remote sensing observations from visible to TIR domain based on a common physical Radiative Transfer (RT) model and a data assimilation framework. The coupled PROSPECT-VISIR and 4SAIL RT model were used for canopy reflectance modeling. At first, LAI was estimated using a data assimilation method that combines MODIS daily reflectance observation and a phenology model. The estimated LAI values were then input into the RT model to simulate surface spectral emissivity and surface albedo. Besides, the background albedo and the transmittance of solar radiation, and the canopy albedo were also calculated to produce FAPAR. Once the spectral emissivity of seven MODIS MIR to TIR bands were retrieved, LST can be estimated from the atmospheric corrected surface radiance by exploiting an optimization method. At last, the upward longwave radiation were estimated using the retrieved LST, broadband emissivity (converted from spectral emissivity) and the downward longwave radiation (modeled by MODTRAN). These seven parameters were validated over several representative sites with different biome type, and compared with MODIS and GLASS product. Results showed that this unified inversion algorithm can retrieve temporally complete and physical consistent land surface parameters with high accuracy.

SCOPE model applied for rapeseed in Spain.

PubMed

Pardo, Nuria; Sánchez, M Luisa; Su, Zhongbo; Pérez, Isidro A; García, M Angeles

2018-06-15

The integrated SCOPE (Soil, Canopy Observation, Photochemistry and Energy balance) model, coupling radiative transfer theory and biochemistry, was applied to a biodiesel crop grown in a Spanish agricultural area. Energy fluxes and CO 2 exchange were simulated with this model for the period spanning January 2008 to October 2008. Results were compared to experimental measurements performed using eddy covariance and meteorological instrumentation. The reliability of the model was proven by simulating latent (LE) and sensible (H) heat fluxes, soil heat flux (G), and CO 2 exchanges (NEE and GPP). LAI data used as input in the model were retrieved from the MODIS and MERIS sensors. SCOPE was able to reproduce similar seasonal trends to those measured for NEE, GPP and LE. When considering H, the modelled values were underestimated for the period covering July 2008 to mid-September 2008. The modelled fluxes reproduced the observed seasonal evolution with determination coefficients of over 0.77 when LE and H were evaluated. The modelled results offered good agreement with observed data for NEE and GPP, regardless of whether LAI data belonged to MODIS or MERIS, showing slopes of 0.87 and 0.91 for NEE-MODIS and NEE-MERIS, and 0.91 and 0.94 for GPP-MODIS and GPP-MERIS, respectively. Moreover, SCOPE was able to reproduce similar seasonal behaviours to those observed for the experimental carbon fluxes, clearly showing the CO 2 sink/source behaviour for the whole period studied. Copyright © 2018 Elsevier B.V. All rights reserved.

LAI (in situ, simulated, Landsat-derived, and MODIS): A comparison within an Oak-Hickory Forest Complex in southwestern Virginia, USA.

EPA Science Inventory

The United States Environmental Protection Agency’s Environmental Sciences and Atmospheric Modeling Analysis Divisions are investigating the viability of simulated (i.e., ‘modeled’) leaf area index (LAI) inputs into various regional and local scale air quality models. Satellite L...

Do state-of-the-art CMIP5 ESMs accurately represent observed vegetation-rainfall feedbacks? Focus on the Sahel

NASA Astrophysics Data System (ADS)

Notaro, M.; Wang, F.; Yu, Y.; Mao, J.; Shi, X.; Wei, Y.

2017-12-01

The semi-arid Sahel ecoregion is an established hotspot of land-atmosphere coupling. Ocean-land-atmosphere interactions received considerable attention by modeling studies in response to the devastating 1970s-90s Sahel drought, which models suggest was driven by sea-surface temperature (SST) anomalies and amplified by local vegetation-atmosphere feedbacks. Vegetation affects the atmosphere through biophysical feedbacks by altering the albedo, roughness, and transpiration and thereby modifying exchanges of energy, momentum, and moisture with the atmosphere. The current understanding of these potentially competing processes is primarily based on modeling studies, with biophysical feedbacks serving as a key uncertainty source in regional climate change projections among Earth System Models (ESMs). In order to reduce this uncertainty, it is critical to rigorously evaluate the representation of vegetation feedbacks in ESMs against an observational benchmark in order to diagnose systematic biases and their sources. However, it is challenging to successfully isolate vegetation's feedbacks on the atmosphere, since the atmospheric control on vegetation growth dominates the atmospheric feedback response to vegetation anomalies and the atmosphere is simultaneously influenced by oceanic and terrestrial anomalies. In response to this challenge, a model-validated multivariate statistical method, Stepwise Generalized Equilibrium Feedback Assessment (SGEFA), is developed, which extracts the forcing of a slowly-evolving environmental variable [e.g. SST or leaf area index (LAI)] on the rapidly-evolving atmosphere. By applying SGEFA to observational and remotely-sensed data, an observational benchmark is established for Sahel vegetation feedbacks. In this work, the simulated responses in key atmospheric variables, including evapotranspiration, albedo, wind speed, vertical motion, temperature, stability, and rainfall, to Sahel LAI anomalies are statistically assessed in Coupled Model Intercomparison Project Phase 5 (CMIP5) ESMs through SGEFA. The dominant mechanism, such as albedo feedback, moisture recycling, or momentum feedback, in each ESM is evaluated against the observed benchmark. SGEFA facilitates a systematic assessment of model biases in land-atmosphere interactions.

Global latitudinal-asymmetric vegetation growth trends and their driving mechanisms: 1982-2009

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mao, Jiafu; Shi, Xiaoying; Thornton, Peter E

2013-01-01

Using a recent Leaf Area Index (LAI) dataset and the Community Land Model version 4 (CLM4), we investigate percent changes and controlling factors of global vegetation growth for the period 1982 to 2009. Over that 28-year period, both the remote-sensing estimate and model simulation show a significant increasing trend in annual vegetation growth. Latitudinal asymmetry appeared in both products, with small increases in the Southern Hemisphere (SH) and larger increases at high latitudes in the Northern Hemisphere (NH). The south-to-north asymmetric land surface warming was assessed to be the principal driver of this latitudinal asymmetry of LAI trend. Heterogeneous precipitationmore » functioned to decrease this latitudinal LAI gradient, and considerably regulated the local LAI change. CO2 fertilization during the last three decades, was simulated to be the dominant cause for the enhanced vegetation growth. Our study, though limited by observational and modeling uncertainties, adds further insight into vegetation growth trends and environmental correlations. These validation exercises also provide new quantitative and objective metrics for evaluation of land ecosystem process models at multiple spatio-temporal scales.« less

Detection and attribution of vegetation growth change in China during the last thirty years

NASA Astrophysics Data System (ADS)

Tan, J.; Wang, X.; Mao, J.; Shi, X.; Peng, S.; Zeng, Z.; Piao, S.

2013-12-01

Enhanced terrestrial vegetation growth in China over the past three decades has been proved by satellite observations. During the same period, China has experienced dramatic land use and land cover changes. Those changes can not only strengthen the vegetation growth by afforestation and agricultural management, but also weaken it by urbanization and overgrazing. Compared to global climate changes, the effect of land use and land cover changes (LULCC) in China vegetation growth is still not clear. A further understanding of the mechanisms for this phenomenon is crucial for projecting future ecosystem dynamics. To investigate the variation of vegetation growth in Chinese provinces and evaluate its responses to external driving factors from 1982 to 2009, two mechanistic terrestrial carbon models (CLM and OCHIDEE) have been applied in this paper. The modeled Leaf Area Index (LAI) from the two models has been increasing, which is consistent to the satellite LAI. On that basis, a series of factorial simulations based on the two models were processed to separate independent contributions of external driving factors to LAI. Besides of climate changing and LULCC, other external driving factors were also considered such as CO2 and nitrogen deposition. The results indicate that the distribution of LAI trend is far from homogeneous at provincial scale and highest LAI trend happened in South China. The dominant influential factor varies in different provinces. Climate-only simulation may not explain the vegetation growth change well in all the provinces. CO2 and LULCC seem to play a more important role in South China which matches the region with sharp increase of LAI. This phenomenon shows that the anthropology-oriented impact cannot be ignored under the background of global climate change and it is vital for further exploration of the effect of human society to vegetation growth.

Spectral estimates of solar radiation intercepted by corn canopies

NASA Technical Reports Server (NTRS)

Bauer, M. E. (Principal Investigator); Daughtry, C. S. T.; Gallo, K. P.

1982-01-01

Reflectance factor data were acquired with a Landsat band radiometer throughout two growing seasons for corn (Zea mays L.) canopies differing in planting dates, populations, and soil types. Agronomic data collected included leaf area index (LAI), biomass, development stage, and final grain yields. The spectral variable, greenness, was associated with 78 percent of the variation in LAI over all treatments. Single observations of LAI or greenness have limited value in predicting corn yields. The proportions of solar radiation intercepted (SRI) by these canopies were estimated using either measured LAI or greenness. Both SRI estimates, when accumulated over the growing season, accounted for approximately 65 percent of the variation in yields. Models which simulated the daily effects of weather and intercepted solar radiation on growth had the highest correlations to grain yields. This concept of estimating intercepted solar radiation using spectral data represents a viable approach for merging spectral and meteorological data for crop yield models.

Bayesian estimation of seasonal course of canopy leaf area index from hyperspectral satellite data

NASA Astrophysics Data System (ADS)

Varvia, Petri; Rautiainen, Miina; Seppänen, Aku

2018-03-01

In this paper, Bayesian inversion of a physically-based forest reflectance model is investigated to estimate of boreal forest canopy leaf area index (LAI) from EO-1 Hyperion hyperspectral data. The data consist of multiple forest stands with different species compositions and structures, imaged in three phases of the growing season. The Bayesian estimates of canopy LAI are compared to reference estimates based on a spectral vegetation index. The forest reflectance model contains also other unknown variables in addition to LAI, for example leaf single scattering albedo and understory reflectance. In the Bayesian approach, these variables are estimated simultaneously with LAI. The feasibility and seasonal variation of these estimates is also examined. Credible intervals for the estimates are also calculated and evaluated. The results show that the Bayesian inversion approach is significantly better than using a comparable spectral vegetation index regression.

Agro-hydrology and multi temporal high resolution remote sensing: toward an explicit spatial processes calibration

NASA Astrophysics Data System (ADS)

Ferrant, S.; Gascoin, S.; Veloso, A.; Salmon-Monviola, J.; Claverie, M.; Rivalland, V.; Dedieu, G.; Demarez, V.; Ceschia, E.; Probst, J.-L.; Durand, P.; Bustillo, V.

2014-07-01

The recent and forthcoming availability of high resolution satellite image series offers new opportunities in agro-hydrological research and modeling. We investigated the perspective offered by improving the crop growth dynamic simulation using the distributed agro-hydrological model, Topography based Nitrogen transfer and Transformation (TNT2), using LAI map series derived from 105 Formosat-2 (F2) images during the period 2006-2010. The TNT2 model (Beaujouan et al., 2002), calibrated with discharge and in-stream nitrate fluxes for the period 1985-2001, was tested on the 2006-2010 dataset (climate, land use, agricultural practices, discharge and nitrate fluxes at the outlet). A priori agricultural practices obtained from an extensive field survey such as seeding date, crop cultivar, and fertilizer amount were used as input variables. Continuous values of LAI as a function of cumulative daily temperature were obtained at the crop field level by fitting a double logistic equation against discrete satellite-derived LAI. Model predictions of LAI dynamics with a priori input parameters showed an temporal shift with observed LAI profiles irregularly distributed in space (between field crops) and time (between years). By re-setting seeding date at the crop field level, we proposed an optimization method to minimize efficiently this temporal shift and better fit the crop growth against the spatial observations as well as crop production. This optimization of simulated LAI has a negligible impact on water budget at the catchment scale (1 mm yr-1 in average) but a noticeable impact on in-stream nitrogen fluxes (around 12%) which is of interest considering nitrate stream contamination issues and TNT2 model objectives. This study demonstrates the contribution of forthcoming high spatial and temporal resolution products of Sentinel-2 satellite mission in improving agro-hydrological modeling by constraining the spatial representation of crop productivity.

LDAS-Monde: Global scale satellite driven Land Data Assimilation System based on SURFEX modelling platform

NASA Astrophysics Data System (ADS)

Munier, Simon; Albergel, Clément; Leroux, Delphine; Calvet, Jean-Christophe

2017-04-01

In the past decades, large efforts have been made to improve our understanding of the dynamics of the terrestrial water cycle, including vertical and horizontal water fluxes as well as water stored in the biosphere. The soil water content is closely related to the development of the vegetation, which is in turn closely related to the water and energy exchanges with the atmosphere (through evapotranspiration) as well as to carbon fluxes. Land Surface Models (LSMs) are usually designed to represent biogeophysical variables, such as Surface and Root Zone Soil Moisture (SSM, RZSM) or Leaf Area Index (LAI), in order to simulate water, energy and carbon fluxes at the interface between land and atmosphere. With the recent increase of satellite missions and derived products, LSMs can benefit from Earth Observations via Data Assimilation systems to improve their representation of different biogeophysical variables. This study, which is part of the eartH2Observe European project (http://www.earth2observe.eu), presents LDAS-Monde, a global Land Data Assimilation System using an implementation of the Simplified Extended Kalman Filter (SEKF) in the Météo-France's modelling platform (SURFEX). SURFEX is based on the coupling of the multilayer, CO2-responsive version of the Interactions Between Soil, Biosphere, and Atmosphere model (ISBA) coupled with Météo-France's version of the Total Runoff Integrating Pathways continental hydrological system (CTRIP). Two global operational datasets derived from satellite observations are assimilated simultaneously: (i) SSM from the ESA Climate Change Initiative and (ii) LAI from the Copernicus Global Land Service project. Atmospheric forcing used in SURFEX are derived from the ERA-Interim reanalysis and corrected from GPCC precipitations. The simulations are conducted at the global scale at a 1 degree spatial resolution over the period 2000-2014. An analysis of the model sensitivity to the assimilated observations is performed over different regions of the globe under various hydro-climatic conditions. The impact of the SEKF on different biogeophysical and hydrological variables is assessed. It is shown that the assimilation scheme greatly improves the representation of the observed variables (SSM and LAI) and that it effectively affects most of the other variables related to the terrestrial water and vegetation cycles. Future developments include the optimization of LDAS-Monde in order to improve the spatial resolution and then take full advantage of the potential of Earth Observations.

Modeling the bidirectional reflectance distribution function of mixed finite plant canopies and soil

NASA Technical Reports Server (NTRS)

Schluessel, G.; Dickinson, R. E.; Privette, J. L.; Emery, W. J.; Kokaly, R.

1994-01-01

An analytical model of the bidirectional reflectance for optically semi-infinite plant canopies has been extended to describe the reflectance of finite depth canopies contributions from the underlying soil. The model depends on 10 independent parameters describing vegetation and soil optical and structural properties. The model is inverted with a nonlinear minimization routine using directional reflectance data for lawn (leaf area index (LAI) is equal to 9.9), soybeans (LAI, 2.9) and simulated reflectance data (LAI, 1.0) from a numerical bidirectional reflectance distribution function (BRDF) model (Myneni et al., 1988). While the ten-parameter model results in relatively low rms differences for the BRDF, most of the retrieved parameters exhibit poor stability. The most stable parameter was the single-scattering albedo of the vegetation. Canopy albedo could be derived with an accuracy of less than 5% relative error in the visible and less than 1% in the near-infrared. Sensitivity were performed to determine which of the 10 parameters were most important and to assess the effects of Gaussian noise on the parameter retrievals. Out of the 10 parameters, three were identified which described most of the BRDF variability. At low LAI values the most influential parameters were the single-scattering albedos (both soil and vegetation) and LAI, while at higher LAI values (greater than 2.5) these shifted to the two scattering phase function parameters for vegetation and the single-scattering albedo of the vegetation. The three-parameter model, formed by fixing the seven least significant parameters, gave higher rms values but was less sensitive to noise in the BRDF than the full ten-parameter model. A full hemispherical reflectance data set for lawn was then interpolated to yield BRDF values corresponding to advanced very high resolution radiometer (AVHRR) scan geometries collected over a period of nine days. The resulting parameters and BRDFs are similar to those for the full sampling geometry, suggesting that the limited geometry of AVHRR measurements might be used to reliably retrieve BRDF and canopy albedo with this model.

[Quantitative relationships between hyper-spectral vegetation indices and leaf area index of rice].

PubMed

Tian, Yong-Chao; Yang, Jie; Yao, Xia; Zhu, Yan; Cao, Wei-Xing

2009-07-01

Based on field experiments with different rice varieties under different nitrogen application levels, the quantitative relationships of rice leaf area index (LAI) with canopy hyper-spectral parameters at different growth stages were analyzed. Rice LAI had good relationships with several hyper-spectral vegetation indices, the correlation coefficient being the highest with DI (difference index), followed by with RI (ratio index), and NI (normalized index), based on the spectral reflectance or the first derivative spectra. The two best spectral indices for estimating LAI were the difference index DI (854, 760) (based on two spectral bands of 850 nm and 760 nm) and the difference index DI (D676, D778) (based on two first derivative bands of 676 nm and 778 nm). In general, the hyper-spectral vegetation indices based on spectral reflectance performed better than the spectral indices based on the first derivative spectra. The tests with independent dataset suggested that the rice LAI monitoring models with difference index DI (854,760) as the variable could give an accurate LAI estimation, being available for estimation of rice LAI.

Estimation of leaf area index using WorldView-2 and Aster satellite image: a case study from Turkey.

PubMed

Günlü, Alkan; Keleş, Sedat; Ercanlı, İlker; Şenyurt, Muammer

2017-10-04

The objective of this study is to estimate the leaf area index (LAI) of a forest ecosystem using two different satellite images, WorldView-2 and Aster. For this purpose, 108 sample plots were taken from pure Crimean pine forest stands of Yenice Forest Management Planning Unit in Ilgaz Forest Management Enterprise, Turkey. Each sample plot was imaged with hemispherical photographs with a fish-eye camera to determine the LAI. These photographs were analyzed with the help of Hemisfer Hemiview software program, and thus, the LAI of each sample plot was estimated. Furthermore, multiple regression analysis method was used to model the statistical relationships between the LAI values and band spectral reflection values and some vegetation indices (Vis) obtained from satellite images. The results show that the high-resolution WorldView-2 satellite image is better than the medium-resolution Aster satellite image in predicting the LAI. It was also seen that the results obtained by using the VIs are better than the bands when the LAI value is predicted with satellite images.

Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors

PubMed Central

Zheng, Guang; Moskal, L. Monika

2009-01-01

The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels. PMID:22574042

Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors.

PubMed

Zheng, Guang; Moskal, L Monika

2009-01-01

The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels.

Spatial Upscaling of Long-term In Situ LAI Measurements from Global Network Sites for Validation of Remotely Sensed Products

NASA Astrophysics Data System (ADS)

Xu, B.; Jing, L.; Qinhuo, L.; Zeng, Y.; Yin, G.; Fan, W.; Zhao, J.

2015-12-01

Leaf area index (LAI) is a key parameter in terrestrial ecosystem models, and a series of global LAI products have been derived from satellite data. To effectively apply these LAI products, it is necessary to evaluate their accuracy reasonablely. The long-term LAI measurements from the global network sites are an important supplement to the product validation dataset. However, the spatial scale mismatch between the site measurement and the pixel grid hinders the utilization of these measurements in LAI product validation. In this study, a pragmatic approach based on the Bayesian linear regression between long-term LAI measurements and high-resolution images is presented for upscaling the point-scale measurements to the pixel-scale. The algorithm was evaluated using high-resolution LAI reference maps provided by the VALERI project at the Järvselja site and was implemented to upscale the long-term LAI measurements at the global network sites. Results indicate that the spatial scaling algorithm can reduce the root mean square error (RMSE) from 0.42 before upscaling to 0.21 after upscaling compared with the aggregated LAI reference maps at the pixel-scale. Meanwhile, the algorithm shows better reliability and robustness than the ordinary least square (OLS) method for upscaling some LAI measurements acquired at specific dates without high-resolution images. The upscaled LAI measurements were employed to validate three global LAI products, including MODIS, GLASS and GEOV1. Results indicate that (i) GLASS and GEOV1 show consistent temporal profiles over most sites, while MODIS exhibits temporal instability over a few forest sites. The RMSE of seasonality between products and upscaled LAI measurement is 0.25-1.72 for MODIS, 0.17-1.29 for GLASS and 0.36-1.35 for GEOV1 along with different sites. (ii) The uncertainty for products varies over different months. The lowest and highest uncertainty for MODIS are 0.67 in March and 1.53 in August, for GLASS are 0.67 in November and 0.99 in July, and for GEOV1 are 0.61 in March and 1.23 in August, respectively. (iii) The overall uncertainty for MODIS, GLASS and GEOV1 is 1.36, 0.90 and 0.99, respectively. According to this study, the long-term LAI measurements can be used to validate time series remote sensing products by spatial upscaling from point-scale to pixel-scale.

Intercomparison and validation of MODIS and GLASS leaf area index (LAI) products over mountain areas: A case study in southwestern China

NASA Astrophysics Data System (ADS)

Jin, Huaan; Li, Ainong; Bian, Jinhu; Nan, Xi; Zhao, Wei; Zhang, Zhengjian; Yin, Gaofei

2017-03-01

The validation study of leaf area index (LAI) products over rugged surfaces not only gives additional insights into data quality of LAI products, but deepens understanding of uncertainties regarding land surface process models depended on LAI data over complex terrain. This study evaluated the performance of MODIS and GLASS LAI products using the intercomparison and direct validation methods over southwestern China. The spatio-temporal consistencies, such as the spatial distributions of LAI products and their statistical relationship as a function of topographic indices, time, and vegetation types, respectively, were investigated through intercomparison between MODIS and GLASS products during the period 2011-2013. The accuracies and change ranges of these two products were evaluated against available LAI reference maps over 10 sampling regions which standed for typical vegetation types and topographic gradients in southwestern China. The results show that GLASS LAI exhibits higher percentage of good quality data (i.e. successful retrievals) and smoother temporal profiles than MODIS LAI. The percentage of successful retrievals for MODIS and GLASS is vulnerable to topographic indices, especially to relief amplitude. Besides, the two products do not capture seasonal dynamics of crop, especially in spring over heterogeneously hilly regions. The yearly mean LAI differences between MODIS and GLASS are within ±0.5 for 64.70% of the total retrieval pixels over southwestern China. The spatial distribution of mean differences and temporal profiles of these two products are inclined to be dominated by vegetation types other than topographic indices. The spatial and temporal consistency of these two products is good over most area of grasses/cereal crops; however, it is poor for evergreen broadleaf forest. MODIS presents more reliable change range of LAI than GLASS through comparison with fine resolution reference maps over most of sampling regions. The accuracies of direct validation are obtained for GLASS LAI (r = 0.35, RMSE = 1.72, mean bias = -0.71) and MODIS LAI (r = 0.49, RMSE = 1.75, mean bias = -0.67). GLASS performs similarly to MODIS, but may be marginally inferior to MODIS based on our direct validation results. The validation experience demonstrates the necessity and importance of topographic consideration for LAI estimation over mountain areas. Considerable attention will be paid to the improvements of surface reflectance, retrieval algorithm and land cover types so as to enhance the quality of LAI products in topographically complex terrain.

Estimation of Spatial Trends in LAI in Heterogeneous Semi-arid Ecosystems using Full Waveform Lidar

NASA Astrophysics Data System (ADS)

Glenn, N. F.; Ilangakoon, N.; Spaete, L.; Dashti, H.

2017-12-01

Leaf area index (LAI) is a key structural trait that is defined by the plant functional type (PFT) and controlled by prevailing climate- and human-driven ecosystem stresses. Estimates of LAI using remote sensing techniques are limited by the uncertainties of vegetation inter and intra-gap fraction estimates; this is especially the case in sparse, low stature vegetated ecosystems. Small footprint full waveform lidar digitizes the total amount of return energy with the direction information as a near continuous waveform at a high vertical resolution (1 ns). Thus waveform lidar provides additional data matrices to capture vegetation gaps as well as PFTs that can be used to constrain the uncertainties of LAI estimates. In this study, we calculated a radiometrically calibrated full waveform parameter called backscatter cross section, along with other data matrices from the waveform to estimate vegetation gaps across plots (10 m x 10 m) in a semi-arid ecosystem in the western US. The LAI was then estimated using empirical relationships with directional gap fraction. Full waveform-derived gap fraction based LAI showed a high correlation with field observed shrub LAI (R2 = 0.66, RMSE = 0.24) compared to discrete return lidar based LAI (R2 = 0.01, RMSE = 0.5). The data matrices derived from full waveform lidar classified a number of deciduous and evergreen tree species, shrub species, and bare ground with an overall accuracy of 89% at 10 m. A similar analysis was performed at 1m with overall accuracy of 80%. The next step is to use these relationships to map the PFTs LAI at 10 m spatial scale across the larger study regions. The results show the exciting potential of full waveform lidar to identify plant functional types and LAI in low-stature vegetation dominated semi-arid ecosystems, an ecosystem in which many other remote sensing techniques fail. These results can be used to assess ecosystem state, habitat suitability as well as to constrain model uncertainties in vegetation dynamic models with a combination of other remote sensing techniques. Multi-spatial resolution (1 m and 10 m) studies provide basic information on the applicability and detection thresholds of future global satellite sensors designed at coarser spatial resolutions (e.g. GEDI, ICESat-2) in semi-arid ecosystems.

Detection and attribution of vegetation greening trend in China over the last 30 years

DOE PAGES

Piao, Shilong; Yin, Guodong; Tan, Jianguang; ...

2014-11-04

The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. Here, we use three different satellite-derived Leaf Area Index (LAI) datasets for detection as well as five different process-based ecosystem models for attribution. Rising atmospheric CO 2 concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41%more » of the average growing-season LAI trend (LAI GS) estimated by satellite datasets (average trend of 0.0070yr -1, ranging from 0.0035yr -1 to 0.0127yr -1), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAI GS at the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai-Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAI GS (P<0.05), and marginally significantly (P=0.07) correlated with the residual of LAI(GS) trend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO 2 concentration and nitrogen deposition, across different provinces. In conclusion, this result highlights the important role of China's afforestation program in explaining the spatial patterns of trend in vegetation growth.« less

Detection and attribution of vegetation greening trend in China over the last 30 years

DOE Office of Scientific and Technical Information (OSTI.GOV)

Piao, Shilong; Yin, Guodong; Tan, Jianguang

The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. Here, we use three different satellite-derived Leaf Area Index (LAI) datasets for detection as well as five different process-based ecosystem models for attribution. Rising atmospheric CO 2 concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41%more » of the average growing-season LAI trend (LAI GS) estimated by satellite datasets (average trend of 0.0070yr -1, ranging from 0.0035yr -1 to 0.0127yr -1), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAI GS at the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai-Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAI GS (P<0.05), and marginally significantly (P=0.07) correlated with the residual of LAI(GS) trend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO 2 concentration and nitrogen deposition, across different provinces. In conclusion, this result highlights the important role of China's afforestation program in explaining the spatial patterns of trend in vegetation growth.« less

Modelling climate change responses in tropical forests: similar productivity estimates across five models, but different mechanisms and responses

NASA Astrophysics Data System (ADS)

Rowland, L.; Harper, A.; Christoffersen, B. O.; Galbraith, D. R.; Imbuzeiro, H. M. A.; Powell, T. L.; Doughty, C.; Levine, N. M.; Malhi, Y.; Saleska, S. R.; Moorcroft, P. R.; Meir, P.; Williams, M.

2015-04-01

Accurately predicting the response of Amazonia to climate change is important for predicting climate change across the globe. Changes in multiple climatic factors simultaneously result in complex non-linear ecosystem responses, which are difficult to predict using vegetation models. Using leaf- and canopy-scale observations, this study evaluated the capability of five vegetation models (Community Land Model version 3.5 coupled to the Dynamic Global Vegetation model - CLM3.5-DGVM; Ecosystem Demography model version 2 - ED2; the Joint UK Land Environment Simulator version 2.1 - JULES; Simple Biosphere model version 3 - SiB3; and the soil-plant-atmosphere model - SPA) to simulate the responses of leaf- and canopy-scale productivity to changes in temperature and drought in an Amazonian forest. The models did not agree as to whether gross primary productivity (GPP) was more sensitive to changes in temperature or precipitation, but all the models were consistent with the prediction that GPP would be higher if tropical forests were 5 °C cooler than current ambient temperatures. There was greater model-data consistency in the response of net ecosystem exchange (NEE) to changes in temperature than in the response to temperature by net photosynthesis (An), stomatal conductance (gs) and leaf area index (LAI). Modelled canopy-scale fluxes are calculated by scaling leaf-scale fluxes using LAI. At the leaf-scale, the models did not agree on the temperature or magnitude of the optimum points of An, Vcmax or gs, and model variation in these parameters was compensated for by variations in the absolute magnitude of simulated LAI and how it altered with temperature. Across the models, there was, however, consistency in two leaf-scale responses: (1) change in An with temperature was more closely linked to stomatal behaviour than biochemical processes; and (2) intrinsic water use efficiency (IWUE) increased with temperature, especially when combined with drought. These results suggest that even up to fairly extreme temperature increases from ambient levels (+6 °C), simulated photosynthesis becomes increasingly sensitive to gs and remains less sensitive to biochemical changes. To improve the reliability of simulations of the response of Amazonian rainforest to climate change, the mechanistic underpinnings of vegetation models need to be validated at both leaf- and canopy-scales to improve accuracy and consistency in the quantification of processes within and across an ecosystem.

Evaluation of the observation operator Jacobian for leaf area index data assimilation with an extended Kalman filter

NASA Astrophysics Data System (ADS)

Rüdiger, Christoph; Albergel, CléMent; Mahfouf, Jean-FrançOis; Calvet, Jean-Christophe; Walker, Jeffrey P.

2010-05-01

To quantify carbon and water fluxes between the vegetation and the atmosphere in a consistent manner, land surface models now include interactive vegetation components. These models treat the vegetation biomass as a prognostic model state, allowing the model to dynamically adapt the vegetation states to environmental conditions. However, it is expected that the prediction skill of such models can be greatly increased by assimilating biophysical observations such as leaf area index (LAI). The Jacobian of the observation operator, a central aspect of data assimilation methods such as the extended Kalman filter (EKF) and the variational assimilation methods, provides the required linear relationship between the observation and the model states. In this paper, the Jacobian required for assimilating LAI into the Interaction between the Soil, Biosphere and Atmosphere-A-gs land surface model using the EKF is studied. In particular, sensitivity experiments were undertaken on the size of the initial perturbation for estimating the Jacobian and on the length of the time window between initial state and available observation. It was found that small perturbations (0.1% of the state) typically lead to accurate estimates of the Jacobian. While other studies have shown that the assimilation of LAI with 10 day assimilation windows is possible, 1 day assimilation intervals can be chosen to comply with numerical weather prediction requirements. Moreover, the seasonal dependence of the Jacobian revealed contrasted groups of Jacobian values due to environmental factors. Further analyses showed the Jacobian values to vary as a function of the LAI itself, which has important implications for its assimilation in different seasons, as the size of the LAI increments will subsequently vary due to the variability of the Jacobian.

View angle effects on relationships between leaf area index in wheat and vegetation indices

NASA Astrophysics Data System (ADS)

Chen, H.; Li, W.; Huang, W.; Niu, Z.

2016-12-01

The effects of plant types and view angles on the canopy-reflected spectrum can not be ignored in the estimation of leaf area index (LAI) using remote sensing vegetation indices. While vegetation indices derived from nadir-viewing remote sensors are insufficient in leaf area index (LAI) estimation because of its misinterpretation of structural characteristecs, vegetation indices derived from multi-angular remote sensors have potential to improve detection of LAI. However, view angle effects on relationships between these indices and LAI for low standing crops (i.e. wheat) has not been fully evaluated and thus limits them to applied for consistent and accurate monitoring of vegetation. View angles effects of two types of winter wheat (wheat 411, erectophile; and wheat 9507, planophile) on relationship between LAI and spectral reflectance are assessed and compared in this study. An evaluation is conducted with in-situ measurements of LAI and bidirectional reflectance in the principal plane from -60° (back-scattering direction ) ot 60° (forward scattering direction) in the growth cycle of winter wheat. A variety of vegetation indices (VIs) published are calculated by BRDF. Additionally, all combinations of the bands are used in order to calculate Normalized difference Spectral Indices (NDSI) and Simple Subtraction Indices (SSI). The performance of the above indices along with raw reflectance and reflectance derivatives on LAI estimation are examined based on a linearity comparison. The results will be helpful in further developing multi-angle remote sensing models for accurate LAI evaluation.

Retrieval of effective leaf area index (LAIe) and leaf area density (LAD) profile at individual tree level using high density multi-return airborne LiDAR

NASA Astrophysics Data System (ADS)

Lin, Yi; West, Geoff

2016-08-01

As an important canopy structure indicator, leaf area index (LAI) proved to be of considerable implications for forest ecosystem and ecological studies, and efficient techniques for accurate LAI acquisitions have long been highlighted. Airborne light detection and ranging (LiDAR), often termed as airborne laser scanning (ALS), once was extensively investigated for this task but showed limited performance due to its low sampling density. Now, ALS systems exhibit more competing capacities such as high density and multi-return sampling, and hence, people began to ask the questions like-;can ALS now work better on the task of LAI prediction?; As a re-examination, this study investigated the feasibility of LAI retrievals at the individual tree level based on high density and multi-return ALS, by directly considering the vertical distributions of laser points lying within each tree crown instead of by proposing feature variables such as quantiles involving laser point distribution modes at the plot level. The examination was operated in the case of four tree species (i.e. Picea abies, Pinus sylvestris, Populus tremula and Quercus robur) in a mixed forest, with their LAI-related reference data collected by using static terrestrial laser scanning (TLS). In light of the differences between ALS- and TLS-based LAI characterizations, the methods of voxelization of 3D scattered laser points, effective LAI (LAIe) that does not distinguish branches from canopies and unified cumulative LAI (ucLAI) that is often used to characterize the vertical profiles of crown leaf area densities (LADs) was used; then, the relationships between the ALS- and TLS-derived LAIes were determined, and so did ucLAIs. Tests indicated that the tree-level LAIes for the four tree species can be estimated based on the used airborne LiDAR (R2 = 0.07, 0.26, 0.43 and 0.21, respectively) and their ucLAIs can also be derived. Overall, this study has validated the usage of the contemporary high density multi-return airborne LiDARs for LAIe and LAD profile retrievals at the individual tree level, and the contribution are of high potential for advancing forest ecosystem modeling and ecological understanding.

Modeling the Time Course of the Tissue Responses to Intramuscular Long-acting Paliperidone Palmitate Nano-/Microcrystals and Polystyrene Microspheres in the Rat.

PubMed

Darville, Nicolas; van Heerden, Marjolein; Erkens, Tim; De Jonghe, Sandra; Vynckier, An; De Meulder, Marc; Vermeulen, An; Sterkens, Patrick; Annaert, Pieter; Van den Mooter, Guy

2016-02-01

Long-acting injectable (LAI) drug suspensions consist of drug nano-/microcrystals suspended in an aqueous vehicle and enable prolonged therapeutic drug exposure up to several months. The examination of injection site reactions (ISRs) to the intramuscular (IM) injection of LAI suspensions is relevant not only from a safety perspective but also for the understanding of the pharmacokinetics. The aim of this study was to perform a multilevel temporal characterization of the local and lymphatic histopathological/immunological alterations triggered by the IM injection of an LAI paliperidone palmitate suspension and an analog polystyrene suspension in rats and identify critical time points and parameters with regard to the host response. The ISRs showed a moderate to marked chronic granulomatous inflammation, which was mediated by multiple cyto-/chemokines, including interleukin-1β, monocyte Chemoattractant Protein-1, and vascular endothelial growth factor. Lymphatic uptake and lymph node retention of nano-/microparticles were observed, but the contribution to the drug absorption was negligible. A simple image analysis procedure and empirical model were proposed for the accurate evaluation of the depot geometry, cell infiltration, and vascularization. This study was designed as a reference for the evaluation and comparison of future LAIs and to support the mechanistic modeling of the formulation-physiology interplay regulating the drug absorption from LAIs. © The Author(s) 2015.

Land and atmosphere interactions using satellite remote sensing and a coupled mesoscale/land surface model

NASA Astrophysics Data System (ADS)

Hong, Seungbum

Land and atmosphere interactions have long been recognized for playing a key role in climate and weather modeling. However their quantification has been challenging due to the complex nature of the land surface amongst various other reasons. One of the difficult parts in the quantification is the effect of vegetation which are related to land surface processes such soil moisture variation and to atmospheric conditions such as radiation. This study addresses various relational investigations among vegetation properties such as Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), surface temperature (TSK), and vegetation water content (VegWC) derived from satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) and EOS Advanced Microwave Scanning Radiometer (AMSR-E). The study provides general information about a physiological behavior of vegetation for various environmental conditions. Second, using a coupled mesoscale/land surface model, we examined the effects of vegetation and its relationship with soil moisture on the simulated land-atmospheric interactions through the model sensitivity tests. The Weather Research and Forecasting (WRF) model was selected for this study, and the Noah land surface model (Noah LSM) implemented in the WRF model was used for the model coupled system. This coupled model was tested through two parameterization methods for vegetation fraction using MODIS data and through model initialization of soil moisture from High Resolution Land Data Assimilation System (HRLDAS). Then, this study evaluates the model improvements for each simulation method.

How Universal Is the Relationship Between Remotely Sensed Vegetation Indices (VI) and Crop Leaf Area Index (LAI)?

NASA Technical Reports Server (NTRS)

Kang, Yanghui; Ozdogan, Mutlu; Zipper, Samuel C.; Roman, Miguel

2016-01-01

Global LAI-VI relationships are statistically significant, crop-specific, and mostly non-linear. This research enables the operationalization of large-area crop modeling and, by extension, has relevance to both fundamental and applied agroecosystem research.

Leaf area index retrieval using Hyperion EO-1 data-based vegetation indices in Himalayan forest system

NASA Astrophysics Data System (ADS)

Singh, Dharmendra; Singh, Sarnam

2016-04-01

Present Study is being taken to retrieve Leaf Area Indexn(LAI) in Himalayan forest system using vegetation indices developed from Hyperion EO-1 hyperspectral data. Hemispherical photograph were captured in the month of March and April, 2012 at 40 locations, covering moist tropical Sal forest, subtropical Bauhinia and pine forest and temperate Oak forest and analysed using an open source GLA software. LAI in the study region was ranging in between 0.076 m2/m2 to 6.00 m2/m2. These LAI values were used to develop spectral models with the FLAASH corrected Hyperion measurements.Normalized difference vegetation index (NDVI) was used taking spectral reflectance values of all the possible combinations of 170 atmospherically corrected channels. The R2 was ranging from lowest 0.0 to highest 0.837 for the band combinations of spectral region 640 nm and 670 nm. The spectral model obtained was, spectral reflectance (y) = 0.02x LAI(x) - 0.0407.

LAI is the major cause of divergence in CO2 fertilization effect in land surface models

NASA Astrophysics Data System (ADS)

Li, Q.; Luo, Y.; Lu, X.; Wang, Y.; Huang, X.; Lin, G., Sr.

2017-12-01

Concentration-carbon feedback (β), also called CO2 fertilization effect, is an important feedback between terrestrial ecosystems and atmosphere to alleviate global climate change. However, models participating in C4MIP and CMIP5 predicted diverse CO2 fertilization effects under future CO2 inceasing scenarios. Hence identifing the key processes dominating the divergence of β in land surface models is of significance. We calculated CO2 fertilization effects from leaf level, canopy gross productivity level, net ecosystem productivity level and ecosystem carbon stock level in Community Atmosphere Biosphere Land Exchange (CABLE) model. Our results identified LAI is the key factor dominating the divergence of β among C3 plants in CABLE model. Saturation of the ecosystem productivity to increasing CO2 is not only regulated by leaf-level response, but also the response of LAI to increasing CO2. The greatest variation among C3 plants at ecosystem level suggests that other processes such as different allocation patterns and soil carbon dynamics of various vegetation types are also responsible for the divergence. Our results indicate that processes regarding to LAI need to be better calibrated according to experiments and observations in order to better represent the response of ecosystem productivity to increasing CO2.

A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description, Validation, and Case Study

NASA Technical Reports Server (NTRS)

Claverie, Martin; Matthews, Jessica L.; Vermote, Eric F.; Justice, Christopher O.

2016-01-01

In- land surface models, which are used to evaluate the role of vegetation in the context ofglobal climate change and variability, LAI and FAPAR play a key role, specifically with respect to thecarbon and water cycles. The AVHRR-based LAIFAPAR dataset offers daily temporal resolution,an improvement over previous products. This climate data record is based on a carefully calibratedand corrected land surface reflectance dataset to provide a high-quality, consistent time-series suitablefor climate studies. It spans from mid-1981 to the present. Further, this operational dataset is availablein near real-time allowing use for monitoring purposes. The algorithm relies on artificial neuralnetworks calibrated using the MODIS LAI/FAPAR dataset. Evaluation based on cross-comparisonwith MODIS products and in situ data show the dataset is consistent and reliable with overalluncertainties of 1.03 and 0.15 for LAI and FAPAR, respectively. However, a clear saturation effect isobserved in the broadleaf forest biomes with high LAI (greater than 4.5) and FAPAR (greater than 0.8) values.

Discerning spatial and temporal LAI and clear-sky FAPAR variability during summer at the Toolik Lake vegetation monitoring grid (North Slope, Alaska)

NASA Astrophysics Data System (ADS)

Heim, B.; Beamish, A. L.; Walker, D. A.; Epstein, H. E.; Sachs, T.; Chabrillat, S.; Buchhorn, M.; Prakash, A.

2016-12-01

Ground data for the validation of satellite-derived terrestrial Essential Climate Variables (ECVs) at high latitudes are sparse. Also for regional model evaluation (e.g. climate models, land surface models, permafrost models), we lack accurate ranges of terrestrial ground data and face the problem of a large mismatch in scale. Within the German research programs `Regional Climate Change' (REKLIM) and the Environmental Mapping and Analysis Program (EnMAP), we conducted a study on ground data representativeness for vegetation-related variables within a monitoring grid at the Toolik Lake Long-Term Ecological Research station; the Toolik Lake station lies in the Kuparuk River watershed on the North Slope of the Brooks Mountain Range in Alaska. The Toolik Lake grid covers an area of 1 km2 containing Eight five grid points spaced 100 meters apart. Moist acidic tussock tundra is the most dominant vegetation type within the grid. Eight five permanent 1 m2 plots were also established to be representative of the individual gridpoints. Researchers from the University of Alaska Fairbanks have undertaken assessments at these plots, including Leaf Area Index (LAI) and field spectrometry to derive the Normalized Difference Vegetation Index (NDVI). During summer 2016, we conducted field spectrometry and LAI measurements at selected plots during early, peak and late summer. We experimentally measured LAI on more spatially extensive Elementary Sampling Units (ESUs) to investigate the spatial representativeness of the permanent 1 m2 plots and to map ESUs for various tundra types. LAI measurements are potentially influenced by landscape-inherent microtopography, sparse vascular plant cover, and dead woody matter. From field spectrometer measurements, we derived a clear-sky mid-day Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). We will present the first data analyses comparing FAPAR and LAI, and maps of biophysically-focused ESUs for evaluation of the use of remote sensing data to estimate these ecosystem properties.

Combined application of Sentinel2A data and growth modelling for novel monitoring and prediction of pasture yields

NASA Astrophysics Data System (ADS)

Verhoef, A.; Punalekar, S.; Quaife, T. L.; Humphries, D.; Reynolds, C.

2017-12-01

Currently, 30% of the world's land area is covered by permanent pasture. Grazing ruminants convert forage materials into milk and meat for human consumption; ruminant production is a key agricultural enterprise. Management of pasture farms (nutrient and herbi-/pesticides application, grazing rotations) is often suboptimal. Furthermore, adverse weather can have negative effects on pasture growth and quality. Near real-time herbage monitoring and prediction could help improve farm profitability. While the use of remote sensing (RS) in the context of arable crop growth prediction is becoming more established, the same is not true for pasture. However, recently launched Sentinel satellites offer real opportunities to exploit high spatio-temporal resolution datasets for effective monitoring of pastures, as well as crops. A perennial grazed ryegrass field in the Southwest of the UK was monitored regularly using field hyperspectral spectro-radiometers. Simultaneously, leaf area index (LAI) was measured using a ceptometer, and yield was measured, indirectly using a `plate meter' and directly by destructive sampling. Two sets of spectral data were used to retrieve LAI with the PROSAIL radiative transfer model: (i) Sentinel-2A bands convolved from field spectral data, (ii) actual Sentinel-2A image pixels for the sampling plots. Retrieved LAI was compared against field observations. LAI estimates were assimilated in a bespoke growth model (including grazing and management), driven by weather data, for calibration of sensitive parameters using a 4D-Var scheme, to obtain pasture biomass. The developed approach was used to study a pasture farm in the South of the UK, for which a large number of Sentinel-2A images were available throughout 2016-17. Retrieved LAI compared well with in-situ LAI, and significantly improved yield estimates. The calibrated model parameters compared well with literature values. The model, guided by satellite data and general information on farm operations, was able to estimate pasture biomass reasonably well. Assimilation of RS information in grass growth models offers a potential tool to monitor pasture growth in response to weather and management and may be used effectively in pasture prediction tools for dairy farmers.

A Study toward the Evaluation of ALOS Images for LAI Estimation in Rice Fields

NASA Astrophysics Data System (ADS)

Sharifi Hashjin, Sh.; Darvishzadeh, R.; Khandan, R.

2013-10-01

For expanding and managing agricultural sources, satellite data have a key role in determining required information about different factors in plants Including Leaf Area Index (LAI).This paper has studied the potential of spectral indices in estimating rice canopy LAI in Amol city as one of the main sources of rice production in Iran. Due to its importance in provision of food and calorie of a major portion of population, rice product was chosen for study. A field campaign was conducted when rice was in the max growth stage (late of June). Also, two satellite images from ALOS-AVNIR-2 were used (simultaneous with conducted field works) to extract and determine vegetation indices. Then the Regression between measured data and vegetation indices, derived from combination of different bands, was evaluated and after that suitable vegetation indices were realized. Finally, statistics and calculations for introduction of a suitable model were presented. After examination of models, the results showed that RDVI and SAVI2, by determination coefficient and RMSE of 0.12-0.59 and 0.24-0.62, have more accuracy in LAI estimation. Results of present study demonstrated the potential of ALOS images, for LAI estimation and their significant role in monitoring and managing the rice plant.

An Analytical Solution for the Impact of Vegetation Changes on Hydrological Partitioning Within the Budyko Framework

NASA Astrophysics Data System (ADS)

Zhang, Shulei; Yang, Yuting; McVicar, Tim R.; Yang, Dawen

2018-01-01

Vegetation change is a critical factor that profoundly affects the terrestrial water cycle. Here we derive an analytical solution for the impact of vegetation changes on hydrological partitioning within the Budyko framework. This is achieved by deriving an analytical expression between leaf area index (LAI) change and the Budyko land surface parameter (n) change, through the combination of a steady state ecohydrological model with an analytical carbon cost-benefit model for plant rooting depth. Using China where vegetation coverage has experienced dramatic changes over the past two decades as a study case, we quantify the impact of LAI changes on the hydrological partitioning during 1982-2010 and predict the future influence of these changes for the 21st century using climate model projections. Results show that LAI change exhibits an increasing importance on altering hydrological partitioning as climate becomes drier. In semiarid and arid China, increased LAI has led to substantial streamflow reductions over the past three decades (on average -8.5% in 1990s and -11.7% in 2000s compared to the 1980s baseline), and this decreasing trend in streamflow is projected to continue toward the end of this century due to predicted LAI increases. Our result calls for caution regarding the large-scale revegetation activities currently being implemented in arid and semiarid China, which may result in serious future water scarcity issues here. The analytical model developed here is physically based and suitable for simultaneously assessing both vegetation changes and climate change induced changes to streamflow globally.

Estimation of Leaf Area Index and Plant Area Index of a Submerged Macrophyte Canopy Using Digital Photography

PubMed Central

Zhao, Dehua; Xie, Dong; Zhou, Hengjie; Jiang, Hao; An, Shuqing

2012-01-01

Non-destructive estimation using digital cameras is a common approach for estimating leaf area index (LAI) of terrestrial vegetation. However, no attempt has been made so far to develop non-destructive approaches to LAI estimation for aquatic vegetation. Using the submerged plant species Potamogeton malainus, the objective of this study was to determine whether the gap fraction derived from vertical photographs could be used to estimate LAI of aquatic vegetation. Our results suggested that upward-oriented photographs taken from beneath the water surface were more suitable for distinguishing vegetation from other objects than were downward-oriented photographs taken from above the water surface. Exposure settings had a substantial influence on the identification of vegetation in upward-oriented photographs. Automatic exposure performed nearly as well as the optimal trial exposure, making it a good choice for operational convenience. Similar to terrestrial vegetation, our results suggested that photographs taken for the purpose of distinguishing gap fraction in aquatic vegetation should be taken under diffuse light conditions. Significant logarithmic relationships were observed between the vertical gap fraction derived from upward-oriented photographs and plant area index (PAI) and LAI derived from destructive harvesting. The model we developed to depict the relationship between PAI and gap fraction was similar to the modified theoretical Poisson model, with coefficients of 1.82 and 1.90 for our model and the theoretical model, respectively. This suggests that vertical upward-oriented photographs taken from below the water surface are a feasible alternative to destructive harvesting for estimating PAI and LAI for the submerged aquatic plant Potamogeton malainus. PMID:23226557

Joint Leaf chlorophyll and leaf area index retrieval from Landsat data using a regularized model inversion system

USDA-ARS?s Scientific Manuscript database

Leaf area index (LAI) and leaf chlorophyll (Chl) content represent key biophysical and biochemical controls on water, energy and carbon exchange processes in the terrestrial biosphere. In combination, LAI and leaf Chl content provide critical information on vegetation density, vitality and photosynt...

[Simulation of vegetation indices optimizing under retrieval of vegetation biochemical parameters based on PROSPECT + SAIL model].

PubMed

Wu, Ling; Liu, Xiang-Nan; Zhou, Bo-Tian; Liu, Chuan-Hao; Li, Lu-Feng

2012-12-01

This study analyzed the sensitivities of three vegetation biochemical parameters [chlorophyll content (Cab), leaf water content (Cw), and leaf area index (LAI)] to the changes of canopy reflectance, with the effects of each parameter on the wavelength regions of canopy reflectance considered, and selected three vegetation indices as the optimization comparison targets of cost function. Then, the Cab, Cw, and LAI were estimated, based on the particle swarm optimization algorithm and PROSPECT + SAIL model. The results showed that retrieval efficiency with vegetation indices as the optimization comparison targets of cost function was better than that with all spectral reflectance. The correlation coefficients (R2) between the measured and estimated values of Cab, Cw, and LAI were 90.8%, 95.7%, and 99.7%, and the root mean square errors of Cab, Cw, and LAI were 4.73 microg x cm(-2), 0.001 g x cm(-2), and 0.08, respectively. It was suggested that to adopt vegetation indices as the optimization comparison targets of cost function could effectively improve the efficiency and precision of the retrieval of biochemical parameters based on PROSPECT + SAIL model.

[Comparison of precision in retrieving soybean leaf area index based on multi-source remote sensing data].

PubMed

Gao, Lin; Li, Chang-chun; Wang, Bao-shan; Yang Gui-jun; Wang, Lei; Fu, Kui

2016-01-01

With the innovation of remote sensing technology, remote sensing data sources are more and more abundant. The main aim of this study was to analyze retrieval accuracy of soybean leaf area index (LAI) based on multi-source remote sensing data including ground hyperspectral, unmanned aerial vehicle (UAV) multispectral and the Gaofen-1 (GF-1) WFV data. Ratio vegetation index (RVI), normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), difference vegetation index (DVI), and triangle vegetation index (TVI) were used to establish LAI retrieval models, respectively. The models with the highest calibration accuracy were used in the validation. The capability of these three kinds of remote sensing data for LAI retrieval was assessed according to the estimation accuracy of models. The experimental results showed that the models based on the ground hyperspectral and UAV multispectral data got better estimation accuracy (R² was more than 0.69 and RMSE was less than 0.4 at 0.01 significance level), compared with the model based on WFV data. The RVI logarithmic model based on ground hyperspectral data was little superior to the NDVI linear model based on UAV multispectral data (The difference in E(A), R² and RMSE were 0.3%, 0.04 and 0.006, respectively). The models based on WFV data got the lowest estimation accuracy with R2 less than 0.30 and RMSE more than 0.70. The effects of sensor spectral response characteristics, sensor geometric location and spatial resolution on the soybean LAI retrieval were discussed. The results demonstrated that ground hyperspectral data were advantageous but not prominent over traditional multispectral data in soybean LAI retrieval. WFV imagery with 16 m spatial resolution could not meet the requirements of crop growth monitoring at field scale. Under the condition of ensuring the high precision in retrieving soybean LAI and working efficiently, the approach to acquiring agricultural information by UAV remote sensing could yet be regarded as an optimal plan. Therefore, in the case of more and more available remote sensing information sources, agricultural UAV remote sensing could become an important information resource for guiding field-scale crop management and provide more scientific and accurate information for precision agriculture research.

From field to region yield predictions in response to pedo-climatic variations in Eastern Canada

NASA Astrophysics Data System (ADS)

JÉGO, G.; Pattey, E.; Liu, J.

2013-12-01

The increase in global population coupled with new pressures to produce energy and bioproducts from agricultural land requires an increase in crop productivity. However, the influence of climate and soil variations on crop production and environmental performance is not fully understood and accounted for to define more sustainable and economical management strategies. Regional crop modeling can be a great tool for understanding the impact of climate variations on crop production, for planning grain handling and for assessing the impact of agriculture on the environment, but it is often limited by the availability of input data. The STICS ("Simulateur mulTIdisciplinaire pour les Cultures Standard") crop model, developed by INRA (France) is a functional crop model which has a built-in module to optimize several input parameters by minimizing the difference between calculated and measured output variables, such as Leaf Area Index (LAI). STICS crop model was adapted to the short growing season of the Mixedwood Plains Ecozone using field experiments results, to predict biomass and yield of soybean, spring wheat and corn. To minimize the numbers of inference required for regional applications, 'generic' cultivars rather than specific ones have been calibrated in STICS. After the calibration of several model parameters, the root mean square error (RMSE) of yield and biomass predictions ranged from 10% to 30% for the three crops. A bit more scattering was obtained for LAI (20%

Scaling Sap Flow Results Over Wide Areas Using High-Resolution Aerial Multispectral Digital Imaging, Leaf Area Index (LAI) and MODIS Satellite Imagery in Saltcedar Stands on the Lower Colorado River

NASA Astrophysics Data System (ADS)

Murray, R.; Neale, C.; Nagler, P. L.; Glenn, E. P.

2008-12-01

Heat-balance sap flow sensors provide direct estimates of water movement through plant stems and can be used to accurately measure leaf-level transpiration (EL) and stomatal conductance (GS) over time scales ranging from 20-minutes to a month or longer in natural stands of plants. However, their use is limited to relatively small branches on shrubs or trees, as the gauged stem section needs to be uniformly heated by the heating coil to produce valid measurements. This presents a scaling problem in applying the results to whole plants, stands of plants, and larger landscape areas. We used high-resolution aerial multispectral digital imaging with green, red and NIR bands as a bridge between ground measurements of EL and GS, and MODIS satellite imagery of a flood plain on the Lower Colorado River dominated by saltcedar (Tamarix ramosissima). Saltcedar is considered to be a high-water-use plant, and saltcedar removal programs have been proposed to salvage water. Hence, knowledge of actual saltcedar ET rates is needed on western U.S. rivers. Scaling EL and GS to large landscape units requires knowledge of leaf area index (LAI) over large areas. We used a LAI model developed for riparian habitats on Bosque del Apache, New Mexico, to estimate LAI at our study site on the Colorado River. We compared the model estimates to ground measurements of LAI, determined with a Li-Cor LAI-2000 Plant Canopy Analyzer calibrated by leaf harvesting to determine Specific Leaf Area (SLA) (m2 leaf area per g dry weight leaves) of the different species on the floodplain. LAI could be adequately predicted from NDVI from aerial multispectral imagery and could be cross-calibrated with MODIS NDVI and EVI. Hence, we were able to project point measurements of sap flow and LAI over multiple years and over large areas of floodplain using aerial multispectral imagery as a bridge between ground and satellite data. The methods are applicable to riparian corridors throughout the western U.S.

Elucidating Inherent Uncertainties in Data Assimilation for Predictions Incorporating Non-stationary Processes - Focus on Predictive Phenology

NASA Astrophysics Data System (ADS)

Lowman, L.; Barros, A. P.

2017-12-01

Data assimilation (DA) is the widely accepted procedure for estimating parameters within predictive models because of the adaptability and uncertainty quantification offered by Bayesian methods. DA applications in phenology modeling offer critical insights into how extreme weather or changes in climate impact the vegetation life cycle. Changes in leaf onset and senescence, root phenology, and intermittent leaf shedding imply large changes in the surface radiative, water, and carbon budgets at multiple scales. Models of leaf phenology require concurrent atmospheric and soil conditions to determine how biophysical plant properties respond to changes in temperature, light and water demand. Presently, climatological records for fraction of photosynthetically active radiation (FPAR) and leaf area index (LAI), the modelled states indicative of plant phenology, are not available. Further, DA models are typically trained on short periods of record (e.g. less than 10 years). Using limited records with a DA framework imposes non-stationarity on estimated parameters and the resulting predicted model states. This talk discusses how uncertainty introduced by the inherent non-stationarity of the modeled processes propagates through a land-surface hydrology model coupled to a predictive phenology model. How water demand is accounted for in the upscaling of DA model inputs and analysis period serves as a key source of uncertainty in the FPAR and LAI predictions. Parameters estimated from different DA effectively calibrate a plant water-use strategy within the land-surface hydrology model. For example, when extreme droughts are included in the DA period, the plants are trained to uptake water, transpire, and assimilate carbon under favorable conditions and quickly shut down at the onset of water stress.

Fewer not more leaves - Key to obtaining the needed jump in crop yield potential and water use efficiency

NASA Astrophysics Data System (ADS)

Srinivasan, V.; Kumar, P.; Long, S.

2013-12-01

Word food and feed supply needs to increase by 75% by 2050 to meet the increasing demands of our growing population. Soybean which is the world`s fourth most important crop in terms of total production at 250 million Mt/yr is a key protein source, and together with rice and wheat, are experiencing declining global yield increases year on year. At present rates of improvement, 2050 targets cannot be reached without new innovations. In this study we demonstrate an innovative approach that could provide a yield jump. While, natural selection favors individual plants to maximize leaf production to maximize light interception and shade competitors, the presence of this trait in domestic crops could be disadvantageous. In addition, rising CO2 causes increased leaf production further exacerbating the problem. Here, we show by mathematical model and field experiment that, a modern cultivar growing at the center of US soy cultivation produces too many leaves and reduction to an optimal level would increase yield. Our model results indicate that an LAI of 3.5 and 3.8 produces maximal rates of net canopy assimilation under ambient and elevated CO2 conditions respectively. However, observed peak LAI values are 6.9 and 7.5 under ambient and elevated CO2 conditions respectively. This results in a NPP loss of 30% and 20% under ambient and elevated CO2 conditions respectively. Furthermore, the optimal LAI results in a decreased transpiration of up to 30% thus increasing water use efficiency. We show that as LAI increases, the tradeoffs between diminishing day time gains in NPP, and increasing losses in respiration is responsible for this effect. By designing a more optimum canopy, we can increase NPP and this potentially translates to increased seed yield. To test this model result, we perform canopy manipulation experiments on soybean plants, where we artificially decrease LAI by periodically removing young and emerging leaves throughout the growing season (after pod onset), and measure the seed yield under ambient and elevated CO2 conditions. Our experimental results show that an LAI reduction of 0.5 results in an increased seed yield of 8.1% validating our model results. We propose that, by achieving a stronger LAI reduction, we can improve seed yields by up to 24% providing the much needed jump in yield to achieve future food security.

Digital Cover Photography for Estimating Leaf Area Index (LAI) in Apple Trees Using a Variable Light Extinction Coefficient

PubMed Central

Poblete-Echeverría, Carlos; Fuentes, Sigfredo; Ortega-Farias, Samuel; Gonzalez-Talice, Jaime; Yuri, Jose Antonio

2015-01-01

Leaf area index (LAI) is one of the key biophysical variables required for crop modeling. Direct LAI measurements are time consuming and difficult to obtain for experimental and commercial fruit orchards. Devices used to estimate LAI have shown considerable errors when compared to ground-truth or destructive measurements, requiring tedious site-specific calibrations. The objective of this study was to test the performance of a modified digital cover photography method to estimate LAI in apple trees using conventional digital photography and instantaneous measurements of incident radiation (Io) and transmitted radiation (I) through the canopy. Leaf area of 40 single apple trees were measured destructively to obtain real leaf area index (LAID), which was compared with LAI estimated by the proposed digital photography method (LAIM). Results showed that the LAIM was able to estimate LAID with an error of 25% using a constant light extinction coefficient (k = 0.68). However, when k was estimated using an exponential function based on the fraction of foliage cover (ff) derived from images, the error was reduced to 18%. Furthermore, when measurements of light intercepted by the canopy (Ic) were used as a proxy value for k, the method presented an error of only 9%. These results have shown that by using a proxy k value, estimated by Ic, helped to increase accuracy of LAI estimates using digital cover images for apple trees with different canopy sizes and under field conditions. PMID:25635411

Hydrological model uncertainty due to spatial evapotranspiration estimation methods

NASA Astrophysics Data System (ADS)

Yu, Xuan; Lamačová, Anna; Duffy, Christopher; Krám, Pavel; Hruška, Jakub

2016-05-01

Evapotranspiration (ET) continues to be a difficult process to estimate in seasonal and long-term water balances in catchment models. Approaches to estimate ET typically use vegetation parameters (e.g., leaf area index [LAI], interception capacity) obtained from field observation, remote sensing data, national or global land cover products, and/or simulated by ecosystem models. In this study we attempt to quantify the uncertainty that spatial evapotranspiration estimation introduces into hydrological simulations when the age of the forest is not precisely known. The Penn State Integrated Hydrologic Model (PIHM) was implemented for the Lysina headwater catchment, located 50°03‧N, 12°40‧E in the western part of the Czech Republic. The spatial forest patterns were digitized from forest age maps made available by the Czech Forest Administration. Two ET methods were implemented in the catchment model: the Biome-BGC forest growth sub-model (1-way coupled to PIHM) and with the fixed-seasonal LAI method. From these two approaches simulation scenarios were developed. We combined the estimated spatial forest age maps and two ET estimation methods to drive PIHM. A set of spatial hydrologic regime and streamflow regime indices were calculated from the modeling results for each method. Intercomparison of the hydrological responses to the spatial vegetation patterns suggested considerable variation in soil moisture and recharge and a small uncertainty in the groundwater table elevation and streamflow. The hydrologic modeling with ET estimated by Biome-BGC generated less uncertainty due to the plant physiology-based method. The implication of this research is that overall hydrologic variability induced by uncertain management practices was reduced by implementing vegetation models in the catchment models.

Generating Vegetation Leaf Area Index Earth System Data Record from Multiple Sensors. Part 2; Implementation, Analysis and Validation

NASA Technical Reports Server (NTRS)

Ganguly, Sangram; Samanta, Arindam; Schull, Mitchell A.; Shabanov, Nikolay V.; Milesi, Cristina; Nemani, Ramajrushna R,; Knyazikhin, Yuri; Myneni, Ranga B.

2008-01-01

The evaluation of a new global monthly leaf area index (LAI) data set for the period July 1981 to December 2006 derived from AVHRR Normalized Difference Vegetation Index (NDVI) data is described. The physically based algorithm is detailed in the first of the two part series. Here, the implementation, production and evaluation of the data set are described. The data set is evaluated both by direct comparisons to ground data and indirectly through inter-comparisons with similar data sets. This indirect validation showed satisfactory agreement with existing LAI products, importantly MODIS, at a range of spatial scales, and significant correlations with key climate variables in areas where temperature and precipitation limit plant growth. The data set successfully reproduced well-documented spatio-temporal trends and inter-annual variations in vegetation activity in the northern latitudes and semi-arid tropics. Comparison with plot scale field measurements over homogeneous vegetation patches indicated a 7% underestimation when all major vegetation types are taken into account. The error in mean values obtained from distributions of AVHRR LAI and high-resolution field LAI maps for different biomes is within 0.5 LAI for six out of the ten selected sites. These validation exercises though limited by the amount of field data, and thus less than comprehensive, indicated satisfactory agreement between the LAI product and field measurements. Overall, the intercomparison with short-term LAI data sets, evaluation of long term trends with known variations in climate variables, and validation with field measurements together build confidence in the utility of this new 26 year LAI record for long term vegetation monitoring and modeling studies.

Evaluating the condition of a mangrove forest of the Mexican Pacific based on an estimated leaf area index mapping approach.

PubMed

Kovacs, J M; King, J M L; Flores de Santiago, F; Flores-Verdugo, F

2009-10-01

Given the alarming global rates of mangrove forest loss it is important that resource managers have access to updated information regarding both the extent and condition of their mangrove forests. Mexican mangroves in particular have been identified as experiencing an exceptional high annual rate of loss. However, conflicting studies, using remote sensing techniques, of the current state of many of these forests may be hindering all efforts to conserve and manage what remains. Focusing on one such system, the Teacapán-Agua Brava-Las Haciendas estuarine-mangrove complex of the Mexican Pacific, an attempt was made to develop a rapid method of mapping the current condition of the mangroves based on estimated LAI. Specifically, using an AccuPAR LP-80 Ceptometer, 300 indirect in situ LAI measurements were taken at various sites within the black mangrove (Avicennia germinans) dominated forests of the northern section of this system. From this sample, 225 measurements were then used to develop linear regression models based on their relationship with corresponding values derived from QuickBird very high resolution optical satellite data. Specifically, regression analyses of the in situ LAI with both the normalized difference vegetation index (NDVI) and the simple ration (SR) vegetation index revealed significant positive relationships [LAI versus NDVI (R (2) = 0.63); LAI versus SR (R (2) = 0.68)]. Moreover, using the remaining sample, further examination of standard errors and of an F test of the residual variances indicated little difference between the two models. Based on the NDVI model, a map of estimated mangrove LAI was then created. Excluding the dead mangrove areas (i.e. LAI = 0), which represented 40% of the total 30.4 km(2) of mangrove area identified in the scene, a mean estimated LAI value of 2.71 was recorded. By grouping the healthy fringe mangrove with the healthy riverine mangrove and by grouping the dwarf mangrove together with the poor condition mangrove, mean estimated LAI values of 4.66 and 2.39 were calculated, respectively. Given that the former healthy group only represents 8% of the total mangrove area examined, it is concluded that this mangrove system, considered one of the most important of the Pacific coast of the Americas, is currently experiencing a considerable state of degradation. Furthermore, based on the results of this investigation it is suggested that this approach could provide resource managers and scientists alike with a very rapid and effective method for monitoring the state of remaining mangrove forests of the Mexican Pacific and, possibly, other areas of the tropics.

Interactions between Canopy Structure and Herbaceous Biomass along Environmental Gradients in Moist Forest and Dry Miombo Woodland of Tanzania.

PubMed

Shirima, Deo D; Pfeifer, Marion; Platts, Philip J; Totland, Ørjan; Moe, Stein R

2015-01-01

We have limited understanding of how tropical canopy foliage varies along environmental gradients, and how this may in turn affect forest processes and functions. Here, we analyse the relationships between canopy leaf area index (LAI) and above ground herbaceous biomass (AGBH) along environmental gradients in a moist forest and miombo woodland in Tanzania. We recorded canopy structure and herbaceous biomass in 100 permanent vegetation plots (20 m × 40 m), stratified by elevation. We quantified tree species richness, evenness, Shannon diversity and predominant height as measures of structural variability, and disturbance (tree stumps), soil nutrients and elevation as indicators of environmental variability. Moist forest and miombo woodland differed substantially with respect to nearly all variables tested. Both structural and environmental variables were found to affect LAI and AGBH, the latter being additionally dependent on LAI in moist forest but not in miombo, where other factors are limiting. Combining structural and environmental predictors yielded the most powerful models. In moist forest, they explained 76% and 25% of deviance in LAI and AGBH, respectively. In miombo woodland, they explained 82% and 45% of deviance in LAI and AGBH. In moist forest, LAI increased non-linearly with predominant height and linearly with tree richness, and decreased with soil nitrogen except under high disturbance. Miombo woodland LAI increased linearly with stem density, soil phosphorous and nitrogen, and decreased linearly with tree species evenness. AGBH in moist forest decreased with LAI at lower elevations whilst increasing slightly at higher elevations. AGBH in miombo woodland increased linearly with soil nitrogen and soil pH. Overall, moist forest plots had denser canopies and lower AGBH compared with miombo plots. Further field studies are encouraged, to disentangle the direct influence of LAI on AGBH from complex interrelationships between stand structure, environmental gradients and disturbance in African forests and woodlands.

Canopy cover and leaf area index relationships for wheat, triticale, and corn

USDA-ARS?s Scientific Manuscript database

The AquaCrop model requires canopy cover (CC) measurements to define crop growth and development. Some previously collected data sets that would be useful for calibrating and validating AquaCrop contain only leaf area index (LAI) data, but could be used if relationships were available relating LAI t...

Enzymatic conversion of D-galactose to D-tagatose: cloning, overexpression and characterization of L-arabinose isomerase from Pediococcus pentosaceus PC-5.

PubMed

Men, Yan; Zhu, Yueming; Zhang, Lili; Kang, Zhenkui; Izumori, Ken; Sun, Yuanxia; Ma, Yanhe

2014-01-01

The gene encoding L-arabinose isomerase from food-grade strain Pediococcus pentosaceus PC-5 was cloned and overexpressed in Escherichia coli. The recombinant protein was purified and characterized. It was optimally active at 50 °C and pH 6.0. Furthermore, this enzyme exhibited a weak requirement for metallic ions for its maximal activity evaluated at 0.6 mM Mn(2+) or 0.8 mM Co(2+). Interestingly, this enzyme was distinguished from other L-AIs, it could not use L-arabinose as its substrate. In addition, a three-dimensional structure of L-AI was built by homology modeling and L-arabinose and D-galactose were docked into the active site pocket of PPAI model to explain the interaction between L-AI and its substrate. The purified P. pentosaceus PC-5 L-AI converted D-galactose into D-tagatose with a high conversion rate of 52% after 24 h at 50 °C, suggesting its excellent potential in D-tagatose production. Crown Copyright © 2013. Published by Elsevier GmbH. All rights reserved.

A 3D Joint Simulation Platform for Multiband_A Case Study in the Huailai Soybean and Maize Field

NASA Astrophysics Data System (ADS)

Zhang, Y.; Qinhuo, L.; Du, Y.; Huang, H.

2016-12-01

Canopy radiation and scattering signal contains abundant vegetation information. One can quantitatively retrieve the biophysical parameters by building canopy radiation and scattering models and inverting them. Joint simulation of the 3D models for different spectral (frequency) domains may produce complementary advantages and improves the precision. However, most of the currently models were based on one or two spectral bands (e.g. visible and thermal inferred bands, or visible and microwave bands). This manuscript established a 3D radiation and scattering simulation system which can simulate the BRDF, DBT, and backscattering coefficient based on the same structural description. The system coupled radiosity graphic model, Thermal RGM model and coherent microwave model by Yang Du for VIS/NIR, TIR, and MW, respectively. The models simulating the leaf spectral characteristics, component temperatures and dielectric properties were also coupled into the joint simulation system to convert the various parameters into fewer but more unified parameters. As a demonstration of our system, we applied the established system to simulate a mixed field with soybeans and maize based on the Huailai experiment data in August, 2014. With the help of Xfrog software, we remodeled soybean and maize in ".obj" and ".mtl" format. We extracted the structure information of the soybean and maize by statistics of the ".obj" files. We did simulations on red, NIR, TIR, C and L band. The simulation results were validated by the multi-angular observation data of Huailai experiment. Also, the spacial distribution (horizontal and vertical), leaf area index (LAI), leaf angle distribution (LAD), vegetation water content (VWC) and the incident observation geometry were analyzed in details. Validated by the experiment data, we indicate that the simulations of multiband were quite well. Because the crops were planted in regular rows and the maize and soybeans were with different height, different LAI, different LAD and different VWC, we did the sensitive analysis by changing on one of them and fixed the other parameters. The analysis showed that the parameters influenced the radiation and scattering signal of different spectral (frequency) with varying degrees.

Modeling biophysical/biogeochemical/ecological/ocean/atmosphere two-way interactions using NCEP CFS/SSiB5/TRIFFID/DAYCENT: challenge and promising

NASA Astrophysics Data System (ADS)

Xue, Y.; Liu, Y.; Cox, P. M.; De Sales, F.; Lee, J.; Marx, L.; Hartman, M. D.; Yang, R.; Parton, W. J.; Qiu, B.; Ek, M. B.

2016-12-01

Evaluations of several dynamic vegetation models' (DVM) performances in the offline experiments and in the CMIP5 simulations suggest that most of the DVMs substantially overestimate leaf area index (LAI) and length of the growing season, which contribute to overestimation in their coupled models' precipitation. These results suggest important deficiencies in today's DVMs but also show the importance of proper ecological processes in the Earth System Modeling. We have developed a water-carbon-energy balance-based ecosystem model (SSiB4/TRIFFID) and verified it with field and satellite measurement at seasonal to decadal and longer scales. In the global offline tests, the model was integrated from 1950 to 2010 driven by observed meteorological forcing. The simulated trend and decadal variabilities in surface ecosystem conditions (e.g., Plant functional types, LAI, GPP), and surface water and energy balances are analyzed; further experiments and analyses are carried to isolate the contribution due to elevated atmospheric carbon concentration, global warming, soil moisture, and climate variability. How nitrogen processes simulated by the DayCent model Climate Forecast System (CFS) model, which has consistently shown improvements in simulated atmospheric & ocean conditions compared with those runs with specified vegetation conditions. In an experiment, two parametrizations that calculate the mean water potential in soil layers, which affect transpiration and plants' mortality, are tested. It shows that these two methods have substantial impact on global decadal variability of precipitation and surface temperature, with even opposite signs over some regions in the worlds. These results show the uncertainty in DVM modeling with significant implication for the future prediction. It is imperative to evaluate DVMs with comprehensive observational data.

Simulating crop phenology in the Community Land Model and its impact on energy and carbon fluxes

NASA Astrophysics Data System (ADS)

Chen, Ming; Griffis, Tim J.; Baker, John; Wood, Jeffrey D.; Xiao, Ke

2015-02-01

A reasonable representation of crop phenology and biophysical processes in land surface models is necessary to accurately simulate energy, water, and carbon budgets at the field, regional, and global scales. However, the evaluation of crop models that can be coupled to Earth system models is relatively rare. Here we evaluated two such models (CLM4-Crop and CLM3.5-CornSoy), both implemented within the Community Land Model (CLM) framework, at two AmeriFlux corn-soybean sites to assess their ability to simulate phenology, energy, and carbon fluxes. Our results indicated that the accuracy of net ecosystem exchange and gross primary production simulations was intimately connected to the phenology simulations. The CLM4-Crop model consistently overestimated early growing season leaf area index, causing an overestimation of gross primary production, to such an extent that the model simulated a carbon sink instead of the measured carbon source for corn. The CLM3.5-CornSoy-simulated leaf area index (LAI), energy, and carbon fluxes showed stronger correlations with observations compared to CLM4-Crop. Net radiation was biased high in both models and was especially pronounced for soybeans. This was primarily caused by the positive LAI bias, which led to a positive net long-wave radiation bias. CLM4-Crop underestimated soil water content during midgrowing season in all soil layers at the two sites, which caused unrealistic water stress, especially for soybean. Future work regarding the mechanisms that drive early growing season phenology and soil water dynamics is needed to better represent crops including their net radiation balance, energy partitioning, and carbon cycle processes.

Relation of MODIS EVI and LAI across time, vegetation types and hydrological regimes

NASA Astrophysics Data System (ADS)

Alexandridis, Thomas; Ovakoglou, George

2015-04-01

Estimation of the Leaf Area Index (LAI) of a landscape is considered important to describe the ecosystems activity and is used as an important input parameter in hydrological and biogeochemical models related to water and carbon cycle, desertification risk, etc. The measurement of LAI in the field is a laborious and costly process and is mainly done by indirect methods, such as hemispherical photographs that are processed by specialized software. For this reason there have been several attempts to estimate LAI with multispectral satellite images, using theoretical biomass development models, or empirical equations using vegetation indices and land cover maps. The aim of this work is to study the relation of MODIS EVI and LAI across time, vegetation type, and hydrological regime. This was achieved by studying 120 maps of EVI and LAI which cover a hydrological year and five hydrologically diverse areas: river Nestos in Greece, Queimados catchment in Brazil, Rijnland catchment in The Netherlands, river Tamega in Portugal, and river Umbeluzi in Mozambique. The following Terra MODIS composite datasets were downloaded for the hydrological year 2012-2013: MOD13A2 "Vegetation Indices" and MCD15A2 "LAI and FPAR", as well as the equivalent quality information layers (QA). All the pixels that fall in a vegetation land cover (according to the MERIS GLOBCOVER map) were sampled for the analysis, with the exception of those that fell at the border between two vegetation or other land cover categories, to avoid the influence of mixed pixels. Using linear regression analysis, the relationship between EVI and LAI was identified per date, vegetation type and study area. Results show that vegetation type has the highest influence in the variation of the relationship between EVI and LAI in each study area. The coefficient of determination (R2) is high and statistically significant (ranging from 0.41 to 0.83 in 90% of the cases). When plotting the EVI factor from the regression equation across time, there is an evident temporal change in all test sites. The sensitivity of EVI to LAI is smaller in periods of high biomass production. The range of fluctuation is different across sites, and is related to biomass quantity and type. Higher fluctuation is noted in the winter season in Tamega, possibly due to cloud infected pixels that the QA and compositing algorithms did not successfully detect. Finally, there was no significant difference in the R2 and EVI factor when including in the analyses pixels indicated as "low and marginal quality" by the QA layers, thus suggesting that the use of low quality pixels can be justified when good quality pixels are not enough. Future work will study the transferability of these relations across scales and sensors. This study is supported by the Research Committee of Aristotle University of Thessaloniki project "Improvement of the estimation of Leaf Area Index (LAI) at basin scale using satellite images". MODIS data are provided by USGS.

Assessing biomass of diverse coastal marsh ecosystems using statistical and machine learning models

NASA Astrophysics Data System (ADS)

Mo, Yu; Kearney, Michael S.; Riter, J. C. Alexis; Zhao, Feng; Tilley, David R.

2018-06-01

The importance and vulnerability of coastal marshes necessitate effective ways to closely monitor them. Optical remote sensing is a powerful tool for this task, yet its application to diverse coastal marsh ecosystems consisting of different marsh types is limited. This study samples spectral and biophysical data from freshwater, intermediate, brackish, and saline marshes in Louisiana, and develops statistical and machine learning models to assess the marshes' biomass with combined ground, airborne, and spaceborne remote sensing data. It is found that linear models derived from NDVI and EVI are most favorable for assessing Leaf Area Index (LAI) using multispectral data (R2 = 0.7 and 0.67, respectively), and the random forest models are most useful in retrieving LAI and Aboveground Green Biomass (AGB) using hyperspectral data (R2 = 0.91 and 0.84, respectively). It is also found that marsh type and plant species significantly impact the linear model development (P < .05 in both cases). Sensors with coarser spatial resolution yield lower LAI values because the fine water networks are not detected and mixed into the vegetation pixels. The Landsat OLI-derived map shows the LAI of coastal mashes in Louisiana mostly ranges from 0 to 5.0, and is highest for freshwater marshes and for marshes in the Atchafalaya Bay delta. The CASI-derived maps show that LAI of saline marshes at Bay Batiste typically ranges from 0.9 to 1.5, and the AGB is mostly less than 900 g/m2. This study provides solutions for assessing the biomass of Louisiana's coastal marshes using various optical remote sensing techniques, and highlights the impacts of the marshes' species composition on the model development and the sensors' spatial resolution on biomass mapping, thereby providing useful tools for monitoring the biomass of coastal marshes in Louisiana and diverse coastal marsh ecosystems elsewhere.

Validation of Leaf Area Index measurements based on the Wireless Sensor Network platform

NASA Astrophysics Data System (ADS)

Song, Q.; Li, X.; Liu, Q.

2017-12-01

The leaf area index (LAI) is one of the important parameters for estimating plant canopy function, which has significance for agricultural analysis such as crop yield estimation and disease evaluation. The quick and accurate access to acquire crop LAI is particularly vital. In the study, LAI measurement of corn crops is mainly through three kinds of methods: the leaf length and width method (LAILLW), the instruments indirect measurement method (LAII) and the leaf area index sensor method(LAIS). Among them, LAI value obtained from LAILLW can be regarded as approximate true value. LAI-2200,the current widespread LAI canopy analyzer,is used in LAII. LAIS based on wireless sensor network can realize the automatic acquisition of crop images,simplifying the data collection work,while the other two methods need person to carry out field measurements.Through the comparison of LAIS and other two methods, the validity and reliability of LAIS observation system is verified. It is found that LAI trend changes are similar in three methods, and the rate of change of LAI has an increase with time in the first two months of corn growth when LAIS costs less manpower, energy and time. LAI derived from LAIS is more accurate than LAII in the early growth stage,due to the small blade especially under the strong light. Besides, LAI processed from a false color image with near infrared information is much closer to the true value than true color picture after the corn growth period up to one and half months.

Predicting large wildfires across western North America by modeling seasonal variation in soil water balance.

PubMed

Waring, Richard H; Coops, Nicholas C

A lengthening of the fire season, coupled with higher temperatures, increases the probability of fires throughout much of western North America. Although regional variation in the frequency of fires is well established, attempts to predict the occurrence of fire at a spatial resolution <10 km 2 have generally been unsuccessful. We hypothesized that predictions of fires might be improved if depletion of soil water reserves were coupled more directly to maximum leaf area index (LAI max ) and stomatal behavior. In an earlier publication, we used LAI max and a process-based forest growth model to derive and map the maximum available soil water storage capacity (ASW max ) of forested lands in western North America at l km resolution. To map large fires, we used data products acquired from NASA's Moderate Resolution Imaging Spectroradiometers (MODIS) over the period 2000-2009. To establish general relationships that incorporate the major biophysical processes that control evaporation and transpiration as well as the flammability of live and dead trees, we constructed a decision tree model (DT). We analyzed seasonal variation in the relative availability of soil water ( fASW ) for the years 2001, 2004, and 2007, representing respectively, low, moderate, and high rankings of areas burned. For these selected years, the DT predicted where forest fires >1 km occurred and did not occur at ~100,000 randomly located pixels with an average accuracy of 69 %. Extended over the decade, the area predicted burnt varied by as much as 50 %. The DT identified four seasonal combinations, most of which included exhaustion of ASW during the summer as critical; two combinations involving antecedent conditions the previous spring or fall accounted for 86 % of the predicted fires. The approach introduced in this paper can help identify forested areas where management efforts to reduce fire hazards might prove most beneficial.

A Comparison of Simulated and Field-Derived Leaf Area Index (LAI) and Canopy Height Values from Four Forest Complexes in the Southeastern USA

EPA Science Inventory

Vegetative leaf area is a critical input to models that simulate human and ecosystem exposure to atmospheric pollutants. Leaf area index (LAI) can be measured in the field or numerically simulated, but all contain some inherent uncertainty that is passed to the exposure assessmen...

Uncertainty Analysis in the Creation of a Fine-Resolution Leaf Area Index (LAI) Reference Map for Validation of Moderate Resolution LAI Products

EPA Science Inventory

The validation process for a moderate resolution leaf area index (LAI) product (i.e., MODIS) involves the creation of a high spatial resolution LAI reference map (Lai-RM), which when scaled to the moderate LAI resolution (i.e., >1 km) allows for comparison and analysis with this ...

Cost-effectiveness analysis of atypical long-acting antipsychotics for treating chronic schizophrenia in Finland.

PubMed

Einarson, Thomas R; Pudas, Hanna; Zilbershtein, Roman; Jensen, Rasmus; Vicente, Colin; Piwko, Charles; Hemels, Michiel E H

2013-09-01

In Finland, regional rates of schizophrenia exceed those in most countries, impacting the healthcare burden. This study determined the cost-effectiveness of long-acting antipsychotic (LAI) drugs paliperidone palmitate (PP-LAI), olanzapine pamoate (OLZ-LAI), and risperidone (RIS-LAI) for chronic schizophrenia. This study adapted a decision tree analysis from Norway for the Finnish National Health Service. Country-specific data were sought from the literature and public documents, guided by clinical experts. Costs of health services and products were retrieved from literature sources and current price lists. This simulation study estimated average 1-year costs for treating patients with each LAI, average remission days, rates of hospitalization and emergency room visits and quality-adjusted life-years (QALY). PP-LAI was dominant. Its estimated annual average cost was €10,380/patient and was associated with 0.817 QALY; OLZ-LAI cost €12,145 with 0.810 QALY; RIS-LAI cost €12,074 with 0.809 QALY. PP-LAI had the lowest rates of hospitalization, emergency room visits, and relapse days. This analysis was robust against most variations in input values except adherence rates. PP-LAI was dominant over OLZ-LAI and RIS-LAI in 77.8% and 85.9% of simulations, respectively. Limitations include the 1-year time horizon (as opposed to lifetime costs), omission of the costs of adverse events, and the assumption of universal accessibility. In Finland, PP-LAI dominated the other LAIs as it was associated with a lower cost and better clinical outcomes.

The effect of satellite-derived surface soil moisture and leaf area index land data assimilation on streamflow simulations over France

NASA Astrophysics Data System (ADS)

Fairbairn, David; Lavinia Barbu, Alina; Napoly, Adrien; Albergel, Clément; Mahfouf, Jean-François; Calvet, Jean-Christophe

2017-04-01

This study evaluates the impact of assimilating surface soil moisture (SSM) and leaf area index (LAI) observations into a land surface model using the SAFRAN-ISBA-MODCOU (SIM) hydrological suite. SIM consists of three stages: (1) an atmospheric reanalysis (SAFRAN) over France, which forces (2) the three-layer ISBA land surface model, which then provides drainage and runoff inputs to (3) the MODCOU hydro-geological model. The drainage and runoff outputs from ISBA are validated by comparing the simulated river discharge from MODCOU with over 500 river-gauge observations over France and with a subset of stations with low-anthropogenic influence, over several years. This study makes use of the A-gs version of ISBA that allows for physiological processes. The atmospheric forcing for the ISBA-A-gs model underestimates direct shortwave and long-wave radiation by approximately 5 % averaged over France. The ISBA-A-gs model also substantially underestimates the grassland LAI compared with satellite retrievals during winter dormancy. These differences result in an underestimation (overestimation) of evapotranspiration (drainage and runoff). The excess runoff flowing into the rivers and aquifers contributes to an overestimation of the SIM river discharge. Two experiments attempted to resolve these problems: (i) a correction of the minimum LAI model parameter for grasslands and (ii) a bias-correction of the model radiative forcing. Two data assimilation experiments were also performed, which are designed to correct random errors in the initial conditions: (iii) the assimilation of LAI observations and (iv) the assimilation of SSM and LAI observations. The data assimilation for (iii) and (iv) was done with a simplified extended Kalman filter (SEKF), which uses finite differences in the observation operator Jacobians to relate the observations to the model variables. Experiments (i) and (ii) improved the median SIM Nash scores by about 9 % and 18 % respectively. Experiment (iii) reduced the LAI phase errors in ISBA-A-gs but had little impact on the discharge Nash efficiency of SIM. In contrast, experiment (iv) resulted in spurious increases in drainage and runoff, which degraded the median discharge Nash efficiency by about 7 %. The poor performance of the SEKF originates from the observation operator Jacobians. These Jacobians are dampened when the soil is saturated and when the vegetation is dormant, which leads to positive biases in drainage and/or runoff and to insufficient corrections during winter, respectively. Possible ways to improve the model are discussed, including a new multi-layer diffusion model and a more realistic response of photosynthesis to temperature in mountainous regions. The data assimilation should be advanced by accounting for model and forcing uncertainties.

Spatial and Temporal Dynamics of the Leaf Area Index of the Caatinga Biome

NASA Astrophysics Data System (ADS)

Alves Rodrigues Pinheiro, Everton; de Jong van Lier, Quirijn; Metselaar, Klaas

2015-04-01

Leaf Area Index (LAI) is an important characteristic of ecosystems with a prominent role in processes such as transpiration, photosynthesis and interception. The Caatinga biome is a unique semiarid ecosystem ocurring in a specific region of Brazil. An important main feature of this biome is the leaf shedding and regenerative capacity of its species. The aim of this study was to quantify both spatial and temporal dynamics of the LAI of the Caatinga biome in the Aiuaba Experimental Basin, an integrally-preserved Caatinga reserve, coordinates 6°42'S; 40°17'W. The research site (12 km2) was divided into three main Soil and Vegatation Associations (SVA). For each SVA the soil type and root depth are respectively, Acrisol -0.8 m, Luvisol - 0.6 m and Regosol - 0.4 m. The LAI was estimated by SEBAL algorithm applied to eleven satellite images from Landsat 5. The values of LAI estimated by SEBAL were correlated to the mean soil water content of the 15 days previous to the satellite image date. Eight images were used to generate a simple regression model, yielding a range of coefficient of determination from 0.89 to 0.92. Three other images were used to validate the equations. The Nash-Sutcliffe efficiency coefficient ranged from 0.76 to 0.94. Using the validated correlations, the LAI was calculated over the time for each of the three SVA, from 2004 to 2012. For SVA1, SVA2 and SVA3, the avarage values of LAI during the rainy season were 0.97, 1.12 and 1.07, respectively. During the dry season, the mean values were 0.15 for SVA1 and 0.11 for SVA2 and SVA3. The vegetation showed abrupt LAI changes, and the average previous 15 days soil water content was a good indicator for this. The study has shown that the maximum LAI was relatively stable over the years, occurring between March and April. The spatial behavior of LAI appeared to be similar, independently of the soil type and root depth.

Comparison of satellite-derived LAI and precipitation anomalies over Brazil with a thermal infrared-based Evaporative Stress Index for 2003-2013

NASA Astrophysics Data System (ADS)

Anderson, Martha C.; Zolin, Cornelio A.; Hain, Christopher R.; Semmens, Kathryn; Tugrul Yilmaz, M.; Gao, Feng

2015-07-01

Shortwave vegetation index (VI) and leaf area index (LAI) remote sensing products yield inconsistent depictions of biophysical response to drought and pluvial events that have occurred in Brazil over the past decade. Conflicting reports of severity of drought impacts on vegetation health and functioning have been attributed to cloud and aerosol contamination of shortwave reflectance composites, particularly over the rainforested regions of the Amazon basin which are subject to prolonged periods of cloud cover and episodes of intense biomass burning. This study compares timeseries of satellite-derived maps of LAI from the Moderate Resolution Imaging Spectroradiometer (MODIS) and precipitation from the Tropical Rainfall Mapping Mission (TRMM) with a diagnostic Evaporative Stress Index (ESI) retrieved using thermal infrared remote sensing over South America for the period 2003-2013. This period includes several severe droughts and floods that occurred both over the Amazon and over unforested savanna and agricultural areas in Brazil. Cross-correlations between absolute values and standardized anomalies in monthly LAI and precipitation composites as well as the actual-to-reference evapotranspiration (ET) ratio used in the ESI were computed for representative forested and agricultural regions. The correlation analyses reveal strong apparent anticorrelation between MODIS LAI and TRMM precipitation anomalies over the Amazon, but better coupling over regions vegetated with shorter grass and crop canopies. The ESI was more consistently correlated with precipitation patterns over both landcover types. Temporal comparisons between ESI and TRMM anomalies suggest longer moisture buffering timescales in the deeper rooted rainforest systems. Diagnostic thermal-based retrievals of ET and ET anomalies, such as used in the ESI, provide independent information on the impacts of extreme hydrologic events on vegetation health in comparison with VI and precipitation-based drought indicators, and used in concert may provide a more reliable evaluation of natural and managed ecosystem response to variable climate regimes.

Generation of High Resolution Land Surface Parameters in the Community Land Model

NASA Astrophysics Data System (ADS)

Ke, Y.; Coleman, A. M.; Wigmosta, M. S.; Leung, L.; Huang, M.; Li, H.

2010-12-01

The Community Land Model (CLM) is the land surface model used for the Community Atmosphere Model (CAM) and the Community Climate System Model (CCSM). It examines the physical, chemical, and biological processes across a variety of spatial and temporal scales. Currently, efforts are being made to improve the spatial resolution of the CLM, in part, to represent finer scale hydrologic characteristics. Current land surface parameters of CLM4.0, in particular plant functional types (PFT) and leaf area index (LAI), are generated from MODIS and calculated at a 0.05 degree resolution. These MODIS-derived land surface parameters have also been aggregated to coarser resolutions (e.g., 0.5, 1.0 degrees). To evaluate the response of CLM across various spatial scales, higher spatial resolution land surface parameters need to be generated. In this study we examine the use of Landsat TM/ETM+ imagery and data fusion techniques for generating land surface parameters at a 1km resolution within the Pacific Northwest United States. . Land cover types and PFTs are classified based on Landsat multi-season spectral information, DEM, National Land Cover Database (NLCD) and the USDA-NASS Crop Data Layer (CDL). For each PFT, relationships between MOD15A2 high quality LAI values, Landsat-based vegetation indices, climate variables, terrain, and laser-altimeter derived vegetation height are used to generate monthly LAI values at a 30m resolution. The high-resolution PFT and LAI data are aggregated to create a 1km model grid resolution. An evaluation and comparison of CLM land surface response at both fine and moderate scale is presented.

Light-absorbing impurities in a southern Tibetan Plateau glacier: Variations and potential impact on snow albedo and radiative forcing

NASA Astrophysics Data System (ADS)

Li, Xiaofei; Kang, Shichang; Zhang, Guoshuai; Qu, Bin; Tripathee, Lekhendra; Paudyal, Rukumesh; Jing, Zhefan; Zhang, Yulan; Yan, Fangping; Li, Gang; Cui, Xiaoqing; Xu, Rui; Hu, Zhaofu; Li, Chaoliu

2018-02-01

Light-absorbing impurities (LAIs), such as organic carbon (OC), black carbon (BC), and mineral dust (MD), deposited on the surface snow of glacier can reduce the surface albedo. As there exists insufficient knowledge to completely characterize LAIs variations and difference in LAIs distributions, it is essential to investigate the behaviors of LAIs and their influence on the glaciers across the Tibetan Plateau (TP). Therefore, surface snow and snowpit samples were collected during September 2014 to September 2015 from Zhadang (ZD) glacier in the southern TP to investigate the role of LAIs in the glacier. LAIs concentrations were observed to be higher in surface aged snow than in the fresh snow possibly due to post-depositional processes such as melting or sublimation. The LAIs concentrations showed a significant spatial distribution and marked negative relationship with elevation. Impurity concentrations varied significantly with depth in the vertical profile of the snowpit, with maximum LAIs concentrations frequently occurred in the distinct dust layers which were deposited in non-monsoon, and the bottom of snowpit due to the eluviation in monsoon. Major ions in snowpit and backward trajectory analysis indicated that regional activities and South Asian emissions were the major sources. According to the SNow ICe Aerosol Radiative (SNICAR) model, the average simulated albedo caused by MD and BC in aged snow collected on 31 May 2015 accounts for about 13% ± 3% and 46% ± 2% of the albedo reduction. Furthermore, we also found that instantaneous RF caused by MD and BC in aged snow collected on 31 May 2015 varied between 4-16 W m- 2 and 7-64 W m- 2, respectively. The effect of BC exceeds that of MD on albedo reduction and instantaneous RF in the study area, indicating that BC played a major role on the surface of the ZD glacier.

In-situ measurements of light-absorbing impurities in snow of glacier on Mt. Yulong and implications for radiative forcing estimates.

PubMed

Niu, Hewen; Kang, Shichang; Shi, Xiaofei; Paudyal, Rukumesh; He, Yuanqing; Li, Gang; Wang, Shijin; Pu, Tao; Shi, Xiaoyi

2017-03-01

The Tibetan Plateau (TP) or the third polar cryosphere borders geographical hotspots for discharges of black carbon (BC). BC and dust play important roles in climate system and Earth's energy budget, particularly after they are deposited on snow and glacial surfaces. BC and dust are two kinds of main light-absorbing impurities (LAIs) in snow and glaciers. Estimating concentrations and distribution of LAIs in snow and glacier ice in the TP is of great interest because this region is a global hotspot in geophysical research. Various snow samples, including surface aged-snow, superimposed ice and snow meltwater samples were collected from a typical temperate glacier on Mt. Yulong in the snow melt season in 2015. The samples were determined for BC, Organic Carbon (OC) concentrations using an improved thermal/optical reflectance (DRI Model 2001) method and gravimetric method for dust concentrations. Results indicated that the LAIs concentrations were highly elevation-dependent in the study area. Higher contents and probably greater deposition at relative lower elevations (generally <5000masl) of the glacier was observed. Temporal difference of LAIs contents demonstrated that LAIs in snow of glacier gradually increased as snow melting progressed. Evaluations of the relative absorption of BC and dust displayed that the impact of dust on snow albedo and radiative forcing (RF) is substantially larger than BC, particularly when dust contents are higher. This was verified by the absorption factor, which was <1.0. In addition, we found the BC-induced albedo reduction to be in the range of 2% to nearly 10% during the snow melting season, and the mean snow albedo reduction was 4.63%, hence for BC contents ranging from 281 to 894ngg -1 in snow of a typical temperate glacier on Mt. Yulong, the associated instantaneous RF will be 76.38-146.96Wm -2 . Further research is needed to partition LAIs induced glacial melt, modeling researches in combination with long-term in-situ observations of LAIs in glaciers is also urgent needed in the future work. Copyright © 2017 Elsevier B.V. All rights reserved.

Phenology of forest-grassland transition zones in the Community Land Model

NASA Astrophysics Data System (ADS)

Dahlin, K.; Fisher, R. A.

2013-12-01

Forest-grassland transition zones (savannas, woodlands, wooded grasslands, and shrublands) are highly sensitive to climate and may already be changing due to warming, changes in precipitation patterns, and/or CO2 fertilization. Shifts between closed canopy forest and open grassland, as well as shifts in phenology, could have large impacts on the global carbon cycle, water balance, albedo, and on the humans and other animals that depend on these regions. From an earth system perspective these impacts may then feed back into the climate system and impact how, when, and where climate change occurs. Here we compare 29 years of monthly leaf area index (LAI) outputs from several offline versions of the Community Land Model (CLM), the land component of the Community Earth System Model, to LAI derived from the AVHRR NDVI3g product (LAI3g). Specifically, we focus on seasonal patterns in regions dominated by tropical broadleaved deciduous trees (T-BDT), broadleaved deciduous shrubs (BDS) and grasslands (C3 and C4) in CLM, all of which follow a 'stress deciduous' phenological algorithm. We consider and compare two versions of CLM (v. 4CN and v. 4.5BGC) to the satellite derived product. We found that both versions of CLM were able to capture seasonal variations in grasslands relatively well at the regional scale, but that the 'stress deciduous' phenology algorithm did not perform well in areas dominated by T-BDT or BDS. When we compared the performance of the models at single points we found slight improvements in CLM4.5BGC over CLM4CN, but generally that the magnitude of seasonality was too low in CLM as compared to the LAI3g satellite product. To explore the parameters within CLM that had the most leverage on seasonality of LAI, we used a Latin hypercube approach to vary values for critical soil water potential (threshold at which plants drop leaves), the critical number of days that soil water potential must be too low for leaves to drop, and the carbon allocation scheme. In single-point simulations we found that changing how carbon is allocated improved the 'flat-topped' nature of the CLM LAI during summer, which is not present in LAI3g, while adjustments to the soil water potential parameters allowed for less extreme and fewer switches between leaf-on and leaf-off. Future work will include applying a subset of the new parameter values to global runs of the model to assess whether the improvements to phenology at single points improve global phenological patterns and/or other components of the CLM carbon cycle.

Neural network simulation of soil NO3 dynamic under potato crop system

NASA Astrophysics Data System (ADS)

Goulet-Fortin, Jérôme; Morais, Anne; Anctil, François; Parent, Léon-Étienne; Bolinder, Martin

2013-04-01

Nitrate leaching is a major issue in sandy soils intensively cropped to potato. Modelling could test and improve management practices, particularly as regard to the optimal N application rates. Lack of input data is an important barrier for the application of classical process-based models to predict soil NO3 content (SNOC) and NO3 leaching (NOL). Alternatively, data driven models such as neural networks (NN) could better take into account indicators of spatial soil heterogeneity and plant growth pattern such as the leaf area index (LAI), hence reducing the amount of soil information required. The first objective of this study was to evaluate NN and hybrid models to simulate SNOC in the 0-40 cm soil layer considering inter-annual variations, spatial soil heterogeneity and differential N application rates. The second objective was to evaluate the same methodology to simulate seasonal NOL dynamic at 1 m deep. To this aim, multilayer perceptrons with different combinations of driving meteorological variables, functions of the LAI and state variables of external deterministic models have been trained and evaluated. The state variables from external models were: drainage estimated by the CLASS model and the soil temperature estimated by an ICBM subroutine. Results of SNOC simulations were compared to field data collected between 2004 and 2011 at several experimental plots under potato cropping systems in Québec, Eastern Canada. Results of NOL simulation were compared to data obtained in 2012 from 11 suction lysimeters installed in 2 experimental plots under potato cropping systems in the same region. The most performing model for SNOC simulation was obtained using a 4-input hybrid model composed of 1) cumulative LAI, 2) cumulative drainage, 3) soil temperature and 4) day of year. The most performing model for NOL simulation was obtained using a 5-input NN model composed of 1) N fertilization rate at spring, 2) LAI, 3) cumulative rainfall, 4) the day of year and 5) the percentage of clay content. The MAE was 22% for SNOC simulation and 23% for NOL simulation. High sensitivity to LAI suggests that the model may take into account field and sub-field spatial variability and support N management. Further studies are needed to fully validate the method, particularly in the case of NOL simulation.

Monitoring boreal forest leaf area index across a Siberian burn chronosequence: a MODIS validation study

USGS Publications Warehouse

Cheng, X.; Vierling, Lee; Deering, D.; Conley, A.

2005-01-01

Landscapes containing differing amounts of ecological disturbance provide an excellent opportunity to validate and better understand the emerging Moderate Resolution Imaging Spectrometer (MODIS) vegetation products. Four sites, including 1‐year post‐fire coniferous, 13‐year post‐fire deciduous, 24‐year post‐fire deciduous, and >100 year old post‐fire coniferous forests, were selected to serve as a post‐fire chronosequence in the central Siberian region of Krasnoyarsk (57.3°N, 91.6°E) with which to study the MODIS leaf area index (LAI) and vegetation index (VI) products. The collection 4 MODIS LAI product correctly represented the summer site phenologies, but significantly underestimated the LAI value of the >100 year old coniferous forest during the November to April time period. Landsat 7‐derived enhanced vegetation index (EVI) performed better than normalized difference vegetation index (NDVI) to separate the deciduous and conifer forests, and both indices contained significant correlation with field‐derived LAI values at coniferous forest sites (r 2 = 0.61 and r 2 = 0.69, respectively). The reduced simple ratio (RSR) markedly improved LAI prediction from satellite measurements (r 2 = 0.89) relative to NDVI and EVI. LAI estimates derived from ETM+ images were scaled up to evaluate the 1 km resolution MODIS LAI product; from this analysis MODIS LAI overestimated values in the low LAI deciduous forests (where LAI<5) and underestimated values in the high LAI conifer forests (where LAI>6). Our results indicate that further research on the MODIS LAI product is warranted to better understand and improve remote LAI quantification in disturbed forest landscapes over the course of the year.

Seasonal LAI in slash pine estimated with LANDSAT TM

NASA Technical Reports Server (NTRS)

Curran, Paul J.; Dungan, Jennifer L.; Gholz, Henry L.

1990-01-01

The leaf area index (LAI, total area of leaves per unit area of ground) of most forest canopies varies throughout the year, yet for logistical reasons it is difficult to estimate anything more detailed than a seasonal maximum LAI. To determine if remotely sensed data can be used to estimate LAI seasonally, field measurements of LAI were compared to normalized difference vegetation index (NDVI) values derived using LANDSAT Thematic Mapper (TM) data, for 16 fertilized and control slash pine plots on 3 dates. Linear relationships existed between NDVI and LAI with R(sup 2) values of 0.35, 0.75, and 0.86 for February 1988, September 1988, and March, 1989, respectively. This is the first reported study in which NDVI is related to forest LAI recorded during the month of sensor overpass. Predictive relationships based on data from eight of the plots were used to estimate the LAI of the other eight plots with a root-mean-square error of 0.74 LAI, which is 15.6 percent of the mean LAI. This demonstrates the potential use of LANDSAT TM data for studying seasonal dynamics in forest canopies.

Relating the radar backscattering coefficient to leaf-area index

NASA Technical Reports Server (NTRS)

Ulaby, F. T. (Principal Investigator); Allen, C.; Eger, G.; Kanemasu, E.

1983-01-01

The relationship between the radar backscattering coefficient of a vegetation canopy, sigma(0) sub can, and the canopy's leaf area index (LAI) is examined. The relationship is established through the development of a model for corn and sorghum and another for wheat. Both models are extensions of the cloud model of Attema and Ulaby (1978). Analysis of experimental data measured at 8.6, 13.0, 17.0, and 35.6 GHz indicates that most of the temporal variations of sigma(0) sub can can be accounted for through variations in green LAI alone, if the latter is greater than 0.5.

Carbon stocks and fluxes in the high latitudes: using site-level data to evaluate Earth system models

NASA Astrophysics Data System (ADS)

Chadburn, Sarah E.; Krinner, Gerhard; Porada, Philipp; Bartsch, Annett; Beer, Christian; Belelli Marchesini, Luca; Boike, Julia; Ekici, Altug; Elberling, Bo; Friborg, Thomas; Hugelius, Gustaf; Johansson, Margareta; Kuhry, Peter; Kutzbach, Lars; Langer, Moritz; Lund, Magnus; Parmentier, Frans-Jan W.; Peng, Shushi; Van Huissteden, Ko; Wang, Tao; Westermann, Sebastian; Zhu, Dan; Burke, Eleanor J.

2017-11-01

It is important that climate models can accurately simulate the terrestrial carbon cycle in the Arctic due to the large and potentially labile carbon stocks found in permafrost-affected environments, which can lead to a positive climate feedback, along with the possibility of future carbon sinks from northward expansion of vegetation under climate warming. Here we evaluate the simulation of tundra carbon stocks and fluxes in three land surface schemes that each form part of major Earth system models (JSBACH, Germany; JULES, UK; ORCHIDEE, France). We use a site-level approach in which comprehensive, high-frequency datasets allow us to disentangle the importance of different processes. The models have improved physical permafrost processes and there is a reasonable correspondence between the simulated and measured physical variables, including soil temperature, soil moisture and snow. We show that if the models simulate the correct leaf area index (LAI), the standard C3 photosynthesis schemes produce the correct order of magnitude of carbon fluxes. Therefore, simulating the correct LAI is one of the first priorities. LAI depends quite strongly on climatic variables alone, as we see by the fact that the dynamic vegetation model can simulate most of the differences in LAI between sites, based almost entirely on climate inputs. However, we also identify an influence from nutrient limitation as the LAI becomes too large at some of the more nutrient-limited sites. We conclude that including moss as well as vascular plants is of primary importance to the carbon budget, as moss contributes a large fraction to the seasonal CO2 flux in nutrient-limited conditions. Moss photosynthetic activity can be strongly influenced by the moisture content of moss, and the carbon uptake can be significantly different from vascular plants with a similar LAI. The soil carbon stocks depend strongly on the rate of input of carbon from the vegetation to the soil, and our analysis suggests that an improved simulation of photosynthesis would also lead to an improved simulation of soil carbon stocks. However, the stocks are also influenced by soil carbon burial (e.g. through cryoturbation) and the rate of heterotrophic respiration, which depends on the soil physical state. More detailed below-ground measurements are needed to fully evaluate biological and physical soil processes. Furthermore, even if these processes are well modelled, the soil carbon profiles cannot resemble peat layers as peat accumulation processes are not represented in the models. Thus, we identify three priority areas for model development: (1) dynamic vegetation including (a) climate and (b) nutrient limitation effects; (2) adding moss as a plant functional type; and an (3) improved vertical profile of soil carbon including peat processes.

Estimating gross primary productivity of a tropical forest ecosystem over north-east India using LAI and meteorological variables

NASA Astrophysics Data System (ADS)

Deb Burman, Pramit Kumar; Sarma, Dipankar; Williams, Mathew; Karipot, Anandakumar; Chakraborty, Supriyo

2017-10-01

Tropical forests act as a major sink of atmospheric carbon dioxide, and store large amounts of carbon in biomass. India is a tropical country with regions of dense vegetation and high biodiversity. However due to the paucity of observations, the carbon sequestration potential of these forests could not be assessed in detail so far. To address this gap, several flux towers were erected over different ecosystems in India by Indian Institute of Tropical Meteorology as part of the MetFlux India project funded by MoES (Ministry of Earth Sciences, Government of India). A 50 m tall tower was set up over a semi-evergreen moist deciduous forest named Kaziranga National Park in north-eastern part of India which houses a significant stretch of local forest cover. Climatically this region is identified to be humid sub-tropical. Here we report first generation of the in situ meteorological observations and leaf area index (LAI) measurements from this site. LAI obtained from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) is compared with the in situ measured LAI. We use these in situ measurements to calculate the total gross photosynthesis (or gross primary productivity, GPP) of the forest using a calibrated model. LAI and GPP show prominent seasonal variation. LAI ranges between 0.75 in winter to 3.25 in summer. Annual GPP is estimated to be 2.11 kg C m^{-2} year^{-1}.

Vegetation Coverage Mapping and Soil Effect Correction in Estimating Vegetation Water Content and Dry Biomass from Satellites

NASA Astrophysics Data System (ADS)

Huang, J.; Chen, D.

2005-12-01

Vegetation water content (VWC) attracts great research interests in hydrology research in recent years. As an important parameter describing the horizontal expansion of vegetation, vegetation coverage is essential to implement soil effect correction for partially vegetated fields to estimate VWC accurately. Ground measurements of corn and soybeans in SMEX02 resulted in an identical expolinear relationship between vegetation coverage and leaf area index (LAI), which is used for vegetation coverage mapping. Results illustrated two parts of LAI growth quantitatively: the horizontal expansion of leaf coverage and the vertical accumulation of leaf layers. It is believed that the former part contributes significantly to LAI growth at initial vegetation growth stage and the latter is more dominant after vegetation coverage reaches a certain level. The Normalized Difference Water Index (NDWI) using short-wave infrared bands is convinced for its late saturation at high LAI values, in contrast to the Normalized Difference Vegetation Index (NDVI). NDWI is then utilized to estimate LAI, via another expolinear relationship, which is evidenced having vegetation species independency in study of corn and soybeans in SMEX02 sites. It is believed that the surface reflectance measured at satellites spectral bands are the mixed results of signals reflected from vegetation and bare soil, especially at partially vegetated fields. A simple linear mixture model utilizing vegetation coverage information is proposed to correct soil effect in such cases. Surface reflectance fractions for -rpure- vegetation are derived from the model. Comparing with ground measurements, empirical models using soil effect corrected vegetation indices to estimate VWC and dry biomass (DB) are generated. The study enhanced the in-depth understanding of the mechanisms how vegetation growth takes effect on satellites spectral reflectance with and without soil effect, which are particularly useful for modeling in hydrology, agriculture, forestry and meteorology etc.

Plant functional diversity increases grassland productivity-related water vapor fluxes: an Ecotron and modeling approach.

PubMed

Milcu, Alexandru; Eugster, Werner; Bachmann, Dörte; Guderle, Marcus; Roscher, Christiane; Gockele, Annette; Landais, Damien; Ravel, Olivier; Gessler, Arthur; Lange, Markus; Ebeling, Anne; Weisser, Wolfgang W; Roy, Jacques; Hildebrandt, Anke; Buchmann, Nina

2016-08-01

The impact of species richness and functional diversity of plants on ecosystem water vapor fluxes has been little investigated. To address this knowledge gap, we combined a lysimeter setup in a controlled environment facility (Ecotron) with large ecosystem samples/monoliths originating from a long-term biodiversity experiment (The Jena Experiment) and a modeling approach. Our goals were (1) quantifying the impact of plant species richness (four vs. 16 species) on day- and nighttime ecosystem water vapor fluxes; (2) partitioning ecosystem evapotranspiration into evaporation and plant transpiration using the Shuttleworth and Wallace (SW) energy partitioning model; and (3) identifying the most parsimonious predictors of water vapor fluxes using plant functional-trait-based metrics such as functional diversity and community weighted means. Daytime measured and modeled evapotranspiration were significantly higher in the higher plant diversity treatment, suggesting increased water acquisition. The SW model suggests that, at low plant species richness, a higher proportion of the available energy was diverted to evaporation (a non-productive flux), while, at higher species richness, the proportion of ecosystem transpiration (a productivity-related water flux) increased. While it is well established that LAI controls ecosystem transpiration, here we also identified that the diversity of leaf nitrogen concentration among species in a community is a consistent predictor of ecosystem water vapor fluxes during daytime. The results provide evidence that, at the peak of the growing season, higher leaf area index (LAI) and lower percentage of bare ground at high plant diversity diverts more of the available water to transpiration, a flux closely coupled with photosynthesis and productivity. Higher rates of transpiration presumably contribute to the positive effect of diversity on productivity. © 2016 by the Ecological Society of America.

Global remote sensing of water-chlorophyll ratio in terrestrial plant leaves.

PubMed

Kushida, Keiji

2012-10-01

I evaluated the use of global remote sensing techniques for estimating plant leaf chlorophyll a + b (C(ab); μg cm(-2)) and water (C(w); mg cm(-2)) concentrations as well as the ratio of C(w)/C(ab) with the PROSAIL model under possible distributions for leaf and soil spectra, leaf area index (LAI), canopy geometric structure, and leaf size. First, I estimated LAI from the normalized difference vegetation index. I found that, at LAI values <2, C(ab), C(w), and C(w)/C(ab) could not be reliably estimated. At LAI values >2, C(ab) and C(w) could be estimated for only restricted ranges of the canopy structure; however, the ratio of C(w)/C(ab) could be reliably estimated for a variety of possible canopy structures with coefficients of determination (R(2)) ranging from 0.56 to 0.90. The remote estimation of the C(w)/C(ab) ratio from satellites offers information on plant condition at a global scale.

Light Diffusion in the Tropical Dry Forest of Costa Rica

NASA Astrophysics Data System (ADS)

Calvo-Rodriguez, S.; Sanchez-Azofeifa, G. A.

2016-06-01

Leaf Area Index (LAI) has been defined as the total leaf area (one-sided) in relation to the ground. LAI has an impact on tree growth and recruitment through the interception of light, which in turn affects primary productivity. Even though many instruments exist for estimating LAI from ground, they are often laborious and costly to run continuously. Measurements of LAI from the field using traditional sensors (e.g., LAI-2000) require multiple visits to the field under very specific sky conditions, making them unsuitable to operate in inaccessible areas and forests with dense vegetation, as well as areas where persistent sunny conditions are the norm like tropical dry forests. With this context, we proposed a methodology to characterize light diffusion based on NDVI and LAI measurements taken from the field in two successional stages in the tropical dry forest of Santa Rosa National Park in Costa Rica. We estimate a "K" coefficient to characterize light diffusion by the canopy, based on field NDVI measurements derived from optical phenology instruments and MODIS NDVI. From the coefficients determined, we estimated LAI values and compared them with ground measurements of LAI. In both successional stages ground measurements of LAI had no significant difference to the tower-derived LAI and the estimated LAI from MODIS NDVI.

Monitoring and mapping leaf area index of rubber and oil palm in small watershed area

NASA Astrophysics Data System (ADS)

Rusli, N.; Majid, M. R.

2014-02-01

Existing conventional methods to determine LAI are tedious and time consuming for implementation in small or large areas. Thus, raster LAI data which are available free were downloaded for 4697.60 km2 of Sungai Muar watershed area in Johor. The aim of this study is to monitor and map LAI changes of rubber and oil palm throughout the years from 2002 to 2008. Raster datasets of LAI value were obtained from the National Aeronautics and Space Administration (NASA) website of available years from 2002 to year 2008. These data, were mosaicked and subset utilizing ERDAS Imagine 9.2. Next, the LAI raster dataset was multiplied by a scale factor of 0.1 to derive the final LAI value. Afterwards, to determine LAI values of rubber and oil palms, the boundaries of each crop from land cover data of the years 2002, 2006 and 2008 were exploited to overlay with LAI raster dataset. A total of 5000 sample points were generated utilizing the Hawths Tool (extension in ARcGIS 9.2) within these boundaries area and utilized for extracting LAI value of oil palm and rubber. In integration, a wide range of literature review was conducted as a guideline to derive LAI value of oil palm and rubber which range from 0 to 6. The results show, an overall mean LAI value from year 2002 to 2008 as decremented from 4.12 to 2.5 due to land cover transition within these years. In 2002, the mean LAI value of rubber and oil palm is 2.65 and 2.53 respectively. Meanwhile in 2006, the mean LAI value for rubber and oil palm is 2.54 and 2.82 respectively. In 2008, the mean LAI value for both crops is 0.85 for rubber and 1.04 for oil palm. In conclusion, apart from the original function of LAI which is related to the growth and metabolism of vegetation, the changes of LAI values from year 2002 to 2008 also capable to explain the process of land cover changes in a watershed area.

Quantitative retrieving forest ecological parameters based on remote sensing in Liping County of China

NASA Astrophysics Data System (ADS)

Tian, Qingjiu; Chen, Jing M.; Zheng, Guang; Xia, Xueqi; Chen, Junying

2006-09-01

Forest ecosystem is an important component of terrestrial ecosystem and plays an important role in global changes. Aboveground biomass (AGB) of forest ecosystem is an important factor in global carbon cycle studies. The purpose of this study was to retrieve the yearly Net Primary Productivity (NPP) of forest from the 8-days-interval MODIS-LAI images of a year and produce a yearly NPP distribution map. The LAI, DBH (diameter at breast height), tree height, and tree age field were measured in different 80 plots for Chinese fir, Masson pine, bamboo, broadleaf, mix forest in Liping County. Based on the DEM image and Landsat TM images acquired on May 14th, 2000, the geometric correction and terrain correction were taken. In addition, the "6S"model was used to gain the surface reflectance image. Then the correlation between Leaf Area Index (LAI) and Reduced Simple Ratio (RSR) was built. Combined with the Landcover map, forest stand map, the LAI, aboveground biomass, tree age map were produced respectively. After that, the 8-days- interval LAI images of a year, meteorology data, soil data, forest stand image and Landcover image were inputted into the BEPS model to get the NPP spatial distribution. At last, the yearly NPP spatial distribution map with 30m spatial resolution was produced. The values in those forest ecological parameters distribution maps were quite consistent with those of field measurements. So it's possible, feasible and time-saving to estimate forest ecological parameters at a large scale by using remote sensing.

Evapotranspiration partitioning for three agro-ecosystems with contrasting moisture conditions: a comparison of an isotope method and a two-source model calculation

NASA Astrophysics Data System (ADS)

Wei, Z.; Lee, X.; Wen, X.; Xiao, W.

2017-12-01

Quantification of the contribution of transpiration (T) to evapotranspiration (ET) is a requirement for understanding changes in carbon assimilation and water cycling in a changing environment. So far, few studies have examined seasonal variability of T/ET and compared different ET partitioning methods under natural conditions across diverse agro-ecosystems. In this study, we apply a two-source model to partition ET for three agro-ecosystems (rice, wheat and corn). The model-estimated T/ET ranges from 0 to 1, with a near continuous increase over time in the early growing season when leaf area index (LAI) is less than 2.5 and then convergence towards a stable value beyond LAI of 2.5. The seasonal change in T/ET can be described well as a function of LAI, implying that LAI is a first-order factor affecting ET partitioning. The two-source model results show that the growing-season (May - September for rice, April - June for wheat and June to September for corn) T/ET is 0.50, 0.84 and 0.64, while an isotopic approach shows that T/ET is 0.74, 0.93 and 0.81 for rice, wheat and maize, respectively. The two-source model results are supported by soil lysimeter and eddy covariance measurements made during the same time period for wheat (0.87). Uncertainty analysis suggests that further improvements to the Craig-Gordon model prediction of the evaporation isotope composition and to measurement of the isotopic composition of ET are necessary to achieve accurate flux partitioning at the ecosystem scale using water isotopes as tracers.

[Applicability of agricultural production systems simulator (APSIM) in simulating the production and water use of wheat-maize continuous cropping system in North China Plain].

PubMed

Wang, Lin; Zheng, You-fei; Yu, Qiang; Wang, En-li

2007-11-01

The Agricultural Production Systems Simulator (APSIM) was applied to simulate the 1999-2001 field experimental data and the 2002-2003 water use data at the Yucheng Experiment Station under Chinese Ecosystem Research Network, aimed to verify the applicability of the model to the wheat-summer maize continuous cropping system in North China Plain. The results showed that the average errors of the simulations of leaf area index (LAI), biomass, and soil moisture content in 1999-2000 and 2000-2001 field experiments were 27.61%, 24.59% and 7.68%, and 32.65%, 35.95% and 10.26%, respectively, and those of LAI and biomass on the soils with high and low moisture content in 2002-2003 were 26.65% and 14.52%, and 23.91% and 27.93%, respectively. The simulations of LAI and biomass accorded well with the measured values, with the coefficients of determination being > 0.85 in 1999-2000 and 2002-2003, and 0.78 in 2000-2001, indicating that APSIM had a good applicability in modeling the crop biomass and soil moisture content in the continuous cropping system, but the simulation error of LAI was a little larger.

A methodology to estimate representativeness of LAI station observation for validation: a case study with Chinese Ecosystem Research Network (CERN) in situ data

NASA Astrophysics Data System (ADS)

Xu, Baodong; Li, Jing; Liu, Qinhuo; Zeng, Yelu; Yin, Gaofei

2014-11-01

Leaf Area Index (LAI) is known as a key vegetation biophysical variable. To effectively use remote sensing LAI products in various disciplines, it is critical to understand the accuracy of them. The common method for the validation of LAI products is firstly establish the empirical relationship between the field data and high-resolution imagery, to derive LAI maps, then aggregate high-resolution LAI maps to match moderate-resolution LAI products. This method is just suited for the small region, and its frequencies of measurement are limited. Therefore, the continuous observing LAI datasets from ground station network are important for the validation of multi-temporal LAI products. However, due to the scale mismatch between the point observation in the ground station and the pixel observation, the direct comparison will bring the scale error. Thus it is needed to evaluate the representativeness of ground station measurement within pixel scale of products for the reasonable validation. In this paper, a case study with Chinese Ecosystem Research Network (CERN) in situ data was taken to introduce a methodology to estimate representativeness of LAI station observation for validating LAI products. We first analyzed the indicators to evaluate the observation representativeness, and then graded the station measurement data. Finally, the LAI measurement data which can represent the pixel scale was used to validate the MODIS, GLASS and GEOV1 LAI products. The result shows that the best agreement is reached between the GLASS and GEOV1, while the lowest uncertainty is achieved by GEOV1 followed by GLASS and MODIS. We conclude that the ground station measurement data can validate multi-temporal LAI products objectively based on the evaluation indicators of station observation representativeness, which can also improve the reliability for the validation of remote sensing products.

Evaluation of land surface model representation of phenology: an analysis of model runs submitted to the NACP Interim Site Synthesis

NASA Astrophysics Data System (ADS)

Richardson, A. D.; Nacp Interim Site Synthesis Participants

2010-12-01

Phenology represents a critical intersection point between organisms and their growth environment. It is for this reason that phenology is a sensitive and robust integrator of the biological impacts of year-to-year climate variability and longer-term climate change on natural systems. However, it is perhaps equally important that phenology, by controlling the seasonal activity of vegetation on the land surface, plays a fundamental role in regulating ecosystem processes, competitive interactions, and feedbacks to the climate system. Unfortunately, the phenological sub-models implemented in most state-of-the-art ecosystem models and land surface schemes are overly simplified. We quantified model errors in the representation of the seasonal cycles of leaf area index (LAI), gross ecosystem photosynthesis (GEP), and net ecosystem exchange of CO2. Our analysis was based on site-level model runs (14 different models) submitted to the North American Carbon Program (NACP) Interim Synthesis, and long-term measurements from 10 forested (5 evergreen conifer, 5 deciduous broadleaf) sites within the AmeriFlux and Fluxnet-Canada networks. Model predictions of the seasonality of LAI and GEP were unacceptable, particularly in spring, and especially for deciduous forests. This is despite an historical emphasis on deciduous forest phenology, and the perception that controls on spring phenology are better understood than autumn phenology. Errors of up to 25 days in predicting “spring onset” transition dates were common, and errors of up to 50 days were observed. For deciduous sites, virtually every model was biased towards spring onset being too early, and autumn senescence being too late. Thus, models predicted growing seasons that were far too long for deciduous forests. For most models, errors in the seasonal representation of deciduous forest LAI were highly correlated with errors in the seasonality of both GPP and NEE, indicating the importance of getting the underlying canopy dynamics correct. Most of the models in this comparison were unable to successfully predict the observed interannual variability in either spring or autumn transition dates. And, perhaps surprisingly, the seasonal cycles of models using phenology prescribed by remote sensing observations was, in general, no better than that that predicted by models with prognostic phenology. Reasons for the poor performance of both approaches will be discussed. These results highlight the need for improved understanding of the environmental controls on vegetation phenology. Existing models are unlikely to accurately predict future responses of phenology to climate change, and therefore will misrepresent the seasonality of key biosphere-atmosphere feedbacks and interactions in coupled model runs. New data sets, as for example from webcam-based monitoring networks (e.g. PhenoCam) or citizen science efforts (USA National Phenology Network) should prove valuable in this regard.

Development of a real-time hydrological cycle - rice growth coupled simulation system as a tool for farmers' decision making in an ungauged basin in Cambodia for the better agricultural water resources management

NASA Astrophysics Data System (ADS)

Tsujimoto, K.; Ohta, T.; Yasukawa, M.; Koike, T.; Kitsuregawa, M.; Homma, K.

2013-12-01

The entire country of Cambodia depends on agriculture for its economy. Rice is the staple food, making it the major agricultural product (roughly 80% of total national production). The target area of this study is western Cambodia, where rice production is the greatest in the country and most land is rainfed. Since most farmers rely only on their (non-science-based) experience, they would not adjust to changing rainfall and degraded water resources under climate change, so food security in the region would be seriously threatened (Monichoth et al., 2013). Under this condition, irrigation master plans are being considered by several ODA projects. This study aims to contribute to the design of such irrigation plans through the development of a real-time hydrological cycle - rice growth coupled simulation system. The purpose of the development of this system is to support decision making 1) for determining the necessary agricultural water resources and 2) for allocating limited water resources to various sectors. Rice growing condition as affected by water stress due to the water shortage is supposed to be shown for both of the cases with and without irrigation for several rainfall patterns. A dynamically coupled model of a distributed hydrological model (WEB-DHM., Wang et al., 2009) and a rice growth model (SIMRIW-rainfed, Homma et al., 2009) has been developed with a simple irrigation model. The target basin, a small basin in western Cambodia, is basically an ungauged basin and the model was validated by soil moisture, LAI, dry matter production of the rice crop, and rice yield, using both intensive field observation and satellite observations. Calibrating hourly satellite precipitation dataset (GSMaP/NRT) using ground rain gauges, hydrological cycle (soil moisture at three layers, river discharge, irrigatable water amount, water level of each paddy field, water demand of each paddy field, etc.) and rice growth (LAI, developmental index of the rice crop, dry matter production of the rice crop, etc.) are being calculated on near real time basis and opened to the Cambodian governmental staff by a website with only 5-hour delay. This system enables the Cambodian local government to virtually experience the effectiveness of irrigation and to get qualitative information for the examination on whether or how much they will investigate for irrigation.

The Importance of Measurement Errors for Deriving Accurate Reference Leaf Area Index Maps for Validation of Moderate-Resolution Satellite LAI Products

NASA Technical Reports Server (NTRS)

Huang, Dong; Yang, Wenze; Tan, Bin; Rautiainen, Miina; Zhang, Ping; Hu, Jiannan; Shabanov, Nikolay V.; Linder, Sune; Knyazikhin, Yuri; Myneni, Ranga B.

2006-01-01

The validation of moderate-resolution satellite leaf area index (LAI) products such as those operationally generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor data requires reference LAI maps developed from field LAI measurements and fine-resolution satellite data. Errors in field measurements and satellite data determine the accuracy of the reference LAI maps. This paper describes a method by which reference maps of known accuracy can be generated with knowledge of errors in fine-resolution satellite data. The method is demonstrated with data from an international field campaign in a boreal coniferous forest in northern Sweden, and Enhanced Thematic Mapper Plus images. The reference LAI map thus generated is used to assess modifications to the MODIS LAI/fPAR algorithm recently implemented to derive the next generation of the MODIS LAI/fPAR product for this important biome type.

Model of Management (Mo.Ma) for the patient with schizophrenia: crisis control, maintenance, relapse prevention, and recovery with long-acting injectable antipsychotics (LAIs).

PubMed

Brugnoli, Roberto; Rapinesi, Chiara; Kotzalidis, Georgios D; Marcellusi, Andrea; Mennini, Francesco S; De Filippis, Sergio; Carrus, Dario; Ballerini, Andrea; Francomano, Antonio; Ducci, Giuseppe; Del Casale, Antonio; Girardi, Paolo

2016-01-01

Schizophrenia is a severe mental disease that affects approximately 1% of the population with a relevant chronic impact on social and occupational functioning and daily activities. People with schizophrenia are 2-2.5 times more likely to die early than the general population. Non-adherence to antipsychotic medications, both in chronic and first episode schizophrenia, is one of the most important risk factors for relapse and hospitalization, that consequently contributes to increased costs due to psychiatric hospitalization. Atypical long-acting injectable (LAI) antipsychotics can improve treatment adherence and decrease re-hospitalization rates in patients with schizophrenia since its onset. The primary goals in the management of schizophrenia are directed not only at symptom reduction in the short and long term, but also at maintaining physical and mental functioning, improving quality of life, and promoting patient recovery. To propose a scientific evidence-based integrated model that provides an algorithm for recovery of patients with schizophrenia and to investigate the effectiveness and safety of antipsychotics LAI in the treatment, maintenance, relapse prevention, and recovery of schizophrenia. After an accurate literature review we identified, collected and analyzed the crucial points in taking care schizophrenia patients, through which we defined the steps described in the model of management and the choice of the better treatment option. Results. In the management model we propose, the choice of a second generation long acting antipsychotic, could allow from the earliest stages of illness better patient management, especially for young individuals with schizophrenia onset, a better recovery and significant reductions of relapse and health care costs. LAI formulations of antipsychotics are valuable, because they help patients to remain adherent to their medication through regular contact with healthcare professionals and to prevent covert non-adherence. The proposed schizophrenia model of management could allow better patient management and recovery, in which the treatment with LAI formulation is a safe and effective therapeutic option. This new therapeutic approach could change the cost structure of schizophrenia by decreasing costs with efficient economic resource allocation guaranteed from efficient diagnostic and therapeutic pathways.

Integration of ALS and TLS for calibration and validation of LAI profiles from large footprint lidar

NASA Astrophysics Data System (ADS)

Armston, J.; Tang, H.; Hancock, S.; Hofton, M. A.; Dubayah, R.; Duncanson, L.; Fatoyinbo, T. E.; Blair, J. B.; Disney, M.

2016-12-01

The Global Ecosystem Dynamics Investigation (GEDI) is designed to provide measurements of forest vertical structure and above-ground biomass density (AGBD) over tropical and temperate regions. The GEDI is a multi-beam waveform lidar that will acquire transects of forest canopy vertical profiles in conditions of up to 99% canopy cover. These are used to produce a number of canopy height and profile metrics to model habitat suitability and AGBD. These metrics include vertical leaf area index (LAI) profiles, which require some pre-launch refinement of large-footprint waveform processing methods for separating canopy and ground returns and estimation of their reflectance. Previous research developments in modelling canopy gap probability to derive canopy and ground reflectance from waveforms have primarily used data from small-footprint instruments, however development of a generalized spatial model with uncertainty will be useful for interpreting and modelling waveforms from large-footprint instruments such as the NASA Land Vegetation and Ice Sensor (LVIS) with a view to implementation for GEDI. Here we present an analysis of waveform lidar data from the NASA Land Vegetation and Ice Sensor (LVIS), which were acquired in Gabon in February 2016 to support the NASA/ESA AfriSAR campaign. AfriSAR presents a unique opportunity to test refined methods for retrieval of LAI profiles in high above-ground biomass rainforests (up to 600 Mg/ha) with dense canopies (>90% cover), where the greatest uncertainty exists. Airborne and Terrestrial Laser Scanning data (TLS) were also collected, enabling quantification of algorithm performance in plots of dense canopy cover. Refinement of canopy gap probability and LAI profile modelling from large-footprint lidar was based on solving for canopy and ground reflectance parameters spatially by penalized least-squares. The sensitivities of retrieved cover and LAI profiles to variation in canopy and ground reflectance showed improvement compared to assuming a constant ratio. We evaluated the use of spatially proximate simple waveforms to interpret more complex waveforms with poor separation of canopy and ground returns. This work has direct implications for GEDI algorithm refinement.

Analysis of Biogenic VOCs Emissions During the MAPS-Seoul Aircraft Field Campaign

NASA Astrophysics Data System (ADS)

Lee, Y.; Woo, J. H.; Kim, Y.; Bu, C.; Kim, J.; Kim, H. K.; Lee, M. H.; Eo, Y.

2016-12-01

The MAPS-Seoul (Megacity Air Pollution Studies-Seoul) aircraft mission was conducted in May - June 2016 to understand atmospheric environment over the South Korea. BVOCs emissions forecasting, along with other components, were conducted daily in support of the aircraft mission planning. The biogenic emissions as well as anthropogenic ones were very important factor to model and analyze atmospheric environment since more than 80% of global VOCs emission comes from biogenic sources. This also could be true for South Korea, since more than 70% of its land area are vegetated such as forest, cropland. For modeling-based BVOC emission estimation, geographical distribution of PFT (plant functional type) and LAI (Leaf Area Index) are considered as very important driving variables. Most of cases, PFTs and LAI were derived from the low-resolution satellite-based information which are not quite ideal for relatively small area like South Korea. In this study, we developed the more reliable Korean PFT and LAI cover derived from Korean landcover maps and modeled satellite images. The WRF-MEGAN modeling framework over South Korea for the period of May to June 2016 was used to estimate re-analysis BVOCs emission field. Analysis of different PFT and LAI inputs affected local and national biogenic emission estimations will be presented at site. Acknowledgements : This subject is supported by Korea Ministry of Environment as "Climate Change Correspondence Program". This work was supported by a grant from the National Institute of Environment Research (NIER), funded by the Ministry of Environment (MOE) of the Republic of Korea.

Initiating/maintaining long-acting injectable antipsychotics in schizophrenia/schizoaffective or bipolar disorder - expert consensus survey part 2.

PubMed

Sajatovic, Martha; Ross, Ruth; Legacy, Susan N; Byerly, Matthew; Kane, John M; DiBiasi, Faith; Fitzgerald, Heather; Correll, Christoph U

2018-01-01

The aim of this study was to provide recommendations on initiating and maintaining long-acting injectable antipsychotics (LAIs) in individuals with schizophrenia/schizoaffective or bipolar disorder. A 50-question survey comprising 916 response options was completed by 34 expert researchers and high prescribers with extensive LAI experience, rating relative appropriateness/importance on a 9-point scale. Consensus was determined using chi-square test of score distributions. Results of 21 questions comprising 339 response options regarding LAI initiation, maintenance treatment, adequate trial definition, identifying treatment nonresponse, and switching are reported. Experts agreed that the most important LAI selection factor was patient response/tolerability to previous antipsychotics. An adequate therapeutic LAI trial was defined as the time to steady state ± 1-2 injection cycles. Experts suggested that oral efficacy and tolerability should be established before switching to an LAI, without consensus on the required time, and that the time for oral supplementation and next injection interval should be determined by the time to attainment of therapeutic LAI levels. Most experts agreed that ≥1 adequate LAI trial is needed to identify the lack of efficacy. There was little agreement about strategies for switching between LAIs. Expert guidance may aid clinicians in their decisions regarding initiating/maintaining LAIs in individuals with schizophrenia/schizoaffective or bipolar disorder.

Development and assessment of an efficient vadose zone module solving the 1D Richards' equation and including root extraction by plants

NASA Astrophysics Data System (ADS)

Varado, N.; Braud, I.; Ross, P. J.

2006-05-01

From the non iterative numerical method proposed by [Ross, P.J., 2003. Modeling soil water and solute transport—fast, simplified numerical solutions. Agronomy Journal 95, 1352-1361] for solving the 1D Richards' equation, an unsaturated zone module for large scale hydrological model is developed by the inclusion of a root extraction module and a formulation of interception. Two root water uptake modules, first proposed by [Lai, C.-T. and Katul, G., 2000. The dynamic role of rott-water uptake in coupling potential to actual transpiration. Adv. Water Res. 23: 427-439; Li, K.Y., De Jong, R. and Boisvert, J.B., 2001. An exponential root-water-uptake model with water stress compensation. J. Hydrol. 252: 189-204], were included as the sink term in the Richards' equation. They express root extraction as a linear function of potential transpiration and take into account water stress and compensation mechanism allowing water to be extracted in wetter layers. The vadose zone module is tested in a systematic way with synthetic data sets covering a wide range of soil characteristics, climate forcing, and vegetation cover. A detailed SVAT model providing an accurate solution of the coupled heat and water transfer in the soil and the surface energy balance is used as a reference. The accuracy of the numerical solution using only the SVAT soil module, and the loss of accuracy when using a potential evapotranspiration instead of solving the energy budget are both investigated. The vadose zone module is very accurate with errors of less than a few percent for cumulative transpiration. Soil evaporation is less accurately simulated as it leads to a systematic underestimation of soil evaporation amounts. The [Lai, C.-T. and Katul, G., 2000. The dynamic role of rott-water uptake in coupling potential to actual transpiration. Adv. Water Res. 23: 427-439] module is not adapted for sandy soils, due to a weakness in the compensation term formulation. When using a potential evapotranspiration instead of the surface energy balance, we evidenced a difference in partitioning the energy between the soil and the vegetation. A Beer-Lambert law is not able to take into account the complex interactions at the soil-vegetation-atmopshere interface. However, under field conditions, the accuracy of the vadose zone module is satisfactory provided that a correct crop coefficient could be defined. As a conclusion the numerical method proposed by [Ross, P.J., 2003. Modeling soil water and solute transport—fast, simplified numerical solutions. Agronomy Journal 95, 1352-1361] coupled with the [Li, K.Y., De Jong, R. and Boisvert, J.B., 2001. An exponential root-water-uptake model with water stress compensation. J. Hydrol. 252: 189-204] root extraction module provides an efficient and accurate solution for inclusion as a physically-based infiltration-evapotranspiration module into larger scale watershed models.

Assessing the Transferability of Statistical Predictive Models for Leaf Area Index Between Two Airborne Discrete Return LiDAR Sensor Designs Within Multiple Intensely Managed Loblolly Pine Forest Locations in the South-Eastern USA

NASA Technical Reports Server (NTRS)

Sumnall, Matthew; Peduzzi, Alicia; Fox, Thomas R.; Wynne, Randolph H.; Thomas, Valerie A.; Cook, Bruce

2016-01-01

Leaf area is an important forest structural variable which serves as the primary means of mass and energy exchange within vegetated ecosystems. The objective of the current study was to determine if leaf area index (LAI) could be estimated accurately and consistently in five intensively managed pine plantation forests using two multiple-return airborne LiDAR datasets. Field measurements of LAI were made using the LiCOR LAI2000 and LAI2200 instruments within 116 plots were established of varying size and within a variety of stand conditions (i.e. stand age, nutrient regime and stem density) in North Carolina and Virginia in 2008 and 2013. A number of common LiDAR return height and intensity distribution metrics were calculated (e.g. average return height), in addition to ten indices, with two additional variants, utilized in the surrounding literature which have been used to estimate LAI and fractional cover, were calculated from return heights and intensity, for each plot extent. Each of the indices was assessed for correlation with each other, and was used as independent variables in linear regression analysis with field LAI as the dependent variable. All LiDAR derived metrics were also entered into a forward stepwise linear regression. The results from each of the indices varied from an R2 of 0.33 (S.E. 0.87) to 0.89 (S.E. 0.36). Those indices calculated using ratios of all returns produced the strongest correlations, such as the Above and Below Ratio Index (ABRI) and Laser Penetration Index 1 (LPI1). The regression model produced from a combination of three metrics did not improve correlations greatly (R2 0.90; S.E. 0.35). The results indicate that LAI can be predicted over a range of intensively managed pine plantation forest environments accurately when using different LiDAR sensor designs. Those indices which incorporated counts of specific return numbers (e.g. first returns) or return intensity correlated poorly with field measurements. There were disparities between the number of different types of returns and intensity values when comparing the results from two LiDAR sensors, indicating that predictive models developed using such metrics are not transferable between datasets with different acquisition parameters. Each of the indices were significantly correlated with one another, with one exception (LAI proxy), in particular those indices calculated from all returns, which indicates similarities in information content for those indices. It can then be argued that LiDAR indices have reached a similar stage in development to those calculated from optical-spectral sensors, but which offer a number of advantages, such as the reduction or removal of saturation issues in areas of high biomass.

Estimating Leaf Area Index (LAI) in Vineyards Using the PocketLAI Smart-App.

PubMed

Orlando, Francesca; Movedi, Ermes; Coduto, Davide; Parisi, Simone; Brancadoro, Lucio; Pagani, Valentina; Guarneri, Tommaso; Confalonieri, Roberto

2016-11-26

Estimating leaf area index (LAI) of Vitis vinifera using indirect methods involves some critical issues, related to its discontinuous and non-homogeneous canopy. This study evaluates the smart app PocketLAI and hemispherical photography in vineyards against destructive LAI measurements. Data were collected during six surveys in an experimental site characterized by a high level of heterogeneity among plants, allowing us to explore a wide range of LAI values. During the last survey, the possibility to combine remote sensing data and in-situ PocketLAI estimates (smart scouting) was evaluated. Results showed a good agreement between PocketLAI data and direct measurements, especially for LAI ranging from 0.13 to 1.41 ( R ² = 0.94, RRMSE = 17.27%), whereas the accuracy decreased when an outlying value (vineyard LAI = 2.84) was included ( R ² = 0.77, RRMSE = 43.00%), due to the saturation effect in case of very dense canopies arising from lack of green pruning. The hemispherical photography showed very high values of R ², even in presence of the outlying value ( R ² = 0.94), although it showed a marked and quite constant overestimation error (RRMSE = 99.46%), suggesting the need to introduce a correction factor specific for vineyards. During the smart scouting, PocketLAI showed its reliability to monitor the spatial-temporal variability of vine vigor in cordon-trained systems, and showed a potential for a wide range of applications, also in combination with remote sensing.

Constrained variability of modeled T:ET ratio across biomes

NASA Astrophysics Data System (ADS)

Fatichi, Simone; Pappas, Christoforos

2017-07-01

A large variability (35-90%) in the ratio of transpiration to total evapotranspiration (referred here as T:ET) across biomes or even at the global scale has been documented by a number of studies carried out with different methodologies. Previous empirical results also suggest that T:ET does not covary with mean precipitation and has a positive dependence on leaf area index (LAI). Here we use a mechanistic ecohydrological model, with a refined process-based description of evaporation from the soil surface, to investigate the variability of T:ET across biomes. Numerical results reveal a more constrained range and higher mean of T:ET (70 ± 9%, mean ± standard deviation) when compared to observation-based estimates. T:ET is confirmed to be independent from mean precipitation, while it is found to be correlated with LAI seasonally but uncorrelated across multiple sites. Larger LAI increases evaporation from interception but diminishes ground evaporation with the two effects largely compensating each other. These results offer mechanistic model-based evidence to the ongoing research about the patterns of T:ET and the factors influencing its magnitude across biomes.

Modelling insights on the partition of evapotranspiration components across biomes

NASA Astrophysics Data System (ADS)

Fatichi, Simone; Pappas, Christoforos

2017-04-01

Recent studies using various methodologies have found a large variability (from 35 to 90%) in the ratio of transpiration to total evapotranspiration (denoted as T:ET) across biomes or even at the global scale. Concurrently, previous results suggest that T:ET is independent of mean precipitation and has a positive correlation with Leaf Area Index (LAI). We used the mechanistic ecohydrological model, T&C, with a refined process-based description of soil resistance and a detailed treatment of canopy biophysics and ecophysiology, to investigate T:ET across multiple biomes. Contrary to observation-based estimates, simulation results highlight a well-constrained range of mean T:ET across biomes that is also robust to perturbations of the most sensitive parameters. Simulated T:ET was confirmed to be independent of average precipitation, while it was found to be uncorrelated with LAI across biomes. Higher values of LAI increase evaporation from interception but suppress ground evaporation with the two effects largely cancelling each other in many sites. These results offer mechanistic, model-based, evidence to the ongoing research about the range of T:ET and the factors affecting its magnitude across biomes.

Assimilating Remote Sensing Observations of Leaf Area Index and Soil Moisture for Wheat Yield Estimates: An Observing System Simulation Experiment

NASA Technical Reports Server (NTRS)

Nearing, Grey S.; Crow, Wade T.; Thorp, Kelly R.; Moran, Mary S.; Reichle, Rolf H.; Gupta, Hoshin V.

2012-01-01

Observing system simulation experiments were used to investigate ensemble Bayesian state updating data assimilation of observations of leaf area index (LAI) and soil moisture (theta) for the purpose of improving single-season wheat yield estimates with the Decision Support System for Agrotechnology Transfer (DSSAT) CropSim-Ceres model. Assimilation was conducted in an energy-limited environment and a water-limited environment. Modeling uncertainty was prescribed to weather inputs, soil parameters and initial conditions, and cultivar parameters and through perturbations to model state transition equations. The ensemble Kalman filter and the sequential importance resampling filter were tested for the ability to attenuate effects of these types of uncertainty on yield estimates. LAI and theta observations were synthesized according to characteristics of existing remote sensing data, and effects of observation error were tested. Results indicate that the potential for assimilation to improve end-of-season yield estimates is low. Limitations are due to a lack of root zone soil moisture information, error in LAI observations, and a lack of correlation between leaf and grain growth.

Estimation of Regional Net CO2 Exchange over the Southern Great Plains

NASA Astrophysics Data System (ADS)

Biraud, S. C.; Riley, W. J.; Fischer, M. L.; Torn, M. S.; Cooley, H. S.

2004-12-01

Estimating spatially distributed ecosystem CO2 exchange is an important component of the North American Carbon Program. We describe here a methodology to estimate Net Ecosystem Exchange (NEE) over the Southern Great Plains, using: (1) data from the Department Of Energy's Atmospheric Radiation Measurement (ARM) sites in Oklahoma and Kansas; (2) meteorological forcing data from the Mesonet facilities; (3) soil and vegetation types from 1 km resolution USGS databases; (4) vegetation status (e.g., LAI) from 1 km satellite measurements of surface reflectance (MODIS); (5) a tested land-surface model; and (6) a coupled land-surface and meteorological model (MM5/ISOLSM). This framework allows us to simulate regional surface fluxes in addition to ABL and free troposphere concentrations of CO2 at a continental scale with fine-scale nested grids centered on the ARM central facility. We use the offline land-surface and coupled models to estimate regional NEE, and compare predictions to measurements from the 9 Extended Facility sites with eddy correlation measurements. Site level comparisons to portable ECOR measurements in several crop types are also presented. Our approach also allows us to extend bottom-up estimates to periods and areas where meteorological forcing data are unavailable.

Allometric method to estimate leaf area index for row crops

USDA-ARS?s Scientific Manuscript database

Leaf area index (LAI) is critical for predicting plant metabolism, biomass production, evapotranspiration, and greenhouse gas sequestration, but direct LAI measurements are difficult and labor intensive. Several methods are available to measure LAI indirectly or calculate LAI using allometric method...

Remote sensing and modelling of vegetation dynamics for early estimation and spatial analysis of grain yields in semiarid context in central Tunisia

NASA Astrophysics Data System (ADS)

Chahbi, Aicha; Zribi, Mehrez; Lili-Chabaane, Zohra

2016-04-01

In arid and semi-arid areas, population growth, urbanization, food security and climate change have an impact on agriculture in general and particular on the cereal production. Therefore to improve food security in arid countries, crop canopy monitoring and yield forecasting cereals are needed. Many models, based on the use of remote sensing or agro-meteorological models, have been developed to estimate the biomass and grain yield of cereals. Through the use of a rich database, acquired over a period of two years for more than 80 test fields, and from optical satellite SPOT/HRV images, the aim of the present study is to evaluate the feasibility of two yield prediction approaches. The first approach is based on the application of the semi-empirical growth model SAFY, developed to simulate the dynamics of the LAI and the grain yield, at the field scale. The model is able to reproduce the time evolution of the leaf area index of all fields with acceptable error. However, an inter-comparison between ground yield measurements and SAFY model simulations reveals that the yields are under-estimated by this model. We can explain the limits of the semi-empirical model SAFY by its simplicity and also by various factors that were not considered (fertilization, irrigation,...). To improve the yield estimation, a new approach is proposed: the grain yield is estimated in function of the LAI in the growth period between 25 March and 5 April. The LAI of this period is estimated by SAFY model. A linear relationship is developed between the measured grain yield and the LAI area of the maximum growth period.This approach is robust, the measured and estimated grain yields are well correlated. Following the validation of this approach, yield estimations are proposed for the entire studied site using the SPOT/HRV images.

Evaluation and Validation of Updated MODIS C6 and VIIRS LAI/FPAR

NASA Astrophysics Data System (ADS)

Yan, K.; Park, T.; Chen, C.; Yang, B.; Yan, G.; Knyazikhin, Y.; Myneni, R. B.; CHOI, S.

2015-12-01

Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (0.4-0.7 μm) absorbed by vegetation (FPAR) play a key role in characterizing vegetation canopy functioning and energy absorption capacity. With radiative transfer realization, MODIS onboard NASA EOS Terra and Aqua satellites has provided globally continuous LAI/FPAR since 2000 and continuously updated the products with better quality. And NPP VIIRS shows the measurement capability to extend high-quality LAI/FPAR time series data records as a successor of MODIS. The primary objectives of this study are 1) to evaluate and validate newly updated MODIS Collection 6 (C6) LAI/FPAR product which has finer resolution (500m) and improved biome type input, and 2) to examine and adjust VIIRS LAI/FPAR algorithm for continuity with MODIS'. For MODIS C6 investigation, we basically measure the spatial coverage (i.e., main radiative transfer algorithm execution), continuity and consistency with Collection 5 (C5), and accuracy with field measured LAI/FPAR. And we also validate C6 LAI/FPAR via comparing other possible global LAI/FPAR products (e.g., GLASS and CYCLOPES) and capturing co-varying seasonal signatures with climatic variables (e.g., temperature and precipitation). For VIIRS evaluation and adjustment, we first quantify possible difference between C5 and MODIS heritage based VIIRS LAI/FPAR. Then based on the radiative transfer theory of canopy spectral invariants, we find VIIRS- and biome-specific configurable parameters (single scattering albedo and uncertainty). These two practices for MODIS C6 and VIIRS LAI/FPAR products clearly suggest that (a) MODIS C6 has better coverage and accuracy than C5, (b) C6 shows consistent spatiotemporal pattern with C5, (c) VIIRS has the potential for producing MODIS-like global LAI/FPAR Earth System Data Records.

A Model-Data Intercomparison of Carbon Fluxes, Pools, and LAI in the Community Land Model (CLM) and Alternative Carbon Allocation Schemes

NASA Astrophysics Data System (ADS)

Montane, F.; Fox, A. M.; Arellano, A. F.; Alexander, M. R.; Moore, D. J.

2016-12-01

Carbon (C) allocation to different plant tissues (leaves, stem and roots) remains a central challenge for understanding the global C cycle, as it determines C residence time. We used a diverse set of observations (AmeriFlux eddy covariance towers, biomass estimates from tree-ring data, and Leaf Area Index measurements) to compare C fluxes, pools, and Leaf Area Index (LAI) data with the Community Land Model (CLM). We ran CLM for seven temperate forests in North America (including evergreen and deciduous sites) between 1980 and 2013 using different C allocation schemes: i) standard C allocation scheme in CLM, which allocates C to the stem and leaves as a dynamic function of annual net primary productivity (NPP); ii) two fixed C allocation schemes, one representative of evergreen and the other one of deciduous forests, based on Luyssaert et al. 2007; iii) an alternative C allocation scheme, which allocated C to stem and leaves, and to stem and coarse roots, as a dynamic function of annual NPP, based on Litton et al. 2007. At our sites CLM usually overestimated gross primary production and ecosystem respiration, and underestimated net ecosystem exchange. Initial aboveground biomass in 1980 was largely overestimated for deciduous forests, whereas aboveground biomass accumulation between 1980 and 2011 was highly underestimated for both evergreen and deciduous sites due to the lower turnover rate in the sites than the one used in the model. CLM overestimated LAI in both evergreen and deciduous sites because the Leaf C-LAI relationship in the model did not match the observed Leaf C-LAI relationship in our sites. Although the different C allocation schemes gave similar results for aggregated C fluxes, they translated to important differences in long-term aboveground biomass accumulation and aboveground NPP. For deciduous forests, one of the alternative C allocation schemes used (iii) gave more realistic stem C/leaf C ratios, and highly reduced the overestimation of initial aboveground biomass, and accumulated aboveground NPP for deciduous forests by CLM. Our results would suggest using different C allocation schemes for evergreen and deciduous forests. It is crucial to improve CLM in the near future to minimize data-model mismatches, and to address some of the current model structural errors and parameter uncertainties.

GEOLAND2 global LAI, FAPAR Essential Climate Variables for terrestrial carbon modeling: principles and validation

NASA Astrophysics Data System (ADS)

Baret, F.; Weiss, M.; Lacaze, R.; Camacho, F.; Smets, B.; Pacholczyk, P.; Makhmara, H.

2010-12-01

LAI and fAPAR are recognized as Essential Climate Variables providing key information for the understanding and modeling of canopy functioning. Global remote sensing observations at medium resolution are routinely acquired since the 80’s mainly with AVHRR, SEAWIFS, VEGETATION, MODIS and MERIS sensors. Several operational products have been derived and provide global maps of LAI and fAPAR at daily to monthly time steps. Inter-comparison between MODIS, CYCLOPES, GLOBCARBON and JRC-FAPAR products showed generally consistent seasonality, while large differences in magnitude and smoothness may be observed. One of the objectives of the GEOLAND2 European project is to develop such core products to be used in a range of application services including the carbon monitoring. Rather than generating an additional product from scratch, the version 1 of GEOLAND2 products was capitalizing on the existing products by combining them to retain their pros and limit their cons. For these reasons, MODIS and CYCLOPES products were selected since they both include LAI and fAPAR while having relatively close temporal sampling intervals (8 to 10 days). GLOBCARBON products were not used here because of the too long monthly time step inducing large uncertainties in the seasonality description. JRC-FAPAR was not selected as well to preserve better consistency between LAI and fAPAR products. MODIS and CYCLOPES products were then linearly combined to take advantage of the good performances of CYCLOPES products for low to medium values of LAI and fAPAR while benefiting from the better MODIS performances for the highest LAI values. A training database representative of the global variability of vegetation type and conditions was thus built. A back-propagation neural network was then calibrated to estimate the new LAI and fAPAR products from VEGETATION preprocessed observations. Similarly, the vegetation cover fraction (fCover) was also derived by scaling the original CYCLOPES fCover products. Validation results achieved following the principles proposed by CEOS-LPV show that the new product called GEOV1 behaves as expected with good performances over the whole range of LAI and fAPAR in a temporally smooth and spatially consistent manner. These products will be processed and delivered by VITO in near real time at 1 km spatial resolution and 10 days frequency using a pre-operational production quality tracking system. The entire VEGETATION archive, from 1999 will be processed to provide a consistent time series over both VEGETATION sensors at the same spatial and temporal sampling. A climatology of products computed over the VEGETATION period will be also delivered at the same spatial and temporal sampling, showing average values, between year variability and possible trends over the decade. Finally, the VEGETATION derived time series starting back to 1999 will be completed with consistent products at 4 km spatial resolution derived from the NOAA/AVHRR series to cover the 1981-2010 period.

Modeling spatial patterns of soil respiration in maize fields from vegetation and soil property factors with the use of remote sensing and geographical information system.

PubMed

Huang, Ni; Wang, Li; Guo, Yiqiang; Hao, Pengyu; Niu, Zheng

2014-01-01

To examine the method for estimating the spatial patterns of soil respiration (Rs) in agricultural ecosystems using remote sensing and geographical information system (GIS), Rs rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson's correlation analysis, leaf area index (LAI), canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC) content, and soil total nitrogen content were selected as the factors that affected spatial variability in Rs during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected Rs. Meanwhile, other factors indirectly affected Rs through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI) showed the best correlation with LAI and was thus used as a proxy for LAI to estimate Rs at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in Rs during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of Rs in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R2 of 0.69 and root-mean-square error of 0.51 µmol CO2 m(-2) s(-1). The conclusions from this study provide valuable information for estimates of Rs during the peak growing season of maize in three counties in North China.

Modeling Spatial Patterns of Soil Respiration in Maize Fields from Vegetation and Soil Property Factors with the Use of Remote Sensing and Geographical Information System

PubMed Central

Huang, Ni; Wang, Li; Guo, Yiqiang; Hao, Pengyu; Niu, Zheng

2014-01-01

To examine the method for estimating the spatial patterns of soil respiration (Rs) in agricultural ecosystems using remote sensing and geographical information system (GIS), Rs rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson's correlation analysis, leaf area index (LAI), canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC) content, and soil total nitrogen content were selected as the factors that affected spatial variability in Rs during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected Rs. Meanwhile, other factors indirectly affected Rs through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI) showed the best correlation with LAI and was thus used as a proxy for LAI to estimate Rs at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in Rs during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of Rs in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R2 of 0.69 and root-mean-square error of 0.51 µmol CO2 m−2 s−1. The conclusions from this study provide valuable information for estimates of Rs during the peak growing season of maize in three counties in North China. PMID:25157827

Recovery outcomes of schizophrenia patients treated with paliperidone palmitate in a community setting: patient and provider perspectives on recovery.

PubMed

Williams, Wesley; McKinney, Christopher; Martinez, Larry; Benson, Carmela

2016-01-01

This study evaluated the effect of paliperidone palmitate long-acting injectable (LAI) antipsychotic on recovery-oriented mental health outcomes from the perspective of healthcare providers and patients during the treatment of patients with schizophrenia or schizoaffective disorders. Archival data for patients with a primary diagnosis of schizophrenia or schizoaffective disorder receiving ≥6 months of paliperidone palmitate LAI were retrieved from the electronic medical records system at the Mental Health Center of Denver. Mental health recovery was assessed from both a provider's (Recovery Markers Inventory [RMI]) and patient's (Consumer Recovery Measure [CRM]) perspective. A three-level hierarchical linear model (HLM) was utilized to determine changes in CRM and RMI scores by including independent variables in the models: intercept, months from treatment (slope), treatment time period (pretreatment and treatment), age, gender, primary diagnosis, substance abuse diagnosis, concurrent medications, and adherence to paliperidone palmitate LAI. A total of 219 patients were identified and included in the study. Results of the final three-level HLMs indicated an overall increase in CRM scores (p < 0.05), an overall increase (p < 0.01), and an increased rate of change (p < 0.05) in RMI scores during the paliperidone palmitate LAI treatment period vs the pre-treatment period. This study contained a retrospective, non-comparative design, and did not adjust for multiplicity Conclusions: The current study demonstrates that changes in recovery-oriented mental health outcomes can be detected following the administration of a specific antipsychotic treatment in persons with schizophrenia or schizoaffective disorders. Furthermore, patients receiving paliperidone palmitate LAI can effectively improve recovery-oriented outcomes, thereby supporting the drug's use as schizophrenia treatment from a recovery-oriented perspective.

Patient and prescriber perspectives on long-acting injectable (LAI) antipsychotics and analysis of in-office discussion regarding LAI treatment for schizophrenia

PubMed Central

2013-01-01

Background The research goal is to better understand prescriber, patient, and caregiver perspectives about long-acting injectable (LAI) antipsychotic therapy and how these perspectives affect LAI use. Addressing these perspectives in the clinic may lead to greater success in achieving therapeutic goals for the patient with schizophrenia. Methods Ethnographic information was collected from a non-random sample of 69 prescriber-patient conversations (60 with community mental health center [CMHC] psychiatrists; 9 with nurse-practitioners) recorded during treatment visits from August 2011 to February 2012, transcribed and analyzed. Discussions were categorized according to 11 predetermined CMHC topics. In-person observations were also conducted at 4 CMHCs, including home visits by researchers (n = 15 patients) prior to the CMHC visit and observations of patients receiving injections and interacting with staff. Telephone in-depth interviews with psychiatrists, patients, and caregivers to gather additional information on LAI discussion, prescription, or use were conducted. Results Antipsychotic treatment decisions were made without patient or caregiver input in 40 of 60 (67%) of psychiatrist-patient conversations. Involvement of patients or caregivers in treatment decisions was greater when discussing LAI (15 of 60 [25%]) vs oral antipsychotic treatment (5 of 60 [8%]). LAIs were not discussed by psychiatrists in 11 of 22 (50%) patients taking oral antipsychotics. When offered, more LAI-naïve patients expressed neutral (9 of 19 [47%]) rather than favorable (3 of 19 [16%]) or unfavorable (7 of 19 [37%]) responses. Prescribers were most concerned about potentially damaging the therapeutic relationship and side-effects when discussing LAIs while patient resistance was often related to negative feelings about injections. Psychiatrists had some success in overcoming patient objections to LAIs by addressing and decomposing initial resistance. More than half (11 of 19 [58%]) of LAI-naïve patients agreed to start LAI treatment following office visits. Patient-described benefits of LAIs vs orals included perceived rapid symptom improvement and greater overall efficacy. Conclusions In this study, many psychiatrists did not offer LAIs and most patients and caregivers were not involved in antipsychotic treatment decision making. Opportunities to increase active patient engagement, address resistances, guide patient drug-formulation selection, and provide better LAI-relevant information for more individualized approaches to treating the patient with schizophrenia were present. PMID:24131801

Application of remote sensing in crop growth simulation and an ensembles approach to reduce model uncertainties

NASA Astrophysics Data System (ADS)

Setiyono, T. D.; Nelson, A.; Ravis, J.; Maunahan, A.; Villano, L.; Li, T.; Bouman, B.

2012-12-01

A semi-empirical model derived from the water-cloud model was used to convert synthetic- aperture radar (SAR) backscattering data into LAI. The SAR-based LAI at early rice growth stages were in a close agreement (90%) with LAI derived from MODIS data for the same study location in Nueva Ecija, Philippines. ORYZA2000 simulated rice yield of 4.5 Mg ha-1 for the 2008 wet season in Nueva Ejica, Philippines when using LAI inputs derived from SAR data, which is closer to the observed yield of 3.9 Mg ha-1, whereas simulated yield without SAR-derived LAI inputs was 5.4 Mg ha-1. The dynamic water and nitrogen balances were accounted in these simulations based on site-specific soil properties and actual fertilizer N and water management. The use of remote sensing data was promising for model application to approximate actual growth conditions and to compensate for limitations in the model due to relevant underlining processes absent in model formulations such as detailed tillering, leaf shading effect, etc., and also limiting factors not accounted in the model such as biotic factors and abiotic factors other than water and N shortages. This study also demonstrated the use an ensembles approach for provincial level rice yield estimation in the Philippines. Such ensembles approach involved statistical classifications of agronomic management settings into 25% percentile, median, and 75% levels followed by generation of factorial combinations. For irrigated lowland system, 4 factors were considered that include transplanting date, plant density, fertilizer N rate, and amount of irrigation water. For rainfed lowland system, there were 3 agronomic management factors (transplanting date, plant density, fertilizer N) and 1 soil parameter (depth of ground water table). These 4 management/soil factors and 3 statistical levels resulted in 81 total factorial combinations representing simulation scenarios for each area of interest (province in the Philippines) and water environments (irrigated vs. rainfed). Finally a normal distribution was assumed and applied to the simulations outputs. This ensembles approach provided an efficient and yet effective method of aggregating point-based crop model results into a larger spatial level of interest. Lack of access to accurate model parameters (e.g. depth of ground water table) could be solved with this approach. The use of process-based crop growth model was critical because the ultimate aim of this study was not just to establish a reliable rice yield estimation system but also to allow yield estimation outputs explainable by the underlining agronomic practices such as transplanting date, fertilizer N application, and water management.

Validation and Temporal Analysis of Lai and Fapar Products Derived from Medium Resolution Sensor

NASA Astrophysics Data System (ADS)

Claverie, M.; Vermote, E. F.; Baret, F.; Weiss, M.; Hagolle, O.; Demarez, V.

2012-12-01

Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) have been defined as Essential Climate Variables. Many Earth surface monitoring applications are based on global estimation combined with a relatively high frequency. The medium spatial resolution sensors (MRS), such as SPOT-VGT, MODIS or MERIS, have been widely used to provide land surface products (mainly LAI and FAPAR) to the scientific community. These products require quality assessment and consistency. However, due to consistency of the ground measurements spatial sampling, the medium resolution is not appropriate for direct validation with in situ measurements sampling. It is thus more adequate to use high spatial resolution sensors which can integrate the spatial variability. The recent availability of combined high spatial (8 m) and temporal resolutions (daily) Formosat-2 data allows to evaluate the accuracy and the temporal consistency of medium resolution sensors products. In this study, we proposed to validate MRS products over a cropland area and to analyze their spatial and temporal consistency. As a matter of fact, this study belongs to the Stage 2 of the validation, as defined by the Land Product Validation sub-group of the Earth Observation Satellites. Reference maps, derived from the aggregation of Formosat-2 data (acquired during the 2006-2010 period over croplands in southwest of France), were compared with (i) two existing global biophysical variables products (GEOV1/VGT and MODIS-15 coll. 5), and (ii) a new product (MODdaily) derived from the inversion of PROSAIL radiative transfer model (EMMAH, INRA Avignon) applied on MODIS BRDF-corrected daily reflectance. Their uncertainty was calculated with 105 LAI and FAPAR reference maps, which uncertainties (22 % for LAI and 12% for FAPAR) were evaluated with in situ measurements performed over maize, sunflower and soybean. Inter-comparison of coarse resolution (0.05°) products showed that LAI and FAPAR have consistent phenology (Figure). The GEOLAND-2 showed the smoothest time series due to a 30-day composite, while MODdaily noise was satisfactory (<12%). The RMSE of LAI calculated for the period 2006-2010 were 0.46 for GEOV1/VGT, 0.19 for MODIS-15 and 0.16 for MODdaily. A significant overestimation (bias=0.43) of the LAI peak were observed for GEOV1/VGT products, while MOD-15 showed a small underestimation (bias=-0.14) of highest LAI. Finally, over a larger area (a quarter of France) covered by cropland, grassland and forest, the products displayed a good spatial consistency.; LAI 2006-2010 time-series of a coarse resolution pixel of cropland (extent in upper-left corner). Products are compared to Formosat-2 reference maps.

A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape.

PubMed

Lausch, Angela; Pause, Marion; Merbach, Ines; Zacharias, Steffen; Doktor, Daniel; Volk, Martin; Seppelt, Ralf

2013-02-01

Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combining hyperspectral remote sensing data on different spatial and temporal scales. We demonstrate the potential of using the "One Sensor at Different Scales" (OSADIS) approach for the laboratory (plot), field (local), and landscape (regional) scales. By implementing the OSADIS approach, we are able (1) to develop suitable stress-controlled vegetation indices for selected variables such as the Leaf Area Index (LAI), chlorophyll, photosynthesis, water content, nutrient content, etc. over a whole vegetation period. Focused laboratory monitoring can help to document additive and counteractive factors and processes of the vegetation and to correctly interpret their spectral response; (2) to transfer the models obtained to the landscape level; (3) to record imaging hyperspectral information on different spatial scales, achieving a true comparison of the structure and process results; (4) to minimize existing errors from geometrical, spectral, and temporal effects due to sensor- and time-specific differences; and (5) to carry out a realistic top- and downscaling by determining scale-dependent correction factors and transfer functions. The first results of OSADIS experiments are provided by controlled whole vegetation experiments on barley under water stress on the plot scale to model LAI using the vegetation indices Normalized Difference Vegetation Index (NDVI) and green NDVI (GNDVI). The regression model ascertained from imaging hyperspectral AISA-EAGLE/HAWK (DUAL) data was used to model LAI. This was done by using the vegetation index GNDVI with an R (2) of 0.83, which was transferred to airborne hyperspectral data on the local and regional scales. For this purpose, hyperspectral imagery was collected at three altitudes over a land cover gradient of 25 km within a timeframe of a few minutes, yielding a spatial resolution from 1 to 3 m. For all recorded spatial scales, both the LAI and the NDVI were determined. The spatial properties of LAI and NDVI of all recorded hyperspectral images were compared using semivariance metrics derived from the variogram. The first results show spatial differences in the heterogeneity of LAI and NDVI from 1 to 3 m with the recorded hyperspectral data. That means that differently recorded data on different scales might not sufficiently maintain the spatial properties of high spatial resolution hyperspectral images.

Evaluation and attribution of vegetation contribution to seasonal climate predictability

NASA Astrophysics Data System (ADS)

Catalano, Franco; Alessandri, Andrea; De Felice, Matteo

2015-04-01

The land surface model of EC-Earth has been modified to include dependence of vegetation densities on the Leaf Area Index (LAI), based on the Lambert-Beer formulation. Effective vegetation fractional coverage can now vary at seasonal and interannual time-scales and therefore affect biophysical parameters such as the surface roughness, albedo and soil field capacity. The modified model is used to perform a real predictability seasonal hindcast experiment. LAI is prescribed using a recent observational dataset based on the third generation GIMMS and MODIS satellite data. Hindcast setup is: 7 months forecast length, 2 start dates (1st May and 1st November), 10 members, 28 years (1982-2009). The effect of the realistic LAI prescribed from observation is evaluated with respect to a control experiment where LAI does not vary. Hindcast results demonstrate that a realistic representation of vegetation significantly improves the forecasts of temperature and precipitation. The sensitivity is particularly large for temperature during boreal winter over central North America and Central Asia. This may be attributed in particular to the effect of the high vegetation component on the snow cover. Summer forecasts are improved in particular for precipitation over Europe, Sahel, North America, West Russia and Nordeste. Correlation improvements depends on the links between targets (temperature and precipitation) and drivers (surface heat fluxes, albedo, soil moisture, evapotranspiration, moisture divergence) which varies from region to region.

Impact of the Mountain Pine Beetle on the Forest Carbon Cycle in British Columbia from 1999 TO 2008 (Invited)

NASA Astrophysics Data System (ADS)

Chen, J. M.; Czurylowicz, P.; Mo, G.; Black, T. A.

2013-12-01

The unprecedented mountain pine beetle (Dendroctonus ponderosae) (MPB) outbreak in British Columbia starting in 1998 affected about 50% of the lodgepole pine (Pinus contorta var. latifolia) forests occupying about 50% of the land area of the province. The impact of this outbreak on the C cycle is assessed in this study. Annual leaf area index (LAI) maps of the affected area from 1999 to 2008 were produced using SPOT VEGETATION data, and net ecosystem production (NEP) was modeled using inputs of LAI, land cover, soil texture and daily meteorological data with the Boreal Ecosystem Productivity Simulator (BEPS). Both LAI and NEP were validated using field measurements. LAI was found to decrease on average by 20% compared to pre-outbreak conditions, while NEP decreased on average by 90%. Annual NEP values ranged from 2.4 to -8.0 Tg C between 1999 and 2008, with the ecosystem changing from a carbon sink to a carbon source in 2000. The annual average NEP was -2.9 Tg C over the 10 years, resulting in a total loss of carbon of 29 Tg C to the atmosphere. The inter-annual variability of both LAI and NEP was characterized by substantial initial decreases followed by steady increases from 2006 to 2008 with NEP returning to near carbon neutrality in 2008 (-1.8 Pg C/y). The impact of this MPB outbreak appears to be less dramatic than previously anticipated. The apparent fast recovery of LAI and NEP after MPB attacks is examined under the framework of ecosystem resilience which was manifested in the form of secondary overstory and understory growth and increased production of non-attacked host trees.

Generating Global Leaf Area Index from Landsat: Algorithm Formulation and Demonstration

NASA Technical Reports Server (NTRS)

Ganguly, Sangram; Nemani, Ramakrishna R.; Zhang, Gong; Hashimoto, Hirofumi; Milesi, Cristina; Michaelis, Andrew; Wang, Weile; Votava, Petr; Samanta, Arindam; Melton, Forrest;

Deriving Leaf Area Index (LAI) from multiple lidar remote sensing systems

NASA Astrophysics Data System (ADS)

Tang, H.; Dubayah, R.; Zhao, F.

2012-12-01

LAI is an important biophysical variable linking biogeochemical cycles of earth systems. Observations with passive optical remote sensing are plagued by saturation and results from different passive and active sensors are often inconsistent. Recently lidar remote sensing has been applied to derive vertical canopy structure including LAI and its vertical profile. In this research we compare LAI retrievals from three different types of lidar sensors. The study areas include the La Selva Biological Station in Costa Rica and Sierra Nevada Forest in California. We first obtain independent LAI estimates from different lidar systems including airborne lidar (LVIS), spaceborne lidar (GLAS) and ground lidar (Echidna). LAI retrievals are then evaluated between sensors as a function of scale, land cover type and sensor characteristics. We also assess the accuracy of these LAI products against ground measurements. By providing a link between ground observations, ground lidar, aircraft and space-based lidar we hope to demonstrate a path for deriving more accurate estimates of LAI on a global basis, and to provide a more robust means of validating passive optical estimates of this important variable.

Efficacy, tolerability, and safety of aripiprazole once-monthly versus other long-acting injectable antipsychotic therapies in the maintenance treatment of schizophrenia: a mixed treatment comparison of double-blind randomized clinical trials.

PubMed

Majer, Istvan M; Gaughran, Fiona; Sapin, Christophe; Beillat, Maud; Treur, Maarten

2015-01-01

Treatment with long-acting injectable (LAI) antipsychotic medication is an important element of relapse prevention in schizophrenia. Recently, the intramuscular once-monthly formulation of aripiprazole received marketing approval in Europe and the United States for schizophrenia. This study aimed to compare aripiprazole once-monthly with other LAI antipsychotics in terms of efficacy, tolerability, and safety. A systematic literature review was conducted to identify relevant double-blind randomized clinical trials of LAIs conducted in the maintenance treatment of schizophrenia. MEDLINE, MEDLINE In-Process, Embase, the Cochrane Library, PsycINFO, conference proceedings, clinical trial registries, and the reference lists of key review articles were searched. The literature search covered studies dating from January 2002 to May 2013. Studies were required to have ≥24 weeks of follow-up. Patients had to be stable at randomization. Studies were not eligible for inclusion if efficacy of acute and maintenance phase treatment was not reported separately. Six trials were identified (0.5% of initially identified studies), allowing comparisons of aripiprazole once-monthly, risperidone LAI, paliperidone palmitate, olanzapine pamoate, haloperidol depot, and placebo. Data extracted included study details, study duration, the total number of patients in each treatment arm, efficacy, tolerability, and safety outcomes. The efficacy outcome contained the number of patients that experienced a relapse, tolerability outcomes included the number of patients that discontinued treatment due to treatment-related adverse events (AEs), and that discontinued treatment due to reasons other than AEs (e.g., loss to follow-up). Safety outcomes included the incidence of clinically relevant weight gain and extrapyramidal symptoms. Data were analyzed by applying a mixed treatment comparison competing risks model (efficacy) and using binary models (safety). There was no statistically significant difference between any study outcome, including the risk of relapse, the risk of discontinuations, and safety outcomes. Aripiprazole once-monthly is similarly efficacious to other LAIs with relatively low rates of discontinuation due to AEs and due to reasons other than AEs than other LAIs.

Gender difference in the impact of gynoid and android fat masses on the progression of hepatic steatosis in Japanese patients with type 2 diabetes.

PubMed

Bouchi, Ryotaro; Fukuda, Tatsuya; Takeuchi, Takato; Nakano, Yujiro; Murakami, Masanori; Minami, Isao; Izumiyama, Hajime; Hashimoto, Koshi; Yoshimoto, Takanobu; Ogawa, Yoshihiro

2017-01-01

Increased visceral adiposity is strongly associated with non-alcoholic fatty liver disease (NAFLD). However, little attention has been paid to the association between the change in subcutaneous adipose mass and the progression of non-alcoholic fatty liver disease (NAFLD). We aimed to investigate whether increased subcutaneous adipose tissue (gynoid fat mass) could be protective against the progression of NAFLD in Japanese patients with type 2 diabetes. This is a retrospective observational study of 294 Japanese patients with type 2 diabetes (65 ± 10 years old, 40% female). Liver attenuation index (LAI) measured by abdominal computed tomography was used for the assessment of hepatic steatosis. Both gynoid (kg) and android (kg) fat masses were measured by the whole body dual-energy X-ray absorptiometry. One-year changes in LAI, gynoid, and android fat masses were evaluated in both male and female patients. Linear regression analysis with a stepwise procedure was used for the statistical analyses to investigate the association of the changes in gynoid and android fat masses with the change in LAI. LAI levels at baseline were 1.15 ± 0.31 and 1.10 ± 0.34 in female and male patients ( p  = 0.455). The change in gynoid fat mass was significantly and positively associated with the change in LAI in both univariate (standardized β 0.331, p  = 0.049) and multivariate (standardized β 0.360, p  = 0.016) models in the female patients. However, no significant association was observed in males. In contrast, the increase in android fat mass was significantly associated with the reduced LAI in both genders in the multivariate models (standardized β -0.651, p  < 0.001 in females and standardized β -0.519, p  = 0.042 in males). This study provides evidence that increased gynoid fat mass may be protective against the progression of NAFLD in female Japanese patients with type 2 diabetes.

A Model-based Approach to Scaling GPP and NPP in Support of MODIS Land Product Validation

NASA Astrophysics Data System (ADS)

Turner, D. P.; Cohen, W. B.; Gower, S. T.; Ritts, W. D.

2003-12-01

Global products from the Earth-orbiting MODIS sensor include land cover, leaf area index (LAI), FPAR, 8-day gross primary production (GPP), and annual net primary production (NPP) at the 1 km spatial resolution. The BigFoot Project was designed specifically to validate MODIS land products, and has initiated ground measurements at 9 sites representing a wide array of vegetation types. An ecosystem process model (Biome-BGC) is used to generate estimates of GPP and NPP for each 5 km x 5 km BigFoot site. Model inputs include land cover and LAI (from Landsat ETM+), daily meteorological data (from a centrally located eddy covariance flux tower), and soil characteristics. Model derived outputs are validated against field-measured NPP and flux tower-derived GPP. The resulting GPP and NPP estimates are then aggregated to the 1 km resolution for direct spatial comparison with corresponding MODIS products. At the high latitude sites (tundra and boreal forest), the MODIS GPP phenology closely tracks the BigFoot GPP, but there is a high bias in the MODIS GPP. In the temperate zone sites, problems with the timing and magnitude of the MODIS FPAR introduce differences in MODIS GPP compared to the validation data at some sites. However, the MODIS LAI/FPAR data are currently being reprocessed (=Collection 4) and new comparisons will be made for 2002. The BigFoot scaling approach permits precise overlap in spatial and temporal resolution between the MODIS products and BigFoot products, and thus permits the evaluation of specific components of the MODIS NPP algorithm. These components include meteorological inputs from the NASA Data Assimilation Office, LAI and FPAR from other MODIS algorithms, and biome-specific parameters for base respiration rate and light use efficiency.

Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA).

PubMed

Reichenau, Tim G; Korres, Wolfgang; Montzka, Carsten; Fiener, Peter; Wilken, Florian; Stadler, Anja; Waldhoff, Guido; Schneider, Karl

2016-01-01

The ratio of leaf area to ground area (leaf area index, LAI) is an important state variable in ecosystem studies since it influences fluxes of matter and energy between the land surface and the atmosphere. As a basis for generating temporally continuous and spatially distributed datasets of LAI, the current study contributes an analysis of its spatial variability and spatial structure. Soil-vegetation-atmosphere fluxes of water, carbon and energy are nonlinearly related to LAI. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation (150 m resolution) with field measurements and a detailed land use map. Test area is the arable land in the fertile loess plain of the Rur catchment on the Germany-Belgium-Netherlands border. LAI from remote sensing and simulation compares well with field measurements. Based on the simulation results, we describe characteristic crop-specific temporal patterns of LAI spatial variability. By means of these patterns, we explain the complex multimodal frequency distributions of LAI in the remote sensing data. In the test area, variability between agricultural fields is higher than within fields. Therefore, spatial resolutions less than the 5 m of the remote sensing scenes are sufficient to infer LAI spatial variability. Frequency distributions from the simulation agree better with the multimodal distributions from remote sensing than normal distributions do. The spatial structure of LAI in the test area is dominated by a short distance referring to field sizes. Longer distances that refer to soil and weather can only be derived from remote sensing data. Therefore, simulations alone are not sufficient to characterize LAI spatial structure. It can be concluded that a comprehensive picture of LAI spatial heterogeneity and its temporal course can contribute to the development of an approach to create spatially distributed and temporally continuous datasets of LAI.

Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA)

PubMed Central

Korres, Wolfgang; Montzka, Carsten; Fiener, Peter; Wilken, Florian; Stadler, Anja; Waldhoff, Guido; Schneider, Karl

2016-01-01

The ratio of leaf area to ground area (leaf area index, LAI) is an important state variable in ecosystem studies since it influences fluxes of matter and energy between the land surface and the atmosphere. As a basis for generating temporally continuous and spatially distributed datasets of LAI, the current study contributes an analysis of its spatial variability and spatial structure. Soil-vegetation-atmosphere fluxes of water, carbon and energy are nonlinearly related to LAI. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation (150 m resolution) with field measurements and a detailed land use map. Test area is the arable land in the fertile loess plain of the Rur catchment on the Germany-Belgium-Netherlands border. LAI from remote sensing and simulation compares well with field measurements. Based on the simulation results, we describe characteristic crop-specific temporal patterns of LAI spatial variability. By means of these patterns, we explain the complex multimodal frequency distributions of LAI in the remote sensing data. In the test area, variability between agricultural fields is higher than within fields. Therefore, spatial resolutions less than the 5 m of the remote sensing scenes are sufficient to infer LAI spatial variability. Frequency distributions from the simulation agree better with the multimodal distributions from remote sensing than normal distributions do. The spatial structure of LAI in the test area is dominated by a short distance referring to field sizes. Longer distances that refer to soil and weather can only be derived from remote sensing data. Therefore, simulations alone are not sufficient to characterize LAI spatial structure. It can be concluded that a comprehensive picture of LAI spatial heterogeneity and its temporal course can contribute to the development of an approach to create spatially distributed and temporally continuous datasets of LAI. PMID:27391858

Derivation of global vegetation biophysical parameters from EUMETSAT Polar System

NASA Astrophysics Data System (ADS)

García-Haro, Francisco Javier; Campos-Taberner, Manuel; Muñoz-Marí, Jordi; Laparra, Valero; Camacho, Fernando; Sánchez-Zapero, Jorge; Camps-Valls, Gustau

2018-05-01

This paper presents the algorithm developed in LSA-SAF (Satellite Application Facility for Land Surface Analysis) for the derivation of global vegetation parameters from the AVHRR (Advanced Very High Resolution Radiometer) sensor on board MetOp (Meteorological-Operational) satellites forming the EUMETSAT (European Organization for the Exploitation of Meteorological Satellites) Polar System (EPS). The suite of LSA-SAF EPS vegetation products includes the leaf area index (LAI), the fractional vegetation cover (FVC), and the fraction of absorbed photosynthetically active radiation (FAPAR). LAI, FAPAR, and FVC characterize the structure and the functioning of vegetation and are key parameters for a wide range of land-biosphere applications. The algorithm is based on a hybrid approach that blends the generalization capabilities offered by physical radiative transfer models with the accuracy and computational efficiency of machine learning methods. One major feature is the implementation of multi-output retrieval methods able to jointly and more consistently estimate all the biophysical parameters at the same time. We propose a multi-output Gaussian process regression (GPRmulti), which outperforms other considered methods over PROSAIL (coupling of PROSPECT and SAIL (Scattering by Arbitrary Inclined Leaves) radiative transfer models) EPS simulations. The global EPS products include uncertainty estimates taking into account the uncertainty captured by the retrieval method and input errors propagation. A sensitivity analysis is performed to assess several sources of uncertainties in retrievals and maximize the positive impact of modeling the noise in training simulations. The paper discusses initial validation studies and provides details about the characteristics and overall quality of the products, which can be of interest to assist the successful use of the data by a broad user's community. The consistent generation and distribution of the EPS vegetation products will constitute a valuable tool for monitoring of earth surface dynamic processes.

Improved Model of Isoprene Emissions in Africa using Ozone Monitoring Instrument (OMI) Satellite Observations of Formaldehyde: Implications for Oxidants and Particulate Matter

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marais, E. A.; Jacob, D.; Guenther, Alex B.

We use a 2005-2009 record of isoprene emissions over Africa derived from OMI satellite observations of formaldehyde (HCHO) to better understand the factors controlling isoprene emission on the scale of the continent and evaluate the impact of isoprene emissions on atmospheric composition in Africa. OMI-derived isoprene emissions show large seasonality over savannas driven by temperature and leaf area index (LAI), and much weaker seasonality over equatorial forests driven by temperature. The commonly used MEGAN (version 2.1) global 31 isoprene emission model reproduces this seasonality but is biased high, particularly for 32 equatorial forests, when compared to OMI and relaxed-eddy accumulationmore » measurements. 33 Isoprene emissions in MEGAN are computed as the product of an emission factor Eo, LAI, and 34 activity factors dependent on environmental variables. We use the OMI-derived emissions to 35 provide improved estimates of Eo that are in good agreement with direct leaf measurements from 36 field campaigns (r = 0.55, bias = -19%). The largest downward corrections to MEGAN Eo values are for equatorial forests and semi-arid environments, and this is consistent with latitudinal transects of isoprene over West Africa from the AMMA aircraft campaign. Total emission of isoprene in Africa is estimated to be 77 Tg C a-1, compared to 104 Tg C a-1 in MEGAN. Simulations with the GEOS-Chem oxidant-aerosol model suggest that isoprene emissions increase mean surface ozone in West Africa by up to 8 ppbv, and particulate matter by up to 1.5 42 μg m-3, due to coupling with anthropogenic influences.« less

Concurrent Oral Antipsychotic Drug Use Among Schizophrenia Patients Initiated on Long-Acting Injectable Antipsychotics Post-Hospital Discharge.

PubMed

Doshi, Jalpa A; Pettit, Amy R; Stoddard, Jeffrey J; Zummo, Jacqueline; Marcus, Steven C

2015-08-01

Pharmacological treatment is central to effective management of schizophrenia. Prescribing clinicians have an increasing array of options from which to choose, and oral antipsychotic polypharmacy is common in routine clinical practice. Practice guidelines recommend long-acting injectable (LAI) formulations, typically viewed as monotherapeutic alternatives, for patients with established nonadherence. Yet there are limited data on the prevalence and nature of concurrent oral antipsychotic prescriptions in patients receiving LAIs. Our observational, claims-based study examined the frequency and duration of concurrent oral prescriptions in 340 Medicaid patients receiving LAI therapy. Specifically, we examined patients with a recent history of nonadherence and hospitalization for schizophrenia and included both first-generation antipsychotic depot medications (fluphenazine decanoate, haloperidol decanoate) and more recently available second-generation injectables (LAI risperidone, paliperidone palmitate). Of all patients initiated on LAIs, 75.9% had a concurrent oral antipsychotic prescription in the 6 months post-hospital discharge. Patients receiving concurrent prescriptions were frequently prescribed an oral formulation of their LAI agent, but many first-generation LAI users received a concurrent second-generation oral medication. The lowest rate of concurrent prescribing (58.8%) was found with paliperidone palmitate, whereas the highest rate was with LAI risperidone (88.9%). Overlap in oral and LAI prescriptions typically occurred for a substantial period of time (ie, >30 days) and for a notable percentage of the days covered by LAIs (often 50% or more). Our findings highlight the need to further examine such prescribing patterns, to probe the reasons for them, and to clarify the optimal roles of different antipsychotic treatments in clinical practice.

Indirect Field Measurement of Wine-Grape Vineyard Canopy Leaf Area Index

NASA Technical Reports Server (NTRS)

Johnson, Lee F.; Pierce, Lars L.; Skiles, J. W. (Technical Monitor)

2002-01-01

Leaf area index (LAI) indirect measurements were made at 12 study plots in California's Napa Valley commercial wine-grape vineyards with a LI-COR LI-2000 Plant Canopy Analyzer (PCA). The plots encompassed different trellis systems, biological varieties, and planting densities. LAI ranged from 0.5 - 2.25 sq m leaf area/ sq m ground area according to direct (defoliation) measurements. Indirect LAI reported by the PCA was significantly related to direct LAI (r(exp 2) = 0.78, p less than 001). However, the PCA tended to underestimate direct LAI by about a factor of two. Narrowing the instrument's conical field of view from 148 deg to 56 deg served to increase readings by approximately 30%. The PCA offers a convenient way to discern relative differences in vineyard canopy density. Calibration by direct measurement (defoliation) is recommended in cases where absolute LAI is desired. Calibration equations provided herein may be inverted to retrieve actual vineyard LAI from PCA readings.

Sources of Uncertainty in the Prediction of LAI / fPAR from MODIS

NASA Technical Reports Server (NTRS)

Dungan, Jennifer L.; Ganapol, Barry D.; Brass, James A. (Technical Monitor)

2002-01-01

To explicate the sources of uncertainty in the prediction of biophysical variables over space, consider the general equation: where z is a variable with values on some nominal, ordinal, interval or ratio scale; y is a vector of input variables; u is the spatial support of y and z ; x and u are the spatial locations of y and z , respectively; f is a model and B is the vector of the parameters of this model. Any y or z has a value and a spatial extent which is called its support. Viewed in this way, categories of uncertainty are from variable (e.g. measurement), parameter, positional. support and model (e.g. structural) sources. The prediction of Leaf Area Index (LAI) and the fraction of absorbed photosynthetically active radiation (fPAR) are examples of z variables predicted using model(s) as a function of y variables and spatially constant parameters. The MOD15 algorithm is an example of f, called f(sub 1), with parameters including those defined by one of six biome types and solar and view angles. The Leaf Canopy Model (LCM)2, a nested model that combines leaf radiative transfer with a full canopy reflectance model through the phase function, is a simpler though similar radiative transfer approach to f(sub 1). In a previous study, MOD15 and LCM2 gave similar results for the broadleaf forest biome. Differences between these two models can be used to consider the structural uncertainty in prediction results. In an effort to quantify each of the five sources of uncertainty and rank their relative importance for the LAI/fPAR prediction problem, we used recent data for an EOS Core Validation Site in the broadleaf biome with coincident surface reflectance, vegetation index, fPAR and LAI products from the Moderate Resolution Imaging Spectrometer (MODIS). Uncertainty due to support on the input reflectance variable was characterized using Landsat ETM+ data. Input uncertainties were propagated through the LCM2 model and compared with published uncertainties from the MOD15 algorithm.

Multi Source Remote Sensing for Monitoring Light-Absorbing Impurities on Snow and Ice in the European Alps

NASA Astrophysics Data System (ADS)

Colombo, R.; Baccolo, G.; Garzonio, R.; Massabò, D.; Julitta, T.; Rossini, M.; Ferrero, L.; Delmonte, B.; Maggi, V.; Mattavelli, M.; Panigada, C.; Cogliati, S.; Cremonese, E.; Di Mauro, B.

2016-12-01

The European Alps are located close to one of the most industrialized areas of the planet and they are 3.000 km from the largest desert of the Earth. Light-absorbing impurities (LAI) emitted from these sources can reach the Alpine chain and deposit on snow covered areas and mountain glaciers. Although several studies show that LAI have important impacts on the optical properties of snow and ice, reducing the albedo and promoting the melt, this impact has been poorly characterized in the Alps. In this contribution, we present the results of a multisource remote sensing approach aimed to study the LAI impact on snow and ice properties in the Alpine area. This process has been observed by means of remote and proximal sensing methods, using satellite (Landsat 8, Hyperion and MODIS data), field spectroscopy (ASD measurements), Automatic Weather Stations, aerial surveys (Unmanned Aerial Vehicle), radiative transfer modeling (SNICAR and TARTES) and laboratory analysis (hyperspectral imaging system). Furthermore, particle size (Coulter Counter), geochemical (Instrumental Neutron Activation Analysis, INAA) and optical (Multi-Wavelength Absorbance Analyzer, MWAA) analyses have been applied to determine the nature and radiative properties of particulate material deposited on snow and ice or aggregated into cryoconite holes. Our results demonstrate that LAI can be monitored from remote sensing at different scale. LAI showed to have a strong impact on the Alpine cryosphere, paving the way for the assessment of their role in melting processes.

Probabilistic landslide susceptibility mapping in the Lai Chau province of Vietnam: focus on the relationship between tectonic fractures and landslides

NASA Astrophysics Data System (ADS)

Lee, Saro; Dan, Nguyen Tu

2005-09-01

This study evaluates the susceptibility of landslides in the Lai Chau province of Vietnam using Geographic Information System (GIS) and remote sensing data to focus on the relationship between tectonic fractures and landslides. Landslide locations were identified from aerial photographs and field surveys. Topographic, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS data and image-processing techniques. A scheme of the tectonic fracturing of crust in the Lai Chau region was established. Lai Chau was identified as a region with many crustal fractures, where the grade of tectonic fracture is closely related to landslide occurrence. The influencing factors of landslide occurrence were: distance from a tectonic fracture, slope, aspect, curvature, soil, and vegetative land cover. Landslide prone areas were analyzed and mapped using the landslide occurrence factors employing the probability-frequency ratio model. The results of the analysis were verified using landslide location data and showed 83.47% prediction accuracy. That emphasized a strong relationship between the susceptibility map and the existing landslide location data. The results of this study can form a basis stable development and land use planning for the region.

The Darkening of the Greenland Ice Sheet: Trends, Drivers and Projections (1981-2100)

NASA Technical Reports Server (NTRS)

Tedesco, Marco; Doherty, Sarah; Fettweis, Xavier; Alexander, Patrick; Jeyaratnam, Jeyavinoth; Stroeve, Julienne

2016-01-01

The surface energy balance and meltwater production of the Greenland ice sheet (GrIS) are modulated by snow and ice albedo through the amount of absorbed solar radiation. Here we show, using space-borne multispectral data collected during the 3 decades from 1981 to 2012, that summertime surface albedo over the GrIS decreased at a statistically significant (99 %) rate of 0.02 decade(sup -1) between 1996 and 2012. Over the same period, albedo modelled by the Modele Atmospherique Regionale (MAR) also shows a decrease, though at a lower rate (approximately -0.01 decade(sup -1)) than that obtained from space-borne data. We suggest that the discrepancy between modelled and measured albedo trends can be explained by the absence in the model of processes associated with the presence of light-absorbing impurities. The negative trend in observed albedo is confined to the regions of the GrIS that undergo melting in summer, with the dry snow zone showing no trend. The period 1981-1996 also showed no statistically significant trend over the whole GrIS. Analysis of MAR outputs indicates that the observed albedo decrease is attributable to the combined effects of increased near-surface air temperatures, which enhanced melt and promoted growth in snow grain size and the expansion of bare ice areas, and to trends in light-absorbing impurities (LAI) on the snow and ice surfaces. Neither aerosol models nor in situ and remote sensing observations indicate increasing trends in LAI in the atmosphere over Greenland. Similarly, an analysis of the number of fires and BC emissions from fires points to the absence of trends for such quantities. This suggests that the apparent increase of LAI in snow and ice might be related to the exposure of a "dark band" of dirty ice and to increased consolidation of LAI at the surface with melt, not to increased aerosol deposition. Albedo projections through to the end of the century under different warming scenarios consistently point to continued darkening, with albedo anomalies averaged over the whole ice sheet lower by 0.08 in 2100 than in 2000, driven solely by a warming climate. Future darkening is likely underestimated because of known underestimates in modelled melting (as seen in hindcasts) and because the model albedo scheme does not currently include the effects of LAI, which have a positive feedback on albedo decline through increased melting, grain growth, and darkening.

Applicability of linear regression equation for prediction of chlorophyll content in rice leaves

NASA Astrophysics Data System (ADS)

Li, Yunmei

2005-09-01

A modeling approach is used to assess the applicability of the derived equations which are capable to predict chlorophyll content of rice leaves at a given view direction. Two radiative transfer models, including PROSPECT model operated at leaf level and FCR model operated at canopy level, are used in the study. The study is consisted of three steps: (1) Simulation of bidirectional reflectance from canopy with different leaf chlorophyll contents, leaf-area-index (LAI) and under storey configurations; (2) Establishment of prediction relations of chlorophyll content by stepwise regression; and (3) Assessment of the applicability of these relations. The result shows that the accuracy of prediction is affected by different under storey configurations and, however, the accuracy tends to be greatly improved with increase of LAI.

LAI-2000 Accuracy, Precision, and Application to Visual Estimation of Leaf Area Index of Loblolly Pine

Treesearch

Jason A. Gatch; Timothy B. Harrington; James P. Castleberry

2002-01-01

Leaf area index (LAI) is an important parameter of forest stand productivity that has been used to diagnose stand vigor and potential fertilizer response of southern pines. The LAI-2000 was tested for its ability to provide accurate and precise estimates of LAI of loblolly pine (Pinus taeda L.). To test instrument accuracy, regression was used to...

Effects of Controlled-Release Fertilizer on Leaf Area Index and Fruit Yield in High-Density Soilless Tomato Culture Using Low Node-Order Pinching

PubMed Central

Kinoshita, Takafumi; Yano, Takayoshi; Sugiura, Makoto; Nagasaki, Yuji

2014-01-01

To further development of a simplified fertigation system using controlled-release fertilizers (CRF), we investigated the effects of differing levels of fertilizers and plant density on leaf area index (LAI), fruit yields, and nutrient use in soilless tomato cultures with low node-order pinching and high plant density during spring-summer (SS), summer-fall (SF), and fall-winter (FW) seasons. Plants were treated with 1 of 3 levels of CRF in a closed system, or with liquid fertilizer (LF) with constant electrical conductivity (EC) in a drip-draining system. Two plant densities were examined for each fertilizer treatment. In CRF treatments, LAI at pinching increased linearly with increasing nutrient supply for all cropping seasons. In SS, both light interception by plant canopy at pinching and total marketable fruit yield increased linearly with increasing LAI up to 6 m2·m−2; the maximization point was not reached for any of the treatments. In FW, both light interception and yield were maximized at an LAI of approximately 4. These results suggest that maximizing the LAI in SS and FW to the saturation point for light interception is important for increasing yield. In SF, however, the yield maximized at an LAI of approximately 3, although the light interception linearly increased with increasing LAI, up to 4.5. According to our results, the optimal LAI at pinching may be 6 in SS, 3 in SF, and 4 in FW. In comparing LAI values with similar fruit yield, we found that nutrient supply was 32−46% lower with the CRF method than with LF. In conclusion, CRF application in a closed system enables growers to achieve a desirable LAI to maximize fruit yield with a regulated amount of nutrient supply per unit area. Further, the CRF method greatly reduced nutrient use without decreasing fruit yield at similar LAIs, as compared to the LF method. PMID:25402478

Structural classification of marshes with Polarimetric SAR highlighting the temporal mapping of marshes exposed to oil

USGS Publications Warehouse

Ramsey, Elijah W.; Rangoonwala, Amina; Jones, Cathleen E.

2015-01-01

Empirical relationships between field-derived Leaf Area Index (LAI) and Leaf Angle Distribution (LAD) and polarimetric synthetic aperture radar (PolSAR) based biophysical indicators were created and applied to map S. alterniflora marsh canopy structure. PolSAR and field data were collected near concurrently in the summers of 2010, 2011, and 2012 in coastal marshes, and PolSAR data alone were acquired in 2009. Regression analyses showed that LAI correspondence with the PolSAR biophysical indicator variables equaled or exceeded those of vegetation water content (VWC) correspondences. In the final six regressor model, the ratio HV/VV explained 49% of the total 77% explained LAI variance, and the HH-VV coherence and phase information accounted for the remainder. HV/HH dominated the two regressor LAD relationship, and spatial heterogeneity and backscatter mechanism followed by coherence information dominated the final three regressor model that explained 74% of the LAD variance. Regression results applied to 2009 through 2012 PolSAR images showed substantial changes in marsh LAI and LAD. Although the direct cause was not substantiated, following a release of freshwater in response to the 2010 Deepwater Horizon oil spill, the fairly uniform interior marsh structure of 2009 was more vertical and dense shortly after the oil spill cessation. After 2010, marsh structure generally progressed back toward the 2009 uniformity; however, the trend was more disjointed in oil impact marshes.             

Combining remote sensing and watershed modeling for regional-scale carbon cycling studies in disturbance-prone systems

NASA Astrophysics Data System (ADS)

Hanan, E. J.; Tague, C.; Choate, J.; Liu, M.; Adam, J. C.

2016-12-01

Disturbance is a major force regulating C dynamics in terrestrial ecosystems. Evaluating future C balance in disturbance-prone systems requires understanding the underlying mechanisms that drive ecosystem processes over multiple scales of space and time. Simulation modeling is a powerful tool for bridging these scales, however, model projections are limited by large uncertainties in the initial state of vegetation C and N stores. Watershed models typically use one of two methods to initialize these stores. Spin up involves running a model until vegetation reaches steady state based on climate. This "potential" state however assumes the vegetation across the entire watershed has reached maturity and has a homogeneous age distribution. Yet to reliably represent C and N dynamics in disturbance-prone systems, models should be initialized to reflect their non-equilibrium conditions. Alternatively, remote sensing of a single vegetation parameter (typically leaf area index; LAI) can be combined with allometric relationships to allocate C and N to model stores and can reflect non-steady-state conditions. However, allometric relationships are species and region specific and do not account for environmental variation, thus resulting in C and N stores that may be unstable. To address this problem, we developed a new approach for initializing C and N pools using the watershed-scale ecohydrologic model RHESSys. The new approach merges the mechanistic stability of spinup with the spatial fidelity of remote sensing. Unlike traditional spin up, this approach supports non-homogeneous stand ages. We tested our approach in a pine-dominated watershed in central Idaho, which partially burned in July of 2000. We used LANDSAT and MODIS data to calculate LAI across the watershed following the 2000 fire. We then ran three sets of simulations using spin up, direct measurements, and the combined approach to initialize vegetation C and N stores, and compared our results to remotely sensed LAI following the simulation period. Model estimates of C, N, and water fluxes varied depending on which approach was used. The combined approach provided the best LAI estimates after 10 years of simulation. This method shows promise for improving projections of C, N, and water fluxes in disturbance-prone watersheds.

Quantifying the Accuracy of Digital Hemispherical Photography for Leaf Area Index Estimates on Broad-Leaved Tree Species.

PubMed

Gilardelli, Carlo; Orlando, Francesca; Movedi, Ermes; Confalonieri, Roberto

2018-03-29

Digital hemispherical photography (DHP) has been widely used to estimate leaf area index (LAI) in forestry. Despite the advancement in the processing of hemispherical images with dedicated tools, several steps are still manual and thus easily affected by user's experience and sensibility. The purpose of this study was to quantify the impact of user's subjectivity on DHP LAI estimates for broad-leaved woody canopies using the software Can-Eye. Following the ISO 5725 protocol, we quantified the repeatability and reproducibility of the method, thus defining its precision for a wide range of broad-leaved canopies markedly differing for their structure. To get a complete evaluation of the method accuracy, we also quantified its trueness using artificial canopy images with known canopy cover. Moreover, the effect of the segmentation method was analysed. The best results for precision (restrained limits of repeatability and reproducibility) were obtained for high LAI values (>5) with limits corresponding to a variation of 22% in the estimated LAI values. Poorer results were obtained for medium and low LAI values, with a variation of the estimated LAI values that exceeded the 40%. Regardless of the LAI range explored, satisfactory results were achieved for trees in row-structured plantations (limits almost equal to the 30% of the estimated LAI). Satisfactory results were achieved for trueness, regardless of the canopy structure. The paired t -test revealed that the effect of the segmentation method on LAI estimates was significant. Despite a non-negligible user effect, the accuracy metrics for DHP are consistent with those determined for other indirect methods for LAI estimates, confirming the overall reliability of DHP in broad-leaved woody canopies.

How do large-scale agricultural investments affect land use and the environment on the western slopes of Mount Kenya? Empirical evidence based on small-scale farmers' perceptions and remote sensing.

PubMed

Zaehringer, Julie G; Wambugu, Grace; Kiteme, Boniface; Eckert, Sandra

2018-05-01

Africa has been heavily targeted by large-scale agricultural investments (LAIs) throughout the last decade, with scarcely known impacts on local social-ecological systems. In Kenya, a large number of LAIs were made in the region northwest of Mount Kenya. These large-scale farms produce vegetables and flowers mainly for European markets. However, land use in the region remains dominated by small-scale crop and livestock farms with less than 1 ha of land each, who produce both for their own subsistence and for the local markets. We interviewed 100 small-scale farmers living near five different LAIs to elicit their perceptions of the impacts that these LAIs have on their land use and the overall environment. Furthermore, we analyzed remotely sensed land cover and land use data to assess land use change in the vicinity of the five LAIs. While land use change did not follow a clear trend, a number of small-scale farmers did adapt their crop management to environmental changes such as a reduced river water flows and increased pests, which they attributed to the presence of LAIs. Despite the high number of open conflicts between small-scale land users and LAIs around the issue of river water abstraction, the main environmental impact, felt by almost half of the interviewed land users, was air pollution with agrochemicals sprayed on the LAIs' land. Even though only a low percentage of local land users and their household members were directly involved with LAIs, a large majority of respondents favored the presence of LAIs nearby, as they are believed to contribute to the region's overall economic development. Copyright © 2018 Elsevier Ltd. All rights reserved.

Quantifying the Accuracy of Digital Hemispherical Photography for Leaf Area Index Estimates on Broad-Leaved Tree Species

PubMed Central

Gilardelli, Carlo; Orlando, Francesca; Movedi, Ermes; Confalonieri, Roberto

2018-01-01

Digital hemispherical photography (DHP) has been widely used to estimate leaf area index (LAI) in forestry. Despite the advancement in the processing of hemispherical images with dedicated tools, several steps are still manual and thus easily affected by user’s experience and sensibility. The purpose of this study was to quantify the impact of user’s subjectivity on DHP LAI estimates for broad-leaved woody canopies using the software Can-Eye. Following the ISO 5725 protocol, we quantified the repeatability and reproducibility of the method, thus defining its precision for a wide range of broad-leaved canopies markedly differing for their structure. To get a complete evaluation of the method accuracy, we also quantified its trueness using artificial canopy images with known canopy cover. Moreover, the effect of the segmentation method was analysed. The best results for precision (restrained limits of repeatability and reproducibility) were obtained for high LAI values (>5) with limits corresponding to a variation of 22% in the estimated LAI values. Poorer results were obtained for medium and low LAI values, with a variation of the estimated LAI values that exceeded the 40%. Regardless of the LAI range explored, satisfactory results were achieved for trees in row-structured plantations (limits almost equal to the 30% of the estimated LAI). Satisfactory results were achieved for trueness, regardless of the canopy structure. The paired t-test revealed that the effect of the segmentation method on LAI estimates was significant. Despite a non-negligible user effect, the accuracy metrics for DHP are consistent with those determined for other indirect methods for LAI estimates, confirming the overall reliability of DHP in broad-leaved woody canopies. PMID:29596376

A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning

NASA Astrophysics Data System (ADS)

Houborg, Rasmus; McCabe, Matthew F.

2018-01-01

With an increasing volume and dimensionality of Earth observation data, enhanced integration of machine-learning methodologies is needed to effectively analyze and utilize these information rich datasets. In machine-learning, a training dataset is required to establish explicit associations between a suite of explanatory 'predictor' variables and the target property. The specifics of this learning process can significantly influence model validity and portability, with a higher generalization level expected with an increasing number of observable conditions being reflected in the training dataset. Here we propose a hybrid training approach for leaf area index (LAI) estimation, which harnesses synergistic attributes of scattered in-situ measurements and systematically distributed physically based model inversion results to enhance the information content and spatial representativeness of the training data. To do this, a complimentary training dataset of independent LAI was derived from a regularized model inversion of RapidEye surface reflectances and subsequently used to guide the development of LAI regression models via Cubist and random forests (RF) decision tree methods. The application of the hybrid training approach to a broad set of Landsat 8 vegetation index (VI) predictor variables resulted in significantly improved LAI prediction accuracies and spatial consistencies, relative to results relying on in-situ measurements alone for model training. In comparing the prediction capacity and portability of the two machine-learning algorithms, a pair of relatively simple multi-variate regression models established by Cubist performed best, with an overall relative mean absolute deviation (rMAD) of ∼11%, determined based on a stringent scene-specific cross-validation approach. In comparison, the portability of RF regression models was less effective (i.e., an overall rMAD of ∼15%), which was attributed partly to model saturation at high LAI in association with inherent extrapolation and transferability limitations. Explanatory VIs formed from bands in the near-infrared (NIR) and shortwave infrared domains (e.g., NDWI) were associated with the highest predictive ability, whereas Cubist models relying entirely on VIs based on NIR and red band combinations (e.g., NDVI) were associated with comparatively high uncertainties (i.e., rMAD ∼ 21%). The most transferable and best performing models were based on combinations of several predictor variables, which included both NDWI- and NDVI-like variables. In this process, prior screening of input VIs based on an assessment of variable relevance served as an effective mechanism for optimizing prediction accuracies from both Cubist and RF. While this study demonstrated benefit in combining data mining operations with physically based constraints via a hybrid training approach, the concept of transferability and portability warrants further investigations in order to realize the full potential of emerging machine-learning techniques for regression purposes.

Improved meteorology from an updated WRF/CMAQ modeling ...

EPA Pesticide Factsheets

Realistic vegetation characteristics and phenology from the Moderate Resolution Imaging Spectroradiometer (MODIS) products improve the simulation for the meteorology and air quality modeling system WRF/CMAQ (Weather Research and Forecasting model and Community Multiscale Air Quality model) that employs the Pleim-Xiu land surface model (PX LSM). Recently, PX LSM WRF/CMAQ has been updated in vegetation, soil, and boundary layer processes resulting in improved 2 m temperature (T) and mixing ratio (Q), 10 m wind speed, and surface ozone simulations across the domain compared to the previous version for a period around August 2006. Yearlong meteorology simulations with the updated system demonstrate that MODIS input helps reduce bias of the 2 m Q estimation during the growing season from April to September. Improvements follow the green-up in the southeast from April and move toward the west and north through August. From October to March, MODIS input does not have much influence on the system because vegetation is not as active. The greatest effects of MODIS input include more accurate phenology, better representation of leaf area index (LAI) for various forest ecosystems and agricultural areas, and realistically sparse vegetation coverage in the western drylands. Despite the improved meteorology, MODIS input causes higher bias for the surface O3 simulation in April, August, and October in areas where MODIS LAI is much less than the base LAI. Thus, improvement

Comparison Between the Use of SAR and Optical Data for Wheat Yield Estimations Using Crop Model Assimilation

NASA Astrophysics Data System (ADS)

Silvestro, Paolo Cosmo; Yang, Hao; Jin, X. L.; Yang, Guijun; Casa, Raffaele; Pignatti, Stefano

2016-08-01

The ultimate aim of this work is to develop methods for the assimilation of the biophysical variables estimated by remote sensing in a suitable crop growth model. Two strategies were followed, one based on the use of Leaf Area Index (LAI) estimated by optical data, and the other based on the use of biomass estimated by SAR. The first one estimates LAI from the reflectance measured by the optical sensors on board of HJ1A, HJ1B and Landsat, using a method based on the training of artificial neural networks (ANN) with PROSAIL model simulations. The retrieved LAI is used to improve wheat yield estimation, using assimilation methods based on the Ensemble Kalman Filter, which assimilate the biophysical variables into growth crop model. The second strategy estimates biomass from SAR imagery. Polarimetric decomposition methods were used based on multi-temporal fully polarimetric Radarsat-2 data during the entire growing season. The estimated biomass was assimilating to FAO Aqua crop model for improving the winter wheat yield estimation, with the Particle Swarm Optimization (PSO) method. These procedures were used in a spatial application with data collected in the rural area of Yangling (Shaanxi Province) in 2014 and were validated for a number of wheat fields for which ground yield data had been recorded and according to statistical yield data for the area.

Identifying patients and clinical scenarios for use of long-acting injectable antipsychotics - expert consensus survey part 1.

PubMed

Sajatovic, Martha; Ross, Ruth; Legacy, Susan N; Correll, Christoph U; Kane, John M; DiBiasi, Faith; Fitzgerald, Heather; Byerly, Matthew

2018-01-01

To assess expert consensus on barriers and facilitators for long-acting injectable antipsychotic (LAI) use and provide clinical recommendations on issues where clinical evidence is lacking, including identifying appropriate clinical situations for LAI use. A 50-question survey comprising 916 response options was distributed to 42 research experts and high prescribers with extensive LAI experience. Respondents rated options on relative appropriateness/importance using a 9-point scale. Consensus was determined using chi-square test of score distributions. Mean (standard deviation) ratings were calculated. Responses to 29 questions (577 options) relating to appropriate patients and clinical scenarios for LAI use are reported. Recommendations aligned with research on risk factors for nonadherence and poor outcomes for patients with schizophrenia/schizoaffective or bipolar disorder. Findings suggested, contrary to general practice patterns, that LAI use may be appropriate earlier in the disease course and in younger patients. Results for bipolar disorder were similar to those for schizophrenia but with less consensus. Numerous facilitators of LAI prescribing were considered important, particularly that LAIs may reduce relapses and improve outcomes. Findings support wider use of LAIs in patients with schizophrenia/schizoaffective and bipolar disorders beyond the setting of poor adherence and earlier use in the disease course.

Effect of vertical canopy architecture on transpiration, thermoregulation and carbon assimilation

DOE PAGES

Banerjee, Tirtha; Linn, Rodman Ray

2018-04-11

Quantifying the impact of natural and anthropogenic disturbances such as deforestation, forest fires and vegetation thinning among others on net ecosystem—atmosphere exchanges of carbon dioxide, water vapor and heat—is an important aspect in the context of modeling global carbon, water and energy cycles. The absence of canopy architectural variation in horizontal and vertical directions is a major source of uncertainty in current climate models attempting to address these issues. This work demonstrates the importance of considering the vertical distribution of foliage density by coupling a leaf level plant biophysics model with analytical solutions of wind flow and light attenuation inmore » a horizontally homogeneous canopy. It is demonstrated that plant physiological response in terms of carbon assimilation, transpiration and canopy surface temperature can be widely different for two canopies with the same leaf area index (LAI) but different leaf area density distributions, under several conditions of wind speed, light availability, soil moisture availability and atmospheric evaporative demand.« less

Effect of vertical canopy architecture on transpiration, thermoregulation and carbon assimilation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Banerjee, Tirtha; Linn, Rodman Ray

Quantifying the impact of natural and anthropogenic disturbances such as deforestation, forest fires and vegetation thinning among others on net ecosystem—atmosphere exchanges of carbon dioxide, water vapor and heat—is an important aspect in the context of modeling global carbon, water and energy cycles. The absence of canopy architectural variation in horizontal and vertical directions is a major source of uncertainty in current climate models attempting to address these issues. This work demonstrates the importance of considering the vertical distribution of foliage density by coupling a leaf level plant biophysics model with analytical solutions of wind flow and light attenuation inmore » a horizontally homogeneous canopy. It is demonstrated that plant physiological response in terms of carbon assimilation, transpiration and canopy surface temperature can be widely different for two canopies with the same leaf area index (LAI) but different leaf area density distributions, under several conditions of wind speed, light availability, soil moisture availability and atmospheric evaporative demand.« less

Estimates of leaf area index from spectral reflectance of wheat under different cultural practices and solar angle

NASA Technical Reports Server (NTRS)

Asrar, G.; Kanemasu, E. T.; Yoshida, M.

1985-01-01

The influence of management practices and solar illumination angle on the leaf area index (LAI) was estimated from measurements of wheat canopy reflectance evaluated by two methods, a regression formula and an indirect technique. The date of planting and the time of irrigation in relation to the stage of plant growth were found to have significant effects on the development of leaves in spring wheat. A reduction in soil moisture adversely affected both the duration and magnitude of the maximum LAI for late planting dates. In general, water stress during vegetative stages resulted in a reduction in maximum LAI, while water stress during the reproductive period shortened the duration of green LAI in spring wheat. Canopy geometry and solar angle also affected the spectral properties of the canopies, and hence the estimated LAI. Increase in solar zenith angles resulted in a general increase in estimated LAI obtained from both methods.

Analysis of MODIS 250 m Time Series Product for LULC Classification and Retrieval of Crop Biophysical Parameter

NASA Astrophysics Data System (ADS)

Verma, A. K.; Garg, P. K.; Prasad, K. S. H.; Dadhwal, V. K.

2016-12-01

Agriculture is a backbone of Indian economy, providing livelihood to about 70% of the population. The primary objective of this research is to investigate the general applicability of time-series MODIS 250m Normalized difference vegetation index (NDVI) and Enhanced vegetation index (EVI) data for various Land use/Land cover (LULC) classification. The other objective is the retrieval of crop biophysical parameter using MODIS 250m resolution data. The Uttar Pradesh state of India is selected for this research work. A field study of 38 farms was conducted during entire crop season of the year 2015 to evaluate the applicability of MODIS 8-day, 250m resolution composite images for assessment of crop condition. The spectroradiometer is used for ground reflectance and the AccuPAR LP-80 Ceptometer is used to measure the agricultural crops Leaf Area Index (LAI). The AccuPAR measures Photosynthetically Active Radiation (PAR) and can invert these readings to give LAI for plant canopy. Ground-based canopy reflectance and LAI were used to calibrate a radiative transfer model to create look-up table (LUT) that was used to simulate LAI. The seasonal trend of MODIS-derived LAI was used to find crop parameter by adjusting the LAI simulated from climate-based crop yield model. Cloud free MODIS images of 250m resolution (16 day composite period) were downloaded using LP-DAAC website over a period of 12 months (Jan to Dec 2015). MODIS both the VI products were found to have sufficient spectral, spatial and temporal resolution to detect unique signatures for each class (water, fallow land, urban, dense vegetation, orchard, sugarcane and other crops). Ground truth data were collected using JUNO GPS. Multi-temporal VI signatures for vegetation classes were consistent with its general phenological characteristic and were spectrally separable at some point during the growing season. The MODIS NDVI and EVI multi-temporal images tracked similar seasonal responses for all croplands and were highly correlated across the growing season. The confusion matrix method is used for accuracy assessment and reference data which has been taken during the field visit. Total 520 pixels have been selected for various classes to determine the accuracy. The classification accuracy and kappa coefficient is found to be 79.76% and 0.78 respectively.

A Bayesian alternative for multi-objective ecohydrological model specification

NASA Astrophysics Data System (ADS)

Tang, Yating; Marshall, Lucy; Sharma, Ashish; Ajami, Hoori

2018-01-01

Recent studies have identified the importance of vegetation processes in terrestrial hydrologic systems. Process-based ecohydrological models combine hydrological, physical, biochemical and ecological processes of the catchments, and as such are generally more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov chain Monte Carlo (MCMC) techniques. The Bayesian approach offers an appealing alternative to traditional multi-objective hydrologic model calibrations by defining proper prior distributions that can be considered analogous to the ad-hoc weighting often prescribed in multi-objective calibration. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological modeling framework based on a traditional Pareto-based model calibration technique. In our study, a Pareto-based multi-objective optimization and a formal Bayesian framework are implemented in a conceptual ecohydrological model that combines a hydrological model (HYMOD) and a modified Bucket Grassland Model (BGM). Simulations focused on one objective (streamflow/LAI) and multiple objectives (streamflow and LAI) with different emphasis defined via the prior distribution of the model error parameters. Results show more reliable outputs for both predicted streamflow and LAI using Bayesian multi-objective calibration with specified prior distributions for error parameters based on results from the Pareto front in the ecohydrological modeling. The methodology implemented here provides insight into the usefulness of multiobjective Bayesian calibration for ecohydrologic systems and the importance of appropriate prior distributions in such approaches.

BOREAS RSS-7 LAI, Gap Fraction, and FPAR Data

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Nickeson, Jaime (Editor); Chen, Jing

2000-01-01

The BOREAS RSS-7 team collected various data sets to develop and validate an algorithm to allow the retrieval of the spatial distribution of Leaf Area Index (LAI) from remotely sensed images. Ground measurements of LAI and Fraction of Photosynthetically Active Radiation (FPAR) absorbed by the plant canopy were made using the LAI-2000 and TRAC optical instruments during focused periods from 09-Aug-1993 to 19-Sep-1994. The measurements were intensive at the NSA and SSA tower sites, but were made just once or twice at auxiliary sites. The final processed LAI and FPAR data set is contained in tabular ASCII files. The data files are available on a CD-ROM (see document number 20010000884).

Performance Evaluation of Machine Learning Methods for Leaf Area Index Retrieval from Time-Series MODIS Reflectance Data

PubMed Central

Wang, Tongtong; Xiao, Zhiqiang; Liu, Zhigang

2017-01-01

Leaf area index (LAI) is an important biophysical parameter and the retrieval of LAI from remote sensing data is the only feasible method for generating LAI products at regional and global scales. However, most LAI retrieval methods use satellite observations at a specific time to retrieve LAI. Because of the impacts of clouds and aerosols, the LAI products generated by these methods are spatially incomplete and temporally discontinuous, and thus they cannot meet the needs of practical applications. To generate high-quality LAI products, four machine learning algorithms, including back-propagation neutral network (BPNN), radial basis function networks (RBFNs), general regression neutral networks (GRNNs), and multi-output support vector regression (MSVR) are proposed to retrieve LAI from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data in this study and performance of these machine learning algorithms is evaluated. The results demonstrated that GRNNs, RBFNs, and MSVR exhibited low sensitivity to training sample size, whereas BPNN had high sensitivity. The four algorithms performed slightly better with red, near infrared (NIR), and short wave infrared (SWIR) bands than red and NIR bands, and the results were significantly better than those obtained using single band reflectance data (red or NIR). Regardless of band composition, GRNNs performed better than the other three methods. Among the four algorithms, BPNN required the least training time, whereas MSVR needed the most for any sample size. PMID:28045443

Performance Evaluation of Machine Learning Methods for Leaf Area Index Retrieval from Time-Series MODIS Reflectance Data.

PubMed

Wang, Tongtong; Xiao, Zhiqiang; Liu, Zhigang

2017-01-01

Leaf area index (LAI) is an important biophysical parameter and the retrieval of LAI from remote sensing data is the only feasible method for generating LAI products at regional and global scales. However, most LAI retrieval methods use satellite observations at a specific time to retrieve LAI. Because of the impacts of clouds and aerosols, the LAI products generated by these methods are spatially incomplete and temporally discontinuous, and thus they cannot meet the needs of practical applications. To generate high-quality LAI products, four machine learning algorithms, including back-propagation neutral network (BPNN), radial basis function networks (RBFNs), general regression neutral networks (GRNNs), and multi-output support vector regression (MSVR) are proposed to retrieve LAI from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data in this study and performance of these machine learning algorithms is evaluated. The results demonstrated that GRNNs, RBFNs, and MSVR exhibited low sensitivity to training sample size, whereas BPNN had high sensitivity. The four algorithms performed slightly better with red, near infrared (NIR), and short wave infrared (SWIR) bands than red and NIR bands, and the results were significantly better than those obtained using single band reflectance data (red or NIR). Regardless of band composition, GRNNs performed better than the other three methods. Among the four algorithms, BPNN required the least training time, whereas MSVR needed the most for any sample size.

Attitudes towards the administration of long-acting antipsychotics: a survey of physicians and nurses

PubMed Central

2013-01-01

Background Discontinuation of antipsychotic treatment for schizophrenia can interrupt improvement and exacerbate the illness. Reasons for discontinuing treatment are multifactorial and include adherence, efficacy and tolerability issues. Poor adherence may be addressed through non-pharmacological approaches as well as through pharmacological ones, ie ensured delivery of medication, such as that achieved with long-acting injectable (LAI) antipsychotics. However, attitudes of healthcare professionals (HCPs) towards LAI antipsychotics may influence their prescribing decisions and may influence medication choices offered to patients. We therefore conducted a survey to investigate factors driving LAI use as well as physician and nurse attitudes to LAI antipsychotics and to different injection sites. Methods An independent market research agency conducted the survey of HCPs across Europe. Participants were recruited by telephone and completed the survey online. Using conjoint analyses (a multivariate statistical technique analysing preferences on the basis of ranking a limited number of attributes which are presented repetitively), attitudes to oral versus LAI medication and gluteal versus deltoid injection routes were assessed. Results A total of 891 HCPs across Europe were surveyed. Of these, 40% would choose LAI antipsychotics for first episode patients whereas 90% would select LAI antipsychotics for chronic patients with two to five psychotic episodes. Dominant elements in antipsychotic choice were low sedation but no tardive dyskinesia, no or mild pain at injection and low risk of embarrassment or impact upon therapeutic alliance. Eighty-six per cent of respondents considered that having the choice of a deltoid as well as gluteal administration site was beneficial over not having that choice. Two thirds of respondents said they agreed that medication administration via the deltoid muscle may reduce social embarrassment associated with LAI antipsychotics and most respondents (61%) believed that administration of LAI antipsychotics into the deltoid muscle as opposed to the gluteal muscle may be more respectful to the patient. Conclusions In this survey of physicians and nurses, attitudes towards LAI antipsychotics compared with oral medication were generally positive. Respondents considered that the availability of a deltoid administration route would offer increased choice in LAI antipsychotic administration and may be perceived as more respectful and less socially embarrassing. PMID:23414331

Evaluation of Global LAI/FPAR Products from VIIRS and MODIS: Spatiotemporal Consistency and Uncertainty

NASA Astrophysics Data System (ADS)

Xu, B.; Park, T.; Yan, K.; Chen, C.; Jing, L.; Qinhuo, L.; Song, W.; Knyazikhin, Y.; Myneni, R.

2017-12-01

The operational EOS MODIS LAI/FPAR algorithm has been successfully transitioned to Suomi-NPP VIIRS by optimizing a small set of configurable parameters in Look-Up-Tables (LUTs). Our preliminary evaluation results show a reasonable agreement between VIIRS and MODIS LAI/FPAR retrievals. However, we still need more comprehensive investigations to assure the continuity of multi-sensor based global LAI/FPAR time series, as the preliminary evaluation was spatiotemporally limited. Here, we used a multi-year (2012-2016) global LAI/FPAR product generated from VIIRS Version 1 and MODIS Collection 6 to evaluate their spatiotemporal consistency at global and site scales. We also quantified the uncertainty of the product by defining and measuring theoretical and physical terms. For both consistency and uncertainty evaluation, we accounted varying biome types and temporal resolutions (i.e., 8-day, seasonal and annual steps). A newly developed approach (a.k.a., Grading and Upscaling Ground Measurements, GUGM) generating accurate validation datasets was implemented to help validating both products. Our results clearly indicate that the LAI/FPAR retrievals from VIIRS and MODIS are quite consistent at different spatio- (i.e., global and site) and temporal- (i.e., 8-day, seasonal and annual) scales. It is also worthy to note that the rate of retrievals from the radiative transfer based main algorithm is also comparable. However, we also saw a relatively larger LAI/FPAR discrepancy over highly dense tropical forests and a slightly less retrieval rate (main algorithm) from VIIRS over high latitude regions. For the uncertainty assessment, the theoretical uncertainty of VIIRS LAI (FPAR) is less than 0.2 (0.06) for non-forest and 0.9 (0.08) for forest, which is nearly identical to those of MODIS. The physical uncertainties of VIIRS and MODIS LAI (FPAR) products assessed by comparing to ground measurements are estimated by 0.60 (0.10) and 0.55 (0.11), respectively. All of the results presented here imbue confidence in assuring the consistency between VIIRS and MODIS LAI/FPAR retrievals, and the feasibility of generating long-term multi-sensor LAI/FPAR time series.

Comparative effectiveness of long-acting risperidone in New Zealand: a report of resource utilization and costs in a 12-month mirror-image analysis.

PubMed

Carswell, Christopher; Wheeler, Amanda; Vanderpyl, Jane; Robinson, Elizabeth

2010-01-01

Schizophrenia affects approximately 1% of the population and is associated with a considerable economic burden to society. The healthcare costs of the disorder are high and are compounded by substantial productivity losses. Failure to adhere to medication regimens, with subsequent relapse and hospitalization, is a key driver of these costs. A long-acting injectable formulation of the second generation antipsychotic risperidone (risperidone long-acting injection [risperidone LAI]) was licensed in New Zealand and received full government funding in October 2005. Second generation antipsychotics may have some efficacy advantages, be associated with fewer adverse effects and could improve adherence. However, the acquisition cost of risperidone LAI is higher than that of first generation antipsychotics and healthcare decision makers need information that allows them to determine whether risperidone LAI represents a cost-effective investment in terms of improved outcomes. To explore real-world outcomes and costs of patients treated with risperidone LAI within New Zealand. A mirror-image retrospective study was conducted comparing outcomes and costs 12 months post- versus 12 months pre-initiation of risperidone LAI in all adults receiving approval for risperidone LAI between 1 October 2005 and 31 October 2006 in five health services. Continuation rates, compulsory treatment status, psychiatric hospitalization (admission number, bed-stay and cost) and treatment data were collected from clinical files and patient information systems for the 12 months on either side of the first risperidone LAI prescription. Hospitalization costs were valued using estimates for cost per admission and cost per hospital day ($NZ, year 2009 values). 58.3% of patients remained on risperidone LAI 12 months after initiation. Compared with the pre-risperidone LAI treatment period the mean number of admissions for the total study population was significantly lower in the post-risperidone LAI treatment period (1.38 vs 0.61, p<0.001) but the mean length of bed-stay increased (37.2 vs 53.3 days, p<0.001), as did compulsory treatment use. Overall hospital bed-nights (hospitalization days) increased by 6877 in the post-index period, driven mostly by those who discontinued treatment. Patients who continued risperidone LAI had fewer admissions and days in hospital post-risperidone LAI than patients who discontinued risperidone LAI use in the first year. The reduction in total hospital admission rates between the two treatment periods was significantly greater in the continuation group and mean difference in bed-days between the two treatment periods was significantly less for continuers (5.4 vs 31.1 days, p<0.001). Applying a cost per admission, hospitalization costs reduced by approximately $NZ1.7 million in the post risperidone LAI-period. Applying a daily hospitalization cost resulted in an increase of approximately $NZ3.5 million in the post-risperidone LAI period. This study suggests that patients have reduced hospital admissions but longer bed-stay after starting risperidone LAI. Longer admissions were driven by those that discontinued treatment and continuation was associated with improved resource and cost outcomes compared with those who discontinued. These findings have potential implications for payers, providers and patients that require further investigation over a longer time frame.

Predicting moisture dynamics of fine understory fuels in a moist tropical rainforest system: results of a pilot study undertaken to identify proxy variables useful for rating fire danger.

PubMed

Ray, David; Nepstad, Dan; Brando, Paulo

2010-08-01

*The use of fire as a land management tool in the moist tropics often has the unintended consequence of degrading adjacent forest, particularly during severe droughts. Reliable models of fire danger are needed to help mitigate these impacts. *Here, we studied the moisture dynamics of fine understory fuels in the east-central Brazilian Amazon during the 2003 dry season. Drying stations established under varying amounts of canopy cover (leaf area index (LAI) = 0 - 5.3) were subjected to a range of water inputs (5-15 mm) and models were developed to forecast litter moisture content (LMC). Predictions were then compared with independent field data. *A multiple linear regression relating litter moisture content to forest structure (LAI), ambient vapor pressure deficit (VPD(M)) and an index of elapsed time since a precipitation event (d(-1)) was identified as the best-fit model (adjusted R(2) = 0.89). Relative to the independent observations, model predictions were relatively unbiased when the LMC was

How consistent are global long-term satellite LAI products in terms of interannual variability and trend?

NASA Astrophysics Data System (ADS)

Jiang, C.; Ryu, Y.; Fang, H.

2016-12-01

Proper usage of global satellite LAI products requires comprehensive evaluation. To address this issue, the Committee on Earth Observation Satellites (CEOS) Land Product Validation (LPV) subgroup proposed a four-stage validation hierarchy. During the past decade, great efforts have been made following this validation framework, mainly focused on absolute magnitude, seasonal trajectory, and spatial pattern of those global satellite LAI products. However, interannual variability and trends of global satellite LAI products have been investigated marginally. Targeting on this gap, we made an intercomparison between seven global satellite LAI datasets, including four short-term ones: MODIS C5, MODIS C6, GEOV1, MERIS, and three long-term products ones: LAI3g, GLASS, and GLOBMAP. We calculated global annual LAI time series for each dataset, among which we found substantial differences. During the overlapped period (2003 - 2011), MODIS C5, GLASS and GLOBMAP have positive correlation (r > 0.6) between each other, while MODIS C6, GEOV1, MERIS, and LAI3g are highly consistent (r > 0.7) in interannual variations. However, the previous three datasets show negative trends, all of which use MODIS C5 reflectance data, whereas the latter four show positive trends, using MODIS C6, SPOT/VGT, ENVISAT/MERIS, and NOAA/AVHRR, respectively. During the pre-MODIS era (1982 - 1999), the three AVHRR-based datasets (LAI3g, GLASS and GLOBMAP) agree well (r > 0.7), yet all of them show oscillation related with NOAA platform changes. In addition, both GLASS and GLOBMAP show clear cut-points around 2000 when they move from AVHRR to MODIS. Such inconsistency is also visible for GEOV1, which uses SPOT-4 and SPOT-5 before and after 2002. We further investigate the map-to-map deviations among these products. This study highlights that continuous sensor calibration and cross calibration are essential to obtain reliable global LAI time series.

Water regime of mechanical-biological pretreated waste materials under fast-growing trees.

PubMed

Rüth, Björn; Lennartz, Bernd; Kahle, Petra

2007-10-01

In this study mechanical-biological pre-treated waste material (MBP) was tested for suitability to serve as an alternative surface layer in combination with fast-growing and water-consumptive trees for final covers at landfill sites. The aim was to quantify evapotranspiration and seepage losses by numerical model simulations for two sites in Germany. In addition, the leaf area index (LAI) of six tree species over the growing season as the driving parameter for transpiration calculations was determined experimentally. The maximum LAI varied between 3.8 and 6.1 m2 m(-2) for poplar and willow clones, respectively. The evapotranspiration calculations revealed that the use of MBP waste material for re-cultivation enhanced evapotranspiration by 40 mm year(-1) (10%) over an 11 year calculation period compared to a standard mineral soil. Between 82% (for LAI(max) = 3.8) and 87% (for LAI(max) = 6.1) of the average annual precipitation (506 mm) could be retained from the surface layer assuming eastern German climate conditions, compared with a retention efficiency between 79 and 82% for a mineral soil. Although a MBP layer in conjunction with water-consumptive trees can reduce vertical water losses as compared to mineral substrates, the effect is not sufficient to meet legal regulations.

Enhancement of vegetation-rainfall feedbacks on the Australian summer monsoon by the Madden-Julian Oscillation

NASA Astrophysics Data System (ADS)

Notaro, Michael

2018-01-01

A regional climate modeling analysis of the Australian monsoon system reveals a substantial modulation of vegetation-rainfall feedbacks by the Madden Julian Oscillation (MJO), both of which operate at similar sub-seasonal time scales, as evidence that the intensity of land-atmosphere interactions is sensitive to the background atmospheric state. Based on ensemble experiments with imposed modification of northern Australian leaf area index (LAI), the atmospheric responses to LAI anomalies are composited for negative and positive modes of the propagating MJO. In the regional climate model (RCM), northern Australian vegetation feedbacks are characterized by evapotranspiration (ET)-driven rainfall responses, with the moisture feedback mechanism dominating over albedo and roughness feedback mechanisms. During November-April, both Tropical Rainfall Measuring Mission and RCM data reveal MJO's pronounced influence on rainfall patterns across northern Australia, tropical Indian Ocean, Timor Sea, Arafura Sea, and Gulf of Carpentaria, with the MJO dominating over vegetation feedbacks in terms of regulating monsoon rainfall variability. Convectively-active MJO phases support an enhancement of positive vegetation feedbacks on monsoon rainfall. While the MJO imposes minimal regulation of ET responses to LAI anomalies, the vegetation feedback-induced responses in precipitable water, cloud water, and rainfall are greatly enhanced during convectively-active MJO phases over northern Australia, which are characterized by intense low-level convergence and efficient precipitable water conversion. The sub-seasonal response of vegetation-rainfall feedback intensity to the MJO is complex, with significant enhancement of rainfall responses to LAI anomalies in February during convectively-active MJO phases compared to minimal modulation by the MJO during prior and subsequent calendar months.

Assessing the comparative effectiveness of long-acting injectable vs. oral antipsychotic medications in the prevention of relapse provides a case study in comparative effectiveness research in psychiatry.

PubMed

Kane, John M; Kishimoto, Taishiro; Correll, Christoph U

2013-08-01

As psychopathology and social functioning can worsen with repeated psychotic episodes in schizophrenia, relapse prevention is critical. Because high nonadherence rates limit the efficacy of pharmacotherapy, the use of long-acting injectable (LAI) antipsychotics is considered an important treatment option. To date, many studies comparing LAIs and oral antipsychotics have been conducted; however, the results are mixed, and careful interpretation of the data is required. Selective review of existing literature regarding LAIs. We especially focused the discussion on the impact of the design of studies with different approaches comparing LAIs and oral antipsychotics in preventing relapse. The results were diverse and were influenced by the design used, that is, randomized controlled trials (RCTs) showed LAIs and oral antipsychotics to have similar effects, whereas mirror-image and some large cohort studies showed LAIs to be superior to oral antipsychotics. Divergent results from studies using different methodologies create a dilemma for comparative effectiveness research, and LAI studies may serve as an example of a situation in which a conventional RCT is not the gold standard. Traditional RCTs generally increase adherence compared with clinical practice and, therefore, might not be well suited to detect differences between LAIs and oral medications, because any increase in adherence affects patients on oral medications more than those on LAIs and thus leads to an underestimation of any potential difference in effectiveness. A possible solution would be the implementation of a true effectiveness trial in which post-randomization involvement would be kept to a minimum to better reflect routine practice. Copyright © 2013 Elsevier Inc. All rights reserved.

Effects of oral versus long-acting antipsychotics on social functioning: A psychiatrists' survey in India.

PubMed

Gundugurti, Prasad Rao; Nagpal, Rajesh; Sheth, Ashit; Narang, Prashant; Gawande, Sonal; Singh, Vikram

2017-12-01

Schizophrenia is associated with functional challenges for patients; relapses in schizophrenia may lead to increased treatment costs and poor quality of life. This SUSTAIN-I study was conducted to establish psychiatrists' perspective on impact of long-acting injectables (LAIs) antipsychotics on the socio-economic and functional burden of schizophrenia. This cross-sectional, survey-based study was conducted in 5 cities in India. Psychiatrists (≥5years of experience) working in clinics, psychiatric, government hospitals and rehabilitation centers were included and administered a specially designed questionnaire to elicit information on their clinical practice and prescription patterns. Perceived treatment costs for LAI versus oral antipsychotic treatments (OATs) and relapse rates were assessed. Descriptive statistics were used to summarize results. Total 31 physicians completed this survey. In acute phase, OAT prescription was higher whereas chronic patients were treated with either OATs or LAIs. Treatment with LAIs was the preferred treatment in 9% of chronic cases. Reduced relapse rates were observed with LAI treatment: 12% patients on LAIs relapsed as compared with 60% patients on OATs. Monthly medication cost for oral medications was lower ($8-$17) than short-acting injectables ($22-$50). For chronic cases, atypical antipsychotics cost (oral: $11.7-25, LAI: $150-167) was higher than typical antipsychotics (oral: $4-5, LAI: $5-25). Of the total expenses incurred, cost for hospital admissions was the largest component (78%). Despite enhanced treatment adherence and potential to lower risk of rehospitalizations from relapse, LAIs are not the preferred treatment choice for patients with schizophrenia in India, owing to their perceived high costs. Copyright © 2017 Elsevier B.V. All rights reserved.

Vegetation controls on the biophysical surface properties at global scale

NASA Astrophysics Data System (ADS)

Forzieri, Giovanni; Cescatti, Alessandro

2016-04-01

Leaf area index (LAI) plays an important role in determining resistances to heat, moisture and momentum exchanges between the land surface and atmosphere. Exploring how variations in LAI may induce changes in the surface energy balance is a key to understanding vegetation-climate interactions and for predicting biophysical climate impacts associated to changes in land cover. To this end, we analyzed remote sensing-observed dynamics in LAI, surface energy fluxes and climate drivers at global scale. We investigated the link between interannual variability of LAI and the components of the surface energy budget under diverse climate gradients. Results show that a 25% increase in annual LAI may induce up to 2% increase in available surface energy, as consequence of higher short wave absorption due to reduced albedos, up to 20% increase and 10% decrease in latent and sensible heat, respectively, leading to a decrease of the Bowen ratio in densely vegetated canopies. Opposite patterns are found for a reduction in LAI of similar magnitude. Such changes are strongly modulated by concurrent year-to-year variations and climatological means of air temperature, precipitation and snow cover as well as by land cover-specific physiological processes. Boreal and semi-arid regions appear to be mostly exposed to large changes in biophysical surface processes induced by interannual fluctuations in LAI. The combination of the emergent patters translates into variations in the long-wave outgoing radiation that reflect the surface warming/cooling associated to LAI changes. These findings provide a deeper understanding of the vegetation control on biophysical surface properties and define a set of observational-based diagnostics of LAI-dependent land surface-atmosphere interactions.

A radiosity model for heterogeneous canopies in remote sensing

NASA Astrophysics Data System (ADS)

GarcíA-Haro, F. J.; Gilabert, M. A.; Meliá, J.

1999-05-01

A radiosity model has been developed to compute bidirectional reflectance from a heterogeneous canopy approximated by an arbitrary configuration of plants or clumps of vegetation, placed on the ground surface in a prescribed manner. Plants are treated as porous cylinders formed by aggregations of layers of leaves. This model explicitly computes solar radiation leaving each individual surface, taking into account multiple scattering processes between leaves and soil, and occlusion of neighboring plants. Canopy structural parameters adopted in this study have served to simplify the computation of the geometric factors of the radiosity equation, and thus this model has enabled us to simulate multispectral images of vegetation scenes. Simulated images have shown to be valuable approximations of satellite data, and then a sensitivity analysis to the dominant parameters of discontinuous canopies (plant density, leaf area index (LAI), leaf angle distribution (LAD), plant dimensions, soil optical properties, etc.) and scene (sun/ view angles and atmospheric conditions) has been undertaken. The radiosity model has let us gain a deep insight into the radiative regime inside the canopy, showing it to be governed by occlusion of incoming irradiance, multiple scattering of radiation between canopy elements and interception of upward radiance by leaves. Results have indicated that unlike leaf distribution, other structural parameters such as LAI, LAD, and plant dimensions have a strong influence on canopy reflectance. In addition, concepts have been developed that are useful to understand the reflectance behavior of the canopy, such as an effective LAI related to leaf inclination.

New Features of the Collection 4 MODIS LAI and FPAR Product

NASA Astrophysics Data System (ADS)

Bin, T.; Yang, W.; Dong, H.; Shabanov, N.; Knyazikhin, Y.; Myneni, R.

2003-12-01

An algorithm based on physics of radiative transfer in vegetation canopies for the retrieval of vegetation green leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) from MODIS surface reflectance data was developed, prototyped and is in operational production at NASA computing facilities since June 2000. This poster highlights recent changes in the operational MODIS LAI and FPAR algorithm introduced for collection 4 data reprocessing. The changes to the algorithm are targeted to improve agreement of retrieved LAI and FPAR with corresponding field measurements, improve consistency of Quality Control (QC) definitions and miscellaneous bug fixes as summarized below. * Improvement of LUTs for the main and back-up algorithms for biomes 1 and 3. Benefits: a) increase in quality of retrievals; b) non-physical peaks in the global LAI distribution have been removed; c) improved agreement with field measurements * Improved QA scheme. Benefits: a) consistency between MODLAND and SCF quality flags has been achieved; b)ambiguity in QA has been resolved * New 8-day compositing scheme. Benefits: a) compositing over best quality retrievals, instead of all retrievals; b) lowers LAI values, decreases saturation and number of pixels generated by the back-up * At-launch static IGBP land cover, input to the LAI/FPAR algorithm, was replaced with the MODIS land cover map. Benefits: a) crosswalking of 17 classes IGBP scheme to 6-biome LC has been eliminated; b) uncertainties in the MODIS LAI/FPAR product due to uncertainties in land cover map have been reduced

Analysis on Difference of Forest Phenology Extracted from EVI and LAI Based on PhenoCams

NASA Astrophysics Data System (ADS)

Wang, C.; Jing, L.; Qinhuo, L.

2017-12-01

Land surface phenology can make up for the deficiency of field observation with advantages of capturing the continuous expression of phenology on a large scale. However, there are some variability in phenological metrics derived from different satellite time-series data of vegetation parameters. This paper aims at assessing the difference of phenology information extracted from EVI and LAI time series. To achieve this, some web-camera sites were selected to analyze the characteristics between MODIS-EVI and MODIS-LAI time series from 2010 to 2014 for different forest types, including evergreen coniferous forest, evergreen broadleaf forest, deciduous coniferous forest and deciduous broadleaf forest. At the same time, satellite-based phenological metrics were extracted by the Logistics algorithm and compared with camera-based phenological metrics. Results show that the SOS and EOS that are extracted from LAI are close to bud burst and leaf defoliation respectively, while the SOS and EOS that are extracted from EVI is close to leaf unfolding and leaf coloring respectively. Thus the SOS that is extracted from LAI is earlier than that from EVI, while the EOS that is extracted from LAI is later than that from EVI at deciduous forest sites. Although the seasonal variation characteristics of evergreen forests are not apparent, significant discrepancies exist in LAI time series and EVI time series. In addition, Satellite- and camera-based phenological metrics agree well generally, but EVI has higher correlation with the camera-based canopy greenness (green chromatic coordinate, gcc) than LAI.

Sensitivity of Global Terrestrial Gross Primary Production to Hydrologic States Simulated by the Community Land Model Using Two Runoff Parameterizations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lei, Huimin; Huang, Maoyi; Leung, Lai-Yung R.

2014-09-01

The terrestrial water and carbon cycles interact strongly at various spatio-temporal scales. To elucidate how hydrologic processes may influence carbon cycle processes, differences in terrestrial carbon cycle simulations induced by structural differences in two runoff generation schemes were investigated using the Community Land Model 4 (CLM4). Simulations were performed with runoff generation using the default TOPMODEL-based and the Variable Infiltration Capacity (VIC) model approaches under the same experimental protocol. The comparisons showed that differences in the simulated gross primary production (GPP) are mainly attributed to differences in the simulated leaf area index (LAI) rather than soil moisture availability. More specifically,more » differences in runoff simulations can influence LAI through changes in soil moisture, soil temperature, and their seasonality that affect the onset of the growing season and the subsequent dynamic feedbacks between terrestrial water, energy, and carbon cycles. As a result of a relative difference of 36% in global mean total runoff between the two models and subsequent changes in soil moisture, soil temperature, and LAI, the simulated global mean GPP differs by 20.4%. However, the relative difference in the global mean net ecosystem exchange between the two models is small (2.1%) due to competing effects on total mean ecosystem respiration and other fluxes, although large regional differences can still be found. Our study highlights the significant interactions among the water, energy, and carbon cycles and the need for reducing uncertainty in the hydrologic parameterization of land surface models to better constrain carbon cycle modeling.« less

Influence of ecohydrologic feedbacks from simulated crop growth on integrated regional hydrologic simulations under climate scenarios

NASA Astrophysics Data System (ADS)

van Walsum, P. E. V.; Supit, I.

2012-06-01

Hydrologic climate change modelling is hampered by climate-dependent model parameterizations. To reduce this dependency, we extended the regional hydrologic modelling framework SIMGRO to host a two-way coupling between the soil moisture model MetaSWAP and the crop growth simulation model WOFOST, accounting for ecohydrologic feedbacks in terms of radiation fraction that reaches the soil, crop coefficient, interception fraction of rainfall, interception storage capacity, and root zone depth. Except for the last, these feedbacks are dependent on the leaf area index (LAI). The influence of regional groundwater on crop growth is included via a coupling to MODFLOW. Two versions of the MetaSWAP-WOFOST coupling were set up: one with exogenous vegetation parameters, the "static" model, and one with endogenous crop growth simulation, the "dynamic" model. Parameterization of the static and dynamic models ensured that for the current climate the simulated long-term averages of actual evapotranspiration are the same for both models. Simulations were made for two climate scenarios and two crops: grass and potato. In the dynamic model, higher temperatures in a warm year under the current climate resulted in accelerated crop development, and in the case of potato a shorter growing season, thus partly avoiding the late summer heat. The static model has a higher potential transpiration; depending on the available soil moisture, this translates to a higher actual transpiration. This difference between static and dynamic models is enlarged by climate change in combination with higher CO2 concentrations. Including the dynamic crop simulation gives for potato (and other annual arable land crops) systematically higher effects on the predicted recharge change due to climate change. Crop yields from soils with poor water retention capacities strongly depend on capillary rise if moisture supply from other sources is limited. Thus, including a crop simulation model in an integrated hydrologic simulation provides a valuable addition for hydrologic modelling as well as for crop modelling.

Reduced uncertainty of regional scale CLM predictions of net carbon fluxes and leaf area indices with estimated plant-specific parameters

NASA Astrophysics Data System (ADS)

Post, Hanna; Hendricks Franssen, Harrie-Jan; Han, Xujun; Baatz, Roland; Montzka, Carsten; Schmidt, Marius; Vereecken, Harry

2016-04-01

Reliable estimates of carbon fluxes and states at regional scales are required to reduce uncertainties in regional carbon balance estimates and to support decision making in environmental politics. In this work the Community Land Model version 4.5 (CLM4.5-BGC) was applied at a high spatial resolution (1 km2) for the Rur catchment in western Germany. In order to improve the model-data consistency of net ecosystem exchange (NEE) and leaf area index (LAI) for this study area, five plant functional type (PFT)-specific CLM4.5-BGC parameters were estimated with time series of half-hourly NEE data for one year in 2011/2012, using the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm, a Markov Chain Monte Carlo (MCMC) approach. The parameters were estimated separately for four different plant functional types (needleleaf evergreen temperate tree, broadleaf deciduous temperate tree, C3-grass and C3-crop) at four different sites. The four sites are located inside or close to the Rur catchment. We evaluated modeled NEE for one year in 2012/2013 with NEE measured at seven eddy covariance sites in the catchment, including the four parameter estimation sites. Modeled LAI was evaluated by means of LAI derived from remotely sensed RapidEye images of about 18 days in 2011/2012. Performance indices were based on a comparison between measurements and (i) a reference run with CLM default parameters, and (ii) a 60 instance CLM ensemble with parameters sampled from the DREAM posterior probability density functions (pdfs). The difference between the observed and simulated NEE sum reduced 23% if estimated parameters instead of default parameters were used as input. The mean absolute difference between modeled and measured LAI was reduced by 59% on average. Simulated LAI was not only improved in terms of the absolute value but in some cases also in terms of the timing (beginning of vegetation onset), which was directly related to a substantial improvement of the NEE estimates in spring. In order to obtain a more comprehensive estimate of the model uncertainty, a second CLM ensemble was set up, where initial conditions and atmospheric forcings were perturbed in addition to the parameter estimates. This resulted in very high standard deviations (STD) of the modeled annual NEE sums for C3-grass and C3-crop PFTs, ranging between 24.1 and 225.9 gC m-2 y-1, compared to STD = 0.1 - 3.4 gC m-2 y-1 (effect of parameter uncertainty only, without additional perturbation of initial states and atmospheric forcings). The higher spread of modeled NEE for the C3-crop and C3-grass indicated that the model uncertainty was notably higher for those PFTs compared to the forest-PFTs. Our findings highlight the potential of parameter and uncertainty estimation to support the understanding and further development of land surface models such as CLM.

Evaluating soil moisture and yield of winter wheat in the Great Plains using Landsat data

NASA Technical Reports Server (NTRS)

Heilman, J. L.; Kanemasu, E. T.; Bagley, J. O.; Rasmussen, V. P.

1977-01-01

Locating areas where soil moisture is limiting to crop growth is important for estimating winter-wheat yields on a regional basis. In the 1975-76 growing season, we evaluated soil-moisture conditions and winter-wheat yields for a five-state region of the Great Plains using Landsat estimates of leaf area index (LAI) and an evapotranspiration (ET) model described by Kanemasu et al (1977). Because LAI was used as an input, the ET model responded to changes in crop growth. Estimated soil-water depletions were high for the Nebraska Panhandle, southwestern Kansas, southeastern Colorado, and the Texas Panhandle. Estimated yields in five-state region ranged from 1.0 to 2.9 metric ton/ha.

Benchmarking sensitivity of biophysical processes to leaf area changes in land surface models

NASA Astrophysics Data System (ADS)

Forzieri, Giovanni; Duveiller, Gregory; Georgievski, Goran; Li, Wei; Robestson, Eddy; Kautz, Markus; Lawrence, Peter; Ciais, Philippe; Pongratz, Julia; Sitch, Stephen; Wiltshire, Andy; Arneth, Almut; Cescatti, Alessandro

2017-04-01

Land surface models (LSM) are widely applied as supporting tools for policy-relevant assessment of climate change and its impact on terrestrial ecosystems, yet knowledge of their performance skills in representing the sensitivity of biophysical processes to changes in vegetation density is still limited. This is particularly relevant in light of the substantial impacts on regional climate associated with the changes in leaf area index (LAI) following the observed global greening. Benchmarking LSMs on the sensitivity of the simulated processes to vegetation density is essential to reduce their uncertainty and improve the representation of these effects. Here we present a novel benchmark system to assess model capacity in reproducing land surface-atmosphere energy exchanges modulated by vegetation density. Through a collaborative effort of different modeling groups, a consistent set of land surface energy fluxes and LAI dynamics has been generated from multiple LSMs, including JSBACH, JULES, ORCHIDEE, CLM4.5 and LPJ-GUESS. Relationships of interannual variations of modeled surface fluxes to LAI changes have been analyzed at global scale across different climatological gradients and compared with satellite-based products. A set of scoring metrics has been used to assess the overall model performances and a detailed analysis in the climate space has been provided to diagnose possible model errors associated to background conditions. Results have enabled us to identify model-specific strengths and deficiencies. An overall best performing model does not emerge from the analyses. However, the comparison with other models that work better under certain metrics and conditions indicates that improvements are expected to be potentially achievable. A general amplification of the biophysical processes mediated by vegetation is found across the different land surface schemes. Grasslands are characterized by an underestimated year-to-year variability of LAI in cold climates, ultimately affecting the amount of absorbed radiation. In addition patterns of simulated turbulent fluxes appear opposite to observations. Such systematic errors shed light on the current partial understanding of some of the mechanisms controlling the surface energy balance. In contrast forests appear reasonably well represented with respect to the interactions between LAI and turbulent fluxes across most climatological gradients, while for net radiation this is only true for warm climates. These proven strengths increase the confidence on how certain processes are simulated in LSMs. The model capacity to mimic the vegetation-biophysics interplay has been tested over the real scenario of greening that occurred in the last 30 years. We found that the modeled trends in surface heat fluxes associated with the long-term changes in leaf area could vary largely from those observed, with different discrepancies across models and climate zones. Our findings help to identify knowledge gaps and improve model representation of the sensitivity of biophysical processes to changes in leaf area density. In particular, comparing models and observations over a wide range of climate and vegetation conditions, as analyzed here, allowed capturing non-linearity of system responses that may emerge more frequently in future climate scenarios.

Collection of LAI and FPAR Data Over The Terra Core Sites

NASA Technical Reports Server (NTRS)

Myneni, Ranga B.; Knjazihhin, J.; Tian, Y.; Wang, Y.

2001-01-01

The objective of our effort was to collect and archive data on LAI (leaf area index) and FPAR (Fraction of Photosynthetically active Radiation absorbed by vegetation) at the EOS Core validation sites as well as to validate and evaluate global fields of LAI and FPAR derived from atmospherically corrected MODIS (Moderate Resolution Imaging Spectrometer) surface reflectance data by comparing these fields with the EOS Core validation data set. The above has been accomplished by: (a) the participation in selected field campaigns within the EOS Validation Program; (b) the processing of the collected data so that suitable comparison between field measurements and the MODIS LAI/FPAR fields can be made; (c) the comparison of the MODAS LAI/FRAM fields with the EOS Terra Core validation data set.

A three-part geometric model to predict the radar backscatter from wheat, corn, and sorghum

NASA Technical Reports Server (NTRS)

Ulaby, F. T. (Principal Investigator); Eger, G. W., III; Kanemasu, E. T.

1982-01-01

A model to predict the radar backscattering coefficient from crops must include the geometry of the canopy. Radar and ground-truth data taken on wheat in 1979 indicate that the model must include contributions from the leaves, from the wheat head, and from the soil moisture. For sorghum and corn, radar and ground-truth data obtained in 1979 and 1980 support the necessity of a soil moisture term and a leaf water term. The Leaf Area Index (LAI) is an appropriate input for the leaf contribution to the radar response for wheat and sorghum, however the LAI generates less accurate values for the backscattering coefficient for corn. Also, the data for corn and sorghum illustrate the importance of the water contained in the stalks in estimating the radar response.

Assessment of the Broadleaf Crops Leaf Area Index Product from the Terra MODIS Instrument

NASA Technical Reports Server (NTRS)

Tan, Bin; Hu, Jiannan; Huang, Dong; Yang, Wenze; Zhang, Ping; Shabanov, Nikolay V.; Knyazikhin, Yuri; Nemani, Ramakrishna R.; Myneni, Ranga B.

2005-01-01

The first significant processing of Terra MODIS data, called Collection 3, covered the period from November 2000 to December 2002. The Collection 3 leaf area index (LAI) and fraction vegetation absorbed photosynthetically active radiation (FPAR) products for broadleaf crops exhibited three anomalies (a) high LAI values during the peak growing season, (b) differences in LAI seasonality between the radiative transfer-based main algorithm and the vegetation index based back-up algorithm, and (c) too few retrievals from the main algorithm during the summer period when the crops are at full flush. The cause of these anomalies is a mismatch between reflectances modeled by the algorithm and MODIS measurements. Therefore, the Look-Up-Tables accompanying the algorithm were revised and implemented in Collection 4 processing. The main algorithm with the revised Look-Up-Tables generated retrievals for over 80% of the pixels with valid data. Retrievals from the back-up algorithm, although few, should be used with caution as they are generated from surface reflectances with high uncertainties.

The Impact of CO2-Driven Vegetation Changes on Wildfire Risk

NASA Astrophysics Data System (ADS)

Skinner, C. B.; Poulsen, C. J.

2017-12-01

While wildfires are a key component of natural ecological restoration and succession, they also pose tremendous risks to human life, health, and property. Wildfire frequency is expected to increase in many regions as the radiative effects of elevated CO2 drive warmer surface air temperatures, earlier spring snow melt, and more frequent meteorological drought. However, high CO2 concentrations will also directly impact vegetation growth and physiology, potentially altering wildfire characteristics through changes in fuel amount and surface hydrology. Depending on the biome and time of year, these vegetation-driven responses may mitigate or enhance radiative-driven wildfire changes. In this study, we use a suite of earth system models from the Coupled Model Intercomparison Project 5 with active biogeophysics and biogeochemistry to understand how the vegetation response to high CO2 (CO2 quadrupling) contributes to future changes in wildfire risk across the globe. Across the models, projected CO2 fertilization enhances aboveground biomass (about a 30% leaf area index (LAI) increase averaged across the globe) during the spring and summer months, increasing the availability of wildfire fuel across all biomes. Despite greater LAI, models robustly project widespread reductions in summer season transpiration (about -15% averaged across the globe) in response to reduced stomatal conductance from CO2 physiological forcing. Reduced transpiration warms summer season near surface temperatures and lowers relative humidity across vegetated regions of the mid-to-high latitudes, heightening the risk of wildfire occurrence. However, as transpiration goes down in response to greater plant water use efficiency, a larger fraction of soil water remains in the soil, potentially halting the spread of wildfires in some regions. Given the myriad ways in which the vegetation response to CO2 may alter wildfire risk, and the robustness of the responses across models, an explicit simulation of the wildfire response to CO2-driven vegetation change with the Community Earth System Model will be presented. The results suggest that many atmosphere-centric statistical wildfire metrics do not capture the many processes that will shape future wildfire risk in a high CO2 world and highlight the need for process-based fire modeling.

Evaluation of the DayCent model to predict carbon fluxes in French crop sites

NASA Astrophysics Data System (ADS)

Fujisaki, Kenji; Martin, Manuel P.; Zhang, Yao; Bernoux, Martial; Chapuis-Lardy, Lydie

2017-04-01

Croplands in temperate regions are an important component of the carbon balance and can act as a sink or a source of carbon, depending on pedoclimatic conditions and management practices. Therefore the evaluation of carbon fluxes in croplands by modelling approach is relevant in the context of global change. This study was part of the Comete-Global project funded by the multi-Partner call FACCE JPI. Carbon fluxes, net ecosystem exchange (NEE), leaf area index (LAI), biomass, and grain production were simulated at the site level in three French crop experiments from the CarboEurope project. Several crops were studied, like winter wheat, rapeseed, barley, maize, and sunflower. Daily NEE was measured with eddy covariance and could be partitioned between gross primary production (GPP) and total ecosystem respiration (TER). Measurements were compared to DayCent simulations, a process-based model predicting plant production and soil organic matter turnover at daily time step. We compared two versions of the model: the original one with a simplified plant module and a newer version that simulates LAI. Input data for modelling were soil properties, climate, and management practices. Simulations of grain yields and biomass production were acceptable when using optimized crop parameters. Simulation of NEE was also acceptable. GPP predictions were improved with the newer version of the model, eliminating temporal shifts that could be observed with the original model. TER was underestimated by the model. Predicted NEE was more sensitive to soil tillage and nitrogen applications than measured NEE. DayCent was therefore a relevant tool to predict carbon fluxes in French crops at the site level. The introduction of LAI in the model improved its performance.

A watershed model to integrate EO data

NASA Astrophysics Data System (ADS)

Jauch, Eduardo; Chambel-Leitao, Pedro; Carina, Almeida; Brito, David; Cherif, Ines; Alexandridis, Thomas; Neves, Ramiro

2013-04-01

MOHID LAND is a open source watershed model developed by MARETEC and is part of the MOHID Framework. It integrates four mediums (or compartments): porous media, surface, rivers and atmosphere. The movement of water between these mediums are based on mass and momentum balance equations. The atmosphere medium is not explicity simulated. Instead, it's used as boundary condition to the model through meteorological properties: precipitation, solar radiation, wind speed/direction, relative humidity and air temperature. The surface medium includes the overland runoff and vegetation growth processes and is simulated using a 2D grid. The porous media includes both the unsaturated (soil) and saturated zones (aquifer) and is simulated using a 3D grid. The river flow is simulated through a 1D drainage network. All these mediums are linked through evapotranspiration and flow exchanges (infiltration, river-soil growndwater flow, surface-river overland flow). Besides the water movement, it is also possible to simulate water quality processes and solute/sediment transport. Model setup include the definition of the geometry and the properties of each one of its compartments. After the setup of the model, the only continuous input data that MOHID LAND requires are the atmosphere properties (boundary conditions) that can be provided as timeseries or spacial data. MOHID LAND has been adapted the last 4 years under FP7 and ESA projects to integrate Earth Observation (EO) data, both variable in time and in space. EO data can be used to calibrate/validate or as input/assimilation data to the model. The currently EO data used include LULC (Land Use Land Cover) maps, LAI (Leaf Area Index) maps, EVTP (Evapotranspiration) maps and SWC (Soil Water Content) maps. Model results are improved by the EO data, but the advantage of this integration is that the model can still run without the EO data. This means that model do not stop due to unavailability of EO data and can run on a forecast mode. The LCLU maps are coupled with a database that transforms land use into model properties through lookup tables. The LAI maps, usually based on NDVI satellite images, can be used directly as input to the model. When the vegetation growth is being simulated, the use of a LAI distributed in space improve the model results, by improving, for example, the estimated evapotranspiration, the estimated values of biomass, the nutrient uptake, etc. MOHID LAND calculates a Reference Evapotranspiration (rEVTP), based on the meteorological properties. The Actual Evapotranspiration (aEVTP) is then computed based on vegetation transpiration, soil evaporation and the available water in soil. Alternatively, EO derived maps of EVTP can be used as input to the model, in the place of the rEVTP, or even in the place of the aEVTP, both being provided as boundary condition. The same can be done with SWC maps, that can be used to initialize the model soil water content. The integration of EO data with MOHID LAND was tested and is being continuously developed and applied for support farmers and to help water managers to improve the water management.

Improving energy partitioning and the nighttime energy balance by implementation of a multi-layer energy budget in ORCHIDEE-CAN

NASA Astrophysics Data System (ADS)

Chen, Yiying; Ryder, James; Naudts, Kim; McGrath, Matthew J.; Otto, Juliane; Bastriko, Vladislav; Valade, Aude; Launiainen, Samuli; Ogée, Jérôme; Elbers, Jan A.; Foken, Thomas; Tiedemann, Frank; Heinesch, Bernard; Black, Andrew; Haverd, Vanessa; Loustau, Denis; Ottlé, Catherine; Peylin, Philippe; Polcher, Jan; Luyssaert, Sebastiaan

2015-04-01

Canopy structure is one of the most important vegetation characteristics for land-atmosphere interactions as it determines the energy and scalar exchanges between land surface and overlay air mass. In this study we evaluated the performance of a newly developed multi-layer energy budget (Ryder et al., 2014) in a land surface model, ORCHIDEE-CAN (Naudts et al., 2014), which simulates canopy structure and can be coupled to an atmospheric model using an implicit procedure. Furthermore, a vertical discrete drag parametrization scheme was also incorporated into this model, in order to obtain a better description of the sub-canopy wind profile simulation. Site level datasets, including the top-of-the-canopy and sub-canopy observations made available from eight flux observation sites, were collected in order to conduct this evaluation. The geo-location of the collected observation sites crossed climate zones from temperate to boreal and the vegetation types included deciduous, evergreen broad leaved and evergreen needle leaved forest with maximum LAI ranging from 2.1 to 7.0. First, we used long-term top-of-the-canopy measurements to analyze the performance of the current one-layer energy budget in ORCHIDEE-CAN. Three major processes were identified for improvement through the implementation of a multi-layer energy budget: 1) night time radiation balance, 2) energy partitioning during winter and 3) prediction of the ground heat flux. Short-term sub-canopy observations were used to calibrate the parameters in sub-canopy radiation, turbulence and resistances modules with an automatic tuning process following the maximum gradient of the user-defined objective function. The multi-layer model is able to capture the dynamic of sub-canopy turbulence, temperature and energy fluxes with imposed LAI profile and optimized parameter set at a site level calibration. The simulation result shows the improvement both on the nighttime energy balance and energy partitioning during winter and presents a better Taylor skill score, compared to the result from single layer simulation. The importance of using the multi-layer energy budget in a land surface model for coupling to the atmospheric model will also be discussed in this presentation. Reference: Ryder, J., J. Polcher, P. Peylin, C. Ottlé, Y. Chen, E. Van Gorsel, V. Haverd, M. J. McGrath, K.Naudts, J. Otto, A. Valade, and S. Luyssaert, 2014. "A multi-layer land surface energy budget model for implicit coupling with global atmospheric simulations", Geosci. Model Dev. Discuss. 7, 8649-8701 Naudts, K. J. Ryder, M. J. McGrath, J. Otto, Y. Chen, A. Valade, V. Bellasen, G. Berhongaray, G. Bönisch, M. Campioli, J. Ghattas, T. De Groote, V. Haverd, J. Kattge, N. MacBean, F. Maignan, P. Merilä, J. Penuelas, P. Peylin, B. Pinty, H. Pretzsch, E. D. Schulze, D. Solyga, N. Vuichard, Y. Yan, and S. Luyssaert, 2014. "A vertically discretised canopy description for ORCHIDEE (SVN r2290) and the modifications to the energy, water and carbon fluxes", Geosci. Model Dev. Discuss. 7, 8565-8647

The Elephant in the Room: Spatial Heterogeneity and the Uncertainty of Measurements and Models

NASA Astrophysics Data System (ADS)

Alfieri, J. G.; Kustas, W. P.; Prueger, J. H.; Agam, N.; Neale, C. M. U.; Evett, S. R.

2014-12-01

Variations in surface conditions can significantly influence the exchange of heat and moisture between the land and atmosphere. As a result, measurements of surface fluxes using disparate methods not only may differ, they may fail to represent the surrounding landscape due to localized differences in surface conditions. To illustrate this, data collected over adjacent cotton fields during the Bushland Evapotranspiration and Agricultural Remote Sensing Experiment (BEAREX08) will be used. The evapotranspiration (ET) within each field was determined via lysimetry (LY), mass balance using neutron probe (NP) data, and a pair of eddy covariance (EC) systems. A comparison of the cumulative ET from each field showed that ET from LY was 20% to 25% greater than that derived from NP and 10% to 15% greater than those from EC. Additionally, the cumulative flux for the two fields collected using the same approach differed by 5% to 10%. These discrepancies can be explained, in large part, by the variations in vegetation density within the two fields. Not only were there substantial variations in the leaf area index (LAI) within the source areas of the different measurement systems - for example, the LAI within LY was, on average, 0.4 m2 m-2 greater than the LAI within the source area of NP - there were also significant differences in the LAI between the fields as a whole. The cumulative ET output by the remote sensing-based Two-Source Energy Balance (TSEB) model was also compared to the cumulative ET from each of the three measurement approaches. Depending on which measurement technique is used, the model either underestimated the moisture flux by approximately 5%, in the case of LY, or overestimated the flux by nearly 20%, in the case of NP. Comparison of the model output with EC data also indicated that the model overestimated ET, in this case, by approximately 10%. Clearly, the choice of which dataset is used to validate the model significantly effects the conclusions drawn regarding the model's accuracy and utility in estimating ET. The results of this study also underscores the limitations of each of these measurement techniques and the need to understand those limitations when using observational datasets to make general conclusions about field scale ET and validating model output.

ACCOUNTING FOR ERROR PROPAGATION IN THE DEVELOPMENT OF A LEAF AREA INDEX (LAI) REFERENCE MAP TO ASSESS THE MODIS LAI MODI5A LAI PRODUCT

EPA Science Inventory

The ability to effectively use remotely sensed data for environmental spatial analysis is dependent on understanding the underlying procedures and associated variances attributed to the data processing and image analysis technique. Equally important, also, is understanding the er...

Comparing LAI estimates of corn and soybean from vegetation indices of multi-resolution satellite images

USDA-ARS?s Scientific Manuscript database

Leaf area index (LAI) is important in explaining the ability of the crop to intercept solar energy for biomass production and in understanding the impact of crop management practices. This paper describes a procedure for estimating LAI as a function of image-derived vegetation indices from temporal ...

EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) Comparisons Across mMltiple Spatial Scales RSAD Oral Poster based session

EPA Science Inventory

Leaf Area Index (LAI) is an important parameter in assessing vegetation structure for characterizing forest canopies over large areas at broad spatial scales using satellite remote sensing data. However, satellite-derived LAI products can be limited by obstructed atmospheric cond...

Greening of the Earth and its drivers

DOE PAGES

Zhu, Zaichun; Piao, Shilong; Myneni, Ranga B.; ...

2016-04-25

Global environmental change is rapidly altering the dynamics of terrestrial vegetation, with consequences for the functioning of the Earth system and provision of ecosystem services 1, 2. Yet how global vegetation is responding to the changing environment is not well established. Here we use three long-term satellite leaf area index (LAI) records and ten global ecosystem models to investigate four key drivers of LAI trends during 1982 2009. We show a persistent and widespread increase of growing season integrated LAI (greening) over 25% to 50% of the global vegetated area, whereas less than 4% of the globe shows decreasing LAImore » (browning). Factorial simulations with multiple global ecosystem models suggest that CO 2 fertilization effects explain 70% of the observed greening trend, followed by nitrogen deposition (9%), climate change (8%) and land cover change (LCC) (4%). CO 2 fertilization effects explain most of the greening trends in the tropics, whereas climate change resulted in greening of the high latitudes and the Tibetan Plateau. LCC contributed most to the regional greening observed in southeast China and the eastern United States. In conclusion, the regional effects of unexplained factors suggest that the next generation of ecosystem models will need to explore the impacts of forest demography, differences in regional management intensities for cropland and pastures, and other emerging productivity constraints such as phosphorus availability.« less

Greening of the Earth and its drivers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, Zaichun; Piao, Shilong; Myneni, Ranga B.

Global environmental change is rapidly altering the dynamics of terrestrial vegetation, with consequences for the functioning of the Earth system and provision of ecosystem services 1, 2. Yet how global vegetation is responding to the changing environment is not well established. Here we use three long-term satellite leaf area index (LAI) records and ten global ecosystem models to investigate four key drivers of LAI trends during 1982 2009. We show a persistent and widespread increase of growing season integrated LAI (greening) over 25% to 50% of the global vegetated area, whereas less than 4% of the globe shows decreasing LAImore » (browning). Factorial simulations with multiple global ecosystem models suggest that CO 2 fertilization effects explain 70% of the observed greening trend, followed by nitrogen deposition (9%), climate change (8%) and land cover change (LCC) (4%). CO 2 fertilization effects explain most of the greening trends in the tropics, whereas climate change resulted in greening of the high latitudes and the Tibetan Plateau. LCC contributed most to the regional greening observed in southeast China and the eastern United States. In conclusion, the regional effects of unexplained factors suggest that the next generation of ecosystem models will need to explore the impacts of forest demography, differences in regional management intensities for cropland and pastures, and other emerging productivity constraints such as phosphorus availability.« less

Estimation of vegetation parameters such as Leaf Area Index from polarimetric SAR data

NASA Astrophysics Data System (ADS)

Hetz, Marina; Blumberg, Dan G.; Rotman, Stanley R.

2010-05-01

This work presents the analysis of the capability to use the radar backscatter coefficient in semi-arid zones to estimate the vegetation crown in terms of Leaf Area Index (LAI). The research area is characterized by the presence of a pine forest with shrubs as an underlying vegetation layer (understory), olive trees, natural grove areas and eucalyptus trees. The research area was imaged by an airborne RADAR system in L-band during February 2009. The imagery includes multi-look radar images. All the images were fully polarized i.e., HH, VV, HV polarizations. For this research we used the central azimuth angle (113° ). We measured LAI using the ?T Sun Scan Canopy Analysis System. Verification was done by analytic calculations and digital methods for the leaf's and needle's surface area. In addition, we estimated the radar extinction coefficient of the vegetation volume by comparing point calibration targets (trihedral corner reflectors with 150cm side length) within and without the canopy. The radar extinction in co- polarized images was ~26dB and ~24dB for pines and olives respectively, compared to the same calibration target outside the vegetation. We used smaller trihedral corner reflectors (41cm side length) and covered them with vegetation to measure the correlation between vegetation density, LAI and radar backscatter coefficient for pines and olives under known conditions. An inverse correlation between the radar backscatter coefficient of the trihedral corner reflectors covered by olive branches and the LAI of those branches was observed. The correlation between LAI and the optical transmittance was derived using the Beer-Lambert law. In addition, comparing this law's principle to the principle of the radar backscatter coefficient production, we derived the equation that connects between the radar backscatter coefficient and LAI. After extracting the radar backscatter coefficient of forested areas, all the vegetation parameters were used as inputs for the MIMICS model that simulates the radar backscatter coefficient of pines. The model results show a backscatter of -18dB in HV polarization which is 13dB higher than the mean pines backscatter in the radar images, whereas the co-polarized images revealed a backscatter of -10dB which is 23dB higher than the actual backscatter value deriver from the radar images. Therefore, next step in the research will incorporate other vegetation parameters and attempt to understand the discrepancies between the simulation and the actual data.

Long-Acting Injectable Antipsychotics for Schizophrenia: Sociodemographic Characteristics and Treatment Adherence.

PubMed

McCreath, James; Larson, Essie; Bharatiya, Purabi; Labanieh, Hisham A; Weiss, Zvi; Lozovatsky, Michael

2017-02-23

Long-acting injectable (LAI) antipsychotic medications are employed universally for the treatment of schizophrenia. This study retrospectively assessed the variables that factor into an individual's adherence to LAIs. The data sample was obtained from the adult ambulatory services of a large general hospital mental health center located in Elizabeth, New Jersey. Reports were run in November 2015 to identify patients who had received at least 1 LAI between January 1, 2014, and October 14, 2015. In September 2016, an additional report was run to collect follow-up data. The sample included 120 women and 178 men, ranging in age from 18-81 years, who received at least 1 LAI during a 23-month period. A hazard analysis for single-decrement, nonrepeatable events was used to assess the risk of discontinuation of LAIs during the study period. Separate χ² analyses were conducted to assess differences in discontinuation rates for sociodemographic variables, program type variables, type of long-acting medication, and time effects. The cumulative continuation rate across the study period was 73%. Main effect differences were found in continuation rates for program type (χ²₂undefined= 10.252, P = .006), LAI type (χ²₅ = 23.365, P < .000), and prescribed frequency of LAI (χ²₂ = 7.622, P = .022). In addition, multiple time-dependent effect differences were found. No significant main effect results were found for LAI continuation rates and patient age (χ²₃ = 3.689, P = .297), sex (χ²₁ = 0.904, P = .342), race (χ²₃ = 5.785, P = .123), or enrollment in involuntary outpatient commitment (χ²₁ = 2.989, P = .084). The findings of the current research suggest that medication type, frequency of medication appointments, and program type may be key in increasing and maintaining LAI adherence. © Copyright 2017 Physicians Postgraduate Press, Inc.

Estimation of leaf area index and foliage clumping in deciduous forests using digital photography

NASA Astrophysics Data System (ADS)

Chianucci, Francesco; Cutini, Andrea

2013-04-01

Rapid, reliable and meaningful estimates of leaf area index (LAI) are essential to the characterization of forest ecosystems. In this contribution the accuracy of both fisheye and non-fisheye digital photography for the estimation of forest leaf area in deciduous stands was evaluated. We compared digital hemispherical photography (DHP), the most widely used technique that measures the gap fraction at multiple zenith angles, with methods that measure the gap fraction at a single zenith angle, namely 57.5 degree photography and cover photography (DCP). Comparison with other different gap fraction methods used to calculate LAI such as canopy transmittance measurements from AccuPAR ceptometer and LAI- 2000 Plant Canopy Analyzer (PCA) were also performed. LAI estimated from all these indirect methods were compared with direct measurements obtained by litter traps (LAILT). We applied these methods in 10 deciduous stands of Quercus cerris, Castanea sativa and Fagus sylvatica, the most common deciduous species in Italy, where LAILT ranged from 3.9 to 7.3. DHP and DCP provided good indirect estimates of LAILT, and outperformed the other indirect methods. The DCP method provided estimates of crown porosity, crown cover, foliage cover and the clumping index at the zenith, but required assumptions about the light extinction coefficient at the zenith (k), to accurately estimate LAI. Cover photography provided good indirect estimates of LAI assuming a spherical leaf angle distribution, even though k appeared to decrease as LAI increased, thus affecting the accuracy of LAI estimates in DCP. In contrast, the accuracy of LAI estimates in DHP appeared insensitive to LAILT values, but the method was sensitive to photographic exposure, gamma-correction and was more time-consuming than DCP. Foliage clumping was estimated from all the photographic methods by analyzing either gap size distribution (DCP) or gap fraction distribution (DHP). Foliage clumping was also calculated from PCA and compared with DHP. The studied stands were characterized by fairly homogeneous canopies with higher within-crown clumping than between-crowns clumping; only the segmented analysis of gap fraction for each ring of the fisheye images was found to provide useful clumping index in such homogeneous canopies. By contrast, the 1-azimuth segment method employed in PCA poorly detected clumping in dense canopies. We conclude both fisheye and non-fisheye photographic methods are suitable for dense deciduous forests. Cover photography holds great promise as a means to quickly obtain inexpensive estimates of LAI over large areas. However, in situations where no direct reference measurements of k are available, we recommend using both DHP and DCP, in order to cross-calibrate the two methods; DCP could then be used for more routinely indirect measurement and monitoring of LAI. Keywords: digital hemispherical photography, cover photography, litter trap, AccuPAR ceptometer, LAI-2000.

Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth

NASA Astrophysics Data System (ADS)

Alessandri, Andrea; Catalano, Franco; De Felice, Matteo; Van Den Hurk, Bart; Doblas Reyes, Francisco; Boussetta, Souhail; Balsamo, Gianpaolo; Miller, Paul

2016-04-01

The EC-Earth earth system model has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning retrospective predictions at the decadal (5-years), seasonal and sub-seasonal time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and sub-seasonal time-scales. Significant improvements of the prediction of 2m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste Brazil and South East Asia, mainly related to improved performance in the surface evapotranspiration.

Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth

NASA Astrophysics Data System (ADS)

Alessandri, A.; Catalano, F.; De Felice, M.; van den Hurk, B.; Doblas-Reyes, F. J.; Boussetta, S.; Balsamo, G.; Miller, P. A.

2016-12-01

The European consortium earth system model (EC-Earth; http://www.ec-earth.org) has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (20th Century) simulations and retrospective predictions to the decadal (5-years), seasonal and weather time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste Brazil and South East Asia, mainly related to improved performance in the surface evapotranspiration.

Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth

NASA Astrophysics Data System (ADS)

Alessandri, Andrea; Catalano, Franco; De Felice, Matteo; Van Den Hurk, Bart; Doblas Reyes, Francisco; Boussetta, Souhail; Balsamo, Gianpaolo; Miller, Paul A.

2017-08-01

The EC-Earth earth system model has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (twentieth century) simulations and retrospective predictions to the decadal (5-years), seasonal and weather time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2 m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste Brazil and South East Asia, mainly related to improved performance in the surface evapotranspiration.

Multi-scale enhancement of climate prediction over land by increasing the model sensitivity to vegetation variability in EC-Earth

NASA Astrophysics Data System (ADS)

Alessandri, Andrea; Catalano, Franco; De Felice, Matteo; Van Den Hurk, Bart; Doblas Reyes, Francisco; Boussetta, Souhail; Balsamo, Gianpaolo; Miller, Paul A.

2017-04-01

The EC-Earth earth system model has been recently developed to include the dynamics of vegetation. In its original formulation, vegetation variability is simply operated by the Leaf Area Index (LAI), which affects climate basically by changing the vegetation physiological resistance to evapotranspiration. This coupling has been found to have only a weak effect on the surface climate modeled by EC-Earth. In reality, the effective sub-grid vegetation fractional coverage will vary seasonally and at interannual time-scales in response to leaf-canopy growth, phenology and senescence. Therefore it affects biophysical parameters such as the albedo, surface roughness and soil field capacity. To adequately represent this effect in EC-Earth, we included an exponential dependence of the vegetation cover on the LAI. By comparing two sets of simulations performed with and without the new variable fractional-coverage parameterization, spanning from centennial (20th Century) simulations and retrospective predictions to the decadal (5-years), seasonal and weather time-scales, we show for the first time a significant multi-scale enhancement of vegetation impacts in climate simulation and prediction over land. Particularly large effects at multiple time scales are shown over boreal winter middle-to-high latitudes over Canada, West US, Eastern Europe, Russia and eastern Siberia due to the implemented time-varying shadowing effect by tree-vegetation on snow surfaces. Over Northern Hemisphere boreal forest regions the improved representation of vegetation cover tends to correct the winter warm biases, improves the climate change sensitivity, the decadal potential predictability as well as the skill of forecasts at seasonal and weather time-scales. Significant improvements of the prediction of 2m temperature and rainfall are also shown over transitional land surface hot spots. Both the potential predictability at decadal time-scale and seasonal-forecasts skill are enhanced over Sahel, North American Great Plains, Nordeste Brazil and South East Asia, mainly related to improved performance in the surface evapotranspiration.

Aripiprazole once-monthly long-acting injectable for the treatment of schizophrenia.

PubMed

Potkin, Steven G; Preda, Adrian

2016-01-01

Patient non-adherence increases the risk for relapse and the long-term care of schizophrenia. Long-acting injectable (LAI) antipsychotics can decrease this risk by ensuring adherence. An extended formulation, aripiprazole 400 mg once-monthly (AOM 400) LAI (AOM LAI), received regulatory approval in the year 2013 for the treatment of schizophrenia. AOM LAI is the first dopamine D2 partial agonist available in a long-acting formulation for the treatment of schizophrenia. This review covers data on the efficacy and tolerability/safety of AOM LAI. AOM LAI is a lyophilized powder of aripiprazole, with an elimination half-life of 29.9 - 46.5 days, allowing for a 4-week injection interval. Antipsychotic efficacy was documented in a 12-week double-blind trial (n = 340) and in two maintenance-of-effect trials: a 38-week trial (n = 662) and a 52-week trial (n = 403). The side effect profile is similar to that of oral aripiprazole. Adverse events (≥5% and at least twice that for placebo) were typically mild or moderate and did not lead to discontinuation: increased weight, akathisia, injection site pain and sedation. The 400 mg dose is tolerated by >90% of patients. Injection does not require additional training of health personnel or post-injection observation. AOM LAI is an efficacious and well-tolerated antipsychotic treatment for schizophrenia.

Measurement of tree canopy architecture

NASA Technical Reports Server (NTRS)

Martens, S. N.; Ustin, S. L.; Norman, J. M.

1991-01-01

The lack of accurate extensive geometric data on tree canopies has retarded development and validation of radiative transfer models. A stratified sampling method was devised to measure the three-dimensional geometry of 16 walnut trees which had received irrigation treatments of either 100 or 33 per cent of evapotranspirational (ET) demand for the previous two years. Graphic reconstructions of the three-dimensional geometry were verified by 58 independent measurements. The distributions of stem- and leaf-size classes, lengths, and angle classes were determined and used to calculate leaf area index (LAI), stem area, and biomass. Reduced irrigation trees have lower biomass of stems, leaves and fruit, lower LAI, steeper leaf angles and altered biomass allocation to large stems. These data can be used in ecological models that link canopy processes with remotely sensed measurements.

Forecasting of cereals yields in a semi-arid area using the agrometeorological model «SAFY» combined to optical SPOT/HRV images

NASA Astrophysics Data System (ADS)

Chahbi, Aicha; Zribi, Mehrez; Lili-Chabaane, Zohra; Mougenot, Bernard

2015-10-01

In semi-arid areas, an operational grain yield forecasting system, which could help decision-makers to plan annual imports, is needed. It can be challenging to monitor the crop canopy and production capacity of plants, especially cereals. Many models, based on the use of remote sensing or agro-meteorological models, have been developed to estimate the biomass and grain yield of cereals. Remote sensing has demonstrated its strong potential for the monitoring of the vegetation's dynamics and temporal variations. Through the use of a rich database, acquired over a period of two years for more than 60 test fields, and from 20 optical satellite SPOT/HRV images, the aim of the present study is to evaluate the feasibility of two approaches to estimate the dynamics and yields of cereals in the context of semi-arid, low productivity regions in North Africa. The first approach is based on the application of the semi-empirical growth model SAFY "Simple Algorithm For Yield estimation", developed to simulate the dynamics of the leaf area index and the grain yield, at the field scale. The model is able to reproduce the time evolution of the LAI of all fields. However, the yields are under-estimated. Therefore, we developed a new approach to improve the SAFY model. The grain yield is function of LAI area in the growth period between 25 March and 5 April. This approach is robust, the measured and estimated grain yield are well correlated. Finally, this model is used in combination with remotely sensed LAI measurements to estimate yield for the entire studied site.

Optimizing Sampling Efficiency for Biomass Estimation Across NEON Domains

NASA Astrophysics Data System (ADS)

Abercrombie, H. H.; Meier, C. L.; Spencer, J. J.

2013-12-01

Over the course of 30 years, the National Ecological Observatory Network (NEON) will measure plant biomass and productivity across the U.S. to enable an understanding of terrestrial carbon cycle responses to ecosystem change drivers. Over the next several years, prior to operational sampling at a site, NEON will complete construction and characterization phases during which a limited amount of sampling will be done at each site to inform sampling designs, and guide standardization of data collection across all sites. Sampling biomass in 60+ sites distributed among 20 different eco-climatic domains poses major logistical and budgetary challenges. Traditional biomass sampling methods such as clip harvesting and direct measurements of Leaf Area Index (LAI) involve collecting and processing plant samples, and are time and labor intensive. Possible alternatives include using indirect sampling methods for estimating LAI such as digital hemispherical photography (DHP) or using a LI-COR 2200 Plant Canopy Analyzer. These LAI estimations can then be used as a proxy for biomass. The biomass estimates calculated can then inform the clip harvest sampling design during NEON operations, optimizing both sample size and number so that standardized uncertainty limits can be achieved with a minimum amount of sampling effort. In 2011, LAI and clip harvest data were collected from co-located sampling points at the Central Plains Experimental Range located in northern Colorado, a short grass steppe ecosystem that is the NEON Domain 10 core site. LAI was measured with a LI-COR 2200 Plant Canopy Analyzer. The layout of the sampling design included four, 300 meter transects, with clip harvests plots spaced every 50m, and LAI sub-transects spaced every 10m. LAI was measured at four points along 6m sub-transects running perpendicular to the 300m transect. Clip harvest plots were co-located 4m from corresponding LAI transects, and had dimensions of 0.1m by 2m. We conducted regression analyses with LAI and clip harvest data to determine whether LAI can be used as a suitable proxy for aboveground standing biomass. We also compared optimal sample sizes derived from LAI data, and clip-harvest data from two different size clip harvest areas (0.1m by 1m vs. 0.1m by 2m). Sample sizes were calculated in order to estimate the mean to within a standardized level of uncertainty that will be used to guide sampling effort across all vegetation types (i.e. estimated within × 10% with 95% confidence). Finally, we employed a Semivariogram approach to determine optimal sample size and spacing.

[French Society for Biological Psychiatry and Neuropsychopharmacology task force: Formal Consensus for the prescription of depot antipsychotics].

PubMed

Samalin, L; Abbar, M; Courtet, P; Guillaume, S; Lancrenon, S; Llorca, P-M

2013-12-01

Compliance is often partial with oral antipsychotics and underestimated for patients with serious mental illness. Despite their demonstrated advantages in terms of relapse prevention, depot formulations are still poorly used in routine. As part of a process to improve the quality of care, French Association for Biological Psychiatry and Neuropsychopharmacology (AFPBN) Task Force elaborated a Formal Consensus for the prescription of depot antipsychotics in clinical practice. The Task Force recommends as first-line choice, the use of long-acting injectable (LAI) second-generation antipsychotics in patients with schizophrenia, schizoaffective disorder and delusional disorder. They can be considered as a second-line option as a monotherapy to prevent manic recurrence or in combination with mood stabilizer to prevent depressive recurrence in the maintenance treatment of bipolar disorder. LAI second-generation antipsychotics can also be used after a first episode of schizophrenia. Depot neuroleptics are not recommended during the early course of schizophrenia and are not appropriate in bipolar disorder. They are considered as a second-line option for maintenance treatment in schizophrenia. LAI formulations should be systematically proposed to any patients for whom maintenance antipsychotic treatment is indicated. LAI antipsychotics can be used preferentially for non-compliant patients with frequent relapses or aggressive behaviors. A specific information concerning the advantages and inconveniences of the LAI formulations, in the framework of shared-decision making must be delivered to each patient. Recommendations for switching from one oral/LAI form to another LAI and for using LAI antipsychotics in specific populations (pregnant women, elderly patients, subjects in a precarious situation, and subjects having to be treated in a prison establishment) are also proposed. Copyright © 2013 L’Encéphale. Published by Elsevier Masson SAS.. All rights reserved.

Modelling climate change responses in tropical forests: similar productivity estimates across five models, but different mechanisms and responses

NASA Astrophysics Data System (ADS)

Rowland, L.; Harper, A.; Christoffersen, B. O.; Galbraith, D. R.; Imbuzeiro, H. M. A.; Powell, T. L.; Doughty, C.; Levine, N. M.; Malhi, Y.; Saleska, S. R.; Moorcroft, P. R.; Meir, P.; Williams, M.

2014-11-01

Accurately predicting the response of Amazonia to climate change is important for predicting changes across the globe. However, changes in multiple climatic factors simultaneously may result in complex non-linear responses, which are difficult to predict using vegetation models. Using leaf and canopy scale observations, this study evaluated the capability of five vegetation models (CLM3.5, ED2, JULES, SiB3, and SPA) to simulate the responses of canopy and leaf scale productivity to changes in temperature and drought in an Amazonian forest. The models did not agree as to whether gross primary productivity (GPP) was more sensitive to changes in temperature or precipitation. There was greater model-data consistency in the response of net ecosystem exchange to changes in temperature, than in the response to temperature of leaf area index (LAI), net photosynthesis (An) and stomatal conductance (gs). Modelled canopy scale fluxes are calculated by scaling leaf scale fluxes to LAI, and therefore in this study similarities in modelled ecosystem scale responses to drought and temperature were the result of inconsistent leaf scale and LAI responses among models. Across the models, the response of An to temperature was more closely linked to stomatal behaviour than biochemical processes. Consequently all the models predicted that GPP would be higher if tropical forests were 5 °C colder, closer to the model optima for gs. There was however no model consistency in the response of the An-gs relationship when temperature changes and drought were introduced simultaneously. The inconsistencies in the An-gs relationships amongst models were caused by to non-linear model responses induced by simultaneous drought and temperature change. To improve the reliability of simulations of the response of Amazonian rainforest to climate change the mechanistic underpinnings of vegetation models need more complete validation to improve accuracy and consistency in the scaling of processes from leaf to canopy.

Synergistically combining Optical and Thermal radiative transfer modelswithin the EO-LDAS data assimilation framework to estimate land surfaceand component temperatures from MODIS and Sentinel-3

NASA Astrophysics Data System (ADS)

Timmermans, J.; Gomez-Dans, J. L.; Verhoef, W.; Tol, C. V. D.; Lewis, P.

2017-12-01

Evapotranspiration (ET) cannot be directly measured from space. Instead it relies on modelling approaches that use several land surface parameters (LSP), LAI and LST, in conjunction with meteorological parameters. Such a modelling approach presents two caveats: the validity of the model, and the consistency between the different input parameters. Often this second step is not considered, ignoring that without good inputs no decent output can provided. When LSP- dynamics contradict each other, the output of the model cannot be representative of reality. At present however, the LSPs used in large scale ET estimations originate from different single-sensor retrieval-approaches and even from different satellite sensors. In response, the Earth Observation Land Data Assimilation System (EOLDAS) was developed. EOLDAS uses a multi-sensor approach to couple different satellite observations/types to radiative transfer models (RTM), consistently. It is therefore capable of synergistically estimating a variety of LSPs. Considering that ET is most sensitive to the temperatures of the land surface (components), the goal of this research is to expand EOLDAS to the thermal domain. This research not only focuses on estimating LST, but also on retrieving (soil/vegetation, Sunlit/shaded) component temperatures, to facilitate dual/quad-source ET models. To achieve this, The Soil Canopy Observations of Photosynthesis and Energy (SCOPE) model was integrated into EOLDAS. SCOPE couples key-parameters to key-processes, such as photosynthesis, ET and optical/thermal RT. In this research SCOPE was also coupled to MODTRAN RTM, in order to estimate BOA component temperatures directly from TOA observations. This paper presents the main modelling steps of integrating these complex models into an operational platform. In addition it highlights the actual retrieval using different satellite observations, such as MODIS and Sentinel-3, and meteorological variables from the ERA-Interim.

Quantifying parenchymal tethering in a finite element simulation of a human lung slice under bronchoconstriction.

PubMed

Breen, Barbara J; Donovan, Graham M; Sneyd, James; Tawhai, Merryn H

2012-08-15

Airway hyper-responsiveness (AHR), a hallmark of asthma, is a highly complex phenomenon characterised by multiple processes manifesting over a large range of length and time scales. Multiscale computational models have been derived to embody the experimental understanding of AHR. While current models differ in their derivation, a common assumption is that the increase in parenchymal tethering pressure P(teth) during airway constriction can be described using the model proposed by Lai-Fook (1979), which is based on intact lung experimental data for elastic moduli over a range of inflation pressures. Here we reexamine this relationship for consistency with a nonlinear elastic material law that has been parameterised to the pressure-volume behaviour of the intact lung. We show that the nonlinear law and Lai-Fook's relationship are consistent for small constrictions, but diverge when the constriction becomes large. Copyright © 2012 Elsevier B.V. All rights reserved.

Marsh canopy leaf area and orientation calculated for improved marsh structure mapping

USGS Publications Warehouse

Ramsey, Elijah W.; Rangoonwala, Amina; Jones, Cathleen E.; Bannister, Terri

2015-01-01

An approach is presented for producing the spatiotemporal estimation of leaf area index (LAI) of a highly heterogeneous coastal marsh without reliance on user estimates of marsh leaf-stem orientation. The canopy LAI profile derivation used three years of field measured photosynthetically active radiation (PAR) vertical profiles at seven S. alterniflora marsh sites and iterative transform of those PAR attenuation profiles to best-fit light extinction coefficients (KM). KM sun zenith dependency was removed obtaining the leaf angle distribution (LAD) representing the average marsh orientation and the LAD used to calculate the LAI canopy profile. LAI and LAD reproduced measured PAR profiles with 99% accuracy and corresponded to field documented structures. LAI and LAD better reflect marsh structure and results substantiate the need to account for marsh orientation. The structure indexes are directly amenable to remote sensing spatiotemporal mapping and offer a more meaningful representation of wetland systems promoting biophysical function understanding.

The role of long-acting injectable antipsychotics in schizophrenia: a critical appraisal.

PubMed

Brissos, Sofia; Veguilla, Miguel Ruiz; Taylor, David; Balanzá-Martinez, Vicent

2014-10-01

Despite their widespread use, long-acting injectable (LAI) antipsychotics (APs) are often regarded with some negativity because of the assumption of punishment, control and insufficient evolution towards psychosocial development of patients. However, LAI APs have proved effective in schizophrenia and other severe psychotic disorders because they assure stable blood levels, leading to a reduction of the risk of relapse. Therapeutic opportunities have also arisen after introduction of newer, second-generation LAI APs in recent years. Newer LAI APs are more readily dosed optimally, may be better tolerated and are better suited to integrated rehabilitation programmes. This review outlines the older and newer LAI APs available for the treatment of schizophrenia, with considerations of past and present pharmacological and therapeutic issues. Traditional, evidence-based approaches to systematic reviews and randomized clinical trials are of limited utility in this area so this paper's blending of experimental trials with observational research is particularly appropriate and effective.

Ten-year variability in ecosystem water use efficiency in an oak-dominated temperate forest under a warming climate

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xie, Jing; Chen, Jiquan; Sun, Ge

The impacts of extreme weather events on water-carbon (C) coupling and ecosystem-scale water use efficiency (WUE) over a long term are poorly understood. We analyzed the changes in ecosystem water use efficiency (WUE) from 10 years of eddy-covariance measurements (2004-2013) over an oak-dominated temperate forest in Ohio, USA. The aim was to investigate the long-term response of ecosystem WUE to measured changes in site-biophysical conditions and ecosystem attributes. The oak forest produced new plant biomass of 2.5 +/- 0.2 gC kg(-1) of water loss annually. Monthly evapotranspiration (ET) and gross ecosystem production (GEP) were tightly coupled over the 10-year studymore » period (R-2=0.94). Daily WUE had a linear relationship with air temperature (T-a) in low-temperature months and a unimodal relationship with T-a in high-temperature months during the growing season. On average, daily WUE ceased to increase when T-a exceeded 22 degrees C in warm months for both wet and dry years. Monthly WUE had a strong positive linear relationship with leaf area index (LAI), net radiation (R-n), and T-a and weak logarithmic relationship with water vapor pressure deficit (VPD) and precipitation (P) on a growing-season basis. When exploring the regulatory mechanisms on WUE within each season, spring LAI and P, summer R-n and T-a, and autumnal VPD and R-n were found to be the main explanatory variables for seasonal variation in WUE. The model developed in this study was able to capture 78% of growing-season variation in WUE on a monthly basis. The negative correlation between WUE and A in spring was mainly due to the high precipitation amounts in spring, decreasing GEP and WUE when LAI was still small, adding ET being observed to increase with high levels of evaporation as a result of high SWC in spring. Summer WUE had a significant decreasing trend across the 10 years mainly due to the combined effect of seasonal drought and increasing potential and available energy increasing ET, but decreasing GEP in summer. We concluded that seasonal dynamics of the interchange between precipitation and drought status of the system was an important variable in controlling seasonal WUE in wet years. In contrast, despite the negative impacts of unfavorable warming, available groundwater and an early start of the growing season were important contributing variables in high seasonal GEP, and thus, high seasonal WUE in dry years. (C) 2015 Elsevier B.V. All rights reserved.« less

Ten-year variability in ecosystem water use efficiency in an oak-dominated temperate forest under a warming climate

DOE PAGES

Xie, Jing; Chen, Jiquan; Sun, Ge; ...

2016-01-07

The impacts of extreme weather events on water-carbon (C) coupling and ecosystem-scale water use efficiency (WUE) over a long term are poorly understood. We analyzed the changes in ecosystem water use efficiency (WUE) from 10 years of eddy-covariance measurements (2004-2013) over an oak-dominated temperate forest in Ohio, USA. The aim was to investigate the long-term response of ecosystem WUE to measured changes in site-biophysical conditions and ecosystem attributes. The oak forest produced new plant biomass of 2.5 +/- 0.2 gC kg(-1) of water loss annually. Monthly evapotranspiration (ET) and gross ecosystem production (GEP) were tightly coupled over the 10-year studymore » period (R-2=0.94). Daily WUE had a linear relationship with air temperature (T-a) in low-temperature months and a unimodal relationship with T-a in high-temperature months during the growing season. On average, daily WUE ceased to increase when T-a exceeded 22 degrees C in warm months for both wet and dry years. Monthly WUE had a strong positive linear relationship with leaf area index (LAI), net radiation (R-n), and T-a and weak logarithmic relationship with water vapor pressure deficit (VPD) and precipitation (P) on a growing-season basis. When exploring the regulatory mechanisms on WUE within each season, spring LAI and P, summer R-n and T-a, and autumnal VPD and R-n were found to be the main explanatory variables for seasonal variation in WUE. The model developed in this study was able to capture 78% of growing-season variation in WUE on a monthly basis. The negative correlation between WUE and A in spring was mainly due to the high precipitation amounts in spring, decreasing GEP and WUE when LAI was still small, adding ET being observed to increase with high levels of evaporation as a result of high SWC in spring. Summer WUE had a significant decreasing trend across the 10 years mainly due to the combined effect of seasonal drought and increasing potential and available energy increasing ET, but decreasing GEP in summer. We concluded that seasonal dynamics of the interchange between precipitation and drought status of the system was an important variable in controlling seasonal WUE in wet years. In contrast, despite the negative impacts of unfavorable warming, available groundwater and an early start of the growing season were important contributing variables in high seasonal GEP, and thus, high seasonal WUE in dry years. (C) 2015 Elsevier B.V. All rights reserved.« less

BOREAS RSS-4 1994 Southern Study Area Jack Pine LAI and FPAR Data

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Nickeson, Jaime (Editor); Plummer, Stephen

2000-01-01

The RSS-4 team collected several data sets related to leaf, plant, and stand physical, optical, and chemical properties. This data set contains leaf area indices and FPAR measurements that were taken at the three conifer sites in the BOREAS SSA during August 1993 and at the jack pine tower flux and a subset of auxiliary sites during July and August 1994. The measurements were made with LAI-2000 and Ceptometer instruments. The measurements were taken for the purpose of model parameterization and to test empirical relationships that were hypothesized between biophysical parameters and remotely sensed data. The data are stored in tabular ASCII files.

Impact of Hurricane Iniki on native Hawaiian Acacia koa forests: damage and two-year recovery

Treesearch

Robin A. Harrington; James H. Fownes; Paul G. Scowcroft; Cheryl S. Vann

1997-01-01

Damage to Hawaiian Acacia koa forest by Hurricane Iniki was assessed by comparison with our previous measures of stand structure and leaf area index (LAI) at sites along a precipitation/elevation gradient on western Kauai. Reductions in LAI ranged from 29 to 80% and were correlated with pre-hurricane LAI and canopy height. The canopy damage...

Forest Productivity, Leaf Area, and Terrain in Southern Appalachian Deciduous Forests

Treesearch

Paul V. Bolstad; James M. Vose; Steven G. McNulty

2000-01-01

Leaf area index (LAI) is an important structural characteristic of forest ecosystems which has been shown to be strongly related to forest mass and energy cycles and forest productivity. LAI is more easily measured than forest productivity, and so a strong relationship between LAI and productivity would be a valuable tool in forest management. While a linear...

How universal is the relationship between remotely sensed vegetation indices and crop leaf area index? A global assessment

USDA-ARS?s Scientific Manuscript database

This study aims to assess the relationship between Leaf Area Index (LAI) and remotely sensed Vegetation Indices (VIs) for major crops, based on a globally explicit dataset of in situ LAI measurements over a significant set of locations. We used a total of 1394 LAI measurements from 29 sites spannin...

A new eco-hydrological distributed model for the predictions of the climate change impact on water resources of Mediterranean water-limited basins: the Mulargia basin case study in Sardinia.

NASA Astrophysics Data System (ADS)

Sarigu, Alessio; Montaldo, Nicola

2017-04-01

In the last three decades, climate change and human activities increased desertification process in Mediterranean regions, with dramatic consequences for agriculture and water availability. For instance in the main reservoir systems in Sardinia the average annual runoff in the latter part of the 20th century decreased of more than 50% compared with the previous period, while the precipitation over the Sardinia basin has decreased, but not at such a drastic rate as the discharge, with an high precipitation elasticity to streamflow, highlighting the key role of the rainfall seasonality on runoff production. IPCC climate change scenarios predict a further decrease of winter rainfall, which is the key term for runoff production in these typical Mediterranean climate basins, and air temperature increase, which can potentially impact on evapotranspiration, soil moisture and runoff. Only the use of an accurate ecohydrological physically based distributed model allow to well predict the impact of the climate change scenarios on the basin water resources. A new eco-hydrological model is developed that couples a distributed hydrological model of and a vegetation dynamic model (VDM). The hydrological model estimates the soil water balance of each basin cell using the force-restore method, the Philips model for infiltration estimate and the Penman-Monteith equation for evapotranspiration estimate. The VDM evaluates the changes in biomass over time for each cell and provides the leaf area index (LAI), which is then used by the hydrological model for evapotranspiration and rainfall interception estimates. Case study is the Mulargia basin (Sardinia, basin area of about 70 km2), where an extended field campaign started from 2003, with rain and discharge data observed at the basin outlet, periodic field measurements of soil moisture and LAI all over the basin, and evapotraspiration estimates using an eddy correlation based tower. The Mulargia basin case study is a very interesting laboratory of Mediterranean basins, thanks to its typical Mediterranean climate, its typical physiografic characteristics, its low human activities and influences and its attractive hydrologic database. The model has been successfully and deeply calibrated for the 2003 and validated for the 2004-2005 period, using both field data and satellite Modis data. Three future climate change scenarios has been generated using a stochastic model (Richardson, 1991), opportunely adapted for accounting the future changes of climate conditions. The scenarios (A1-A1B-A2) assume that in the next century there will be a drastic reduction of precipitation (with maximum reduction of 30% in A2) and that will continue the warming process. A reduction of soil moisture (about 40%) is predicted, especially during winter month and also the LAI will drastically decrease (more than 50% for woody vegetation and 75% for grass especially during the spring). Runoff will decrease even more (up to 70%) during the winter season, which is the key season for the water resource management and planning of these Mediterranean basins. These results anticipate a dramatic reduction of water resources availability, a change of vegetation species and ecosystems, increasing the desertification process in this typical Mediterranean area.

The Thermal Infrared Sensor on the Landsat Data Continutiy Mission

USDA-ARS?s Scientific Manuscript database

The REGularized canopy reFLECtance (REGFLEC) modeling tool integrates leaf optics, canopy reflectance, and atmospheric radiative transfer model components, facilitating accurate retrieval of leaf area index (LAI) and leaf chlorophyll content (Cab) directly from at-sensor radiances in green, red and ...

Modeling coupled interactions of carbon, water, and ozone exchange between terrestrial ecosystems and the atmosphere. I: model description.

PubMed

Nikolov, Ned; Zeller, Karl F

2003-01-01

A new biophysical model (FORFLUX) is presented to study the simultaneous exchange of ozone, carbon dioxide, and water vapor between terrestrial ecosystems and the atmosphere. The model mechanistically couples all major processes controlling ecosystem flows trace gases and water implementing recent concepts in plant eco-physiology, micrometeorology, and soil hydrology. FORFLUX consists of four interconnected modules-a leaf photosynthesis model, a canopy flux model, a soil heat-, water- and CO2- transport model, and a snow pack model. Photosynthesis, water-vapor flux and ozone uptake at the leaf level are computed by the LEAFC3 sub-model. The canopy module scales leaf responses to a stand level by numerical integration of the LEAFC3model over canopy leaf area index (LAI). The integration takes into account (1) radiative transfer inside the canopy, (2) variation of foliage photosynthetic capacity with canopy depth, (3) wind speed attenuation throughout the canopy, and (4) rainfall interception by foliage elements. The soil module uses principles of the diffusion theory to predict temperature and moisture dynamics within the soil column, evaporation, and CO2 efflux from soil. The effect of soil heterogeneity on field-scale fluxes is simulated employing the Bresler-Dagan stochastic concept. The accumulation and melt of snow on the ground is predicted using an explicit energy balance approach. Ozone deposition is modeled as a sum of three fluxes- ozone uptake via plant stomata, deposition to non-transpiring plant surfaces, and ozone flux into the ground. All biophysical interactions are computed hourly while model projections are made at either hourly or daily time step. FORFLUX represents a comprehensive approach to studying ozone deposition and its link to carbon and water cycles in terrestrial ecosystems.

P69 Using the NASA-Unified WRF to Assess the Impacts of Real-Time Vegetation on Simulations of Severe Weather

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; LaFontaine, Frank J.; Kumar, Sujay V.; Peters-Lidard, Christa D.

2012-01-01

Since June 2010, the NASA Short-term Prediction Research and Transition (SPoRT; Goodman et al. 2004; Darden et al. 2010; Stano et al. 2012; Fuell et al. 2012) Center has been generating a real-time Normalized Difference Vegetation Index (NDVI) and corresponding Green Vegetation Fraction (GVF) composite based on reflectances from NASA s Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. This dataset is generated at 0.01 resolution across the Continental United States (CONUS), and updated daily. The goal of producing such a vegetation dataset is to improve over the default climatological GVF dataset in land surface and numerical weather prediction models, in order to have better simulations of heat and moisture exchange between the land surface and the planetary boundary layer. Details on the SPoRT/MODIS vegetation composite algorithm are presented in Case et al. (2011). Vegetation indices such as GVF and Leaf Area Index (LAI) are used by land surface models (LSMs) to represent the horizontal and vertical density of plant vegetation (Gutman and Ignatov 1998), in order to calculate transpiration, interception and radiative shading. Both of these indices are related to the NDVI; however, there is an inherent ambiguity in determining GVF and LAI simultaneously from NDVI, as described in Gutman and Ignatov (1998). One practice is to specify the LAI while allowing the GVF to vary both spatially and temporally, as is done in the Noah LSM (Chen and Dudhia 2001; Ek et al. 2003). Operational versions of Noah within several of the National Centers for Environmental Prediction (NCEP) global and regional modeling systems hold the LAI fixed, while the GVF varies according to a global monthly climatology. This GVF climatology was derived from NDVI data on the NOAA Advanced Very High Resolution Radiometer (AVHRR) polar orbiting satellite, using information from 1985 to 1991 (Gutman and Ignatov 1998; Jiang et al. 2010). Representing data at the mid-point of every month, the climatological dataset is on a grid with 0.144 (16 km) spatial resolution and is distributed with the community WRF model (Ek et al. 2003; Jiang et al. 2010; Skamarock et al. 2008).

Use of plant trait data in the ISBA-A-gs model

NASA Astrophysics Data System (ADS)

Calvet, Jean-Christophe

2014-05-01

ISBA-A-gs is a CO2-responsive LSM (Calvet et al., 1998; Gibelin et al., 2006), able to simulate the diurnal cycle of carbon and water vapour fluxes, together with LAI and soil moisture evolution. The various components of ISBA-A-gs are based to a large extent on meta-analyses of trait data. (1) Photosynthesis: ISBA-A-gs uses the model of Goudriaan et al. (1985) modified by Jacobs (1994) and Jacobs et al. (1996). The main parameter is mesophyll conductance (gm). Leaf-level photosynthesis observations were used together with canopy level flux observations to derive gm together with other key parameters of the Jacobs model, including in drought conditions. This permitted implementing detailed representations of the soil moisture stress. Two different types of drought responses are distinguished for both herbaceous vegetation (Calvet, 2000) and forests (Calvet et al., 2004), depending on the evolution of the water use efficiency (WUE) under moderate stress: WUE increases in the early soil water stress stages in the case of the drought-avoiding response, whereas WUE decreases or remains stable in the case of the drought-tolerant response. (2) Plant growth: the leaf biomass is provided by a growth model (Calvet et al., 1998; Calvet and Soussana, 2001) driven by photosynthesis. In contrast to other land surface models, no GDD-based phenology model is used in ISBA-A-gs, as the vegetation growth and senescence are entirely driven by photosynthesis. The leaf biomass is supplied with the carbon assimilated by photosynthesis, and decreased by a turnover and a respiration term. Turnover is increased by a deficit in photosynthesis. The leaf onset is triggered by sufficient photosynthesis levels and a minimum LAI value is prescribed. The maximum annual value of LAI is prognostic, i.e. it can be predicted by the model. LAI is derived from leaf biomass using SLA values. The latter are derived from the leaf nitrogen concentration using plasticity parameters. (3) CO2 effect: the photosynthesis model is able to represent the antitranspirant effect of CO2. The plant growth model represents the fertilization effect of CO2. However, the nitrogen dilution triggered by the CO2 increase has to be represented. A pragmatic solution consists in decreasing the leaf nitrogen concentration parameter in response to CO2, using existing meta-analyses of this parameter (Calvet et al., 2008). The TRY database could be used to improve the current parameterizations, together with the mapping of the model parameters.

Cross-sectional comparison of first-generation antipsychotic long-acting injections vs risperidone long-acting injection: patient-rated attitudes, satisfaction and tolerability

PubMed Central

Singh, Sourabh Moti; Haddad, Peter M.; Husain, Nusrat; Heaney, Eamonn; Tomenson, Barbara; Chaudhry, Imran B.

2016-01-01

Objectives: The objective of this study was to compare patients’ attitudes and satisfaction with medication and patient-rated tolerability between those prescribed a first-generation antipsychotic long-acting injection (FGA-LAI) and those prescribed risperidone long-acting injection (RLAI). Method: A cross-sectional study of a representative sample of outpatients prescribed an FGA-LAI or RLAI for a minimum of 6 months and attending a depot clinic. Attitudes to medication were assessed by the Drug Attitude Inventory (DAI-30), tolerability was measured by the Liverpool University Neuroleptic Side Effect Rating Scale (LUNSERS) and satisfaction with antipsychotic medication was assessed by the Satisfaction with Antipsychotic Medication (SWAM) scale. Results: The RLAI (n = 28) and FGA-LAI (n = 39) groups did not differ in terms of mean age, sex, diagnosis and ethnicity. All individual LAIs were prescribed within British National Formulary limits. The most commonly prescribed FGA-LAI was flupentixol decanoate (n = 22). There was no significant difference between the RLAI and FGA-LAI groups in terms of mean total scores on the DAI-30, LUNSERS and SWAM or the tolerability subscales of the LUNSERS or the two subscales (treatment acceptability and medication insight) of the SWAM. In both LAI groups there was a low level of side effects (LUNSERS) and a generally positive attitude (DAI-30) and reasonable satisfaction (SWAM) with medication. Conclusions: Patients treated with FGA-LAI and RLAI for at least 6 months did not differ in terms of patient-rated tolerability, attitudes and satisfaction with medication. The current design cannot determine whether differences would have been evident earlier on during treatment. These results should be regarded as preliminary and are subject to prescribing bias. Randomized studies avoid prescribing bias and are a superior way to compare specific LAIs. Ideally randomized studies should include patient-rated outcome measures including medication tolerability; assessment of side effects, efficacy and quality of life made by blinded raters; and additional objective side-effect data including changes in weight and key blood parameters. PMID:27354904

Enhancement of understory productivity by asynchronous phenology with overstory competitors in a temperate deciduous forest.

PubMed

Jolly, William M; Nemani, Ramakrishna; Running, Steven W

2004-09-01

Some saplings and shrubs growing in the understory of temperate deciduous forests extend their periods of leaf display beyond that of the overstory, resulting in periods when understory radiation, and hence productivity, are not limited by the overstory canopy. To assess the importance of the duration of leaf display on the productivity of understory and overstory trees of deciduous forests in the north eastern United States, we applied the simulation model, BIOME-BGC with climate data for Hubbard Brook Experimental Forest, New Hampshire, USA and mean ecophysiological data for species of deciduous, temperate forests. Extension of the overstory leaf display period increased overstory leaf area index (LAI) by only 3 to 4% and productivity by only 2 to 4%. In contrast, extending the growing season of the understory relative to the overstory by one week in both spring and fall, increased understory LAI by 35% and productivity by 32%. A 2-week extension of the growing period in both spring and fall increased understory LAI by 53% and productivity by 55%.

What is the role of historical anthropogenically-induced land-cover change on the surface climate of West Africa? Results from the LUCID intercomparison project

NASA Astrophysics Data System (ADS)

Souleymane, S.

2015-12-01

West Africa has been highlighted as a hot spot of land surface-atmosphere interactions. This study analyses the outputs of the project Land-Use and Climate, IDentification of Robust Impacts (LUCID) over West Africa. LUCID used seven atmosphere-land models with a common experimental design to explore the impacts of Land Use induced Land Cover Change (LULCC) that are robust and consistent across the climate models. Focusing the analysis on Sahel and Guinea, this study shows that, even though the seven climate models use the same atmospheric and land cover forcing, there are significant differences of West African Monsoon variability across the climate models. The magnitude of that variability differs significantly from model to model resulting two major "features": (1) atmosphere dynamics models; (2) how the land-surface functioning is parameterized in the Land surface Model, in particular regarding the evapotranspiration partitioning within the different land-cover types, as well as the role of leaf area index (LAI) in the flux calculations and how strongly the surface is coupled to the atmosphere. The major role that the models'sensitivity to land-cover perturbations plays in the resulting climate impacts of LULCC has been analysed in this study. The climate models show, however, significant differences in the magnitude and the seasonal partitioning of the temperature change. The LULCC induced cooling is directed by decreases in net shortwave radiation that reduced the available energy (QA) (related to changes in land-cover properties other than albedo, such as LAI and surface roughness), which decreases during most part of the year. The biophysical impacts of LULCC were compared to the impact of elevated greenhouse gases resulting changes in sea surface temperatures and sea ice extent (CO2SST). The results show that the surface cooling (related a decrease in QA) induced by the biophysical effects of LULCC are insignificant compared to surface warming (related an increase in QA), which is induced by the regional significance effect of CO2SST due to a small LULCC imposed. In contrast, the decrease of surface water balance resulting from LULCC effect is a similar sign to those resulting from CO2SST but the signal resulting of the biophysical effects of LULCC is stronger than the regional CO2SST impact.

Using the ARTMO toolbox for automated retrieval of biophysical parameters through radiative transfer model inversion: Optimizing LUT-based inversion

NASA Astrophysics Data System (ADS)

Verrelst, J.; Rivera, J. P.; Leonenko, G.; Alonso, L.; Moreno, J.

2012-04-01

Radiative transfer (RT) modeling plays a key role for earth observation (EO) because it is needed to design EO instruments and to develop and test inversion algorithms. The inversion of a RT model is considered as a successful approach for the retrieval of biophysical parameters because of being physically-based and generally applicable. However, to the broader community this approach is considered as laborious because of its many processing steps and expert knowledge is required to realize precise model parameterization. We have recently developed a radiative transfer toolbox ARTMO (Automated Radiative Transfer Models Operator) with the purpose of providing in a graphical user interface (GUI) essential models and tools required for terrestrial EO applications such as model inversion. In short, the toolbox allows the user: i) to choose between various plant leaf and canopy RT models (e.g. models from the PROSPECT and SAIL family, FLIGHT), ii) to choose between spectral band settings of various air- and space-borne sensors or defining own sensor settings, iii) to simulate a massive amount of spectra based on a look up table (LUT) approach and storing it in a relational database, iv) to plot spectra of multiple models and compare them with measured spectra, and finally, v) to run model inversion against optical imagery given several cost options and accuracy estimates. In this work ARTMO was used to tackle some well-known problems related to model inversion. According to Hadamard conditions, mathematical models of physical phenomena are mathematically invertible if the solution of the inverse problem to be solved exists, is unique and depends continuously on data. This assumption is not always met because of the large number of unknowns and different strategies have been proposed to overcome this problem. Several of these strategies have been implemented in ARTMO and were here analyzed to optimize the inversion performance. Data came from the SPARC-2003 dataset, which was acquired on the agricultural test site Barrax, Spain. LUTs were created using the 4SAIL and FLIGHT models and were inverted against CHRIS data in order to retrieve maps of chlorophyll content (chl) and leaf area index (LAI). The following inversion steps have been optimized: 1. Cost function. The performances of about 50 different cost functions (i.e. minimum distance functions) were compared. Remarkably, in none of the studied cases the widely used root mean square error (RMSE) led to most accurate results. Depending on the retrieved parameter, more successful functions were: 'Sharma and Mittal', 'Shannońs entropy', 'Hellinger distance', 'Pearsońs chi-square'. 2. Gaussian noise. Earth observation data typically encompass a certain degree of noise due to errors related to radiometric and geometric processing. In all cases, adding 5% Gaussian noise to the simulated spectra led to more accurate retrievals as compared to without noise. 3. Average of multiple best solutions. Because multiple parameter combinations may lead to the same spectra, a way to overcome this problem is not searching for the top best match but for a percentage of best matches. Optimized retrievals were encountered when including an average of 7% (Chl) to 10% (LAI) top best matches. 4. Integration of estimates. The option is provided to integrate estimates of biochemical contents at the canopy level (e.g., total chlorophyll: Chl × LAI, or water: Cw × LAI), which can lead to increased robustness and accuracy. 5. Class-based inversion. This option is probably ARTMÓs most powerful feature as it allows model parameterization depending on the imagés land cover classes (e.g. different soil or vegetation types). Class-based inversion can lead to considerably improved accuracies compared to one generic class. Results suggest that 4SAIL and FLIGHT performed alike for Chl but not for LAI. While both models rely on the leaf model PROSPECT for Chl retrieval, their different nature (e.g. numerical vs. ray tracing) may cause that retrieval of structural parameters such as LAI differ. Finally, it should be noted that the whole analysis can be intuitively performed by the toolbox. ARTMO is freely available to the EO community for further development. Expressions of interest are welcome and should be directed to the corresponding author.

Managing Southeastern US Forests for Increased Water Yield

NASA Astrophysics Data System (ADS)

Acharya, S.; Kaplan, D. A.; Mclaughlin, D. L.; Cohen, M. J.

2017-12-01

Forested lands influence watershed hydrology by affecting water quantity and quality in surface and groundwater systems, making them potentially effective tools for regional water resource planning. In this study, we quantified water use and water yield by pine forests under varying silvicultural management (e.g., high density plantation, thinning, and prescribed burning). Daily forest water use (evapotranspiration, ET) was estimated using continuously monitored soil-moisture in the root-zone at six sites across Florida (USA), each with six plots ranging in forest leaf-area index (LAI). Plots included stands with different rotational ages (from clear-cut to mature pine plantations) and those restored to more historical conditions. Estimated ET relative to potential ET (PET) was strongly associated with LAI, root-zone soil-moisture status, and site hydroclimate; these factors explained 85% of the variation in the ET:PET ratio. Annual water yield (Yw) calculated from these ET estimates and a simple water balance differed significantly among sites and plots (ranging from -0.12 cm/yr to > 100 cm/yr), demonstrating substantive influence of management regimes. LAI strongly influenced Yw in all sites, and a general linear model with forest attributes (LAI and groundcover), hydroclimate, and site characteristics explained >90% of variation in observed Yw. These results can be used to predict water yield changes under different management and climate scenarios and may be useful in the development of payment for ecosystem services approaches that identify water as an important product of forest best management practices.

An improved SWAT vegetation growth module and its evaluation for four tropical ecosystems

NASA Astrophysics Data System (ADS)

Alemayehu, Tadesse; van Griensven, Ann; Taddesse Woldegiorgis, Befekadu; Bauwens, Willy

2017-09-01

The Soil and Water Assessment Tool (SWAT) is a globally applied river basin ecohydrological model used in a wide spectrum of studies, ranging from land use change and climate change impacts studies to research for the development of the best water management practices. However, SWAT has limitations in simulating the seasonal growth cycles for trees and perennial vegetation in the tropics, where rainfall rather than temperature is the dominant plant growth controlling factor. Our goal is to improve the vegetation growth module of SWAT for simulating the vegetation variables - such as the leaf area index (LAI) - for tropical ecosystems. Therefore, we present a modified SWAT version for the tropics (SWAT-T) that uses a straightforward but robust soil moisture index (SMI) - a quotient of rainfall (P) and reference evapotranspiration (ETr) - to dynamically initiate a new growth cycle within a predefined period. Our results for the Mara Basin (Kenya/Tanzania) show that the SWAT-T-simulated LAI corresponds well with the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI for evergreen forest, savanna grassland and shrubland. This indicates that the SMI is reliable for triggering a new annual growth cycle. The water balance components (evapotranspiration and streamflow) simulated by the SWAT-T exhibit a good agreement with remote-sensing-based evapotranspiration (ET-RS) and observed streamflow. The SWAT-T model, with the proposed vegetation growth module for tropical ecosystems, can be a robust tool for simulating the vegetation growth dynamics in hydrologic models in tropical regions.

Remote sensing of temperate coniferous forest lead area index - The influence of canopy closure, understory vegetation and background reflectance

NASA Technical Reports Server (NTRS)

Spanner, Michael A.; Pierce, Lars L.; Running, Steven W.; Peterson, David L.

1990-01-01

Consideration is given to the effects of canopy closure, understory vegetation, and background reflectance on the relationship between Landsat TM data and the leaf area index (LAI) of temperate coniferous forests in the western U.S. A methodology for correcting TM data for atmospheric conditions and sun-surface-sensor geometry is discussed. Strong inverse curvilinear relationships were found between coniferous forest LAI and TM bands 3 and 5. It is suggested that these inverse relationships are due to increased reflectance of understory vegetation and background in open stands of lower LAI and decreased reflectance of the overstory in closed canopy stands with higher LAI.

Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data - potential of unmanned aerial vehicle imagery

NASA Astrophysics Data System (ADS)

Roosjen, Peter P. J.; Brede, Benjamin; Suomalainen, Juha M.; Bartholomeus, Harm M.; Kooistra, Lammert; Clevers, Jan G. P. W.

2018-04-01

In addition to single-angle reflectance data, multi-angular observations can be used as an additional information source for the retrieval of properties of an observed target surface. In this paper, we studied the potential of multi-angular reflectance data for the improvement of leaf area index (LAI) and leaf chlorophyll content (LCC) estimation by numerical inversion of the PROSAIL model. The potential for improvement of LAI and LCC was evaluated for both measured data and simulated data. The measured data was collected on 19 July 2016 by a frame-camera mounted on an unmanned aerial vehicle (UAV) over a potato field, where eight experimental plots of 30 × 30 m were designed with different fertilization levels. Dozens of viewing angles, covering the hemisphere up to around 30° from nadir, were obtained by a large forward and sideways overlap of collected images. Simultaneously to the UAV flight, in situ measurements of LAI and LCC were performed. Inversion of the PROSAIL model was done based on nadir data and based on multi-angular data collected by the UAV. Inversion based on the multi-angular data performed slightly better than inversion based on nadir data, indicated by the decrease in RMSE from 0.70 to 0.65 m2/m2 for the estimation of LAI, and from 17.35 to 17.29 μg/cm2 for the estimation of LCC, when nadir data were used and when multi-angular data were used, respectively. In addition to inversions based on measured data, we simulated several datasets at different multi-angular configurations and compared the accuracy of the inversions of these datasets with the inversion based on data simulated at nadir position. In general, the results based on simulated (synthetic) data indicated that when more viewing angles, more well distributed viewing angles, and viewing angles up to larger zenith angles were available for inversion, the most accurate estimations were obtained. Interestingly, when using spectra simulated at multi-angular sampling configurations as were captured by the UAV platform (view zenith angles up to 30°), already a huge improvement could be obtained when compared to solely using spectra simulated at nadir position. The results of this study show that the estimation of LAI and LCC by numerical inversion of the PROSAIL model can be improved when multi-angular observations are introduced. However, for the potato crop, PROSAIL inversion for measured data only showed moderate accuracy and slight improvements.

Soil Respiration in European Grasslands in Relation to Climate and Assimilate Supply

PubMed Central

Bahn, Michael; Rodeghiero, Mirco; Anderson-Dunn, Margaret; Dore, Sabina; Gimeno, Cristina; Drösler, Matthias; Williams, Michael; Ammann, Christof; Berninger, Frank; Flechard, Chris; Jones, Stephanie; Balzarolo, Manuela; Kumar, Suresh; Newesely, Christian; Priwitzer, Tibor; Raschi, Antonio; Siegwolf, Rolf; Susiluoto, Sanna; Tenhunen, John; Wohlfahrt, Georg; Cernusca, Alexander

2010-01-01

Soil respiration constitutes the second largest flux of carbon (C) between terrestrial ecosystems and the atmosphere. This study provides a synthesis of soil respiration (Rs) in 20 European grasslands across a climatic transect, including ten meadows, eight pastures and two unmanaged grasslands. Maximum rates of Rs (Rsmax), Rs at a reference soil temperature (10°C; Rs10) and annual Rs (estimated for 13 sites) ranged from 1.9 to 15.9 μmol CO2 m−2 s−1, 0.3 to 5.5 μmol CO2 m−2 s−1 and 58 to 1988 g C m−2 y−1, respectively. Values obtained for Central European mountain meadows are amongst the highest so far reported for any type of ecosystem. Across all sites Rsmax was closely related to Rs10. Assimilate supply affected Rs at timescales from daily (but not necessarily diurnal) to annual. Reductions of assimilate supply by removal of aboveground biomass through grazing and cutting resulted in a rapid and a significant decrease of Rs. Temperature-independent seasonal fluctuations of Rs of an intensively managed pasture were closely related to changes in leaf area index (LAI). Across sites Rs10 increased with mean annual soil temperature (MAT), LAI and gross primary productivity (GPP), indicating that assimilate supply overrides potential acclimation to prevailing temperatures. Also annual Rs was closely related to LAI and GPP. Because the latter two parameters were coupled to MAT, temperature was a suitable surrogate for deriving estimates of annual Rs across the grasslands studied. These findings contribute to our understanding of regional patterns of soil C fluxes and highlight the importance of assimilate supply for soil CO2 emissions at various timescales. PMID:20936099

The role of long-acting injectable antipsychotics in schizophrenia: a critical appraisal

PubMed Central

Veguilla, Miguel Ruiz; Taylor, David; Balanzá-Martinez, Vicent

2014-01-01

Despite their widespread use, long-acting injectable (LAI) antipsychotics (APs) are often regarded with some negativity because of the assumption of punishment, control and insufficient evolution towards psychosocial development of patients. However, LAI APs have proved effective in schizophrenia and other severe psychotic disorders because they assure stable blood levels, leading to a reduction of the risk of relapse. Therapeutic opportunities have also arisen after introduction of newer, second-generation LAI APs in recent years. Newer LAI APs are more readily dosed optimally, may be better tolerated and are better suited to integrated rehabilitation programmes. This review outlines the older and newer LAI APs available for the treatment of schizophrenia, with considerations of past and present pharmacological and therapeutic issues. Traditional, evidence-based approaches to systematic reviews and randomized clinical trials are of limited utility in this area so this paper’s blending of experimental trials with observational research is particularly appropriate and effective. PMID:25360245

Clinical observation of lymphocyte active immunotherapy in 380 patients with unexplained recurrent spontaneous abortion.

PubMed

Chen, Jian-Ling; Yang, Jian-Ming; Huang, Ya-Zhe; Li, Ying

2016-11-01

This study aims to investigate the clinical curative effect of lymphocyte active immunotherapy (LAI) on unexplained recurrent spontaneous abortion (RSA). A total of 749 RSA patients who received medical service in our hospital from October 2009 to June 2013 were enrolled into this study. These patients were randomly divided into two groups: LAI group (treatment group) and routine progesterone for maintenance tocolysis group (control group). A comparative analysis on the pregnancy outcomes in these two groups was conducted. Abortion rate was significantly lower in the LAI group than in the control group (P<0.05). Furthermore, pregnancy success rates were 89.7% and 32.2% in patients who received LAI and routine progesterone for maintenance tocolysis, respectively, and the difference was statistically significant (P<0.05). Our analysis suggested that LAI can treat RSA effectively and has an excellent clinical effect. Furthermore, the detection of blocking antibodies showed a positive prediction on pregnancy outcome. Copyright © 2016 Elsevier B.V. All rights reserved.

Relationship between aerodynamic roughness length and bulk sedge leaf area index in a mixed-species boreal mire complex

NASA Astrophysics Data System (ADS)

Alekseychik, P. K.; Korrensalo, A.; Mammarella, I.; Vesala, T.; Tuittila, E.-S.

2017-06-01

Leaf area index (LAI) is an important parameter in natural ecosystems, representing the seasonal development of vegetation and photosynthetic potential. However, direct measurement techniques require labor-intensive field campaigns that are usually limited in time, while remote sensing approaches often do not yield reliable estimates. Here we propose that the bulk LAI of sedges (LAIs) can be estimated alternatively from a micrometeorological parameter, the aerodynamic roughness length for momentum (z0). z0 can be readily calculated from high-response turbulence and other meteorological data, typically measured continuously and routinely available at ecosystem research sites. The regressions of LAI versus z0 were obtained using the data from two Finnish natural sites representative of boreal fen and bog ecosystems. LAIs was found to be well correlated with z0 and sedge canopy height. Superior method performance was demonstrated in the fen ecosystem where the sedges make a bigger contribution to overall surface roughness than in bogs.

Photosynthesis and canopy characteristics in genetically defined families of silver birch (Betula pendula).

PubMed

Wang, T; Tigerstedt, P M; Viherä-Aarnio, A

1995-10-01

Net photosynthetic rates (A) of leaves in upper and lower crown layers (A(upper) and A(lower)), leaf area index (LAI), mean tilt angle (MTA), several leaf characteristics, and volume growth were observed in fast- and slow-growing families of a 14-year-old full-sib and half-sib family progeny test of Betula pendula Roth. Each measure of net photosynthetic rate was calculated after correcting measured net photosynthesis for the effects of environmental variables. The differences in A(upper) and LAI among families were significant. The proportions of the total variance assigned to family for A(upper), A(lower) and LAI were 33.64, 28.93 and 54.99%, respectively. The mean A(upper) and LAI of the fast-growing families were significantly higher than those of the slow-growing families, whereas the mean A(lower) of the fast-growing families was significantly lower than that of the slow-growing families. There were also significant differences among families in leaf size, leaf shape, and the ratios leaf fresh weight/area and leaf dry weight/area. Between 27.55 and 54.55% of the total variance in these characteristics could be assigned to the family effect. Volume growth was positively correlated with A(upper) and LAI, but it was most strongly correlated with A(upper) x LAI.

Satellite-Based Evidence for Shrub and Graminoid Tundra Expansion in Northern Quebec from 1986-2010

NASA Technical Reports Server (NTRS)

McManus, K. M.; Morton, D. C.; Masek, J. G.; Wang, D.; Sexton, J. O.; Nagol, J.; Ropars, P.; Boudreau, S.

2012-01-01

Global vegetation models predict rapid poleward migration of tundra and boreal forest vegetation in response to climate warming. Local plot and air-photo studies have documented recent changes in high-latitude vegetation composition and structure, consistent with warming trends. To bridge these two scales of inference, we analyzed a 24-year (1986-2010) Landsat time series in a latitudinal transect across the boreal forest-tundra biome boundary in northern Quebec province, Canada. This region has experienced rapid warming during both winter and summer months during the last forty years. Using a per-pixel (30 m) trend analysis, 30% of the observable (cloud-free) land area experienced a significant (p < 0.05) positive trend in the Normalized Difference Vegetation Index (NDVI). However, greening trends were not evenly split among cover types. Low shrub and graminoid tundra contributed preferentially to the greening trend, while forested areas were less likely to show significant trends in NDVI. These trends reflect increasing leaf area, rather than an increase in growing season length, because Landsat data were restricted to peak-summer conditions. The average NDVI trend (0.007/yr) corresponds to a leaf-area index (LAI) increase of 0.6 based on the regional relationship between LAI and NDVI from the Moderate Resolution Spectroradiometer (MODIS). Across the entire transect, the area-averaged LAI increase was 0.2 during 1986-2010. A higher area-averaged LAI change (0.3) within the shrub-tundra portion of the transect represents a 20-60% relative increase in LAI during the last two decades. Our Landsat-based analysis subdivides the overall high-latitude greening trend into changes in peak-summer greenness by cover type. Different responses within and among shrub, graminoid, and tree-dominated cover types in this study indicate important fine-scale heterogeneity in vegetation growth. Although our findings are consistent with community shifts in low-biomass vegetation types over multi-decadal time scales, the response in tundra and forest ecosystems to recent warming was not uniform.

Light-absorbing impurities accelerate glacier melt in the Central Tibetan Plateau.

PubMed

Li, Xiaofei; Kang, Shichang; He, Xiaobo; Qu, Bin; Tripathee, Lekhendra; Jing, Zhefan; Paudyal, Rukumesh; Li, Yang; Zhang, Yulan; Yan, Fangping; Li, Gang; Li, Chaoliu

2017-06-01

Light-absorbing impurities (LAIs), such as organic carbon (OC), black carbon (BC), and mineral dust (MD) deposited on the glacier surface can reduce albedo, thus accelerating the glacier melt. Surface fresh snow, aged snow, granular ice, and snowpits samples were collected between August 2014 and October 2015 on the Xiao Dongkemadi (XDKMD) glacier (33°04'N, 92°04'E) in the central Tibetan Plateau (TP). The spatiotemporal variations of LAIs concentrations in the surface snow/ice were observed to be consistent, differing mainly in magnitudes. LAIs concentrations were found to be in the order: granular ice>snowpit>aged snow>fresh snow, which must be because of post-depositional effects and enrichment. In addition, more intense melting led to higher LAIs concentrations exposed to the surface at a lower elevation, suggesting a strong negative relationship between LAIs concentrations and elevation. The scavenging efficiencies of OC and BC were same (0.07±0.02 for OC, 0.07±0.01 for BC), and the highest enrichments was observed in late September and August for surface snow and granular ice, respectively. Meanwhile, as revealed by the changes in the OC/BC ratios, intense glacier melt mainly occurred between August and October. Based on the SNow ICe Aerosol Radiative (SNICAR) model simulations, BC and MD in the surface snow/ice were responsible for about 52%±19% and 25%±14% of the albedo reduction, while the radiative forcing (RF) were estimated to be 42.74±40.96Wm -2 and 21.23±22.08Wm -2 , respectively. Meanwhile, the highest RF was observed in the granular ice, suggesting that the exposed glaciers melt and retreat more easily than the snow distributed glaciers. Furthermore, our results suggest that BC was the main forcing factor compared with MD in accelerating glacier melt during the melt season in the Central TP. Copyright © 2017 Elsevier B.V. All rights reserved.

Parameterization of Forest Canopies with the PROSAIL Model

NASA Astrophysics Data System (ADS)

Austerberry, M. J.; Grigsby, S.; Ustin, S.

2013-12-01

Particularly in forested environments, arboreal characteristics such as Leaf Area Index (LAI) and Leaf Inclination Angle have a large impact on the spectral characteristics of reflected radiation. The reflected spectrum can be measured directly with satellites or airborne instruments, including the MASTER and AVIRIS instruments. This particular project dealt with spectral analysis of reflected light as measured by AVIRIS compared to tree measurements taken from the ground. Chemical properties of leaves including pigment concentrations and moisture levels were also measured. The leaf data was combined with the chemical properties of three separate trees, and served as input data for a sequence of simulations with the PROSAIL Model, a combination of PROSPECT and Scattering by Arbitrarily Inclined Leaves (SAIL) simulations. The output was a computed reflectivity spectrum, which corresponded to the spectra that were directly measured by AVIRIS for the three trees' exact locations within a 34-meter pixel resolution. The input data that produced the best-correlating spectral output was then cross-referenced with LAI values that had been obtained through two entirely separate methods, NDVI extraction and use of the Beer-Lambert law with airborne LiDAR. Examination with regressive techniques between the measured and modeled spectra then enabled a determination of the trees' probable structure and leaf parameters. Highly-correlated spectral output corresponded well to specific values of LAI and Leaf Inclination Angle. Interestingly, it appears that varying Leaf Angle Distribution has little or no noticeable effect on the PROSAIL model. Not only is the effectiveness and accuracy of the PROSAIL model evaluated, but this project is a precursor to direct measurement of vegetative indices exclusively from airborne or satellite observation.

Impacts of including forest understory brightness and foliage clumping information from multiangular measurements on leaf area index mapping over North America

NASA Astrophysics Data System (ADS)

Pisek, Jan; Chen, Jing M.; Alikas, Krista; Deng, Feng

2010-09-01

A new leaf area index (LAI) data set in 10 day intervals with consideration of the understory reflectance and foliage clumping effects over North America for 1 year is developed. The data set brings effectively together measurements from multiple sensors with complementary capabilities (SPOT-VEGETATION, Multiangle Imaging Spectroradiometer, POLDER). First, the temporal consistency analysis indicated the new product is on par with other available LAI data sets currently used by the community. Second, with the removal of the background (understory in forests, moss, litter, and soil) effect on the forest overstory LAI retrieval, slightly different LAI reductions were found between needleleaf and broadleaf forests. This is caused by the more clumped nature of needleleaf forests, especially at higher LAI values, which allows more light to penetrate through the overstory canopy, making the understory more visible for equal LAI as compared to broadleaf forests. This is found over a representative set of 105 CEOS Benchmark Land Multisite Analysis and Intercomparison of Products sites in North America used for indirect validation. Third, the data set was directly validated and compared with Moderate Resolution Imaging Spectroradiometer Collection 5 LAI product using results from the BigFoot project for available forest test sites. This study demonstrates that the fusion of data inputs between multiple sensors can indeed lead to improved products and that multiangle remote sensing can help us to address effectively the issues (separating the signal from the understory and overstory, foliage clumping) that could not be solved via the means of the conventional mono-angle remote sensing.

Investigating the Relationship Between Liquid Water and Leaf Area in Clonal Populus

NASA Technical Reports Server (NTRS)

Roberts, Dar; Brown, K.; Green, R.; Ustin, S.; Hinckley, T.

1998-01-01

Leaf Area Index (LAI) is one of the most commonly employed biophysical parameters used to characterize vegetation canopies and scale leaf physiological processes to larger scales. For example, LAI is a critical parameter used in regional scale estimates of evapotranspiration, photosynthesis, primary productivity, and carbon cycling (Running et al., 1989; Dorman and Sellers, 1989; Potter et al., 1993). LAI is typically estimated using ratio-based techniques, such as the Normalized Difference Vegetation Index (NDVI: e.g. Tucker 1979; Asrar et al., 1989; Sellers 1985, 1987). The physical basis behind this relationship depends on the high spectral contrast between scattered near-infrared (NIR) and absorbed red radiation in canopies. As the number of leaves present in a canopy increases over a unit area, NIR reflectance increases, while red reflectance decreases, resulting in an increase in the ratio. Through time series and image compositing, NDVI provides an additional temporal measure of how these parameters change, providing a means to monitor fluxes and productivity (Tucker et al., 1983). NDVI, while highly successful for agriculture and grassland ecosystems has been found to be less successful in evergreen chaparral and forested ecosystems (Badhwar et al., 1986; Gamon et al., 1993; Hall et al., 1995). Typically, the relationship between NDVI and LAI becomes progressively more asymptotic at LAI values above three (Sellers, 1985), although linear relationships have been observed in conifers at LAis as high as 13 (Spanner et al., 1990). In this paper, we explore an alternative approach for estimating LAI for remotely sensed data from AVIRIS based on estimates of canopy liquid water. Our primary objective is to test the hypothesis that the depth of the liquid water bands expressed in canopy reflectance spectra at 960, 1200, 1400 and 1900 nm increases with increasing LAI in canopies. This study builds from work by Roberts et al. (1997), in which liquid water was shown to increase following a gradient of increasing LAI ranging from grasslands to coniferous forests. In that study, it was observed that forests, which showed little variation in NDVI, showed significant variation in liquid water. In order to test this hypothesis, we analyzed field spectra measured over Populus resprouts of known LAI and monitored changes in liquid water in young Populus stands as they aged over a 4-year time span. The study was conducted in south-central Washington, in a clonal Populus fiber farm owned and operated by Boise-Cascade near the town of Wallula.

Development of a New Model for Accurate Prediction of Cloud Water Deposition on Vegetation

NASA Astrophysics Data System (ADS)

Katata, G.; Nagai, H.; Wrzesinsky, T.; Klemm, O.; Eugster, W.; Burkard, R.

2006-12-01

Scarcity of water resources in arid and semi-arid areas is of great concern in the light of population growth and food shortages. Several experiments focusing on cloud (fog) water deposition on the land surface suggest that cloud water plays an important role in water resource in such regions. A one-dimensional vegetation model including the process of cloud water deposition on vegetation has been developed to better predict cloud water deposition on the vegetation. New schemes to calculate capture efficiency of leaf, cloud droplet size distribution, and gravitational flux of cloud water were incorporated in the model. Model calculations were compared with the data acquired at the Norway spruce forest at the Waldstein site, Germany. High performance of the model was confirmed by comparisons of calculated net radiation, sensible and latent heat, and cloud water fluxes over the forest with measurements. The present model provided a better prediction of measured turbulent and gravitational fluxes of cloud water over the canopy than the Lovett model, which is a commonly used cloud water deposition model. Detailed calculations of evapotranspiration and of turbulent exchange of heat and water vapor within the canopy and the modifications are necessary for accurate prediction of cloud water deposition. Numerical experiments to examine the dependence of cloud water deposition on the vegetation species (coniferous and broad-leaved trees, flat and cylindrical grasses) and structures (Leaf Area Index (LAI) and canopy height) are performed using the presented model. The results indicate that the differences of leaf shape and size have a large impact on cloud water deposition. Cloud water deposition also varies with the growth of vegetation and seasonal change of LAI. We found that the coniferous trees whose height and LAI are 24 m and 2.0 m2m-2, respectively, produce the largest amount of cloud water deposition in all combinations of vegetation species and structures in the experiments.

Spatially Distributed Assimilation of Remotely Sensed Leaf Area Index and Potential Evapotranspiration for Hydrologic Modeling in Wetland Landscapes

NASA Astrophysics Data System (ADS)

Rajib, A.; Evenson, G. R.; Golden, H. E.; Lane, C.

2017-12-01

Evapotranspiration (ET), a highly dynamic flux in wetland landscapes, regulates the accuracy of surface/sub-surface runoff simulation in a hydrologic model. Accordingly, considerable uncertainty in simulating ET-related processes remains, including our limited ability to incorporate realistic ground conditions, particularly those involved with complex land-atmosphere feedbacks, vegetation growth, and energy balances. Uncertainty persists despite using high resolution topography and/or detailed land use data. Thus, a good hydrologic model can produce right answers for wrong reasons. In this study, we develop an efficient approach for multi-variable assimilation of remotely sensed earth observations (EOs) into a hydrologic model and apply it in the 1700 km2 Pipestem Creek watershed in the Prairie Pothole Region of North Dakota, USA. Our goal is to employ EOs, specifically Leaf Area Index (LAI) and Potential Evapotranspiration (PET), as surrogates for the aforementioned processes without overruling the model's built-in physical/semi-empirical process conceptualizations. To do this, we modified the source code of an already-improved version of the Soil and Water Assessment Tool (SWAT) for wetland hydrology (Evenson et al. 2016 HP 30(22):4168) to directly assimilate remotely-sensed LAI and PET (obtained from the 500 m and 1 km Moderate Resolution Imaging Spectroradiometer (MODIS) gridded products, respectively) into each model Hydrologic Response Unit (HRU). Two configurations of the model, one with and one without EO assimilation, are calibrated against streamflow observations at the watershed outlet. Spatio-temporal changes in the HRU-level water balance, based on calibrated outputs, are evaluated using MODIS Actual Evapotranspiration (AET) as a reference. It is expected that the model configuration having remotely sensed LAI and PET, will simulate more realistic land-atmosphere feedbacks, vegetation growth and energy balance. As a result, this will decrease simulated water balance uncertainties compared to the default model configuration.

Responses of plant available water and forest productivity to variably layered coarse textured soils

NASA Astrophysics Data System (ADS)

Huang, Mingbin; Barbour, Lee; Elshorbagy, Amin; Si, Bing; Zettl, Julie

2010-05-01

Reforestation is a primary end use for reconstructed soils following oil sands mining in northern Alberta, Canada. Limited soil water conditions strongly restrict plant growth. Previous research has shown that layering of sandy soils can produce enhanced water availability for plant growth; however, the effect of gradation on these enhancements is not well defined. The objective of this study was to evaluate the effect of soil texture (gradation and layering) on plant available water and consequently on forest productivity for reclaimed coarse textured soils. A previously validated system dynamics (SD) model of soil moisture dynamics was coupled with ecophysiological and biogeochemical processes model, Biome-BGC-SD, to simulate forest dynamics for different soil profiles. These profiles included contrasting 50 cm textural layers of finer sand overlying coarser sand in which the sand layers had either a well graded or uniform soil texture. These profiles were compared to uniform profiles of the same sands. Three tree species of jack pine (Pinus banksiana Lamb.), white spruce (Picea glauce Voss.), and trembling aspen (Populus tremuloides Michx.) were simulated using a 50 year climatic data base from northern Alberta. Available water holding capacity (AWHC) was used to identify soil moisture regime, and leaf area index (LAI) and net primary production (NPP) were used as indices of forest productivity. Published physiological parameters were used in the Biome-BGC-SD model. Relative productivity was assessed by comparing model predictions to the measured above-ground biomass dynamics for the three tree species, and was then used to study the responses of forest leaf area index and potential productivity to AWHC on different soil profiles. Simulated results indicated soil layering could significantly increase AWHC in the 1-m profile for coarse textured soils. This enhanced AWHC could result in an increase in forest LAI and NPP. The increased extent varied with soil textures and vegetative types. The simulated results showed that the presence of 50 cm of coarser graded sand overlying 50 cm of finer graded sand is the most effective reclaimed prescription to increase AWHC and forest productivity among the studied soil profiles.

Incorporating maps of leaf chlorophyll in a thermal-based two-source energy balance scheme for mapping coupled fluxes of carbon and water exchange at a range of scales

NASA Astrophysics Data System (ADS)

Houborg, R.; Anderson, M. C.; Kustas, W. P.

2008-12-01

A light-use efficiency (LUE) based model of canopy resistance was recently implemented within a thermal- based Two-Source Energy Balance (TSEB) scheme facilitating coupled simulations of land-surface fluxes of water, energy and CO2 exchange from field to regional scales (Anderson et al., 2008). The LUE model component computes canopy-scale carbon assimilation and transpiration fluxes and incorporates LUE modifications from biome specific nominal values (Bn) in response to variations in humidity, CO2 concentration, temperature (soil and air), wind speed, and direct beam vs. diffuse light composition. Here we incorporate leaf chlorophyll content (Cab) as a determinant of spatial and temporal variations in Bn as Cab is related to key LUE modulating factors such as crop phenology, vegetation stress and photosynthetic capacity. A linear relationship between Bn and Cab, established from stand-level measurement of LUE for unstressed environmental conditions and a representative set of Cab values for a range of agricultural and natural vegetation groups, is used to distribute Bn over the modeling domain. The technique is tested for an agricultural area near Bushland, Texas by fusing reflective and thermal based remote sensing inputs from SPOT, Landsat, ASTER and aircraft sensor systems. Maps of LAI and Cab are generated by using at-sensor radiances in green, red and near-infrared wavelengths as input to a REGularized canopy reFLECtance (REGFLEC) modeling tool that couples leaf optics (PROSPECT), canopy reflectance (ACRM), and atmospheric radiative transfer (6SV1) model components. Modeled carbon and water fluxes are compared with eddy covariance measurements made in stands of cotton and with fluxes measured by an aircraft flying transects over irrigated and non-irrigated agricultural land and natural vegetation. The technique is flexible and scalable and is portable to continental scales using GOES and MODIS data products. The results demonstrate utility in combining remotely sensed observations in the reflective solar and thermal domains for estimating carbon and water fluxes within a coupled framework.

The response of surface mass and energy balance of a continental glacier to climate variability, western Qilian Mountains, China

NASA Astrophysics Data System (ADS)

Sun, Weijun; Qin, Xiang; Wang, Yetang; Chen, Jizu; Du, Wentao; Zhang, Tong; Huai, Baojuan

2017-08-01

To understand how a continental glacier responds to climate change, it is imperative to quantify the surface energy fluxes and identify factors controlling glacier mass balance using surface energy balance (SEB) model. Light absorbing impurities (LAIs) at the glacial surface can greatly decrease surface albedo and increase glacial melt. An automatic weather station was set up and generated a unique 6-year meteorological dataset for the ablation zone of Laohugou Glacier No. 12. Based on these data, the surface energy budget was calculated and an experiment on the glacial melt process was carried out. The effect of reduced albedo on glacial melting was analyzed. Owing to continuous accumulation of LAIs, the ablation zone had been darkening since 2010. The mean value of surface albedo in melt period (June through September) dropped from 0.52 to 0.43, and the minimum of daily mean value was as small as 0.1. From the records of 2010-2015, keeping the clean ice albedo fixed in the range of 0.3-0.4, LAIs caused an increase of +7.1 to +16 W m-2 of net shortwave radiation and an removal of 1101-2663 mm water equivalent. Calculation with the SEB model showed equivalent increases in glacial melt were obtained by increasing air temperature by 1.3 and 3.2 K, respectively.

Chlorophyll content retrieval from hyperspectral remote sensing imagery.

PubMed

Yang, Xiguang; Yu, Ying; Fan, Wenyi

2015-07-01

Chlorophyll content is the essential parameter in the photosynthetic process determining leaf spectral variation in visible bands. Therefore, the accurate estimation of the forest canopy chlorophyll content is a significant foundation in assessing forest growth and stress affected by diseases. Hyperspectral remote sensing with high spatial resolution can be used for estimating chlorophyll content. In this study, the chlorophyll content was retrieved step by step using Hyperion imagery. Firstly, the spectral curve of the leaf was analyzed, 25 spectral characteristic parameters were identified through the correlation coefficient matrix, and a leaf chlorophyll content inversion model was established using a stepwise regression method. Secondly, the pixel reflectance was converted into leaf reflectance by a geometrical-optical model (4-scale). The three most important parameters of reflectance conversion, including the multiple scattering factor (M 0 ), and the probability of viewing the sunlit tree crown (P T ) and the background (P G ), were estimated by leaf area index (LAI), respectively. The results indicated that M 0 , P T , and P G could be described as a logarithmic function of LAI, with all R (2) values above 0.9. Finally, leaf chlorophyll content was retrieved with RMSE = 7.3574 μg/cm(2), and canopy chlorophyll content per unit ground surface area was estimated based on leaf chlorophyll content and LAI. Chlorophyll content mapping can be useful for the assessment of forest growth stage and diseases.

Spatial variation and linkages of soil and vegetation in the Siberian Arctic tundra - coupling field observations with remote sensing data

NASA Astrophysics Data System (ADS)

Mikola, Juha; Virtanen, Tarmo; Linkosalmi, Maiju; Vähä, Emmi; Nyman, Johanna; Postanogova, Olga; Räsänen, Aleksi; Kotze, D. Johan; Laurila, Tuomas; Juutinen, Sari; Kondratyev, Vladimir; Aurela, Mika

2018-05-01

Arctic tundra ecosystems will play a key role in future climate change due to intensifying permafrost thawing, plant growth and ecosystem carbon exchange, but monitoring these changes may be challenging due to the heterogeneity of Arctic landscapes. We examined spatial variation and linkages of soil and plant attributes in a site of Siberian Arctic tundra in Tiksi, northeast Russia, and evaluated possibilities to capture this variation by remote sensing for the benefit of carbon exchange measurements and landscape extrapolation. We distinguished nine land cover types (LCTs) and to characterize them, sampled 92 study plots for plant and soil attributes in 2014. Moreover, to test if variation in plant and soil attributes can be detected using remote sensing, we produced a normalized difference vegetation index (NDVI) and topographical parameters for each study plot using three very high spatial resolution multispectral satellite images. We found that soils ranged from mineral soils in bare soil and lichen tundra LCTs to soils of high percentage of organic matter (OM) in graminoid tundra, bog, dry fen and wet fen. OM content of the top soil was on average 14 g dm-3 in bare soil and lichen tundra and 89 g dm-3 in other LCTs. Total moss biomass varied from 0 to 820 g m-2, total vascular shoot mass from 7 to 112 g m-2 and vascular leaf area index (LAI) from 0.04 to 0.95 among LCTs. In late summer, soil temperatures at 15 cm depth were on average 14 °C in bare soil and lichen tundra, and varied from 5 to 9 °C in other LCTs. On average, depth of the biologically active, unfrozen soil layer doubled from early July to mid-August. When contrasted across study plots, moss biomass was positively associated with soil OM % and OM content and negatively associated with soil temperature, explaining 14-34 % of variation. Vascular shoot mass and LAI were also positively associated with soil OM content, and LAI with active layer depth, but only explained 6-15 % of variation. NDVI captured variation in vascular LAI better than in moss biomass, but while this difference was significant with late season NDVI, it was minimal with early season NDVI. For this reason, soil attributes associated with moss mass were better captured by early season NDVI. Topographic attributes were related to LAI and many soil attributes, but not to moss biomass and could not increase the amount of spatial variation explained in plant and soil attributes above that achieved by NDVI. The LCT map we produced had low to moderate uncertainty in predictions for plant and soil properties except for moss biomass and bare soil and lichen tundra LCTs. Our results illustrate a typical tundra ecosystem with great fine-scale spatial variation in both plant and soil attributes. Mosses dominate plant biomass and control many soil attributes, including OM % and temperature, but variation in moss biomass is difficult to capture by remote sensing reflectance, topography or a LCT map. Despite the general accuracy of landscape level predictions in our LCT approach, this indicates challenges in the spatial extrapolation of some of those vegetation and soil attributes that are relevant for the regional ecosystem and global climate models.

Combining observations in the reflective solar and thermal domains for improved mapping of carbon, water and energy fluxes

USDA-ARS?s Scientific Manuscript database

The REGularized canopy reFLECtance (REGFLEC) modeling tool integrates leaf optics, canopy reflectance, and atmospheric radiative transfer model components, facilitating accurate retrieval of leaf area index (LAI) and leaf chlorophyll content (Cab) directly from at-sensor radiances in green, red and ...

Changes of global terrestrial carbon budget and major drivers in recent 30 years simulated using the remote sensing driven BEPS model

NASA Astrophysics Data System (ADS)

Ju, W.; Chen, J.; Liu, R.; Liu, Y.

2013-12-01

The process-based Boreal Ecosystem Productivity Simulator (BEPS) model was employed in conjunction with spatially distributed leaf area index (LAI), land cover, soil, and climate data to simulate the carbon budget of global terrestrial ecosystems during the period from 1981 to 2008. The BEPS model was first calibrated and validated using gross primary productivity (GPP), net primary productivity (NPP), and net ecosystem productivity (NEP) measured in different ecosystems across the word. Then, four global simulations were conducted at daily time steps and a spatial resolution of 8 km to quantify the global terrestrial carbon budget and to identify the relative contributions of changes in climate, atmospheric CO2 concentration, and LAI to the global terrestrial carbon sink. The long term LAI data used to drive the model was generated through fusing Moderate Resolution Imaging Spectroradiometer (MODIS) and historical Advanced Very High Resolution Radiometer (AVHRR) data pixel by pixel. The meteorological fields were interpolated from the 0.5° global daily meteorological dataset produced by the land surface hydrological research group at Princeton University. The results show that the BEPS model was able to simulate carbon fluxes in different ecosystems. Simulated GPP, NPP, and NEP values and their temporal trends exhibited distinguishable spatial patterns. During the period from 1981 to 2008, global terrestrial ecosystems acted as a carbon sink. The averaged global totals of GPP NPP, and NEP were 122.70 Pg C yr-1, 56.89 Pg C yr-1, and 2.76 Pg C yr-1, respectively. The global totals of GPP and NPP increased greatly, at rates of 0.43 Pg C yr-2 (R2=0.728) and 0.26 Pg C yr-2 (R2=0.709), respectively. Global total NEP did not show an apparent increasing trend (R2= 0.036), averaged 2.26 Pg C yr-1, 3.21 Pg C yr-1, and 2.72 Pg C yr-1 for the periods from 1981 to 1989, from 1990 to 1999, and from 2000 to 2008, respectively. The magnitude and temporal trend of global terrestrial carbon budget were similar to the values recently reported by the Global Carbon Project. The obvious increases in global GPP and NPP were mainly driven by the enhancement of atmospheric CO2 fertilization. The change of LAI played the secondary role. Climate had a small negative impact on global terrestrial carbon sequestration. The relative importance of changes in climate, atmospheric CO2 concentration, and LAI in altering the temporal trend of carbon sequestration differed spatially. During the period from 2000 to 2008, terrestrial carbon sinks mainly existed in the northern region of South America, the western region of middle Africa, Southeast Asia, Southeast China, Southeast United States, and some regions of Eurasia.

Free-space optical communication through a forest canopy.

PubMed

Edwards, Clinton L; Davis, Christopher C

2006-01-01

We model the effects of the leaves of mature broadleaf (deciduous) trees on air-to-ground free-space optical communication systems operating through the leaf canopy. The concept of leaf area index (LAI) is reviewed and related to a probabilistic model of foliage consisting of obscuring leaves randomly distributed throughout a treetop layer. Individual leaves are opaque. The expected fractional unobscured area statistic is derived as well as the variance around the expected value. Monte Carlo simulation results confirm the predictions of this probabilistic model. To verify the predictions of the statistical model experimentally, a passive optical technique has been used to make measurements of observed sky illumination in a mature broadleaf environment. The results of the measurements, as a function of zenith angle, provide strong evidence for the applicability of the model, and a single parameter fit to the data reinforces a natural connection to LAI. Specific simulations of signal-to-noise ratio degradation as a function of zenith angle in a specific ground-to-unmanned aerial vehicle communication situation have demonstrated the effect of obscuration on performance.

Hydroclimatic Controls over Global Variations in Phenology and Carbon Flux

NASA Technical Reports Server (NTRS)

Koster, Randal; Walker, G.; Thornton, Patti; Collatz, G. J.

2012-01-01

The connection between phenological and hydroclimatological variations are quantified through joint analyses of global NDVI, LAI, and precipitation datasets. The global distributions of both NDVI and LAI in the warm season are strongly controlled by three quantities: mean annual precipitation, the standard deviation of annual precipitation, and Budyko's index of dryness. Upon demonstrating that these same basic (if biased) relationships are produced by a dynamic vegetation model (the dynamic vegetation and carbon storage components of the NCAR Community Land Model version 4 combined with the water and energy balance framework of the Catchment Land Surface Model of the NASA Global Modeling and Assimilation Office), we use the model to perform a sensitivity study focusing on how phenology and carbon flux might respond to climatic change. The offline (decoupled from the atmosphere) simulations show us, for example, where on the globe a given small increment in precipitation mean or variability would have the greatest impact on carbon uptake. The analysis framework allows us in addition to quantify the degree to which climatic biases in a free-running GCM are manifested as biases in simulated phenology.

Hydroclimatic Controls over Global Variations in Phenology and Carbon Flux

NASA Astrophysics Data System (ADS)

Koster, R. D.; Walker, G.; Thornton, P. E.; Collatz, G. J.

2012-12-01

The connection between phenological and hydroclimatological variations are quantified through joint analyses of global NDVI, LAI, and precipitation datasets. The global distributions of both NDVI and LAI in the warm season are strongly controlled by three quantities: mean annual precipitation, the standard deviation of annual precipitation, and Budyko's index of dryness. Upon demonstrating that these same basic (if somewhat biased) relationships are produced by a dynamic vegetation model (the dynamic vegetation and carbon storage components of the NCAR Community Land Model version 4 combined with the water and energy balance framework of the Catchment Land Surface Model of the NASA Global Modeling and Assimilation Office), we use the model to perform a sensitivity study focusing on how phenology and carbon flux might respond to climatic change. The offline (decoupled from the atmosphere) simulations show us, for example, where on the globe a given small increment in precipitation mean or variability would have the greatest impact on carbon uptake. The analysis framework allows us in addition to quantify the degree to which climatic biases in a free-running GCM are manifested as biases in simulated phenology.

Assessing the effects of architectural variations on light partitioning within virtual wheat–pea mixtures

PubMed Central

Barillot, Romain; Escobar-Gutiérrez, Abraham J.; Fournier, Christian; Huynh, Pierre; Combes, Didier

2014-01-01

Background and Aims Predicting light partitioning in crop mixtures is a critical step in improving the productivity of such complex systems, and light interception has been shown to be closely linked to plant architecture. The aim of the present work was to analyse the relationships between plant architecture and light partitioning within wheat–pea (Triticum aestivum–Pisum sativum) mixtures. An existing model for wheat was utilized and a new model for pea morphogenesis was developed. Both models were then used to assess the effects of architectural variations in light partitioning. Methods First, a deterministic model (L-Pea) was developed in order to obtain dynamic reconstructions of pea architecture. The L-Pea model is based on L-systems formalism and consists of modules for ‘vegetative development’ and ‘organ extension’. A tripartite simulator was then built up from pea and wheat models interfaced with a radiative transfer model. Architectural parameters from both plant models, selected on the basis of their contribution to leaf area index (LAI), height and leaf geometry, were then modified in order to generate contrasting architectures of wheat and pea. Key results By scaling down the analysis to the organ level, it could be shown that the number of branches/tillers and length of internodes significantly determined the partitioning of light within mixtures. Temporal relationships between light partitioning and the LAI and height of the different species showed that light capture was mainly related to the architectural traits involved in plant LAI during the early stages of development, and in plant height during the onset of interspecific competition. Conclusions In silico experiments enabled the study of the intrinsic effects of architectural parameters on the partitioning of light in crop mixtures of wheat and pea. The findings show that plant architecture is an important criterion for the identification/breeding of plant ideotypes, particularly with respect to light partitioning. PMID:24907314

Developing Remote Sensing Products for Monitoring and Modeling Great Lakes Coastal Wetland Vulnerability to Climate Change and Land Use

NASA Astrophysics Data System (ADS)

Bourgeau-Chavez, L. L.; Miller, M. E.; Battaglia, M.; Banda, E.; Endres, S.; Currie, W. S.; Elgersma, K. J.; French, N. H. F.; Goldberg, D. E.; Hyndman, D. W.

2014-12-01

Spread of invasive plant species in the coastal wetlands of the Great Lakes is degrading wetland habitat, decreasing biodiversity, and decreasing ecosystem services. An understanding of the mechanisms of invasion is crucial to gaining control of this growing threat. To better understand the effects of land use and climatic drivers on the vulnerability of coastal zones to invasion, as well as to develop an understanding of the mechanisms of invasion, research is being conducted that integrates field studies, process-based ecosystem and hydrological models, and remote sensing. Spatial data from remote sensing is needed to parameterize the hydrological model and to test the outputs of the linked models. We will present several new remote sensing products that are providing important physiological, biochemical, and landscape information to parameterize and verify models. This includes a novel hybrid radar-optical technique to delineate stands of invasives, as well as natural wetland cover types; using radar to map seasonally inundated areas not hydrologically connected; and developing new algorithms to estimate leaf area index (LAI) using Landsat. A coastal map delineating wetland types including monocultures of the invaders (Typha spp. and Phragmites austrailis) was created using satellite radar (ALOS PALSAR, 20 m resolution) and optical data (Landsat 5, 30 m resolution) fusion from multiple dates in a Random Forests classifier. These maps provide verification of the integrated model showing areas at high risk of invasion. For parameterizing the hydrological model, maps of seasonal wetness are being developed using spring (wet) imagery and differencing that with summer (dry) imagery to detect the seasonally wet areas. Finally, development of LAI remote sensing high resolution algorithms for uplands and wetlands is underway. LAI algorithms for wetlands have not been previously developed due to the difficulty of a water background. These products are being used to improve the hydrological model through higher resolution products and parameterization of variables that have previously been largely unknown.

Willingness of community-recruited men who have sex with men in Washington, DC to use long-acting injectable HIV pre-exposure prophylaxis

PubMed Central

Patrick, Rudy; Gamble, Jonjelyn; Rawls, Anthony; Opoku, Jenevieve; Magnus, Manya; Kharfen, Michael; Greenberg, Alan E.; Kuo, Irene

2017-01-01

Objectives Clinical trials are currently investigating the safety and efficacy of long-acting injectable (LAI) agents as HIV pre-exposure prophylaxis (PrEP). Using National HIV Behavioral Surveillance data, we assessed the self-reported willingness of men who have sex with men (MSM) to use LAI PrEP and their preference for LAI versus daily oral PrEP. Methods In 2014, venue-based sampling was used to recruit MSM aged ≥18 years in Washington, DC. Participants completed an interviewer-administered survey followed by voluntary HIV testing. This analysis included MSM who self-reported negative/unknown HIV status at study entry. Correlates of being “very likely” to use LAI PrEP and preferring it to daily oral PrEP were identified using multivariable logistic regression. Results Of 314 participants who self-reported negative/unknown HIV status, 50% were <30 years old, 41% were non-Hispanic Black, 37% were non-Hispanic White, and 14% were Hispanic. If LAI PrEP were offered for free or covered by health insurance, 62% were very likely, 25% were somewhat likely, and 12% were unlikely to use it. Regarding preferred PrEP modality, 67% chose LAI PrEP, 24% chose oral PrEP, and 9% chose neither. Correlates of being very likely versus somewhat likely/unlikely to use LAI PrEP included age <30 years (aOR 1.64; 95% CI 1.00–2.68), reporting ≥6 (vs. 1) sex partners in the last year (aOR 2.60; 95% CI 1.22–5.53), previous oral PrEP use (aOR 3.67; 95% CI 1.20–11.24), and being newly identified as HIV-infected during study testing (aOR 4.83; 95% CI 1.03–22.67). Black (vs. White) men (aOR 0.48; 95% CI 0.24–0.96) and men with an income of <$20,000 (vs. ≥$75,000; aOR 0.37; 95% CI 0.15–0.93) were less likely to prefer LAI to oral PrEP. Conclusions If LAI PrEP were found to be efficacious, its addition to the HIV prevention toolkit could facilitate more complete PrEP coverage among MSM at risk for HIV. PMID:28827821

Ivermectin treatment of bovine psoroptic mange: effects on serum chemistry, hematology, organ weights, and leather quality.

PubMed

Rehbein, S; Visser, M; Meyer, M; Lindner, T

2016-04-01

Psoroptic mange is a skin disease which may result in serious health and welfare problems and important economic losses. Apart from the effect on weight gain, little information is available concerning other responses of the organism consequent to the successful therapy of bovine psoroptic mange. Accordingly, serum chemistry, hematology, organ weights, and leather quality of young bulls with experimentally induced clinical Psoroptes ovis mange and treated with either ivermectin long-acting injection (IVM LAI; IVOMEC(®) GOLD, Merial) or saline (n = 16 each) were examined 8 weeks after treatment when all IVM LAI-treated bulls were free of live P. ovis mites while the saline-treated bulls maintained clinical mange. IVM LAI-treated bulls had higher (p < 0.05) alkaline phosphatase, creatinine, cholesterol, glucose, and albumin levels and lower (p < 0.01) total protein and β- and γ-globulin levels than the saline-treated bulls. Complete blood counts revealed higher leukocyte counts associated with higher eosinophil counts and higher platelet counts in the saline-treated compared to the IVM LAI-treated bulls (p < 0.01). Correlating with body weight, the warm carcass weight of the saline-treated bulls was lower than that of the IVM LAI-treated bulls (p < 0.05). Absolute and relative (organ weight divided by body weight) weights of the spleen, thymus, omental fat, and perirenal fat were higher (p < 0.01) for the IVM LAI-treated bulls than for the saline-treated bulls, while the IVM LAI-treated bulls had lower (p < 0.05) absolute and relative weights of the liver, adrenal glands, and selected lymph nodes than the saline-treated bulls. The leathers produced from the IVM LAI-treated bulls showed significantly (p < 0.001) less severe gouging or etching than leathers from the saline-treated bulls, and significantly (p < 0.05) more leather from the IVM LAI-treated bulls was of usable quality than the size of leather from the saline-treated bulls. Overall, these findings provided evidence that many changes, which are indicative of impaired protein and energy metabolism, immune system function, and performance resultant from clinical psoroptic mange, improved substantially within 8 weeks of successful treatment with injectable ivermectin.

Preferences for a potential longer-acting injectable contraceptive: perspectives from women, providers, and policy makers in Kenya and Rwanda.

PubMed

Tolley, Elizabeth E; McKenna, Kevin; Mackenzie, Caroline; Ngabo, Fidele; Munyambanza, Emmanuel; Arcara, Jennet; Rademacher, Kate H; Lendvay, Anja

2014-05-01

Between 1995 and 2005, injectable use doubled worldwide. However, discontinuation rates remain high, partly because of side effects but also because of missed appointments for reinjection. A longer-acting injectable (LAI) may improve compliance by reducing the required number of reinjection visits, thereby reducing unintentional discontinuation. This study examined acceptability of LAI characteristics comprising the target product profile (TPP). In 2012, we conducted qualitative case studies in Kenya and Rwanda, consisting of 19 focus group discussions (FGDs) with 177 current, previous, or never users of injectables and 46 in-depth interviews (IDIs) with providers, program implementers, and policy makers. FGDs and IDIs assessed current injectable experiences; attitudes toward potential LAI products; and perceptions of TPP attributes, including ranking preferences for the most and least important characteristics. In addition, we obtained completed electronic surveys from 28 international family planning opinion leaders about the perceived need for an LAI, important product characteristics, and challenges to LAI development or introduction. Many FGD participants and interviewees spontaneously expressed strong interest in an LAI, but there was some variation in TPP preferences. The majority of participants ranked effectiveness as the most important TPP attribute. Providers were generally more concerned about side effects than potential users; some potential users suggested that side effects were related less to the product than to their own body chemistry and that side effects were acceptable as long as they did not last a long time or disrupt daily activities. Women and providers, especially in Kenya, preferred a method with a predictable return to fertility. Some participants associated amenorrhea with delayed or reduced fertility. Most women and providers preferred delivery of the LAI in a single, prepackaged, disposable injection system to facilitate injections by providers and to reduce the risk of pain or discomfort for women. While providers and policy makers ranked cost as one of the most important issues, it was among the least important issues for most potential users. Many Kenyan, but few Rwandan, participants appeared willing to pay for an LAI, with some presuming cost savings from reduced menstruation and fewer clinic visits. Some TPP preferences for an LAI have implications for product development decisions about formulation, delivery mechanism, or presentation, while others point to the need for tailored communication and counseling approaches to ensure acceptability and adherence within clinical trials and beyond.

Tree specific traits vs. stand level characteristics - assessing the source depths of plant water uptake in a mixed forest stand

NASA Astrophysics Data System (ADS)

Seeger, Stefan; Brinkmann, Nadine; Kahmen, Ansgar; Weiler, Markus

2017-04-01

Due to differences in fine root distributions, physiological root characteristics and plant plasticity, the spatial and temporal characteristics of plant water uptake are expected to vary between different tree species. This has implications on the overall water budget of a forest stand as well as on the drought sensitivity of particular trees. A four-year time series of climate data, soil moisture, and stable water isotopes in soil and tree xylem was used to investigate plant water uptake dynamics of four tree species (beech - Fagus sylvatica, spruce - Picea abies, ash - Fraxinus excelsior and maple - Acer pseudoplatanus) in a mixed forest stand. Modeling with a modified version of the soil hydrological model Hydrus-1D allowed us to simulate continuous time series of stable water isotopes in plant water uptake, which were compared to the measured values in tree xylem water and soil water. We found that different estimated species specific fine root distributions and root water uptake parameters lead to very similar simulated water balances and soil water isotope depth profiles for all four species. According to our simulations, differences in evaporative demand (i.e. LAI) had the biggest influence on water uptake and soil water distributions. Comparing the isotopic signatures of simulated root water uptake and measured xylem water, the simulations for beech were most suited to predict the observed signatures of all four species. This indicates that isolated, tree specific parametrized 1-D simulations are not suited to predict actual water uptake of different trees in a mixed stand. Due to overlapping root spaces dominant trees (in our case beeches with an LAI of around 5.5) may influence the soil water storage below accompanying trees (spruces, ashes and maples with LAIs between 1.8 and 3.1) in a degree that their actual water uptake cannot be predicted with 1-D simulations based on their smaller LAI values. Consequently, for a mixed forest stand the interplay of trees with different traits has to be accounted for in order to correctly model plant water uptake of single trees.

Afforestation may have little effect on hydrological cycle over the Three-North region of China

NASA Astrophysics Data System (ADS)

Meng, S.; Xie, X.

2017-12-01

Afforestation or reforestation is generally effective to improve environmental conditions, and it may have substantial impact on hydrological cycle by increasing rainfall interception and transpiration. To combat desertification and to control dust storms, China has implemented a few Large-scale afforestation programs since 1980s, including the world's most ambitious afforestation program, the Three-North Forest Shelterbelt (TNFS) program in the arid and semiarid land areas. This afforestation plan covers about 4 million km2 (> 42%) of the land area of China. Although the TNFS program eased environmental problems in the region to some degree, the consequences of large-scale afforestation on hydrological cycles is still controversial. To identify the impact of the afforestation on hydrological cycle at regional scale, we employed a large-scale hydrological model, i.e., the Variable Infiltration Capacity (VIC) model, and satellite remote sensing data sets, i.e., leaf area index (LAI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Global LAnd Surface satellite (GLASS). The VIC modelling was forced with long-term dynamic LAI and gridded atmospheric data. We focused on the period of 2000-2015 when fewer afforestation activities implemented and the vegetation in steady growth stage in the three-north region. The results show that, despite the spatial heterogeneity, LAI in the growing season exhibits a slight increase across the three-north region, which is the contribution of the vegetation growth due to afforestation program. Evapotranspiration (ET) increased at a rate of 3.93 mm/yr over the whole region from 2000 to 2015. The spatial pattern of ET is consistent with the changes in LAI and precipitation, but this does not mean vegetation growth contributed equally. Based on factor-distinguishing simulations, we found that precipitation change has more significant influence on hydrological cycle than vegetation growth. Therefore, the afforestation practices are influential at small-catchment scale, but at regional scale, they may have little effect on the hydrological cycles. For sustainable water resource management, we should pay special attention on climate change rather than the afforestation efforts.

Large-scale Agroecosytem's Resiliency to Extreme Hydrometeorological and Climate Extreme Events in the Missouri River Basin

NASA Astrophysics Data System (ADS)

Munoz-Arriola, F.; Smith, K.; Corzo, G.; Chacon, J.; Carrillo-Cruz, C.

2015-12-01

A major challenge for water, energy and food security relies on the capability of agroecosyststems and ecosystems to adapt to a changing climate and land use changes. The interdependency of these forcings, understood through our ability to monitor and model processes across scales, indicate the "depth" of their impact on agroecosystems and ecosystems, and consequently our ability to predict the system's ability to return to a "normal" state. We are particularly interested in explore two questions: (1) how hydrometeorological and climate extreme events (HCEs) affect sub-seasonal to interannual changes in evapotranspiration and soil moisture? And (2) how agroecosystems recover from the effect of such events. To address those questions we use the land surface hydrologic Variable Infiltration Capacity (VIC) model and the Moderate Resolution Imaging Spectrometer-Leaf Area Index (MODIS-LAI) over two time spans (1950-2013 using a seasonal fixed LAI cycle) and 2001-2013 (an 8-day MODIS-LAI). VIC is forced by daily/16th degree resolution precipitation, minimum and maximum temperature, and wind speed. In this large-scale experiment, resiliency is defined by the capacity of a particular agroecosystem, represented by a grid cell's ET, SM, and LAI to return to a historical average. This broad, yet simplistic definition will contribute to identify the possible components and their scales involved in agroecosystems and ecosystems capacity to adapt to the incidence of HCEs and technologies used to intensify agriculture and diversify their use for food and energy production. Preliminary results show that dynamical changes in land use, tracked by MODIS data, require larger time spans to address properly the influence of technologic improvements in crop production as well as the competition for land for biofuel vs. food production. On the other hand, fixed seasonal changes in land use allow us just to identify hydrologic changes mainly due to climate variability.

Performance of shrub willows (Salix spp.) as an evapotranspiration cover on Solvay wastebeds

NASA Astrophysics Data System (ADS)

Mirck, Jaconette

2009-12-01

Soda ash (Na2CO3) production in the Syracuse New York area created 607 ha of wastebeds over the course of about 100 years. Today the primary concern of the Solvay wastebeds is high chloride concentrations in the leachate and storm water that may end up in the groundwater and nearby Onondaga Lake. The potential of shrub willow evapotranspiration (ET) covers to minimize leaching and to manage storm water was assessed in two studies. A sap flow sensor field study to estimate transpiration rates of four shrub willow varieties over an entire growing season. A greenhouse study focused on recycling saline Solvay storm water onto shrub willows. Annual sap flow and crop coefficients (Kc) were similar among four shrub willows, but differences were present over the course of the growing season. Peak K c values did not coincide with peak leaf area index (LAI), as might be expected if LAI were the main driver of transpiration. Rather than solely being driven by LAI, coupling with the atmosphere was an important factor in stand level sap flow. Estimates of ET were measured during both experiments, the ET/sap flow rankings of the shrub willow varieties were similar; Salix miyabeana (SX64)< S. purpurea (9882-34)< S. miyabeana x S. sachalinensis (9870-23 or 9870-40). In the greenhouse study, Solvay storm water that contained 1,625 mg Cl - L-1 (close to the average storm water concentration) did not significantly decrease ET values or growth for any of the willow varieties. Mass balances of sodium and chloride were carried out to assess the potentials of recycling saline Solvay storm water back onto a shrub willow ET cover during the growing season. During a ten-week study the combination of a shallow depth soil (33 cm) and a high irrigation regime (170% of average precipitation in the Syracuse NY area) resulted in the accumulation of at least 62% of both sodium and chloride in the plant/soil system for all five Solvay storm water treatments. Both studies indicated that shrub willows have the characteristics to be part of a sustainable ET cover on the Solvay wastebeds, which will decrease leaching of sodium and chloride. Key words. Coupling/decoupling, crop coefficient, hydraulic control, leaf area index, mass balance, phytoremediation, sap flow.

Long-acting injectable antipsychotics for prevention and management of violent behaviour in psychotic patients.

PubMed

Mohr, Pavel; Knytl, Pavel; Voráčková, Veronika; Bravermanová, Anna; Melicher, Tomáš

2017-09-01

It has been well established that long-term antipsychotic treatment prevents relapse, lowers number of rehospitalisations, and also effectively reduces violent behaviour. Although violent behaviour is not a typical manifestation of schizophrenia or other psychotic disorders, the diagnosis of psychosis increases the overall risk of violence. One of the few modifiable factors of violence risk is adherence with medication. In contrast, non-adherence with drug treatment and subsequent relapse increases risk of violent acts. Non-adherence can be addressed partially by long-acting injectable antipsychotics (LAI). The aim of our review was to examine the role of antipsychotic drugs, especially LAI, in prevention and management of violent behaviour in psychosis. This is a non-systematic, narrative review of the data from open, naturalistic, retrospective, and population studies, case series, and post hoc analyses of randomised controlled trials. Search of electronic databases (PubMed, Embase) was performed to identify relevant papers. Nine published papers (3 cross-sectional chart reviews, 4 retrospective studies, 2 prospective, randomised trials) were found. The results indicated positive clinical and antiaggressive effects of LAI in psychotic patients with high risk of violent behaviour. Reviewed evidence suggests that secured drug treatment with LAI may have clinical benefit in schizophrenia patients with high risk of violent behaviour. LAI significantly reduced the severity of hostility, aggressivity, number of violent incidents, and criminal offences. These findings are supported further by the empirical evidence from clinical practice, high rates of prescribed LAI to schizophrenia patients in high-security and forensic psychiatric facilities. Available data encourage the use of LAI in forensic psychiatry, especially during court-ordered commitment treatment. © 2017 John Wiley & Sons Ltd.

Modeling the budget impact of long-acting injectable paliperidone palmitate in the treatment of schizophrenia in Japan

PubMed Central

Mahlich, Jörg; Nishi, Masamichi; Saito, Yoshimichi

2015-01-01

Background The cost of schizophrenia in Japan is high and new long-acting injectable (LAI) antipsychotics might be able to reduce costs by causing a reduction of hospital stays. We aim to estimate budget effects of the introduction of a new 1-month LAI, paliperidone palmitate, in Japan. Methods A budget impact analysis was conducted from a payer perspective. The model took direct costs of illness into account (ie, costs for inpatient and outpatient services, as well as drug costs). The robustness of the model was checked using a sensitivity analysis. Results According to our calculations, direct total costs of schizophrenia reach 710,500 million yen a year (US$6 billion). These costs decrease to 691,000 million yen (US$5.9 billion) 3 years after the introduction of paliperidone palmitate. Conclusion From a payer point of view, the introduction of a new treatment for schizophrenia in Japan helps to save resources and is not associated with a higher financial burden. PMID:26045674

Multi-Decadal Pathfinder Data Sets of Global Land Biophysical Variables from AVHRR and MODIS and their Use in GCM Studies of Biogeophysics and Biogeochemistry

NASA Technical Reports Server (NTRS)

Myneni, Ranga

2003-01-01

The problem of how the scale, or spatial resolution, of reflectance data impacts retrievals of vegetation leaf area index (LAI) and fraction absorbed photosynthetically active radiation (PAR) has been investigated. We define the goal of scaling as the process by which it is established that LAI and FPAR values derived from coarse resolution sensor data equal the arithmetic average of values derived independently from fine resolution sensor data. The increasing probability of land cover mixtures with decreasing resolution is defined as heterogeneity, which is a key concept in scaling studies. The effect of pixel heterogeneity on spectral reflectances and LAI/FPAR retrievals is investigated with 1 km Advanced Very High Resolution Radiometer (AVHRR) data aggregated to different coarse spatial resolutions. It is shown that LAI retrieval errors at coarse resolution are inversely related to the proportion of the dominant land cover in such pixel. Further, large errors in LAI retrievals are incurred when forests are minority biomes in non-forest pixels compared to when forest biomes are mixed with one another, and vice-versa. A physically based technique for scaling with explicit spatial resolution dependent radiative transfer formulation is developed. The successful application of this theory to scaling LAI retrievals from AVHRR data of different resolutions is demonstrated

Leaf area index uncertainty estimates for model-data fusion applications

Treesearch

Andrew D. Richardson; D. Bryan Dail; D.Y. Hollinger

2011-01-01

Estimates of data uncertainties are required to integrate different observational data streams as model constraints using model-data fusion. We describe an approach with which random and systematic uncertainties in optical measurements of leaf area index [LAI] can be quantified. We use data from a measurement campaign at the spruce-dominated Howland Forest AmeriFlux...

Towards an understanding of coupled physical and biological processes in the cultivated Sahel - 2. Vegetation and carbon dynamics

NASA Astrophysics Data System (ADS)

Boulain, N.; Cappelaere, B.; Ramier, D.; Issoufou, H. B. A.; Halilou, O.; Seghieri, J.; Guillemin, F.; Oï, M.; Gignoux, J.; Timouk, F.

2009-08-01

SummaryThis paper analyses the dynamics of vegetation and carbon during the West African monsoon season, for millet crop and fallow vegetation covers in the cultivated area of the Sahel. Comparing these two dominant land cover types informs on the impact of cultivation on productivity and carbon fluxes. Biomass, leaf area index (LAI) and carbon fluxes were monitored over a 2-year period for these two vegetation systems in the Wankama catchment of the AMMA (African monsoon multidisciplinary analyses) experimental super-site in West Niger. Carbon fluxes and water use efficiency observed at the field scale are confronted with ecophysiological measurements (photosynthetic response to light, and relation of water use efficiency to air humidity) made at the leaf scale for the dominant plant species in the two vegetation systems. The two rainy seasons monitored were dissimilar with respect to rain patterns, reflecting some of the interannual variability. Distinct responses in vegetation development and in carbon dynamics were observed between the two vegetation systems. Vegetation development in the fallow was found to depend more on rainfall distribution along the season than on its starting date. A quite opposite behaviour was observed for the crop vegetation: the date of first rain appears as a principal factor of millet growth. Carbon flux exchanges were well correlated to vegetation development. High responses of photosynthesis to light were observed for the dominant herbaceous and shrub species of the fallow at the leaf and field scales. Millet showed high response at the leaf scale, but a much lesser response at the field scale. This pattern, also observed for water use efficiency, is to be related to the low density of the millet cover. A simple LAI-based model for scaling up the photosynthetic response from leaf to field scale was found quite successful for the fallow, but was less conclusive for the crop, due to spatial variability of LAI. Time/space variations in leaf distribution for the dominant species are key to scale transition of carbon dynamics. Results obtained for the two vegetation covers are important in light of the major land use/cover change experienced in the Sahel region due to extensive savanna clearing for food production.

Tree crown structural characterization: A study using terrestrial laser scanning and three-dimensional radiative transfer modeling

NASA Astrophysics Data System (ADS)

Moorthy, Inian

Spectroscopic observational data for vegetated environments, have been coupled with 3D physically-based radiative transfer models for retrievals of biochemical and biophysical indicators of vegetation health and condition. With the recent introduction of Terrestrial Laser Scanning (TLS) units, there now exists a means of rapidly measuring intricate structural details of vegetation canopies, which can also serve as input into 3D radiative transfer models. In this investigation, Intelligent Laser Ranging and Imaging System (ILRIS-3D) data was acquired of individual tree crowns in laboratory, and field-based experiments. The ILRIS-3D uses the Time-Of-Flight (TOF) principle to measure the distances of objects based on the time interval between laser pulse exitance and return, upon reflection from an object. At the laboratory-level, this exploratory study demonstrated and validated innovative approaches for retrieving crown-level estimates of Leaf Area Index (LAI) (r2 = 0.98, rmse = 0.26m2/m2), a critical biophysical parameter for vegetation monitoring and modeling. These methods were implemented and expanded in field experiments conducted in olive (Olea europaea L.) orchards in Cordoba, Spain, where ILRIS-3D observations for 24 structurally-variable trees were made. Robust methodologies were developed to characterize diagnostic architectural parameters, such as tree height (r2 = 0.97, rmse = 0.21m), crown width (r 2 = 0.98, rmse = 0.12m), crown height (r2 = 0.81, rmse = 0.11m), crown volume (r2 = 0.99, rmse = 2.6m3), and LAI (r2 = 0.76, rmse = 0.27m2/ m2). These parameters were subsequently used as direct inputs into the Forest LIGHT (FLIGHT) 3D ray tracing model for characterization of the spectral behavior of the olive crowns. Comparisons between FLIGHT-simulated spectra and measured data showed small differences in the visible (< 3%) and near infrared (< 10%) spectral ranges. These differences between model simulations and measurements were significantly correlated to TLS-derived tree crown complexity metrics. The specific implications of internal crown complexity on estimating leaf chlorophyll concentration, a pertinent physiological health indicator, is highlighted. This research demonstrates that TLS systems can potentially be the new observational tool and benchmark for precise characterization of vegetation architecture for synergy with 3D radiative transfer models for improved operational management of agricultural crops.

Long-term records of global radiation, carbon and water fluxes derived from multi-satellite data and a process-based model

NASA Astrophysics Data System (ADS)

Ryu, Youngryel; Jiang, Chongya

2016-04-01

To gain insights about the underlying impacts of global climate change on terrestrial ecosystem fluxes, we present a long-term (1982-2015) global radiation, carbon and water fluxes products by integrating multi-satellite data with a process-based model, the Breathing Earth System Simulator (BESS). BESS is a coupled processed model that integrates radiative transfer in the atmosphere and canopy, photosynthesis (GPP), and evapotranspiration (ET). BESS was designed most sensitive to the variables that can be quantified reliably, fully taking advantages of remote sensing atmospheric and land products. Originally, BESS entirely relied on MODIS as input variables to produce global GPP and ET during the MODIS era. This study extends the work to provide a series of long-term products from 1982 to 2015 by incorporating AVHRR data. In addition to GPP and ET, more land surface processes related datasets are mapped to facilitate the discovery of the ecological variations and changes. The CLARA-A1 cloud property datasets, the TOMS aerosol datasets, along with the GLASS land surface albedo datasets, were input to a look-up table derived from an atmospheric radiative transfer model to produce direct and diffuse components of visible and near infrared radiation datasets. Theses radiation components together with the LAI3g datasets and the GLASS land surface albedo datasets, were used to calculate absorbed radiation through a clumping corrected two-stream canopy radiative transfer model. ECMWF ERA interim air temperature data were downscaled by using ALP-II land surface temperature dataset and a region-dependent regression model. The spatial and seasonal variations of CO2 concentration were accounted by OCO-2 datasets, whereas NOAA's global CO2 growth rates data were used to describe interannual variations. All these remote sensing based datasets are used to run the BESS. Daily fluxes in 1/12 degree were computed and then aggregated to half-month interval to match with the spatial-temporal resolution of LAI3g dataset. The BESS GPP and ET products were compared to other independent datasets including MPI-BGC and CLM. Overall, the BESS products show good agreement with the other two datasets, indicating a compelling potential for bridging remote sensing and land surface models.

Improving the frequency of high spatial resolution leaf area index maps using Landsat OLI and Sentinel-2 MSI

NASA Astrophysics Data System (ADS)

Li, S.; Ganguly, S.; Dungan, J. L.; Zhang, G.; Ju, J.; Claverie, M.

2015-12-01

The European Space Agency's Sentinel-2 mission successfully launched the first of two satellites in June, 2015. Sentinel 2A's MSI instrument is now providing optical data similar to Landsat 8's OLI imagery and, with its global repeat of 10 days, has the potential to increase the availability of 30m resolution high level products such as leaf area index (LAI). Prior to the launch of S-2A, we simulated MSI imagery using EO-1 Hyperion data and estimated green LAI using an algorithm based on canopy spectral invariants theory. Comparison of the resulting LAI maps resulting from the simulated MSI and corresponding maps derived from OLI data showed a RMSE of 0.1875. Uncertainty bounds on actual MSI data promise to be narrower because of the superior signal-to-noise ratio of MSI. A workflow for the production of LAI and other high level products including data ingest, BRDF correction, cloud masking and atmospheric correction is being developed using the NASA Earth Exchange (NEX) and will improve the capability to examine seasonal changes in canopy LAI.

Light-absorbing impurities enhance glacier albedo reduction in the southeastern Tibetan plateau

NASA Astrophysics Data System (ADS)

Zhang, Yulan; Kang, Shichang; Cong, Zhiyuan; Schmale, Julia; Sprenger, Michael; Li, Chaoliu; Yang, Wei; Gao, Tanguang; Sillanpää, Mika; Li, Xiaofei; Liu, Yajun; Chen, Pengfei; Zhang, Xuelei

2017-07-01

Light-absorbing impurities (LAIs) in snow of the southeastern Tibetan Plateau (TP) and their climatic impacts are of interest not only because this region borders areas affected by the South Asian atmospheric brown clouds but also because the seasonal snow and glacier melt from this region form important headwaters of large rivers. In this study, we collected surface snow and snowpit samples from four glaciers in the southeastern TP in June 2015 to investigate the comprehensive observational data set of LAIs. Results showed that the LAI concentrations were much higher in the aged snow and granular ice than in the fresh snow and snowpits due to postdepositional processes. Impurity concentrations fluctuated across snowpits, with maximum LAI concentrations frequently occurring toward the bottom of snowpits. Based on the SNow ICe Aerosol Radiative model, the albedo simulation indicated that black carbon and dust account for approximately 20% of the albedo reduction relative to clean snow. The radiative forcing caused by black carbon and dust deposition on the glaciers were between 1.0-141 W m-2 and 1.5-120 W m-2, respectively. Black carbon (BC) played a larger role in albedo reduction and radiative forcing than dust in the study area, enhancing approximately 15% of glacier melt. Analysis based on the Fire INventory from NCAR indicated that nonbiomass-burning sources of BC played an important role in the total BC deposition, especially during the monsoon season. This study suggests that eliminating anthropogenic BC could mitigate glacier melt in the future of the southeastern TP.

Influence of topographic heterogeneity on the abandance of larch forest in eastern Siberia

NASA Astrophysics Data System (ADS)

Sato, H.; Kobayashi, H.

2016-12-01

In eastern Siberia, larches (Larix spp.) often exist in pure stands, constructing the world's largest coniferous forest, of which changes can significantly affect the earth's albedo and the global carbon balance. We have conducted simulation studies for this vegetation, aiming to forecast its structures and functions under changing climate (1, 2). In previous studies of simulating vegetation at large geographical scales, the examining area is divided into coarse grid cells such as 0.5 * 0.5 degree resolution, and topographical heterogeneities within each grid cell are just ignored. However, in Siberian larch area, which is located on the environmental edge of existence of forest ecosystem, abundance of larch trees largely depends on topographic condition at the scale of tens to hundreds meters. We, therefore, analyzed patterns of within-grid-scale heterogeneity of larch LAI as a function of topographic condition, and examined its underlying reason. For this analysis, larch LAI was estimated for each 1/112 degree from the SPOT-VEGETATION data, and topographic properties such as angularity and aspect direction were estimated form the ASTER-GDEM data. Through this analysis, we found that, for example, sign of correlation between angularity and larch LAI depends on hydrological condition on the grid cell. We then refined the hydrological sub-model of our vegetation model SEIB-DGVM, and validated whether the modified model can reconstruct these patterns, and examined its impact on the estimation of biomass and vegetation productivity of entire larch region. -- References --1. Sato, H., et al. (2010). "Simulation study of the vegetation structure and function in eastern Siberian larch forests using the individual-based vegetation model SEIB-DGVM." Forest Ecology and Management 259(3): 301-311.2. Sato, H., et al. (2016). "Endurance of larch forest ecosystems in eastern Siberia under warming trends." Ecology and Evolution

Model parameters for representative wetland plant functional groups

USGS Publications Warehouse

Williams, Amber S.; Kiniry, James R.; Mushet, David M.; Smith, Loren M.; McMurry, Scott T.; Attebury, Kelly; Lang, Megan; McCarty, Gregory W.; Shaffer, Jill A.; Effland, William R.; Johnson, Mari-Vaughn V.

2017-01-01

Wetlands provide a wide variety of ecosystem services including water quality remediation, biodiversity refugia, groundwater recharge, and floodwater storage. Realistic estimation of ecosystem service benefits associated with wetlands requires reasonable simulation of the hydrology of each site and realistic simulation of the upland and wetland plant growth cycles. Objectives of this study were to quantify leaf area index (LAI), light extinction coefficient (k), and plant nitrogen (N), phosphorus (P), and potassium (K) concentrations in natural stands of representative plant species for some major plant functional groups in the United States. Functional groups in this study were based on these parameters and plant growth types to enable process-based modeling. We collected data at four locations representing some of the main wetland regions of the United States. At each site, we collected on-the-ground measurements of fraction of light intercepted, LAI, and dry matter within the 2013–2015 growing seasons. Maximum LAI and k variables showed noticeable variations among sites and years, while overall averages and functional group averages give useful estimates for multisite simulation modeling. Variation within each species gives an indication of what can be expected in such natural ecosystems. For P and K, the concentrations from highest to lowest were spikerush (Eleocharis macrostachya), reed canary grass (Phalaris arundinacea), smartweed (Polygonum spp.), cattail (Typha spp.), and hardstem bulrush (Schoenoplectus acutus). Spikerush had the highest N concentration, followed by smartweed, bulrush, reed canary grass, and then cattail. These parameters will be useful for the actual wetland species measured and for the wetland plant functional groups they represent. These parameters and the associated process-based models offer promise as valuable tools for evaluating environmental benefits of wetlands and for evaluating impacts of various agronomic practices in adjacent areas as they affect wetlands.

Simulating Energy, Water and Carbon Fluxes at the Shortgrass Steppe Long Term Ecological Research (LTER) Site

NASA Astrophysics Data System (ADS)

Beltran-Przekurat, A. B.; Pielke, R. A.; Morgan, J. A.; Burke, I. C.

2005-12-01

Coupled atmospheric-biospheric models are a particularly valuable tool for studying the potential effects of land-use and land-cover changes on the near-surface atmosphere since the atmosphere and biosphere are allowed to dynamically interact through the surface and canopy energy balance. GEMRAMS is a coupled atmospheric-biospheric model comprised of an atmospheric model, RAMS, and an ecophysiological process-based model, GEMTM. In the first part of this study, the soil-vegetation-atmosphere-transfer (SVAT) scheme, LEAF2, from RAMS, coupled with GEMTM, are used to simulate energy, water and carbon fluxes over different cropping systems (winter wheat and irrigated corn) and over a mixed C3/C4 shortgrass prairie located at the USDA-ARS Central Plains Experimental Range near Nunn, Colorado, the LTER Shortgrass Steppe site. The new SVAT scheme, GEMLEAF, is forced with air temperature and humidity, wind speed and photosynthetic active radiation (PAR). Calculated canopy temperature and relative humidity, soil moisture and temperature and PAR are used to compute sunlit/shaded leaf photosynthesis (for C3 and C4 plant types) and respiration. Photosynthate is allocated to leaves, shoots, roots and reproductive organs with variable partition coefficients, which are functions of soil water conditions. As water stress increases, the fraction of photosynthate allocated to root growth increases. Leaf area index (LAI) is estimated from daily leaf biomass growth, using the vegetation-prescribed specific leaf area. Canopy conductance, computed and based on photosynthesis and relative humidity, is used to calculate latent heat flux. Simulated energy and CO2 fluxes are compared to observations collected using Bowen ratio flux towers during two growing seasons. Seasonality of the fluxes reflecting different plant phenologies agrees well with the observed patterns. In the second part of this study, simulations for two clear days are performed with GEMRAMS over a model domain centered at the SGS site. Simulated spatial differences in the energy fluxes can be associated with the highly heterogeneous landscape in this area.

Estimation of Canopy Sunlit Fraction of Leaf Area from Ground-Based Measurements

NASA Astrophysics Data System (ADS)

Yang, B.; Knyazikhin, Y.; Yan, K.; Chen, C.; Park, T.; CHOI, S.; Mottus, M.; Rautiainen, M.; Stenberg, P.; Myneni, R.; Yan, L.

2015-12-01

The sunlit fraction of leaf area (SFLA) defined as the fraction of the total hemisurface leaf area illuminated by the direct solar beam is a key structural variable in many global models of climate, hydrology, biogeochemistry and ecology. SFLAI is expected to be a standard product from the Earth Polychromatic Imaging Camera (EPIC) on board the joint NOAA, NASA and US Air Force Deep Space Climate Observatory (DSCOVR) mission, which was successfully launched from Cape Canaveral, Florida on February 11, 2015. The DSCOVR EPIC sensor orbiting the Sun-Earth Lagrange L1 point provides multispectral measurements of the radiation reflected by Earth in retro-illumination directions. This poster discusses a methodology for estimating the SFLA using LAI-2000 Canopy Analyzer, which is expected to underlie the strategy for validation of the DSCOVR EPIC land surface products. LAI-2000 data collected over 18 coniferous and broadleaf sites in Hyytiälä, Central Finland, were used to estimate the SFLA. Field data on canopy geometry were used to simulate selected sites. Their SFLAI was calculated using a Monte Carlo (MC) technique. LAI-2000 estimates of SFLA showed a very good agreement with MC results, suggesting validity of the proposed approach.

Modeling regional cropland GPP by empirically incorporating sun-induced chlorophyll fluorescence into a coupled photosynthesis-fluorescence model

NASA Astrophysics Data System (ADS)

Zhang, Y.; Guanter, L.; Van der Tol, C.; Joiner, J.; Berry, J. A.

2015-12-01

Global sun-induced chlorophyll fluorescence (SIF) retrievals are currently available from several satellites. SIF is intrinsically linked to photosynthesis, so the new data sets allow to link remotely-sensed vegetation parameters and the actual photosynthetic activity of plants. In this study, we used space measurements of SIF together with the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) balance model in order to simulate regional photosynthetic uptake of croplands in the US corn belt. SCOPE couples fluorescence and photosynthesis at leaf and canopy levels. To do this, we first retrieved a key parameter of photosynthesis model, the maximum rate of carboxylation (Vcmax), from field measurements of CO2 and water flux during 2007-2012 at some crop eddy covariance flux sites in the Midwestern US. Then we empirically calibrated Vcmax with apparent fluorescence yield which is SIF divided by PAR. SIF retrievals are from the European GOME-2 instrument onboard the MetOp-A platform. The resulting apparent fluorescence yield shows a stronger relationship with Vcmax during the growing season than widely-used vegetation index, EVI and NDVI. New seasonal and regional Vcmax maps were derived based on the calibration model for the cropland of the corn belt. The uncertainties of Vcmax were also estimated through Gaussian error propagation. With the newly derived Vcmax maps, we modeled regional cropland GPP during the growing season for the Midwestern USA, with meteorological data from MERRA reanalysis data and LAI from MODIS product (MCD15A2). The results show the improvement in the seasonal and spatial patterns of cropland productivity in comparisons with both flux tower and agricultural inventory data.

Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest

NASA Astrophysics Data System (ADS)

Tian, Jinyan; Wang, Le; Li, Xiaojuan; Gong, Huili; Shi, Chen; Zhong, Ruofei; Liu, Xiaomeng

2017-09-01

Unmanned Aerial Vehicle (UAV) remote sensing has opened the door to new sources of data to effectively characterize vegetation metrics at very high spatial resolution and at flexible revisit frequencies. Successful estimation of the leaf area index (LAI) in precision agriculture with a UAV image has been reported in several studies. However, in most forests, the challenges associated with the interference from a complex background and a variety of vegetation species have hindered research using UAV images. To the best of our knowledge, very few studies have mapped the forest LAI with a UAV image. In addition, the drawbacks and advantages of estimating the forest LAI with UAV and satellite images at high spatial resolution remain a knowledge gap in existing literature. Therefore, this paper aims to map LAI in a mangrove forest with a complex background and a variety of vegetation species using a UAV image and compare it with a WorldView-2 image (WV2). In this study, three representative NDVIs, average NDVI (AvNDVI), vegetated specific NDVI (VsNDVI), and scaled NDVI (ScNDVI), were acquired with UAV and WV2 to predict the plot level (10 × 10 m) LAI. The results showed that AvNDVI achieved the highest accuracy for WV2 (R2 = 0.778, RMSE = 0.424), whereas ScNDVI obtained the optimal accuracy for UAV (R2 = 0.817, RMSE = 0.423). In addition, an overall comparison results of the WV2 and UAV derived LAIs indicated that UAV obtained a better accuracy than WV2 in the plots that were covered with homogeneous mangrove species or in the low LAI plots, which was because UAV can effectively eliminate the influence from the background and the vegetation species owing to its high spatial resolution. However, WV2 obtained a slightly higher accuracy than UAV in the plots covered with a variety of mangrove species, which was because the UAV sensor provides a negative spectral response function(SRF) than WV2 in terms of the mangrove LAI estimation.

[Comparison of medical and economic benefits of antipsychotics in the treatment of schizophrenia in France].

PubMed

Druais, S; Doutriaux, A; Cognet, M; Godet, A; Lançon, C; Levy, P; Samalin, L; Guillon, P

2017-08-01

The course of schizophrenia can vary widely, and patients experience remission phases alternating with relapse episodes, which generally lead to hospitalisation and have a significant impact on the burden of disease. The prevalence of schizophrenia in France is estimated to be approximately 600,000 people, with an incidence of 10,000 new patients per year. Patients with schizophrenia represent the largest group of hospitalised patients in French public institutions and specialised centres, and the French authorities recognise that the management of schizophrenia is a major public health concern. The Haute Autorité de Santé (HAS) and most of the evidence-based guidelines for the maintenance treatment of schizophrenia recommend long-acting injectable (LAI) antipsychotics to be used predominantly in the prevention of relapse for non-compliant patients; however, in clinical practice, the use of LAIs remains low. This analysis aimed to estimate and to compare the cost-effectiveness of the most common antipsychotic strategies in France in the management of schizophrenia. A Markov model was developed to simulate the progression of a cohort of patients with schizophrenia through four health states (stable treated, stable non-treated, relapse and death) and considered up to three lines of treatment to account for changes in treatment management. Antipsychotics including aripiprazole LAI (ALAI), olanzapine LAI (OLAI), paliperidone LAI (PLAI), risperidone LAI (RLAI), haloperidol decanoate (HD) and oral olanzapine (OO) were compared in terms of costs and clinical outcomes. Thus, costs, quality-adjusted life-years (QALYs) and number of relapses were assessed over five years based on three-month cycles from a French health insurance perspective with a discount rate of 4 %. Patients were considered to be stabilised after clinical decompensation and would enter the model at an initiation phase, followed by a prevention of relapse phase if successful. Data (e.g. relapse or discontinuation rates) for the initiation phase came from randomised clinical trials, whereas relapse rates in the prevention phase were derived from hospitalisation risks based on French real-life data in order to capture adherence effects. Safety and utility data were derived from international publications. Additionally costs were retrieved from French health insurance databases and publications. Robustness of results was assessed through deterministic and probabilistic sensitivity analyses. First and second generations of LAIs were found to have similar costs over five years; i.e. approximately € 55,000, except for PLAI which was associated with a discounted cost of € 50,880. Oral antipsychotics were found to be less costly (i.e. OO cost € 50,379 after five years) but associated with a lower number of QALYs gained and relapse avoided. PLAI and RLAI were associated with the greatest number of QALYs gained; i.e. PLAI dominated ALAI, OLAI and HD and was associated with an incremental costs-effectiveness ratio (ICER) of € 2411 per QALY gained versus OO. Finally, PLAI and OLAI were associated with the lowest number of relapses; i.e. PLAI dominated RLAI, ALAI and HLAI and was associated with an ICER of € 1782 per avoided relapse compared to OO. OO and HD were found to have led to the highest number of relapses. This analysis, to the best of our knowledge, is the first of its kind to assess the cost-effectiveness of antipsychotics based on French observational data. PLAI was associated with the highest probability of being the optimal treatment from the French health insurance perspective. Copyright © 2016. Published by Elsevier Masson SAS.

The review of dynamic monitoring technology for crop growth

NASA Astrophysics Data System (ADS)

Zhang, Hong-wei; Chen, Huai-liang; Zou, Chun-hui; Yu, Wei-dong

2010-10-01

In this paper, crop growth monitoring methods are described elaborately. The crop growth models, Netherlands-Wageningen model system, the United States-GOSSYM model and CERES models, Australia APSIM model and CCSODS model system in China, are introduced here more focus on the theories of mechanism, applications, etc. The methods and application of remote sensing monitoring methods, which based on leaf area index (LAI) and biomass were proposed by different scholars at home and abroad, are highly stressed in the paper. The monitoring methods of remote sensing coupling with crop growth models are talked out at large, including the method of "forced law" which using remote sensing retrieval state parameters as the crop growth model parameters input, and then to enhance the dynamic simulation accuracy of crop growth model and the method of "assimilation of Law" which by reducing the gap difference between the value of remote sensing retrieval and the simulated values of crop growth model and thus to estimate the initial value or parameter values to increasing the simulation accuracy. At last, the developing trend of monitoring methods are proposed based on the advantages and shortcomings in previous studies, it is assured that the combination of remote sensing with moderate resolution data of FY-3A, MODIS, etc., crop growth model, "3S" system and observation in situ are the main methods in refinement of dynamic monitoring and quantitative assessment techniques for crop growth in future.

Combined Use of Airborne Lidar and DBInSAR Data to Estimate LAI in Temperate Mixed Forests

NASA Technical Reports Server (NTRS)

Peduzzi, Alicia; Wynne, Randolph Hamilton; Thomas, Valerie A.; Nelson, Ross F.; Reis, James J.; Sanford, Mark

2012-01-01

The objective of this study was to determine whether leaf area index (LAI) in temperate mixed forests is best estimated using multiple-return airborne laser scanning (lidar) data or dual-band, single-pass interferometric synthetic aperture radar data (from GeoSAR) alone, or both in combination. In situ measurements of LAI were made using the LiCor LAI-2000 Plant Canopy Analyzer on 61 plots (21 hardwood, 36 pine, 4 mixed pine hardwood; stand age ranging from 12-164 years; mean height ranging from 0.4 to 41.2 m) in the Appomattox-Buckingham State Forest, Virginia, USA. Lidar distributional metrics were calculated for all returns and for ten one meter deep crown density slices (a new metric), five above and five below the mode of the vegetation returns for each plot. GeoSAR metrics were calculated from the X-band backscatter coefficients (four looks) as well as both X- and P-band interferometric heights and magnitudes for each plot. Lidar metrics alone explained 69% of the variability in LAI, while GeoSAR metrics alone explained 52%. However, combining the lidar and GeoSAR metrics increased the R2 to 0.77 with a CV-RMSE of 0.42. This study indicates the clear potential for X-band backscatter and interferometric height (both now available from spaceborne sensors), when combined with small-footprint lidar data, to improve LAI estimation in temperate mixed forests.

What do we know about location affordability in U.S. shrinking cities?

DOT National Transportation Integrated Search

2017-07-01

In late 2013, the Department of Housing and Urban Development (HUD) launched the Location Affordability Index (LAI) portal. Their dataset uses models to estimate typical amount households spend on housing and transportation at the block group level, ...

Genetic diversity among major endemic strains of Leptospira interrogans in China

PubMed Central

He, Ping; Sheng, Yue-Ying; Shi, Yao-Zhou; Jiang, Xiu-Gao; Qin, Jin-Hong; Zhang, Zhi-Ming; Zhao, Guo-Ping; Guo, Xiao-Kui

2007-01-01

Background Leptospirosis is a world-widely distributed zoonosis. Humans become infected via exposure to pathogenic Leptospira spp. from contaminated water or soil. The availability of genomic sequences of Leptospira interrogans serovar Lai and serovar Copenhageni opened up opportunities to identify genetic diversity among different pathogenic strains of L. interrogans representing various kinds of serotypes (serogroups and serovars). Results Comparative genomic hybridization (CGH) analysis was used to compare the gene content of L. interrogans serovar Lai strain Lai with that of other 10 L. interrogans strains prevailed in China and one identified from Brazil using a microarray spotted with 3,528 protein coding sequences (CDSs) of strain Lai. The cutoff ratio of sample/reference (S/R) hybridization for detecting the absence of genes from one tested strain was set by comparing the ratio of S/R hybridization and the in silico sequence similarities of strain Lai and serovar Copenhageni strain Fiocruz L1-130. Among the 11 strains tested, 275 CDSs were found absent from at least one strain. The common backbone of the L. interrogans genome was estimated to contain about 2,917 CDSs. The genes encoding fundamental cellular functions such as translation, energy production and conversion were conserved. While strain-specific genes include those that encode proteins related to either cell surface structures or carbohydrate transport and metabolism. We also found two genomic islands (GIs) in strain Lai containing genes divergently absent in other strains. Because genes encoding proteins with potential pathogenic functions are located within GIs, these elements might contribute to the variations in disease manifestation. Differences in genes involved in O-antigen biosynthesis were also identified for strains belonging to different serogroups, which offers an opportunity for future development of genomic typing tools for serological classification. Conclusion CGH analyses for pathogenic leptospiral strains prevailed in China against the L. interrogans serovar Lai strain Lai CDS-spotted microarrays revealed 2,917 common backbone CDSs and strain specific genes encoding proteins mainly related to cell surface structures and carbohydrated transport/metabolism. Of the 275 CDSs considered absent from at least one of the L. interrogans strains tested, most of them were clustered in the rfb gene cluster and two putative genomic islands (GI A and B) in strain Lai. The strain-specific genes detected via this work will provide a knowledge base for further investigating the pathogenesis of L interrogans and/or for the development of effective vaccines and/or diagnostic tools. PMID:17603913

Response of sap flow to environmental factors in the headwater catchment of Miyun Reservoir in subhumid North China

NASA Astrophysics Data System (ADS)

Tie, Qiang; Hu, Hongchang; Tian, Fuqiang; Liu, Yaping; Xu, Ran

2015-04-01

Since the headwater catchment of Miyun Reservoir is the main drinking water conservation area of Beijing, its water cycle is of importance for the regional water resource. Transpiration is an important component of water cycle, which can be estimated by sap flow. In this study, the dynamics of sap flow and its response to environmental factors and relationship with leaf area index (LAI) were analyzed. The field study was conducted in the Xitaizi Experimental Catchment, located in the headwater catchment of Miyun Reservoir in subhumid North China. The Aspen (Populus davidiana) and Epinette (Larix gmelinii) are the two dominant tree species. Sap flow in 15 Aspen (Populus davidiana) trees was monitored using thermal dissipation probes (TDP) during the growing season of 2013 and 2014, and sap flow in another 3 Epinette (Larix gmelinii) trees was also monitored during September and October in 2014 for comparative analysis. Physiological and biometric parameters of the selected trees and the environmental factors, including meteorological variables, soil moisture content and groundwater table depth were measured. Vapor pressure deficit (VPD), variable of transpiration (VT) and reference crop evapotranspiration (ET0) were calculated using the measured environmental factors. The LAI, which is used to characterize phenophase, was calculated using the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product (MCD15A3). Correlation analysis for daily sap flow and air temperature, relative humidity, precipitation, wind speed, solar radiation, VPD, VT and ET0 under different soil moisture and groundwater table depth conditions was performed. Diurnal course and hysteresis of sap flow were analyzed as a function of air temperature, solar radiation, VPD and VT on the typical sunny, cloudy and rainy days under different soil moisture conditions. Correlation analysis between daily sap flow and LAI showed that LAI and phenophase significantly influence sap flow and restrict the maximum value of sap flow. The sap flow and its response to environmental factors were compared between Aspen and Epinette. The result could make contributions to improve empirical transpiration modeling for efficient water resource management in the headwater catchment of subhumid region.

BOREAS RSS-7 Landsat TM LAI IMages of the SSA and NSA

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Nickeson, Jaime (Editor); Chen, Jing; Cihlar, Josef

2000-01-01

The BOReal Ecosystem-Atmosphere Study Remote Sensing Science (BOREAS RSS-7) team used Landsat Thematic Mapper (TM) images processed at CCRS to produce images of Leaf Area Index (LAI) for the BOREAS study areas. Two images acquired on 06-Jun and 09-Aug-1991 were used for the SSA, and one image acquired on 09-Jun-1994 was used for the NSA. The LAI images are based on ground measurements and Landsat TM Reduced Simple Ratio (RSR) images. The data are stored in binary image-format files.

Human-induced greening of the northern extratropical land surface

NASA Astrophysics Data System (ADS)

Mao, J.; Ribes, A.; Yan, B.; Shi, X.; Thornton, P. E.; Seferian, R.; Ciais, P.; Myneni, R. B.; Douville, H.; Piao, S.; Zhu, Z.; Dickinson, R. E.; Dai, Y. J.; Ricciuto, D. M.; Jin, M.; Hoffman, F. M.; Wang, B.; Huang, M.; Lian, X.

2016-12-01

Significant land greening in the northern extratropical latitudes (NEL) has been documented through satellite observations during the past three decades. This enhanced vegetation growth has broad implications for surface energy, water and carbon budgets, and ecosystem services across multiple scales. Discernible human impacts on the Earth's climate system have been revealed by using statistical frameworks of detection-attribution. These impacts, however, were not previously identified on the NEL greening signal, owing to the lack of long-term observational records, possible bias of satellite data, different algorithms used to calculate vegetation greenness, and the lack of suitable simulations from coupled Earth system models (ESMs). Here we have overcome these challenges to attribute recent changes in NEL vegetation activity. We used two 30-year-long remote-sensing-based leaf area index (LAI) data sets, simulations from 19 coupled ESMs with interactive vegetation, and a formal detection and attribution algorithm. Our findings reveal that the observed greening record is consistent with an assumption of anthropogenic forcings, where greenhouse gases play a dominant role, but is not consistent with simulations that include only natural forcings and internal climate variability. These results provide the first clear evidence of a discernible human fingerprint on physiological vegetation changes other than phenology and range shifts.

Human-induced greening of the northern extratropical land surface

NASA Astrophysics Data System (ADS)

Mao, Jiafu; Ribes, Aurélien; Yan, Binyan; Shi, Xiaoying; Thornton, Peter E.; Séférian, Roland; Ciais, Philippe; Myneni, Ranga B.; Douville, Hervé; Piao, Shilong; Zhu, Zaichun; Dickinson, Robert E.; Dai, Yongjiu; Ricciuto, Daniel M.; Jin, Mingzhou; Hoffman, Forrest M.; Wang, Bin; Huang, Mengtian; Lian, Xu

2016-10-01

Significant land greening in the northern extratropical latitudes (NEL) has been documented through satellite observations during the past three decades. This enhanced vegetation growth has broad implications for surface energy, water and carbon budgets, and ecosystem services across multiple scales. Discernible human impacts on the Earth's climate system have been revealed by using statistical frameworks of detection-attribution. These impacts, however, were not previously identified on the NEL greening signal, owing to the lack of long-term observational records, possible bias of satellite data, different algorithms used to calculate vegetation greenness, and the lack of suitable simulations from coupled Earth system models (ESMs). Here we have overcome these challenges to attribute recent changes in NEL vegetation activity. We used two 30-year-long remote-sensing-based leaf area index (LAI) data sets, simulations from 19 coupled ESMs with interactive vegetation, and a formal detection and attribution algorithm. Our findings reveal that the observed greening record is consistent with an assumption of anthropogenic forcings, where greenhouse gases play a dominant role, but is not consistent with simulations that include only natural forcings and internal climate variability. These results provide the first clear evidence of a discernible human fingerprint on physiological vegetation changes other than phenology and range shifts.

Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops

NASA Astrophysics Data System (ADS)

Kross, Angela; McNairn, Heather; Lapen, David; Sunohara, Mark; Champagne, Catherine

2015-02-01

Leaf area index (LAI) and biomass are important indicators of crop development and the availability of this information during the growing season can support farmer decision making processes. This study demonstrates the applicability of RapidEye multi-spectral data for estimation of LAI and biomass of two crop types (corn and soybean) with different canopy structure, leaf structure and photosynthetic pathways. The advantages of Rapid Eye in terms of increased temporal resolution (∼daily), high spatial resolution (∼5 m) and enhanced spectral information (includes red-edge band) are explored as an individual sensor and as part of a multi-sensor constellation. Seven vegetation indices based on combinations of reflectance in green, red, red-edge and near infrared bands were derived from RapidEye imagery between 2011 and 2013. LAI and biomass data were collected during the same period for calibration and validation of the relationships between vegetation indices and LAI and dry above-ground biomass. Most indices showed sensitivity to LAI from emergence to 8 m2/m2. The normalized difference vegetation index (NDVI), the red-edge NDVI and the green NDVI were insensitive to crop type and had coefficients of variations (CV) ranging between 19 and 27%; and coefficients of determination ranging between 86 and 88%. The NDVI performed best for the estimation of dry leaf biomass (CV = 27% and r2 = 090) and was also insensitive to crop type. The red-edge indices did not show any significant improvement in LAI and biomass estimation over traditional multispectral indices. Cumulative vegetation indices showed strong performance for estimation of total dry above-ground biomass, especially for corn (CV ≤ 20%). This study demonstrated that continuous crop LAI monitoring over time and space at the field level can be achieved using a combination of RapidEye, Landsat and SPOT data and sensor-dependant best-fit functions. This approach eliminates/reduces the need for reflectance resampling, VIs inter-calibration and spatial resampling.

A dense camera network for cropland (CropInsight) - developing high spatiotemporal resolution crop Leaf Area Index (LAI) maps through network images and novel satellite data

NASA Astrophysics Data System (ADS)

Kimm, H.; Guan, K.; Luo, Y.; Peng, J.; Mascaro, J.; Peng, B.

2017-12-01

Monitoring crop growth conditions is of primary interest to crop yield forecasting, food production assessment, and risk management of individual farmers and agribusiness. Despite its importance, there are limited access to field level crop growth/condition information in the public domain. This scarcity of ground truth data also hampers the use of satellite remote sensing for crop monitoring due to the lack of validation. Here, we introduce a new camera network (CropInsight) to monitor crop phenology, growth, and conditions that are designed for the US Corn Belt landscape. Specifically, this network currently includes 40 sites (20 corn and 20 soybean fields) across southern half of the Champaign County, IL ( 800 km2). Its wide distribution and automatic operation enable the network to capture spatiotemporal variations of crop growth condition continuously at the regional scale. At each site, low-maintenance, and high-resolution RGB digital cameras are set up having a downward view from 4.5 m height to take continuous images. In this study, we will use these images and novel satellite data to construct daily LAI map of the Champaign County at 30 m spatial resolution. First, we will estimate LAI from the camera images and evaluate it using the LAI data collected from LAI-2200 (LI-COR, Lincoln, NE). Second, we will develop relationships between the camera-based LAI estimation and vegetation indices derived from a newly developed MODIS-Landsat fusion product (daily, 30 m resolution, RGB + NIR + SWIR bands) and the Planet Lab's high-resolution satellite data (daily, 5 meter, RGB). Finally, we will scale up the above relationships to generate high spatiotemporal resolution crop LAI map for the whole Champaign County. The proposed work has potentials to expand to other agro-ecosystems and to the broader US Corn Belt.

Evaluating the effect of alternative carbon allocation schemes in a land surface model (CLM4.5) on carbon fluxes, pools, and turnover in temperate forests

NASA Astrophysics Data System (ADS)

Montané, Francesc; Fox, Andrew M.; Arellano, Avelino F.; MacBean, Natasha; Alexander, M. Ross; Dye, Alex; Bishop, Daniel A.; Trouet, Valerie; Babst, Flurin; Hessl, Amy E.; Pederson, Neil; Blanken, Peter D.; Bohrer, Gil; Gough, Christopher M.; Litvak, Marcy E.; Novick, Kimberly A.; Phillips, Richard P.; Wood, Jeffrey D.; Moore, David J. P.

2017-09-01

How carbon (C) is allocated to different plant tissues (leaves, stem, and roots) determines how long C remains in plant biomass and thus remains a central challenge for understanding the global C cycle. We used a diverse set of observations (AmeriFlux eddy covariance tower observations, biomass estimates from tree-ring data, and leaf area index (LAI) measurements) to compare C fluxes, pools, and LAI data with those predicted by a land surface model (LSM), the Community Land Model (CLM4.5). We ran CLM4.5 for nine temperate (including evergreen and deciduous) forests in North America between 1980 and 2013 using four different C allocation schemes: i. dynamic C allocation scheme (named "D-CLM4.5") with one dynamic allometric parameter, which allocates C to the stem and leaves to vary in time as a function of annual net primary production (NPP); ii. an alternative dynamic C allocation scheme (named "D-Litton"), where, similar to (i), C allocation is a dynamic function of annual NPP, but unlike (i) includes two dynamic allometric parameters involving allocation to leaves, stem, and coarse roots; iii.-iv. a fixed C allocation scheme with two variants, one representative of observations in evergreen (named "F-Evergreen") and the other of observations in deciduous forests (named "F-Deciduous"). D-CLM4.5 generally overestimated gross primary production (GPP) and ecosystem respiration, and underestimated net ecosystem exchange (NEE). In D-CLM4.5, initial aboveground biomass in 1980 was largely overestimated (between 10 527 and 12 897 g C m-2) for deciduous forests, whereas aboveground biomass accumulation through time (between 1980 and 2011) was highly underestimated (between 1222 and 7557 g C m-2) for both evergreen and deciduous sites due to a lower stem turnover rate in the sites than the one used in the model. D-CLM4.5 overestimated LAI in both evergreen and deciduous sites because the leaf C-LAI relationship in the model did not match the observed leaf C-LAI relationship at our sites. Although the four C allocation schemes gave similar results for aggregated C fluxes, they translated to important differences in long-term aboveground biomass accumulation and aboveground NPP. For deciduous forests, D-Litton gave more realistic Cstem / Cleaf ratios and strongly reduced the overestimation of initial aboveground biomass and aboveground NPP for deciduous forests by D-CLM4.5. We identified key structural and parameterization deficits that need refinement to improve the accuracy of LSMs in the near future. These include changing how C is allocated in fixed and dynamic schemes based on data from current forest syntheses and different parameterization of allocation schemes for different forest types. Our results highlight the utility of using measurements of aboveground biomass to evaluate and constrain the C allocation scheme in LSMs, and suggest that stem turnover is overestimated by CLM4.5 for these AmeriFlux sites. Understanding the controls of turnover will be critical to improving long-term C processes in LSMs.

Evaluating the effect of alternative carbon allocation schemes in a land surface model (CLM4.5) on carbon fluxes, pools, and turnover in temperate forests

DOE PAGES

Montané, Francesc; Fox, Andrew M.; Arellano, Avelino F.; ...

2017-09-22

How carbon (C) is allocated to different plant tissues (leaves, stem, and roots) determines how long C remains in plant biomass and thus remains a central challenge for understanding the global C cycle. We used a diverse set of observations (AmeriFlux eddy covariance tower observations, biomass estimates from tree-ring data, and leaf area index (LAI) measurements) to compare C fluxes, pools, and LAI data with those predicted by a land surface model (LSM), the Community Land Model (CLM4.5). We ran CLM4.5 for nine temperate (including evergreen and deciduous) forests in North America between 1980 and 2013 using four different C allocationmore » schemes: i. dynamic C allocation scheme (named "D-CLM4.5") with one dynamic allometric parameter, which allocates C to the stem and leaves to vary in time as a function of annual net primary production (NPP); ii. an alternative dynamic C allocation scheme (named "D-Litton"), where, similar to (i), C allocation is a dynamic function of annual NPP, but unlike (i) includes two dynamic allometric parameters involving allocation to leaves, stem, and coarse roots; iii.–iv. a fixed C allocation scheme with two variants, one representative of observations in evergreen (named "F-Evergreen") and the other of observations in deciduous forests (named "F-Deciduous"). D-CLM4.5 generally overestimated gross primary production (GPP) and ecosystem respiration, and underestimated net ecosystem exchange (NEE). In D-CLM4.5, initial aboveground biomass in 1980 was largely overestimated (between 10 527 and 12 897 g C m -2) for deciduous forests, whereas aboveground biomass accumulation through time (between 1980 and 2011) was highly underestimated (between 1222 and 7557 g C m -2) for both evergreen and deciduous sites due to a lower stem turnover rate in the sites than the one used in the model. D-CLM4.5 overestimated LAI in both evergreen and deciduous sites because the leaf C–LAI relationship in the model did not match the observed leaf C–LAI relationship at our sites. Although the four C allocation schemes gave similar results for aggregated C fluxes, they translated to important differences in long-term aboveground biomass accumulation and aboveground NPP. For deciduous forests, D-Litton gave more realistic C stem/C leaf ratios and strongly reduced the overestimation of initial aboveground biomass and aboveground NPP for deciduous forests by D-CLM4.5. We identified key structural and parameterization deficits that need refinement to improve the accuracy of LSMs in the near future. These include changing how C is allocated in fixed and dynamic schemes based on data from current forest syntheses and different parameterization of allocation schemes for different forest types. Our results highlight the utility of using measurements of aboveground biomass to evaluate and constrain the C allocation scheme in LSMs, and suggest that stem turnover is overestimated by CLM4.5 for these AmeriFlux sites. Understanding the controls of turnover will be critical to improving long-term C processes in LSMs.« less

Evaluating the effect of alternative carbon allocation schemes in a land surface model (CLM4.5) on carbon fluxes, pools, and turnover in temperate forests

DOE Office of Scientific and Technical Information (OSTI.GOV)

Montané, Francesc; Fox, Andrew M.; Arellano, Avelino F.

How carbon (C) is allocated to different plant tissues (leaves, stem, and roots) determines how long C remains in plant biomass and thus remains a central challenge for understanding the global C cycle. We used a diverse set of observations (AmeriFlux eddy covariance tower observations, biomass estimates from tree-ring data, and leaf area index (LAI) measurements) to compare C fluxes, pools, and LAI data with those predicted by a land surface model (LSM), the Community Land Model (CLM4.5). We ran CLM4.5 for nine temperate (including evergreen and deciduous) forests in North America between 1980 and 2013 using four different C allocationmore » schemes: i. dynamic C allocation scheme (named "D-CLM4.5") with one dynamic allometric parameter, which allocates C to the stem and leaves to vary in time as a function of annual net primary production (NPP); ii. an alternative dynamic C allocation scheme (named "D-Litton"), where, similar to (i), C allocation is a dynamic function of annual NPP, but unlike (i) includes two dynamic allometric parameters involving allocation to leaves, stem, and coarse roots; iii.–iv. a fixed C allocation scheme with two variants, one representative of observations in evergreen (named "F-Evergreen") and the other of observations in deciduous forests (named "F-Deciduous"). D-CLM4.5 generally overestimated gross primary production (GPP) and ecosystem respiration, and underestimated net ecosystem exchange (NEE). In D-CLM4.5, initial aboveground biomass in 1980 was largely overestimated (between 10 527 and 12 897 g C m -2) for deciduous forests, whereas aboveground biomass accumulation through time (between 1980 and 2011) was highly underestimated (between 1222 and 7557 g C m -2) for both evergreen and deciduous sites due to a lower stem turnover rate in the sites than the one used in the model. D-CLM4.5 overestimated LAI in both evergreen and deciduous sites because the leaf C–LAI relationship in the model did not match the observed leaf C–LAI relationship at our sites. Although the four C allocation schemes gave similar results for aggregated C fluxes, they translated to important differences in long-term aboveground biomass accumulation and aboveground NPP. For deciduous forests, D-Litton gave more realistic C stem/C leaf ratios and strongly reduced the overestimation of initial aboveground biomass and aboveground NPP for deciduous forests by D-CLM4.5. We identified key structural and parameterization deficits that need refinement to improve the accuracy of LSMs in the near future. These include changing how C is allocated in fixed and dynamic schemes based on data from current forest syntheses and different parameterization of allocation schemes for different forest types. Our results highlight the utility of using measurements of aboveground biomass to evaluate and constrain the C allocation scheme in LSMs, and suggest that stem turnover is overestimated by CLM4.5 for these AmeriFlux sites. Understanding the controls of turnover will be critical to improving long-term C processes in LSMs.« less

Effects of LiDAR point density, sampling size and height threshold on estimation accuracy of crop biophysical parameters.

PubMed

Luo, Shezhou; Chen, Jing M; Wang, Cheng; Xi, Xiaohuan; Zeng, Hongcheng; Peng, Dailiang; Li, Dong

2016-05-30

Vegetation leaf area index (LAI), height, and aboveground biomass are key biophysical parameters. Corn is an important and globally distributed crop, and reliable estimations of these parameters are essential for corn yield forecasting, health monitoring and ecosystem modeling. Light Detection and Ranging (LiDAR) is considered an effective technology for estimating vegetation biophysical parameters. However, the estimation accuracies of these parameters are affected by multiple factors. In this study, we first estimated corn LAI, height and biomass (R² = 0.80, 0.874 and 0.838, respectively) using the original LiDAR data (7.32 points/m²), and the results showed that LiDAR data could accurately estimate these biophysical parameters. Second, comprehensive research was conducted on the effects of LiDAR point density, sampling size and height threshold on the estimation accuracy of LAI, height and biomass. Our findings indicated that LiDAR point density had an important effect on the estimation accuracy for vegetation biophysical parameters, however, high point density did not always produce highly accurate estimates, and reduced point density could deliver reasonable estimation results. Furthermore, the results showed that sampling size and height threshold were additional key factors that affect the estimation accuracy of biophysical parameters. Therefore, the optimal sampling size and the height threshold should be determined to improve the estimation accuracy of biophysical parameters. Our results also implied that a higher LiDAR point density, larger sampling size and height threshold were required to obtain accurate corn LAI estimation when compared with height and biomass estimations. In general, our results provide valuable guidance for LiDAR data acquisition and estimation of vegetation biophysical parameters using LiDAR data.

The effect of financial incentives on adherence to antipsychotic depot medication: does it change over time?

PubMed

Pavlickova, Hana; Bremner, Stephen A; Priebe, Stefan

2015-08-01

A recent cluster-randomized controlled trial found that offering financial incentives improves adherence to long-acting injectable antipsychotics (LAIs). The present study investigates whether the impact of incentives diminishes over time and whether the improvement in adherence is linked to the amount of incentives offered. Seventy-three teams with 141 patients with psychotic disorders (using ICD-10) were randomized to the intervention or control group. Over 1 year, patients in the intervention group received £15 (US $23) for each LAI, while control patients received treatment as usual. Adherence levels, ie, the percentage of prescribed LAIs that were received, were calculated for quarterly intervals. The amount of incentives offered was calculated from the treatment cycle at baseline. Multilevel models were used to examine the time course of the effect of incentives and the effect of the amount of incentives offered on adherence. Adherence increased in both the intervention and the control group over time by an average of 4.2% per quarterly interval (95% CI, 2.8%-5.6%; P < .001). Despite this general increase, adherence in the intervention group remained improved compared to the control group by between 11% and 14% per quarterly interval. There was no interaction effect between time and treatment group. Further, a higher total amount of incentives was associated with poorer adherence (βbootstrapped = -0.11; 95% CIbootstrapped, -0.20 to -0.01; P = .023). A substantial effect of financial incentives on adherence to LAIs occurs within the first 3 months of the intervention and is sustained over 1 year. A higher total amount of incentives does not increase the effect. ISRCTN.com identifier: ISRCTN77769281 and UKCRN.org identifier: 7033. © Copyright 2015 Physicians Postgraduate Press, Inc.

Fusing Cubesat and Landsat 8 data for near-daily mapping of leaf area index at 3 m resolution

NASA Astrophysics Data System (ADS)

McCabe, M.; Houborg, R.

2017-12-01

Constellations of small cubesats are emerging as a relatively inexpensive observational resource with the potential to overcome spatio-temporal constraints of traditional single-sensor satellite missions. With more than 130 compact 3U (i.e., 10 x 10 x 30 cm) cubesats currently in orbit, the company "Planet" has realized near-daily image capture in RGB and the near-infrared (NIR) at 3 m resolution for every location on the earth. However cross-sensor inconsistencies can be a limiting factor, which result from relatively low signal-to-noise ratios, varying overpass times, and sensor-specific spectral response functions. In addition, the sensor radiometric information content is more limited compared to conventional satellite systems such as Landsat. In this study, a synergistic machine-learning framework utilizing Planet, Landsat 8, and MODIS data is developed to produce Landsat 8 consistent LAI with a factor of 10 increase in spatial resolution and a daily observing potential, globally. The Cubist machine-learning technique is used to establish scene-specific links between scale-consistent cubesat RGB+NIR imagery and Landsat 8 LAI. The scheme implements a novel LAI target sampling technique for model training purposes, which accounts for changes in cover conditions over the cubesat and Landsat acquisition timespans. Results over an agricultural region in Saudi Arabia highlight the utility of the approach for detecting high frequency (i.e., near-daily) and fine-scale (i.e., 3 m) intra-field dynamics in LAI with demonstrated potential for timely identification of developing crop risks. The framework maximizes the utility of ultra-high resolution cubesat data for agricultural management and resource efficiency optimization at the precision scale.

Influence of lasing parameters on the cleaning efficacy of laser-activated irrigation with pulsed erbium lasers.

PubMed

Meire, Maarten A; Havelaerts, Sophie; De Moor, Roeland J

2016-05-01

Laser-activated irrigation (LAI) using erbium lasers is an irrigant agitation technique with great potential for improved cleaning of the root canal system, as shown in many in vitro studies. However, lasing parameters for LAI vary considerably and their influence remains unclear. Therefore, this study sought to investigate the influence of pulse energy, pulse frequency, pulse length, irradiation time and fibre tip shape, position and diameter on the cleaning efficacy of LAI. Transparent resin blocks containing standardized root canals (apical diameter of 0.4 mm, 6% taper, 15 mm long, with a coronal reservoir) were used as the test model. A standardized groove in the apical part of each canal wall was packed with stained dentin debris. The canals were filled with irrigant, which was activated by an erbium: yttrium aluminium garnet (Er:YAG) laser (2940 nm, AT Fidelis, Fotona, Ljubljana, Slovenia). In each experiment, one laser parameter was varied, while the others remained constant. In this way, the influence of pulse energy (10-40 mJ), pulse length (50-1000 μs), frequency (5-30 Hz), irradiation time (5-40 s) and fibre tip shape (flat or conical), position (pulp chamber, canal entrance, next to groove) and diameter (300-600 μm) was determined by treating 20 canals per parameter. The amount of debris remaining in the groove after each LAI procedure was scored and compared among the different treatments. The parameters significantly (P < 0.05, Kruskal-Wallis) affecting debris removal from the groove were fibre tip position, pulse length, pulse energy, irradiation time and frequency. Fibre tip shape and diameter had no significant influence on the cleaning efficacy.

Canopy Chlorophyll Density Based Index for Estimating Nitrogen Status and Predicting Grain Yield in Rice

PubMed Central

Liu, Xiaojun; Zhang, Ke; Zhang, Zeyu; Cao, Qiang; Lv, Zunfu; Yuan, Zhaofeng; Tian, Yongchao; Cao, Weixing; Zhu, Yan

2017-01-01

Canopy chlorophyll density (Chl) has a pivotal role in diagnosing crop growth and nutrition status. The purpose of this study was to develop Chl based models for estimating N status and predicting grain yield of rice (Oryza sativa L.) with Leaf area index (LAI) and Chlorophyll concentration of the upper leaves. Six field experiments were conducted in Jiangsu Province of East China during 2007, 2008, 2009, 2013, and 2014. Different N rates were applied to generate contrasting conditions of N availability in six Japonica cultivars (9915, 27123, Wuxiangjing 14, Wuyunjing 19, Yongyou 8, and Wuyunjing 24) and two Indica cultivars (Liangyoupei 9, YLiangyou 1). The SPAD values of the four uppermost leaves and LAI were measured from tillering to flowering growth stages. Two N indicators, leaf N accumulation (LNA) and plant N accumulation (PNA) were measured. The LAI estimated by LAI-2000 and LI-3050C were compared and calibrated with a conversion equation. A linear regression analysis showed significant relationships between Chl value and N indicators, the equations were as follows: PNA = (0.092 × Chl) − 1.179 (R2 = 0.94, P < 0.001, relative root mean square error (RRMSE) = 0.196), LNA = (0.052 × Chl) − 0.269 (R2 = 0.93, P < 0.001, RRMSE = 0.185). Standardized method was used to quantity the correlation between Chl value and grain yield, normalized yield = (0.601 × normalized Chl) + 0.400 (R2 = 0.81, P < 0.001, RRMSE = 0.078). Independent experimental data also validated the use of Chl value to accurately estimate rice N status and predict grain yield. PMID:29163568

Rapid recovery of gross ecosystem production and ecosystem-level respiration in a semiarid sagebrush shrubland following prescribed fire.

NASA Astrophysics Data System (ADS)

Fellows, A.; Flerchinger, G. N.

2016-12-01

The impact of fire remains a key uncertainty in our understanding of the spatio-temporal dynamics of carbon cycling on Western US rangelands. We, therefore, tracked the recovery of carbon fluxes and vegetative carbon stocks following prescribed fire in a sagebrush shrubland located in the Western US Great Basin. We quantified the change in plant function type, Leaf Area Index (LAI), aboveground carbon stocks, Gross Ecosystem Production (GEP), and Ecosystem-level Respiration (Reco) for 2 years before and 5 years following a prescribed fire that burned in 2007. Recruitment of burned sagebrush shrubland by fast growing grasses and forbs drove a rapid recovery of LAI, GEP, and Reco following fire; LAI, GEP, and Reco recovered within 1-3 years. These findings are consistent with previous measurement and modeling work by Flerchinger that demonstrated rooting depths, soil moisture withdrawal, and evapotranspiration also recovered within a few years of this fire. Live aboveground biomass reached 15% of pre-fire aboveground biomass after 5 years. The rapid recovery of LAI, rooting depth, GEP and Reco may partially reflect conducive environmental conditions at this site and at the time of the fire. In particular, the site was wet for a sagebrush shrubland; annual precipitation averaged 545 mm during the study and large-deep snow drifts formed upslope of the site. Post-fire weather was particularly wet, with the second, third and fourth years following fire receiving 587, 614, and 745 mm of water. Grazing was excluded from the burned area, which limited herbivory and may have facilitated vegetation establishment and growth. Lastly, the fire burned in September after many grasses and herbaceous plants had already senesced.

Uncertainties associated to the representation of surface processes in impact studies. A study in the Mediterranean area.

NASA Astrophysics Data System (ADS)

Quéguiner, Solen; Martin, Eric; Lafont, Sébastien; Calvet, Jean-Christophe; Faroux, Stéphanie

2010-05-01

In the framework of the assessment of the impact of climate change, the uncertainty associated to the direct effect of CO2 on plant physiology was seldom addressed, while some other sources of uncertainties have been more studied, such as those related to climate modeling or the downscaling method. A few studies are available at global or continental scale. The purpose of this study is to quantify this effect in a regional study focussed on the Mediterranean area of France. The Safran-Isba-Modcou chain was used. This chain is composed of a meteorological analysis system (SAFRAN), a land surface model describing the exchange with the atmosphere (ISBA) and a hydrogeological model (MODCOU), and has already been used in many studies in France. The present study focuses on the uncertainties related to the representation of carbon cycle and the photosynthesis in the surface model. Two versions of ISBA were used and compared. The standard version simulates the mass and energy exchanges between the continental surface (including vegetation and snow) and the atmosphere. In this version, the LAI (Leaf Area Index) is provided by the ECOCLIMAP2 database and the vegetation is divided into 12 types. The A-gs version accounts for the process of photosynthesis taking into account the vegetation assimilation of atmospheric CO2 concentration, and simulates the evolution of the biomass and the LAI. The domain studied is the French mediterranean basin, in which a sub domain was defined (latitude < 45 °N et height < 1000m) in order to identify the low land area pertaining to a Mediterranean climate. The study focuses on the impact of the climate change on the surface variables (LAI, water balance) and the discharges. The periods chosen to compare the changes are the end of the 20th century (1995-2005) and the end of the 21st century (2090-2099). A first comparison is made for the present climate between the versions of model and the observations of discharges, using two type of meteorological forcing : SAFRAN and data from a continuous high resolution climate scenario, based on the scenario A2, with a coupled atmosphere-mediterranean sea GCM. This scenario was further downscaled to the resolution of the study (a grid mesh of 8x8 km), using a quantile-quantile correction method. Concerning the present climate, the comparison shows a delay of the development of the vegetation simulated by ISBA-A-gs causing an underestimation of evaporation and an overestimation of discharges in the spring compared to the observations and the standard version of ISBA. In future climate, the explicit response of vegetation to the CO2 concentration of the ISBA A-gs version gives an different answer on the surface water budget and flow from the standard version of ISBA. This difference is especially visible in the southern area, the impact on the flow is increased and impact on evaporation is decreased, showing the interest of using a CO2 responsive version of ISBA for impact studies.

Plant canopy gap-size analysis theory for improving optical measurements of leaf-area index

NASA Astrophysics Data System (ADS)

Chen, Jing M.; Cihlar, Josef

1995-09-01

Optical instruments currently available for measuring the leaf-area index (LAI) of a plant canopy all utilize only the canopy gap-fraction information. These instruments include the Li-Cor LAI-2000 Plant Canopy Analyzer, Decagon, and Demon. The advantages of utilizing both the canopy gap-fraction and gap-size information are shown. For the purpose of measuring the canopy gap size, a prototype sunfleck-LAI instrument named Tracing Radiation and Architecture of Canopies (TRAC), has been developed and tested in two pure conifer plantations, red pine (Pinus resinosa Ait.) and jack pine (Pinus banksiana Lamb). A new gap-size-analysis theory is presented to quantify the effect of canopy architecture on optical measurements of LAI based on the gap-fraction principle. The theory is an improvement on that of Lang and Xiang [Agric. For. Meteorol. 37, 229 (1986)]. In principle, this theory can be used for any heterogeneous canopies.

[Estimation of rice LAI by using NDVI at different spectral bandwidths].

PubMed

Wang, Fu-min; Huang, Jing-feng; Tang, Yan-lin; Wang, Xiu-zhen

2007-11-01

The canopy hyperspectral reflectance data of rice at its different development stages were collected from field measurement, and the corresponding NDVIs as well as the correlation coefficients of NDVIs and LAI were computed at extending bandwidth of TM red and near-infrared (NIR) spectra. According to the variation characteristics of best fitted R2 with spectral bandwidth, the optimal bandwidth was determined. The results showed that the correlation coefficients of LAI and ND-VI and the maximum R2 of the best fitted functions at different spectral bandwidths had the same variation trend, i.e., decreased with increasing bandwidth when the bandwidth was less than 60 nm. However, when the bandwidth was beyond 60 nm, the maximum R2 somewhat fluctuated due to the effect of NIR. The analysis of R2 variation with bandwidth indicated that 15 nm was the optimal bandwidth for the estimation of rice LAI by using NDVI.

Development of a distributed air pollutant dry deposition modeling framework

Treesearch

Satoshi Hirabayashi; Charles N. Kroll; David J. Nowak

2012-01-01

A distributed air pollutant dry deposition modeling systemwas developed with a geographic information system (GIS) to enhance the functionality of i-Tree Eco (i-Tree, 2011). With the developed system, temperature, leaf area index (LAI) and air pollutant concentration in a spatially distributed form can be estimated, and based on these and other input variables, dry...

Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation

USDA-ARS?s Scientific Manuscript database

The scale mismatch between remotely sensed observations and crop growth models simulated state variables decreases the reliability of crop yield estimates. To overcome this problem, we used a two-step data assimilation phases: first we generated a complete leaf area index (LAI) time series by combin...

Algorithm for retrieving vegetative canopy and leaf parameters from multi- and hyperspectral imagery

NASA Astrophysics Data System (ADS)

Borel, Christoph

2009-05-01

In recent years hyper-spectral data has been used to retrieve information about vegetative canopies such as leaf area index and canopy water content. For the environmental scientist these two parameters are valuable, but there is potentially more information to be gained as high spatial resolution data becomes available. We developed an Amoeba (Nelder-Mead or Simplex) based program to invert a vegetative canopy radiosity model coupled with a leaf (PROSPECT5) reflectance model and modeled for the background reflectance (e.g. soil, water, leaf litter) to a measured reflectance spectrum. The PROSPECT5 leaf model has five parameters: leaf structure parameter Nstru, chlorophyll a+b concentration Cab, carotenoids content Car, equivalent water thickness Cw and dry matter content Cm. The canopy model has two parameters: total leaf area index (LAI) and number of layers. The background reflectance model is either a single reflectance spectrum from a spectral library() derived from a bare area pixel on an image or a linear mixture of soil spectra. We summarize the radiosity model of a layered canopy and give references to the leaf/needle models. The method is then tested on simulated and measured data. We investigate the uniqueness, limitations and accuracy of the retrieved parameters on canopy parameters (low, medium and high leaf area index) spectral resolution (32 to 211 band hyperspectral), sensor noise and initial conditions.

Steady state estimation of soil organic carbon using satellite-derived canopy leaf area index

DOE PAGES

Fang, Yilin; Liu, Chongxuan; Huang, Maoyi; ...

2014-12-02

Soil organic carbon (SOC) plays a key role in the global carbon cycle that is important for decadal-to-century climate prediction. Estimation of soil organic carbon stock using model-based methods typically requires spin-up (time marching transient simulation) of the carbon-nitrogen (CN) models by performing hundreds to thousands years long simulations until the carbon-nitrogen pools reach dynamic steady-state. This has become a bottleneck for global modeling and analysis, especially when testing new physical and/or chemical mechanisms and evaluating parameter sensitivity. Here we report a new numerical approach to estimate global soil carbon stock that can avoid the long term spin-up of themore » CN model. The approach uses canopy leaf area index (LAI) from satellite data and takes advantage of a reaction-based biogeochemical module NGBGC (Next Generation BioGeoChemical Module) that was recently developed and incorporated in version 4 of the Community Land Model (CLM4). Although NGBGC uses the same CN mechanisms as used in CLM4CN, it can be easily configured to run prognostic or steady state simulations. In this approach, monthly LAI from the multi-year Moderate Resolution Imaging Spectroradiometer (MODIS) data was used to calculate potential annual average gross primary production (GPP) and leaf carbon for the period of the atmospheric forcing. The calculated potential annual average GPP and leaf C are then used by NGBGC to calculate the steady-state distributions of carbon and nitrogen in different vegetation and soil pools by solving the steady-state reaction-network in NGBGC using the Newton-Raphson method. The new approach was applied at point and global scales and compared with SOC derived from long spin-up by running NGBGC in prognostic mode, and SOC from the empirical data of the Harmonized World Soil Database (HWSD). The steady-state solution is comparable to the spin-up value when the MODIS LAI is close to the LAI from the spin-up solution, and largely captured the variability of the HWSD SOC across the different dominant plant functional types (PFTs) at global scale. The numerical correlation between the calculated and HWSD SOC was, however, weak at both point and global scales, suggesting that the models used in describing biogeochemical processes in CLM needs improvements and/or HWSD needs updating as suggested by other studies. Besides SOC, the steady state solution also includes all other state variables simulated by a spin-up run, such as NPP, GPP, total vegetation C etc., which makes the developed approach a promising tool to efficiently estimate global SOC distribution and evaluate and compare different aspects simulated by different CN mechanisms in the model.« less

Seasonality of semi-arid and savanna-type ecosystems in an Earth system model

NASA Astrophysics Data System (ADS)

Dahlin, K.; Swenson, S. C.; Lombardozzi, D.; Kamoske, A.

2016-12-01

Recent work has identified semi-arid and savanna-type (SAST) ecosystems as a critical component of interannual variability in the Earth system (Poulter et al. 2014, Ahlström et al. 2015), yet our understanding of the spatial and temporal patterns present in these systems remains limited. There are three major factors that contribute to the complex behavior of SAST ecosystems, globally. First is leaf phenology, the timing of the appearance, presence, and senescence of plant leaves. Plants grow and drop their leaves in response to a variety of cues, including soil moisture, rainfall, day length, and relative humidity, and alternative phenological strategies might often co-exist in the same location. The second major factor in savannas is soil moisture. The complex nature of soil behavior under extremely dry, then extremely wet conditions is critical to our understanding of how savannas function. The third factor is fire. Globally, virtually all savanna-type ecosystems operate with some non-zero fire return interval. Here we compare model output from the Community Land Model (CLM5-BGC) in SAST regions to remotely sensed data on these three variables - phenology (MODIS LAI), soil moisture (SMAP), and fire (GFED4) - assessing both annual spatial patterns and intra-annual variability, which is critical in these highly variable systems. We present new SAST-specific first- and second-order benchmarks, including numbers of annual LAI peaks (often >1 in SAST systems) and correlations between soil moisture, LAI, and fire. Developing a better understanding of how plants respond to seasonal patterns is a critical first step in understanding how SAST ecosystems will respond to and influence climate under future scenarios.

Long-acting injectables and risk for rehospitalization among patients with schizophrenia in the home care program in Taiwan.

PubMed

Ju, Po-Chung; Chou, Frank Huang-Chih; Lai, Te-Jen; Chuang, Po-Ya; Lin, Yung-Jung; Yang, Ching-Wen Wendy; Tang, Chao-Hsiun

2014-02-01

We aimed at evaluating the relationship between medication and treatment effectiveness in a home care setting among patients with schizophrenia. Patients with schizophrenia hospitalized between 2004 and 2009 with a primary International Classification of Diseases, Ninth Revision, Clinical Modification code of 295 were identified from Psychiatric Inpatient Medical Claims Data released by the National Health Research Institute in Taiwan. Patients who joined the home care program after discharge and were prescribed long-acting injection (LAI) (the LAI group) or oral antipsychotic medications (the oral group) were included as study subjects. The final sample for the study included 810 participants in the LAI group and 945 in the oral group. Logistic regression was performed to examine the independent effect of LAI medication on the risk for rehospitalization within the 12-month observation window after controlling for patient and hospital characteristics and propensity score quintile adjustment. The unadjusted odds ratio for rehospitalization risk was 0.80 (confidence interval, 0.65-0.98) for the LAI group compared to the oral group. The adjusted odds ratio was further reduced to 0.78 (confidence interval, 0.63-0.97). Results remained unchanged when the propensity score quintiles were entered into the regression for further adjustment. In a home care setting, patients treated with long-acting antipsychotic agents are at a significantly lower risk for psychiatric rehospitalization than those treated with oral medication. Consequently, LAI home-based treatment for the prevention of schizophrenia relapse may lead to substantial clinical and economic benefits.

Estimating Soil and Vegetation Parameters using Synergies between Optical and Microwave Observations

NASA Astrophysics Data System (ADS)

Timmermans, J.; Gomez-Dans, J. L.; Lewis, P.; Loew, A.; Schlenz, F.; Mathieu, P. P.; Pounder, N. L.; Styles, J.

2017-12-01

The large amount of remote sensing data available provides a huge potential for various applications, such as crop monitoring. This potential has not been realized yet because inversion-algorithms mostly use a single sensor approach. Consequently, products that combine different low-level observations from different sensors are hard to find. The difficulty in a multi-sensor approach is that 1) different sensor types (microwave/ optical) require different radiative transfer (RT) models and 2) it require consistency between the models. The goal of this research was to investigate the synergistic potential of integrating optical (Opt) and passive microwave (PM) RT models within the Earth Observation Land Data Assimilation System (EOLDAS). EOLDAS uses a Bayesian data assimilation approach together with observation operators such as PROSAIL to estimate state variables. In order to use PM observations, the Community Microwave Emission Model was integrated into the system. Results show a high potential when both Opt and PM observations are used independently. Using only RapidEye only with SAIL RT model, LAI was estimated with R=0.68, with leaf water content and dry matter having lower correlations |R|<0.4. Results for retrieving soil temperature and leaf area index retrievals using only Elbarra observations were good with respectively R=[0.85, 0.79], and for soil moisture also very good with R=0.73 (focusing on dry-spells of at least 9 days only), and with R=0.89 and R=0.77 for respectively the trend and anomalies. Synergistically using Opt and MW observations also shows good potential. Results show that absolute errors decreased (with RMSE=1.22 and S=0.89), but with lower R=0.59; sparse optical observations only improved part of the temporal domain. This shows that PM observations provide good information for the overall trend of the retrieved LAI due to the regular acquisitions, while Opt observations provides better information of the absolute values of the LAI.

DSCOVR EPIC L2 VESDR Data Release Announcement

Atmospheric Science Data Center

2018-06-14

... Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR). The VESDR product contains Leaf Area Index (LAI) ... FPAR, LAI, SLAI are useful for monitoring variability and change in global vegetation due to climate and anthropogenic influences, ...

DSCOVR EPIC L2 VESDR Data Release Announcement

Atmospheric Science Data Center

2018-06-07

... Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR). The VESDR product contains Leaf Area Index (LAI) ... FPAR, LAI, SLAI are useful for monitoring variability and change in global vegetation due to climate and anthropogenic influences, ...

Policy and programmatic considerations for introducing a longer-acting injectable contraceptive: perspectives of stakeholders from Kenya and Rwanda

PubMed Central

McKenna, Kevin; Arcara, Jennet; Rademacher, Kate H; Mackenzie, Caroline; Ngabo, Fidele; Munyambanza, Emmanuel; Wesson, Jennifer; Tolley, Elizabeth E

2014-01-01

ABSTRACT Background: More than 40 million women use injectable contraceptives to prevent pregnancy, and most current or previous injectable users report being satisfied with the method. However, while women may find injectables acceptable, they may not always find them accessible due to stock-outs and difficulties with returning to the clinic for reinjections. FHI 360 is spearheading efforts to develop a longer-acting injectable (LAI) contraceptive that could provide at least 6 months of protection against pregnancy. This article addresses systems-level considerations for the introduction of a new LAI. Methods: We conducted qualitative case studies in Kenya and Rwanda—two countries that have high levels of injectable use but with different service delivery contexts. Between June and September 2012, we conducted in-depth interviews with 27 service providers and 19 policy makers and program implementers focusing on 4 themes: systems-level barriers and facilitators to delivering LAI services; process for introducing an LAI; LAI distribution approaches; and potential LAI characteristics. We also obtained electronic feedback from 28 international family planning opinion leaders. Results: Respondents indicated strong interest in an LAI and thought it would appeal to existing injectable users as well as new family planning clients, both for spacing and for limiting births. Providers appreciated the potential for a lighter workload due to fewer follow-up visits, but they were concerned that fewer visits would also decrease their ability to help women manage side effects. The providers also appreciated the 1-month grace period for follow-up LAI injections; some seemed unaware of the latest international guidance that had increased the grace period from 2 weeks to 4 weeks for the currently available 3-month injectable. The majority of policy makers and program implementers were supportive of letting community health workers provide the method, but many nurses and midwives in Kenya had reservations about the approach. At the policy level, respondents indicated that obtaining regulatory approvals before introducing the new method could be costly and time-consuming. Manufacturing and procurement decisions could also affect cost and availability. Conclusions: Successful introduction of a potential longer-acting injectable may be enhanced by considering broader systemic issues, including managing cost to the health system and users, expanding access through community-based distribution, and training providers on the latest service delivery guidelines. PMID:25611479

The Jet Stream's Precursor of M7.7 Russia Earthquake on 2017/07/17

NASA Astrophysics Data System (ADS)

Wu, H. C.

2017-12-01

Before M>6.0 earthquakes occurred, jet stream in the epicenter area will interrupt or velocity flow lines cross. That meaning is that before earthquake happen, atmospheric pressure in high altitude suddenly dropped during 6 12 hours (Wu & Tikhonov, 2014; Wu et.al,2015). The 70 knots speed line in jet stream was crossed at the epicenter on 2017/07/13, and then M7.7 Russia earthquake happened on 2017/07/17. Lithosphere-atmosphere-ionosphere (LAI) coupling model may be explained this phenomenon : Ionization of the air produced by an increased emanation of radon at epicenter. The water molecules in the air react with these ions, and then release heat. The heat result in temperature rise and pressure drop in the air(Pulinets, Ouzounov, 2011), and then the speed line of jet stream was changed. ps.Russia earthquake:M7.7 2017-07-17 23:34:13 (UTC) 54.471°N 168.815°E 11.0 kmReference: H.C Wu, I.N. Tikhonov, 2014, "Jet streams anomalies as possible short-term precursors of earthquakes with M>6.0", Research in geophysics. H.C.Wu., Ivan N. Tikhonov, and Ariel R. Ćesped,2015, Multi-parametric analysis of earthquake precursors, Russ. J. Earth. Sci., 15, ES3002, doi:10.2205/2015ES000553 S Pulinets, D Ouzounov, 2011,"Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) model-An unified concept for earthquake precursors validation", Journal of Asian Earth Sciences 41 (4), 371-382.

Time series of GNSS-derived ionospheric maps to detect anomalies as possible precursors of high magnitude earthquakes

NASA Astrophysics Data System (ADS)

Barbarella, M.; De Giglio, M.; Galeandro, A.; Mancini, F.

2012-04-01

The modification of some atmospheric physical properties prior to a high magnitude earthquake has been recently debated within the Lithosphere-Atmosphere-Ionosphere (LAI) Coupling model. Among this variety of phenomena the ionization of air at the higher level of the atmosphere, called ionosphere, is investigated in this work. Such a ionization occurrences could be caused by possible leaking of gases from earth crust and their presence was detected around the time of high magnitude earthquakes by several authors. However, the spatial scale and temporal domain over which such a disturbances come into evidence is still a controversial item. Even thought the ionospheric activity could be investigated by different methodologies (satellite or terrestrial measurements), we selected the production of ionospheric maps by the analysis of GNSS (Global Navigation Satellite Data) data as possible way to detect anomalies prior of a seismic event over a wide area around the epicentre. It is well known that, in the GNSS sciences, the ionospheric activity could be probed by the analysis of refraction phenomena occurred on the dual frequency signals along the satellite to receiver path. The analysis of refraction phenomena affecting data acquired by the GNSS permanent trackers is able to produce daily to hourly maps representing the spatial distribution of the ionospheric Total Electron Content (TEC) as an index of the ionization degree in the upper atmosphere. The presence of large ionospheric anomalies could be therefore interpreted in the LAI Coupling model like a precursor signal of a strong earthquake, especially when the appearance of other different precursors (thermal anomalies and/or gas fluxes) could be detected. In this work, a six-month long series of ionospheric maps produced from GNSS data collected by a network of 49 GPS permanent stations distributed within an area around the city of L'Aquila (Abruzzi, Italy), where an earthquake (M = 6.3) occurred on April 6, 2009, were investigated. Basically, the proposed methodology is able to perform a time series analysis of the TEC maps and, eventually, define the spatial and temporal domains of ionospheric disturbances. This goal was achieved by a time series analysis of the spatial dataset able to compare a local pattern of ionospheric activity with its historical mean value and detect areas where the TEC content exhibits anomalous values. This data processing shows some 1 to 2 days long anomalies about 20 days before of the seismic event (confirming also results provided in recent studies by means of ionospheric soundings).

Long-acting injectable antipsychotics update: lengthening the dosing interval and expanding the diagnostic indications.

PubMed

Citrome, Leslie

2017-10-01

Long-acting injectable (LAI) antipsychotics are a useful but underutilized option in the management of schizophrenia. Areas covered: This is a narrative review of newer LAI antipsychotics approved by the US Food and Drug Administration and is an update to a previously published review from 2013. Emphasized are new indications and new dosing intervals. Expert commentary: Ensuring that persons receiving oral antipsychotics are aware that LAI antipsychotics are available is important. The use of LAI antipsychotics can decrease the risk of relapse in both first-episode and chronic schizophrenia. Available treatments differ in terms of specific indications, approved injection sites, needle gauge, injection volume, injection interval, requirements for oral supplementation, availability of pre-filled syringes, storage needs, and post-injection observation period, as well as potential drug-drug interactions and commonly encountered adverse reactions. Approved indications have expanded beyond schizophrenia to also include bipolar maintenance (risperidone microspheres and aripiprazole monohydrate) and schizoaffective disorder (paliperidone palmitate monthly). Intervals between injections can be longer than one month (six-week or two-month aripiprazole lauroxil, and three-month paliperidone palmitate). After a review of the evidence-base, guidance is offered on the appropriate selection among the LAI formulations of both first and second-generation antipsychotics.

Leaf Area Index (LAI) in different type of agroforestry systems based on hemispherical photographs in Cidanau Watershed

NASA Astrophysics Data System (ADS)

Nur Khairiah, Rahmi; Setiawan, Yudi; Budi Prasetyo, Lilik; Ayu Permatasari, Prita

2017-01-01

Ecological functions of agroforestry systems have perceived benefit to people around Cidanau Watershed, especially in the protection of water quality. The main causes of the problems encountered in the Cidanau Watershed are associated with the human factors, especially encroachment and conversion of forest into farmland. The encroachment has made most forest in Cidanau Watershed become bare land. To preserve the ecological function of agroforestry systems in Cidanau Watershed, monitoring of the condition of the vegetation canopy in agroforestry systems is really needed. High intensity thinning of crown density due to deforestation can change stand leaf area index dramatically. By knowing LAI, we can assess the condition of the vegetation canopy in agroforestry systems. LAI in this research was obtained from Hemispherical Photographs analysis using the threshold method in HemiView Canopy Analysis Software. Our research results indicate that there are six types of agroforestry in Cidanau Watershed i.e. Sengon Agroforestry, Clove Agroforestry, Melinjo Agroforestry, Chocolate Agroforestry, Coffee Agroforestry, and Complex Agroforestry. Several factors potentially contribute to variations in the value of LAI in different types of agroforestry. The simple assumptions about differences ranges of LAI values on six types of agroforestry is closely related to leaf area and plant population density.

Evaluation of different shadow detection and restoration methods and their impact on vegetation indices using UAV high-resolution imageries over vineyards

NASA Astrophysics Data System (ADS)

Aboutalebi, M.; Torres-Rua, A. F.; McKee, M.; Kustas, W. P.; Nieto, H.

2017-12-01

Shadows are an unavoidable component of high-resolution imagery. Although shadows can be a useful source of information about terrestrial features, they are a hindrance for image processing and lead to misclassification errors and increased uncertainty in defining surface reflectance properties. In precision agriculture activities, shadows may affect the performance of vegetation indices at pixel and plant scales. Thus, it becomes necessary to evaluate existing shadow detection and restoration methods, especially for applications that makes direct use of pixel information to estimate vegetation biomass, leaf area index (LAI), plant water use and stress, chlorophyll content, just to name a few. In this study, four high-resolution imageries captured by the Utah State University - AggieAir Unmanned Aerial Vehicle (UAV) system flown in 2014, 2015, and 2016 over a commercial vineyard located in the California for the USDA-Agricultural Research Service Grape Remote sensing Atmospheric Profile and Evapotranspiration Experiment (GRAPEX) Program are used for shadow detection and restoration. Four different methods for shadow detection are compared: (1) unsupervised classification, (2) supervised classification, (3) index-based method, and (4) physically-based method. Also, two different shadow restoration methods are evaluated: (1) linear correlation correction, and (2) gamma correction. The models' performance is evaluated over two vegetation indices: normalized difference vegetation index (NDVI) and LAI for both sunlit and shadowed pixels. Histogram and analysis of variance (ANOVA) are used as performance indicators. Results indicated that the performance of the supervised classification and the index-based method are better than other methods. In addition, there is a statistical difference between the average of NDVI and LAI on the sunlit and shadowed pixels. Among the shadow restoration methods, gamma correction visually works better than the linear correlation correction. Moreover, the statistical difference between sunlit and shadowed NDVI and LAI decreases after the application of the gamma restoration method. Potential effects of shadows on modeling surface energy balance and evapotranspiration using very high resolution UAV imagery over the GRAPEX vineyard will be discussed.

Establishment, drought tolerance and recovery, and canopy analysis of turfgrasses in the transition zone

NASA Astrophysics Data System (ADS)

Goldsby, Anthony Lee

Increasing water scarcity may result in greater irrigation restrictions for turfgrass. Drought tolerance and recovery of Kentucky bluegrasses ( Poa. pratensis L.) (KBG) were evaluated during and after 88 and 60 day dry downs in 2010 and 2011, respectively, under a rainout shelter. Changes in green coverage were evaluated with digital images. Green coverage declined slowest during dry downs and increased fastest during recoveries in the cultivar 'Apollo', indicating it had superior drought tolerance. Electrolyte leakage, photosynthesis, and leaf water potential were evaluated in 7 KBG cultivars during and after the dry downs. Soil moisture at 5 and 20 cm was measured. There were generally no differences in physiological parameters among cultivars during or after dry down. The highest reduction in soil moisture at 5 and 20 cm was in Apollo, suggesting it had a better developed root system for mining water from the profile during drought. Weed prevention and turfgrass establishment of 'Legacy' buffalograss (Buchloe dactyloides [Nutt.] Engelm.) and 'Chisholm' zoysiagrass (Zoysia japonica Steud.) grown on turf reinforcement mats (TRM) was evaluated. 'Chisholm' zoysiagrass stolons grew under the TRM; as such, use of TRM for this cultivar is not practical. Buffalograss had 90% or greater coverage when established on TRM in 2010 and 65% or greater coverage in 2011; coverage was similar to that in oxadiazon-treated plots at the end of each year. 'Legacy' buffalograss plugs were established on TRM over plastic for 3 weeks, stored in TRM under tree shade for 7, 14, or 21 days, and evaluated for establishment after storage. In 2010, plugs on mats stored for 7 days had similar coverage to the control, but in 2011 displayed similar coverage to plugs stored on TRM for 14 or 21 day treatments. Green leaf are index (LAI) is an important indicator of turfgrass performance, but its measurement is time consuming and destructive. Measurements using hyperspectral radiometry were compared with destructive measurements of LAI. Results suggest spectral radiometry has potential to accurately predict LAI. The robustness of prediction models varied over the growing season. Finding one model to predict LAI across and entire growing season still seems unrealistic.

Calibration of a biome-biogeochemical cycles model for modeling the net primary production of teak forests through inverse modeling of remotely sensed data

NASA Astrophysics Data System (ADS)

Imvitthaya, Chomchid; Honda, Kiyoshi; Lertlum, Surat; Tangtham, Nipon

2011-01-01

In this paper, we present the results of a net primary production (NPP) modeling of teak (Tectona grandis Lin F.), an important species in tropical deciduous forests. The biome-biogeochemical cycles or Biome-BGC model was calibrated to estimate net NPP through the inverse modeling approach. A genetic algorithm (GA) was linked with Biome-BGC to determine the optimal ecophysiological model parameters. The Biome-BGC was calibrated by adjusting the ecophysiological model parameters to fit the simulated LAI to the satellite LAI (SPOT-Vegetation), and the best fitness confirmed the high accuracy of generated ecophysioligical parameter from GA. The modeled NPP, using optimized parameters from GA as input data, was evaluated using daily NPP derived by the MODIS satellite and the annual field data in northern Thailand. The results showed that NPP obtained using the optimized ecophysiological parameters were more accurate than those obtained using default literature parameterization. This improvement occurred mainly because the model's optimized parameters reduced the bias by reducing systematic underestimation in the model. These Biome-BGC results can be effectively applied in teak forests in tropical areas. The study proposes a more effective method of using GA to determine ecophysiological parameters at the site level and represents a first step toward the analysis of the carbon budget of teak plantations at the regional scale.

PyrE, an interactive fire module within the NASA-GISS Earth System Model

NASA Astrophysics Data System (ADS)

Mezuman, K.; Bauer, S. E.; Tsigaridis, K.

2017-12-01

Fires directly affect the composition of the atmosphere and Earth's radiation balance by emitting a suite of reactive gases and particles. Having an interactive fire module in an Earth System Model allows us to study the natural and anthropogenic drivers, feedbacks, and interactions of biomass burning in different time periods. To do so we have developed PyrE, the NASA-GISS interactive fire emissions model. PyrE uses the flammability, ignition, and suppression parameterization proposed by Pechony and Shindell (2009), and is coupled to a burned area and surface recovery parameterization. The burned area calculation follows CLM's approach (Li et al., 2012), paired with an offline recovery scheme based on Ent's Terrestrial Biosphere Model (Ent TBM) carbon pool turnover time. PyrE is driven by environmental variables calculated by climate simulations, population density data, MODIS fire counts and LAI retrievals, as well as GFED4s emissions. Since the model development required extensive use of reference datasets, in addition to comparing it to GFED4s BA, we evaluate it by studying the effect of fires on atmospheric composition and climate. Our results show good agreement globally, with some regional differences. Finally, we quantify the present day fire radiative forcing. The development of PyrE allowed us for the first time to interactively simulate climate and fire activity with GISS-ModelE3

Noah-MP-Crop: Enhancing cropland representation in the community land surface modeling system

NASA Astrophysics Data System (ADS)

Liu, X.; Chen, F.; Barlage, M. J.; Zhou, G.; Niyogi, D.

2015-12-01

Croplands are important in land-atmosphere interactions and in modifying local and regional weather and climate. Despite their importance, croplands are poorly represented in the current version of the coupled Weather Research and Forecasting (WRF)/ Noah land-surface modeling system, resulting in significant surface temperature and humidity biases across agriculture- dominated regions of the United States. This study aims to improve the WRF weather forecasting and regional climate simulations during the crop growing season by enhancing the representation of cropland in the Noah-MP land model. We introduced dynamic crop growth parameterization into Noah-MP and evaluated the enhanced model (Noah-MP-Crop) at both the field and regional scales with multiple crop biomass datasets, surface fluxes and soil moisture/temperature observations. We also integrated a detailed cropland cover map into WRF, enabling the model to simulate corn and soybean field across the U.S. Great Plains. Results show marked improvement in the Noah-MP-Crop performance in simulating leaf area index (LAI), crop biomass, soil temperature, and surface fluxes. Enhanced cropland representation is not only crucial for improving weather forecasting but can also help assess potential impacts of weather variability on regional hydrometeorology and crop yields. In addition to its applications to WRF, Noah-MP-Crop can be applied in high-spatial-resolution regional crop yield modeling and drought assessments

Utilization of satellite remote sensing data on land surface characteristics in water and heat balance component modeling for vegetation covered territories

NASA Astrophysics Data System (ADS)

Muzylev, Eugene; Uspensky, Alexander; Startseva, Zoya; Volkova, Elena; Kukharsky, Alexander; Uspensky, Sergey

2010-05-01

The model of vertical water and heat transfer in the "soil-vegetation-atmosphere" system (SVAT) for vegetation covered territory has been developed, allowing assimilating satellite remote sensing data on land surface condition as well as accounting for heterogeneities of vegetation and meteorological characteristics. The model provides the calculation of water and heat balance components (such as evapotranspiration Ev, soil water content W, sensible and latent heat fluxes and others ) as well as vertical soil moisture and temperature distributions, temperatures of soil surface and foliage, land surface brightness temperature for any time interval within vegetation season. To describe the landscape diversity soil constants and leaf area index LAI, vegetation cover fraction B, and other vegetation characteristics are used. All these values are considered to be the model parameters. Territory of Kursk region with square about 15 thousands km2 situated in the Black Earth zone of Central Russia was chosen for investigation. Satellite-derived estimates of land surface characteristics have been constructed under cloud-free condition basing AVHRR/NOAA, MODIS/EOS Terra and EOS Aqua, SEVIRI/Meteosat-8, -9 data. The developed technologies of AVHRR data thematic processing have been refined providing the retrieval of surface skin brightness temperature Tsg, air foliage temperature Ta, efficient surface temperature Ts.eff and emissivity E, as well as derivation of vegetation index NDVI, B, and LAI. The linear regression estimators for Tsg, Ta and LAI have been built using representative training samples for 2003-2009 vegetation seasons. The updated software package has been applied for AVHRR data thematic processing to generate named remote sensing products for various dates of the above vegetation seasons. The error statistics of Ta, Ts.eff and Тsg derivation has been investigated for various samples using comparison with in-situ measurements that has given RMS errors in the range 2.0-2.6, 2.5-3.7, and 3.5-4.9°C respectively. The dataset of remote sensing products has been compiled on the base of special technology using Internet resources, that includes MODIS-based estimates of land surface temperature (LST) Tsg, E, NDVI, LAI for the region of interest and the same vegetation seasons. Two types of MODIS-based Тsg and E estimates have been extracted from LP DAAC web-site (for separate dates of 2003-2009 time period): LST/E Daily L3 product (MOD11В1) with spatial resolution ~ 4.8 km and LST/E 5-Min L2 product (MOD11_L2) with spatial resolution ~ 1 km. The verification of Tsg estimates has been performed via comparison with analogous and collocated AVHRR-based ones. Along with this the sample of SEVIRI-based Tsg and E estimates has been accumulated for the Kursk area and surrounding territories for the time interval of several days during 2009 vegetation season. To retrieve Тsg and Е from SEVIRI/Meteosat-8, -9 data the new method has been developed. Being designed as the combination of well-known Split Window Technique and Two Temperature Method algorithms it provides the derivation of Тsg from SEVIRI/Meteosat-9 measurements carried out at three successive times (classified as 100% cloud-free) and covering the region under consideration without accurate a priory knowledge of E. Comparison of the SEVIRI-based Tsg retrievals with the independent collocated Tsg estimates gives the values of RMS deviation in the range of 0.9-1.4°C and it proves (indirectly) the efficiency of proposed approach. To assimilate satellite-derived estimates of vegetation characteristics and LST in the SVAT model some procedures have been developed. These procedures have included: 1) the replacement of LAI and B ground and point-wise estimates by their AVHRR- or MODIS-based analogues. The efficiency of such approach has been proved through comparison between satellite-derived and ground-based seasonal time behaviors of LAI and B, between satellite-derived, modeled, and in-situ measured temperatures as well as through comparison the modeled and actual values of evapotranspiration Ev and soil water content W for one meter soil layer. The discrepancies between mentioned temperatures do not exceed the RMS errors of satellite-derived estimates Ta, Ts.eff and Tsg while the modeled and measured values of Ev and W have been found close to each other within their standard estimation error; 2) the treating AVHRR- or MODIS-based LST as the input model variable within the SVAT model instead their standard ground-based estimates if the satisfactory time-matching of satellite and ground-based observations takes place. The SEVIRI-derived Tsg can be also used for these aims. Permissibility of such replacement has been verified while comparing remote sensed, modeled and ground-based temperatures as well as calculated and measured values of W and Ev. The SEVIRI-based Tsg estimates were found to be very informative and useful due to their high temporal resolution. Moreover the approach has been developed to account for space variability of vegetation cover parameters and meteorological characteristics. This approach includes the development of algorithms and programs for entering AVHRR- and MODIS-derived LAI and B, all named satellite-based LSTs as well as ground-based precipitation, air temperature and humidity data prepared by Inverse Distance Weighted Average Method into the model in each calculation grid unit. The calculations of vertical water and heat fluxes, soil water and heat contents and other water and heat balance components for Kursk region have been carried out with the help of the SVAT model using fields of AVHRR/3- and MODIS-derived LAI and B and AVHRR/3-, MODIS, and SEVIRI-derived LST for various vegetation seasons of 2003-2009. The acceptable accuracy levels of above values assessment have been achieved under all scenarios of parameter and input model variable specification. Thus, the results of this study confirm the opportunity of using area distributed satellite-derived estimates of land surface characteristics for the model calculations of water and heat balance components for large territories especially under the lack of ground observation data. The present study was carried out with support of the Russian Foundation of Basic Researches - grant N 10-05-00807.

Switchgrass growth and effects on biomass accumulation, moisture content, and nutrient removal

USDA-ARS?s Scientific Manuscript database

Temporal patterns of plant growth, composition, and nutrient removal impact development of models for predicting optimal harvest times of switchgrass (Panicum virgatum L.) for bioenergy. Objectives were to characterize seasonal trends in yield, tissue moisture, ash content, leaf area index (LAI), in...

COSMO-SkyMed potentiality to identify crop-specific behavior and monitor phenological parameters

NASA Astrophysics Data System (ADS)

Guarini, Rocchina; Segalini, Federica; Mastronardi, Giovanni; Notarnicola, Claudia; Vuolo, Francesco; Dini, Luigi

2014-10-01

This work aims at investigating the capability of COSMO-SkyMed® (CSK®) constellation of Synthetic Aperture Radar (SAR) system to monitor the Leaf Area Index (LAI) of different crops. The experiment was conducted in the Marchfeld Region, an agricultural Austrian area, and focused on five crop species: sugar beet, soybean, potato, pea and corn. A linear regression analysis was carried out to assess the sensitivity of CSK® backscattering coefficients to crops changes base on LAI values. CSK® backscattering coefficients were averaged at a field scale (<σ°dB>) and were compared to the DEIMOS-1 derived values of estimated LAI. LAI were as well averaged over the corresponding fields (). CSK® data acquired at three polarizations (HH, VV and VH), four incidence angles (23°, 33°, 40° and 57°) and at different pixel spacings (2.5 m and 10 m) were tested to assess whether spatial resolution may influence results at a field scale and to find the best combination of polarizations and CSK® acquisition beams which indicate the highest sensitivity to crop LAI values. The preliminary results show that sugar beet can be well monitored (r = 0.72 - 0.80) by CSK® by using any of the polarization acquisition modes, at moderate to shallow incidence angles (33° - 57°). Slightly weaker correlations were found, at VH polarization only, between CSK® < σ°dB> and for potato (r = 0.65), pea (r = 0.65) and soybean (r = -0.83). Shallower view incidence angles seem to be preferable to steep ones in most cases. CSK® backscattering coefficients were no sensitive at all to LAI changes for already developed corn fields.

Evaluation of the Talar Cartilage in Chronic Lateral Ankle Instability with Lateral Ligament Injury Using Biochemical T2* Mapping: Correlation with Clinical Symptoms.

PubMed

Hu, Yiwen; Tao, Hongyue; Qiao, Yang; Ma, Kui; Hua, Yinghui; Yan, Xu; Chen, Shuang

2018-06-19

This study aims to quantitatively compare T2* measurements of the talar cartilage between chronic lateral ankle instability (LAI) patients with lateral ligament injury and healthy volunteers, and to assess the association of T2* value with American Orthopedic Foot and Ankle Society (AOFAS) score. Nineteen consecutive patients with chronic LAI (LAI group) and 19 healthy individuals (control group) were enrolled. Biochemical magnetic resonance examination of the ankle was performed in all participants using three-dimensional gradient-echo T2* mapping. Total talar cartilage was divided into six subcompartments, including medial anterior (MA), central medial, medial posterior, lateral anterior, central lateral (LC), and lateral posterior regions. T2* values of respective cartilage areas were measured and compared between the two groups using Student t test. AOFAS scoring was performed for clinical evaluation. Then, the association of T2* value with AOFAS score was evaluated by Pearson correlation. The T2* values of total talar cartilage, as well as MA and LC cartilage compartments, in the chronic LAI group were significantly higher than control values (P <.001, P = .039, and P = .014, respectively). Furthermore, the T2* value of MA in the chronic LAI group was negatively correlated with AOFAS score (r =-0.8089, P <.001). Chronic LAI with lateral ligament injury may have a causal connection with early cartilage degeneration in the ankle joint, especially in MA and LC cartilage compartments, as assessed by quantitative T2* measurements. The clinical score correlates highly with T2* value of the MA cartilage compartment, indicating that MA may be the principal cartilage area conferring clinical symptoms. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Hospitalizations and economic analysis in psychotic patients with paliperidone palmitate long-acting injection.

PubMed

Mesones-Peral, Jesús E; Gurillo-Muñoz, Pedro; Sánchez-Sicilia, Mari Paz; Miller, Adam; Griñant-Fernández, Alejandra

Prevent hospitalizations in psychotic disorders is an important aim, so long-acting antipsychotic is a good option that can control better the correct adherence. Moreover, in the current economic context pharmacoeconomic studies are necessary. We estimate the effect in prevention of paliperidone palmitate long-acting injection (PP-LAI) and calculate the economic cost in the 12 months preceding the start of treatment with PP-LAI and 12 months later. Mirror image study of 71 outpatients diagnosed with psychotic disorders and treated with PP-LAI. In a first analysis, we measured along one year: number of hospitalizations/year, number of hospitalization in days, number of emergency assists/year and if there is antipsychotics associated to long-acting treatment. After this phase, we applied Fees Act of Valencia for economic analysis and estimate of the cost per hospitalization (€ 5,640.41) and hospital emergency (€ 187.61). After one year of treatment with PP-LAI (mean dose=130.65mg/month), we obtained greater numbers in assistance variables: total hospitalizations decrease, 78.8% (P=.009); shortening in hospitalization days, 89.4% (P=.009); abridgement of number of emergency assists, 79.1% (P=.002); decrease of rate of antipsychotics associated to long-acting treatment, 21% (P<.0001); increase in monotherapy, 53.8% (P<.0001). Therefore, after 12 months of treatment with PP-LAI we obtained a reduction in inpatient spending (savings of € 175,766.54) and increased spending on antipsychotics 32% (equivalent to € 151,126.92). PP-LAI can be an effective therapy for the treatment of patients with severe psychotic disorders: improves symptomatic stability and can prevent hospitalizations with cost-effective symptom control. Copyright © 2016 SEP y SEPB. Publicado por Elsevier España, S.L.U. All rights reserved.

Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development

PubMed Central

Shafian, Sanaz; Schnell, Ronnie; Bagavathiannan, Muthukumar; Valasek, John; Shi, Yeyin; Olsenholler, Jeff

2018-01-01

Unmanned Aerial Vehicles and Systems (UAV or UAS) have become increasingly popular in recent years for agricultural research applications. UAS are capable of acquiring images with high spatial and temporal resolutions that are ideal for applications in agriculture. The objective of this study was to evaluate the performance of a UAS-based remote sensing system for quantification of crop growth parameters of sorghum (Sorghum bicolor L.) including leaf area index (LAI), fractional vegetation cover (fc) and yield. The study was conducted at the Texas A&M Research Farm near College Station, Texas, United States. A fixed-wing UAS equipped with a multispectral sensor was used to collect image data during the 2016 growing season (April–October). Flight missions were successfully carried out at 50 days after planting (DAP; 25 May), 66 DAP (10 June) and 74 DAP (18 June). These flight missions provided image data covering the middle growth period of sorghum with a spatial resolution of approximately 6.5 cm. Field measurements of LAI and fc were also collected. Four vegetation indices were calculated using the UAS images. Among those indices, the normalized difference vegetation index (NDVI) showed the highest correlation with LAI, fc and yield with R2 values of 0.91, 0.89 and 0.58 respectively. Empirical relationships between NDVI and LAI and between NDVI and fc were validated and proved to be accurate for estimating LAI and fc from UAS-derived NDVI values. NDVI determined from UAS imagery acquired during the flowering stage (74 DAP) was found to be the most highly correlated with final grain yield. The observed high correlations between UAS-derived NDVI and the crop growth parameters (fc, LAI and grain yield) suggests the applicability of UAS for within-season data collection of agricultural crops such as sorghum. PMID:29715311

The incidence and characteristics of supraventricular tachycardia in left atrial isomerism: a high incidence of atrial fibrillation in young patients.

PubMed

Miyazaki, Aya; Sakaguchi, Heima; Ohuchi, Hideo; Yamamoto, Tetsuya; Igarashi, Takehiro; Negishi, Jun; Toyota, Naoki; Kagisaki, Koji; Yagihara, Toshikatsu; Yamada, Osamu

2013-06-20

In left atrial isomerism (LAI), both atria show left atrial morphology. Although bradyarrhythmias are frequent and highly complex in LAI patients, previous studies have reported a low incidence of supraventricular tachycardia (SVT). To evaluate the incidence and characteristics of SVT in LAI, we retrospectively evaluated the clinical characteristics of SVTs in 83 patients with LAI (age at last follow-up, 15.3±10.5 years). There were 27 SVTs in 19 patients (23%), including nine episodes of atrial fibrillation (AF) and eight non-reentrant SVTs. Sixteen of the 19 patients with SVT had histories of atriotomy, but the three patients with AF or non-reentrant tachycardia had no history of atriotomy. The rates of freedom from SVT were 66% and 59% at ages of 20 and 30 years, respectively; the corresponding rates for freedom from AF were 89% and 74%. In multivariate analysis, the predictors of SVT were age (OR, 1.14; 95% CI, 1.06-1.26; p=0.003) and sinus node dysfunction (SND) (OR, 3.88; 95% CI, 1.57-13.34; p=0.01). In patients with LAI, SVTs are common, and AF and non-reentrant SVTs are the major type of SVTs. The incidence of AF was high in young patients with LAI. The lack of anatomical barriers in the atria that allow the formation of macro-reentrant circuits may account for the higher incidence of AF and non-reentrant SVT than macro-reentrant tachycardia. Moreover, the increasing prevalence of SND with age should contribute to a higher incidence of SVT. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Assessing Mechanisms of Climate Change Impact on the Upland Forest Water Balance of the Willamette River Basin

NASA Astrophysics Data System (ADS)

Turner, D. P.; Conklin, D. R.; Vache, K. B.; Schwartz, C.; Nolin, A. W.; Chang, H.; Watson, E.; John, B.

2016-12-01

Projected changes in air temperature, precipitation, and vapor pressure for the Willamette River Basin (Oregon, USA) over the next century will have significant impacts on the river basin water balance, notably on the amount of evapotranspiration (ET). Mechanisms of impact on ET will be both direct and indirect, but there is limited understanding of their absolute and relative magnitudes. Here we developed a spatially-explicit, daily time-step, modeling infrastructure to simulate the basin-wide water balance that accounts for meteorological influences, as well as effects mediated by changing vegetation cover type, leaf area, and ecophysiology. Three CMIP5 climate scenarios (LowClim, Reference, HighClim) were run for the 2010 to 2100 period. Besides warmer temperatures, the climate scenarios were characterized by wetter winters and increasing vapor pressure deficits. In the mid-range Reference scenario, our landscape simulation model (Envision) projected a continuation of forest cover on the uplands but a 3-fold increase in area burned per year. A decline (12-30%) in basin-wide mean leaf area index (LAI) in forests was projected in all scenarios. The lower LAIs drove a corresponding decline in ET. In a sensitivity test, the effect of increasing CO2 on stomatal conductance induced a further substantial decrease (11-18%) in basin-wide mean ET. The net effect of decreases in ET and increases in winter precipitation was an increase in annual streamflow. These results support the inclusion of changes in land cover, land use, LAI, and ecophysiology in efforts to anticipate impacts of climate change on basin-scale water balances.

The stochastic Beer-Lambert-Bouguer law for discontinuous vegetation canopies

NASA Astrophysics Data System (ADS)

Shabanov, N.; Gastellu-Etchegorry, J.-P.

2018-07-01

The 3D distribution of canopy foliage affects the radiation regime and retrievals of canopy biophysical parameters. The gap fraction is one primary indicator of a canopy structure. Historically the Beer-Lambert-Bouguer law and the linear mixture model have served as a basis for multiple technologies for retrievals of the gap (or vegetation) fraction and Leaf Area Index (LAI). The Beer-Lambert-Bouguer law is a form of the Radiative Transfer (RT) equation for homogeneous canopies, which was later adjusted for a correlation between fitoelements using concept of the clumping index. The Stochastic Radiative Transfer (SRT) approach has been developed specifically for heterogeneous canopies, however the approach lacks a proper model of the vegetation fraction. This study is focused on the implementation of the stochastic version of the Beer-Lambert-Bouguer law for heterogeneous canopies, featuring the following principles: 1) two mechanisms perform photon transport- transmission through the turbid medium of foliage crowns and direct streaming through canopy gaps, 2) the radiation field is influenced by a canopy structure (quantified by the statistical moments of a canopy structure) and a foliage density (quantified by the gap fraction as a function of LAI), 3) the notions of canopy transmittance and gap fraction are distinct. The derived stochastic Beer-Lambert-Bouguer law is consistent with the Geometrical Optical and Radiative Transfer (GORT) derivations. Analytical and numerical analysis of the stochastic Beer-Lambert-Bouguer law presented in this study provides the basis to reformulate widely used technologies for retrievals of the gap fraction and LAI from ground and satellite radiation measurements.

Influence of lateral groundwater flow in a shallow aquifer on eco-hydrological process in a shrub-grass coexistence semiarid area

NASA Astrophysics Data System (ADS)

Wang, Siru; Sun, Jinhua; Lei, Huimin; Zhu, Qiande; Jiang, Sanyuan

2017-04-01

Topography has a considerable influence on eco-hydrological processes resulting from the patterns of solar radiation distribution and lateral water flow. However, not much quantitative information on the contribution of lateral groundwater flow on ecological processes such as vegetation growth and evapo-transpiration is available. To fill this gap, we used a simple eco-hydrological model based on water balance with a 3D groundwater module that uses Darcy's law. This model was applied to a non-contributing area of 50km2 dominated by grassland and shrubland with an underlying shallow aquifer. It was calibrated using manually and remotely sensed vegetation data and water flux data observed by eddy covariance system of two flux towers as well as water table data obtained from HOBO recorders of 40 wells. The results demonstrate that the maximum hydraulic gradient and the maximum flux of lateral groundwater flow reached to 0.156m m-1 and 0.093m3 s-1 respectively. The average annual maximum LAI in grassland, predominantly in low-lying areas, improved by about 5.9% while that in shrubland, predominantly in high-lying areas, remained the same when lateral groundwater flow is considered adequately compared to the case without considering lateral groundwater flow. They also show that LAI is positively and nonlinearly related to evapotranspiration, and that the greater the magnitude of evapotranspiration, the smaller the rate of increase of LAI. The results suggest that lateral groundwater flow should not be neglected when simulating eco-hydrological process in areas with a shallow aquifer.

Evaluate dry deposition velocity of the nitrogen oxides using Noah-MP physics ensemble simulations for the Dinghushan Forest, Southern China

NASA Astrophysics Data System (ADS)

Zhang, Qi; Chang, Ming; Zhou, Shengzhen; Chen, Weihua; Wang, Xuemei; Liao, Wenhui; Dai, Jianing; Wu, ZhiYong

2017-11-01

There has been a rapid growth of reactive nitrogen (Nr) deposition over the world in the past decades. The Pearl River Delta region is one of the areas with high loading of nitrogen deposition. But there are still large uncertainties in the study of dry deposition because of its complex processes of physical chemistry and vegetation physiology. At present, the forest canopy parameterization scheme used in WRF-Chem model is a single-layer "big leaf" model, and the simulation of radiation transmission and energy balance in forest canopy is not detailed and accurate. Noah-MP land surface model (Noah-MP) is based on the Noah land surface model (Noah LSM) and has multiple parametric options to simulate the energy, momentum, and material interactions of the vegetation-soil-atmosphere system. Therefore, to investigate the improvement of the simulation results of WRF-Chem on the nitrogen deposition in forest area after coupled with Noah-MP model and to reduce the influence of meteorological simulation biases on the dry deposition velocity simulation, a dry deposition single-point model coupled by Noah- MP and the WRF-Chem dry deposition module (WDDM) was used to simulate the deposition velocity (Vd). The model was driven by the micro-meteorological observation of the Dinghushan Forest Ecosystem Location Station. And a series of numerical experiments were carried out to identify the key processes influencing the calculation of dry deposition velocity, and the effects of various surface physical and plant physiological processes on dry deposition were discussed. The model captured the observed Vd well, but still underestimated the Vd. The self-defect of Wesely scheme applied by WDDM, and the inaccuracy of built-in parameters in WDDM and input data for Noah-MP (e.g. LAI) were the key factors that cause the underestimation of Vd. Therefore, future work is needed to improve model mechanisms and parameterization.

Impact of climate, vegetation, soil and crop management variables on multi-year ISBA-A-gs simulations of evapotranspiration over a Mediterranean crop site

NASA Astrophysics Data System (ADS)

Garrigues, S.; Olioso, A.; Carrer, D.; Decharme, B.; Calvet, J.-C.; Martin, E.; Moulin, S.; Marloie, O.

2015-10-01

Generic land surface models are generally driven by large-scale data sets to describe the climate, the soil properties, the vegetation dynamic and the cropland management (irrigation). This paper investigates the uncertainties in these drivers and their impacts on the evapotranspiration (ET) simulated from the Interactions between Soil, Biosphere, and Atmosphere (ISBA-A-gs) land surface model over a 12-year Mediterranean crop succession. We evaluate the forcing data sets used in the standard implementation of ISBA over France where the model is driven by the SAFRAN (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie) high spatial resolution atmospheric reanalysis, the leaf area index (LAI) time courses derived from the ECOCLIMAP-II land surface parameter database and the soil texture derived from the French soil database. For climate, we focus on the radiations and rainfall variables and we test additional data sets which include the ERA-Interim (ERA-I) low spatial resolution reanalysis, the Global Precipitation Climatology Centre data set (GPCC) and the MeteoSat Second Generation (MSG) satellite estimate of downwelling shortwave radiations. The evaluation of the drivers indicates very low bias in daily downwelling shortwave radiation for ERA-I (2.5 W m-2) compared to the negative biases found for SAFRAN (-10 W m-2) and the MSG satellite (-12 W m-2). Both SAFRAN and ERA-I underestimate downwelling longwave radiations by -12 and -16 W m-2, respectively. The SAFRAN and ERA-I/GPCC rainfall are slightly biased at daily and longer timescales (1 and 0.5 % of the mean rainfall measurement). The SAFRAN rainfall is more precise than the ERA-I/GPCC estimate which shows larger inter-annual variability in yearly rainfall error (up to 100 mm). The ECOCLIMAP-II LAI climatology does not properly resolve Mediterranean crop phenology and underestimates the bare soil period which leads to an overall overestimation of LAI over the crop succession. The simulation of irrigation by the model provides an accurate irrigation amount over the crop cycle but the timing of irrigation occurrences is frequently unrealistic. Errors in the soil hydrodynamic parameters and the lack of irrigation in the simulation have the largest influence on ET compared to uncertainties in the large-scale climate reanalysis and the LAI climatology. Among climate variables, the errors in yearly ET are mainly related to the errors in yearly rainfall. The underestimation of the available water capacity and the soil hydraulic diffusivity induce a large underestimation of ET over 12 years. The underestimation of radiations by the reanalyses and the absence of irrigation in the simulation lead to the underestimation of ET while the overall overestimation of LAI by the ECOCLIMAP-II climatology induces an overestimation of ET over 12 years. This work shows that the key challenges to monitor the water balance of cropland at regional scale concern the representation of the spatial distribution of the soil hydrodynamic parameters, the variability of the irrigation practices, the seasonal and inter-annual dynamics of vegetation and the spatiotemporal heterogeneity of rainfall.

Cost effectiveness of long-acting risperidone in Sweden.

PubMed

Hensen, Marja; Heeg, Bart; Löthgren, Mickael; van Hout, Ben

2010-01-01

In Sweden, risperidone long-acting injectable (RLAI) is generally used in a population of schizophrenia patients who are at a high risk of being non-compliant. However, RLAI might also be suitable for use in the general schizophrenia population. To analyse the clinical and economic effects of RLAI in the Swedish treatment practice using a discrete-event simulation (DES) model. Treatment outcomes and direct costs were analysed for both the high-risk non-compliant patient population and the general schizophrenia population. An existing DES model was adapted to simulate the treatment of schizophrenia in Sweden. Model inputs were based on literature research and supplemented with expert opinion. In the high-risk non-compliant schizophrenia population, RLAI was compared with haloperidol LAI. The analysis was built upon differences in symptom reduction, time between relapses, compliance and adverse effect profile between the two drugs. Main outcomes were the predicted incremental (discounted) costs (€) and effects (QALYs). In the general schizophrenia population, RLAI was compared with oral olanzapine. This analysis was built upon differences in compliance and adverse effects between the drugs. Multivariate probabilistic sensitivity analyses (PSA) were carried out to assess the sensitivity of the results of the two analyses. In the high-risk non-compliant patient population, RLAI was predicted to generate 0.103 QALYs per patient over 3 years while realizing cost savings of €5013 (year 2007 values) compared with haloperidol LAI. The main driver of the cost effectiveness of RLAI was the difference in Positive and Negative Syndrome Scale (PANSS) reduction between the two drugs, followed by the difference in adverse effects. The PSA showed that, in this setting, RLAI had a probability of 100% of being cost effective at a willingness-to-pay (WTP) threshold of €43,300 per QALY gained, compared with haloperidol LAI. The probability that RLAI combines additional effectiveness with cost savings compared with haloperidol LAI was estimated at 94%. When analysing RLAI in the general schizophrenia population, it was predicted to generate 0.043 QALYs and save €239 per patient over 5 years compared with olanzapine. Compliance was the main driver of the cost effectiveness of RLAI in this scenario. In the PSA it was shown that RLAI had a probability of 78% of being cost effective at a WTP threshold of €43,300 per QALY gained, compared with olanzapine. The estimated probability that RLAI combines additional effectiveness with cost savings was 50% and the probability that RLAI is less effective and more costly than olanzapine was negligible (0.2%). Treatment with RLAI is suggested to result in improved QALYs combined with cost savings compared with haloperidol LAI among the Swedish, high-risk non-compliant schizophrenia patient population. In the general schizophrenia population, RLAI also resulted in positive incremental QALYs and cost savings, when compared with olanzapine. However, the estimates used in the model for compliance and symptom reduction need further validation through naturalistic-based studies with reasonable follow-up to more definitely establish the real-life differences between RLAI and the comparators in the considered patient populations and to further reduce the uncertainty of these parameters.

Developing a global mixed-canopy, height-variable vegetation structure dataset for estimating global vegetation albedo and biomass in the NASA Ent Terrestrial Biosphere Model and GISS GCM

NASA Astrophysics Data System (ADS)

Montes, C.; Kiang, N. Y.; Yang, W.; Ni-Meister, W.; Schaaf, C.; Aleinov, I. D.; Jonas, J.; Zhao, F. A.; Yao, T.; Wang, Z.; Sun, Q.

2015-12-01

Processes determining biosphere-atmosphere coupling are strongly influenced by vegetation structure. Thus, ecosystem carbon sequestration and evapotranspiration affecting global carbon and water balances will depend upon the spatial extent of vegetation, its vertical structure, and its physiological variability. To represent this globally, Dynamic Global Vegetation Models (DGVMs) coupled to General Circulation Models (GCMs) make use of satellite and/or model-based vegetation classifications often composed by homogeneous communities. This work aims at developing a new Global Vegetation Structure Dataset (GVSD) by incorporating varying vegetation heights for mixed plant communities to be used as input to the Ent Terrestrial Biosphere Model (TBM), the DGVM coupled to the NASA Goddard Institute for Space Studies (GISS) GCM. Information sources include the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover and plant functional types (PFTs) (Friedl et al., 2010), vegetation height from the Geoscience Laser Altimeter System (GLAS) on board ICESat (Ice, Cloud, and land Elevation Satellite) (Simard et al., 2011; Tang et al., 2014) along with the Global Data Sets of Vegetation Leaf Area Index (LAI)3g (Zhu et al. 2013). Further PFT partitioning is performed according to a climate classification utilizing the Climate Research Unit (CRU) and the NOAA Global Precipitation Climatology Centre (GPCC) data. Final products are a GVSD consisting of mixed plant communities (e.g. mixed forests, savannas, mixed PFTs) following the Ecosystem Demography model (Moorcroft et al., 2001) approach represented by multi-cohort community patches at the sub-grid level of the GCM, which are ensembles of identical individuals whose differences are represented by PFTs, canopy height, density and vegetation structure sensitivity to allometric parameters. To assess the sensitivity of the GISS GCM to vegetation structure, we produce a range of estimates of Ent TBM biomass and plant densities by varying allometric specifications. Ultimately, this GVSD will serve as a template for community data sets, and be used as boundary conditions to the Ent TBM for prediction of canopy albedo in the Analytical Clumped Two-Stream canopy radiative transfer scheme, biomass, primary productivity, respiration, and GISS GCM climate.

Efficient production of D-tagatose using a food-grade surface display system.

PubMed

Liu, Yi; Li, Sha; Xu, Hong; Wu, Lingtian; Xu, Zheng; Liu, Jing; Feng, Xiaohai

2014-07-16

D-tagatose, a functional sweetener, is commonly transformed from D-galactose by L-arabinose isomerase (L-AI). In this study, a novel type of biocatalyst, L-AI from Lactobacillus fermentum CGMCC2921 displayed on the spore surface of Bacillus subtilis 168, was developed for producing D-tagatose. The anchored L-AI, exhibiting the relatively high bioactivity, suggested that the surface display system using CotX as the anchoring protein was successfully constructed. The stability of the anchored L-AI was significantly improved. Specifically, the consolidation of thermal stability representing 87% of relative activity was retained even at 80 °C for 30 min, which remarkably favored the production of D-tagatose. Under the optimal conditions, the robust spores can convert 75% D-galactose (100 g/L) into D-tagatose after 24 h, and the conversion rate remained at 56% at the third cycle. Therefore, this biocatalysis system, which could express the target enzyme on the food-grade vector, was an alternative method for the value-added production of D-tagatose.

Vegetation-climate feedback causes reduced precipitation in CMIP5 regional Earth system model simulation over Africa

NASA Astrophysics Data System (ADS)

Wu, Minchao; Smith, Benjamin; Schurgers, Guy; Lindström, Joe; Rummukainen, Markku; Samuelsson, Patrick

2013-04-01

Terrestrial ecosystems have been demonstrated to play a significant role within the climate system, amplifying or dampening climate change via biogeophysical and biogeochemical exchange with the atmosphere and vice versa (Cox et al. 2000; Betts et al. 2004). Africa is particularly vulnerable to climate change and studies of vegetation-climate feedback mechanisms on Africa are still limited. Our study is the first application of A coupled Earth system model at regional scale and resolution over Africa. We applied a coupled regional climate-vegetation model, RCA-GUESS (Smith et al. 2011), over the CORDEX Africa domain, forced by boundary conditions from a CanESM2 CMIP5 simulation under the RCP8.5 future climate scenario. The simulations were from 1961 to 2100 and covered the African continent at a horizontal grid spacing of 0.44°. RCA-GUESS simulates changes in the phenology, productivity, relative cover and population structure of up to eight plant function types (PFTs) in response to forcing from the climate part of the model. These vegetation changes feedback to simulated climate through dynamic adjustments in surface energy fluxes and surface properties. Changes in the net ecosystem-atmosphere carbon flux and its components net primary production (NPP), heterotrophic respiration and emissions from biomass burning were also simulated but do not feedback to climate in our model. Constant land cover was assumed. We compared simulations with and without vegetation feedback switched "on" to assess the influence of vegetation-climate feedback on simulated climate, vegetation and ecosystem carbon cycling. Both positive and negative warming feedbacks were identified in different parts of Africa. In the Sahel savannah zone near 15°N, reduced vegetation cover and productivity, and mortality caused by a deterioration of soil water conditions led to a positive warming feedback mediated by decreased evapotranspiration and increased sensible heat flux between vegetation and the atmosphere. In the equatorial rainforest stronghold region of central Africa, a feedback syndrome characterised by reduced plant production and LAI, a dominance shift from tropical trees to grasses, reduced soil water and reduced rainfall was identified. The likely underlying mechanism was a decline in evaporative water recycling associated with sparser vegetation cover, reminiscent of Earth system model studies in which a similar feedback mechanism was simulated to force dieback of tropical rainforest and reduced precipitation over the Amazon Basin (Cox et al. 2000; Betts et al. 2004; Malhi et al. 2009). Opposite effects are seen in southern Senegal, southern Mali, northern Guinea and Guinea-Bissau, positive evapotranspiration feedback enhancing the cover of trees in forest and savannah, mitigating warming and promoting local moisture recycling as rainfall. We reveal that LAI-driven evapotranspiration feedback may reduced rainfall in parts of Africa, vegetation-climate feedbacks may significantly impact the magnitude and character of simulated changes in climate as well as vegetation and ecosystems in future scenario studies of this region. They should be accounted for in future studies of climate change and its impacts on Africa. Keywords: vegetation-climate feedback, regional climate model, evapotranspiration, CORDEX. References: Betts, R.A., Cox, P.M., Collins, M., Harris, P.P., Huntingford, C. & Jones, C.D. 2004. The role of ecosystem-atmosphere interactions in simulated Amazonian precipitation decrease and forest dieback under global climate warming. Theoretical and Applied Climatology 78: 157-175. Cox, P.M., Betts, R.A., Jones, C.D., Spall, S.A. & Totterdell, I.J. 2000. Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature 408: 184-187. Samuelsson, P., Jones, C., Wilĺen, U., Gollvik, S., Hansson, U. and coauthors. 2011. The Rossby Centre Regional Climate Model RCA3:Model description and performance. Tellus 63A, 4-23. Smith, B., Prentice, I. C. and Sykes, M. T. 2001. Representation of vegetation dynamics in modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Global Ecol. Biogeog. 10, 621-637 Smith, B., Samuelsson, P., Wramneby, A. & Rummukainen, M. 2011. A model of the coupled dynamics of climate, vegetation and terrestrial ecosystem biogeochemistry for regional applications. Tellus 63A: 87-106.

Observed Oceanic and Terrestrial Drivers of North African Climate

NASA Astrophysics Data System (ADS)

Yu, Y.; Notaro, M.; Wang, F.; Mao, J.; Shi, X.; Wei, Y.

2015-12-01

Hydrologic variability can pose a serious threat to the poverty-stricken regions of North Africa. Yet, the current understanding of oceanic versus terrestrial drivers of North African droughts/pluvials is largely model-based, with vast disagreement among models. In order to identify the observed drivers of North African climate and develop a benchmark for model evaluations, the multivariate Generalized Equilibrium Feedback Assessment (GEFA) is applied to observations, remotely sensed data, and reanalysis products. The identified primary oceanic drivers of North African rainfall variability are the Atlantic, tropical Indian, and tropical Pacific Oceans and Mediterranean Sea. During the summer monsoon, positive tropical eastern Atlantic sea-surface temperature (SST) anomalies are associated with a southward shift of the Inter-Tropical Convergence Zone, enhanced ocean evaporation, and greater precipitable water across coastal West Africa, leading to increased West African monsoon (WAM) rainfall and decreased Sahel rainfall. During the short rains, positive SST anomalies in the western tropical Indian Ocean and negative anomalies in the eastern tropical Indian Ocean support greater easterly oceanic flow, evaporation over the western ocean, and moisture advection to East Africa, thereby enhancing rainfall. The sign, magnitude, and timing of observed vegetation forcing on rainfall vary across North Africa. The positive feedback of leaf area index (LAI) on rainfall is greatest during DJF for the Horn of Africa, while it peaks in autumn and is weakest during the summer monsoon for the Sahel. Across the WAM region, a positive LAI anomaly supports an earlier monsoon onset, increased rainfall during the pre-monsoon, and decreased rainfall during the wet season. Through unique mechanisms, positive LAI anomalies favor enhanced transpiration, precipitable water, and rainfall across the Sahel and Horn of Africa, and increased roughness, ascent, and rainfall across the WAM region. The current study represents the first attempt to separate the observed roles of oceanic and vegetation feedbacks across North Africa, and provides observational benchmark for model evaluation.

A Simulation of the Importance of Length of Growing Season and Canopy Functional Properties on the Seasonal Gross Primary Production of Temperate Alpine Meadows

PubMed Central

Baptist, Florence; Choler, Philippe

2008-01-01

Background and Aims Along snowmelt gradients, the canopies of temperate alpine meadows differ strongly in their structural and biochemical properties. Here, a study is made of the effects of these canopy dissimilarities combined with the snow-induced changes in length of growing season on seasonal gross primary production (GPP). Methods Leaf area index (LAI) and community-aggregated values of leaf angle and leaf nitrogen content were estimated for seven alpine plant canopies distributed along a marked snowmelt gradient, and these were used as input variables in a sun–shade canopy bulk-photosynthesis model. The model was validated for plant communities of early and late snowmelt sites by measuring the instantaneous CO2 fluxes with a canopy closed-chamber technique. A sensitivity analysis was conducted to estimate the relative impact of canopy properties and environmental factors on the daily and seasonal GPP. Key Results Carbon uptake was primarily related to the LAI and total canopy nitrogen content, but not to the leaf angle. For a given level of photosynthetically active radiation, CO2 assimilation was higher under overcast conditions. Sensitivity analysis revealed that increase of the length of the growing season had a higher effect on the seasonal GPP than a similar increase of any other factor. It was also found that the observed greater nitrogen content and larger LAI of canopies in late-snowmelt sites largely compensated for the negative impact of the reduced growing season. Conclusions The results emphasize the primary importance of snow-induced changes in length of growing season on carbon uptake in alpine temperate meadows. It was also demonstrated how using leaf-trait values of the dominants is a useful approach for modelling ecosystem carbon-cycle-related processes, particularly when continuous measurements of CO2 fluxes are technically difficult. The study thus represents an important step in addressing the challenge of using a plant functional-trait approach for biogeochemical modelling. PMID:18182383

[Monitoring temporal dynamics in leaf area index of the temperate broadleaved deciduous forest in Maoershan region, Northeast China with tower-based radiation measurements.

PubMed

Liu, Fan; Wang, Chuan Kuan; Wang, Xing Chang

2016-08-01

Broadband vegetation indices (BVIs) derived from routine radiation measurements on eddy flux towers have the advantage of high temporal resolutions, and thus have the potential to obtain detailed information of dynamics in canopy leaf area index (LAI). Taking the temperate broadleaved deciduous forest around the Maoershan flux tower in Northeast China as a case, we investigated the controlling factors and smoothing method of four BVI time-series, i.e., broadband norma-lized difference vegetation index (NDVI B ), broadband enhanced vegetation index (EVI B ), the ratio of the near-infrared radiation reflectance to photosynthetically active radiation reflectance (SR NP ), and the ratio of the shortwave radiation reflectance to photosynthetically active radiation reflectance (SR SP ). We compared the seasonal courses of the BVIs with the LAI based on litterfall collection method. The values for each BVI were slightly different among the three calculation methods by Huemmrich, Wilson, and Jenkins, but showed similar seasonal patterns. The diurnal variations in BVIs were mainly influenced by the solar elevation and the angle between the solar elevation and slope, but the BVIs were relatively stable around 12:30. The noise of daily BVI time-series could be effectively smoothed by a threshold of clearness index (K). The seasonal courses of BVIs for each time of day around the noon had similar patterns, but their thresholds of K and the percen-tages of remaining data were different. Therefore, the daily values of BVIs might be optimized based on the smoothing and the proportion of remaining data. The NDVI B was closely correlated linearly with the LAI derived from the litterfall collection method, while the EVI B , SR NP , and SR SP had a logarithmic relationship with the LAI. The NDVI B had the advantage in tracking the seasonal dyna-mics in LAI and extrapolating LAI to a broader scale. Given that most eddy flux towers had equipped with energy balance measurements, a network of monitoring canopy LAI could be readily achieved if the reflectance of photosynthetically active radiation was measured synchronously.

Application and Evaluation of MODIS LAI, FPAR, and Albedo Products in the WRF/CMAQ System

EPA Science Inventory

MODIS vegetation and albedo products provide a more realistic representation of surface conditions for input to the WRF/CMAQ modeling system. However, the initial evaluation of ingesting MODIS data into the system showed mixed results, with increased bias and error for 2-m temper...

ASSESSMENT OF MODIS LAI (W4) IN LOBLOLLY PINE (P. TAEDA) FOREST TYPE, APPOMATTOX, VIRGINIA

EPA Science Inventory

The United States Environmental Protection Agency initiated MODIS MODI5A2LAI validation research (2002) in the evergreen needle leaf biome, as defined in the MOD12 classification, in a regional study located in the southeastern United States.

75 FR 8081 - Patrick J. Lais: Debarment Order

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-23

..., among other things, subpotent burn spray, aspirin that had failed dissolution testing, and antacid... as ``Uncoated Aspirin.'' This drug failed its final dissolution testing. Neither Mr. Lais nor the... coated the failed aspirin and renumbered the lot. Part of this lot then was packaged as ``Coated Aspirin...

Thermostable L-arabinose isomerase from Bacillus stearothermophilus IAM 11001 for D-tagatose production: gene cloning, purification and characterisation.

PubMed

Cheng, Lifang; Mu, Wanmeng; Jiang, Bo

2010-06-01

D-Tagatose, as one of the rare sugars, has been found to be a natural and safe low-calorie sweetener in food products and is classified as a GRAS substance. L-Arabinose isomerase (L-AI, EC 5.3.1.4), catalysing the isomerisations of L-arabinose and D-galactose to L-ribulose and D-tagatose respectively, is considered to be the most promising enzyme for the production of D-tagatose. The araA gene encoding an L-AI from Bacillus stearothermophilus IAM 11001 was cloned, sequenced and overexpressed in Escherichia coli. The gene is composed of 1491 bp nucleotides and codes for a protein of 496 amino acid residues. The recombinant L-AI was purified to electrophoretical homogeneity by affinity chromatography. The purified enzyme was optimally active at 65 degrees C and pH 7.5 and had an absolute requirement for the divalent metal ion Mn(2+) for both catalytic activity and thermostability. The enzyme was relatively active and stable at acidic pH of 6. The bioconversion yield of D-galactose to D-tagatose by the purified L-AI after 12 h at 65 degrees C reached 36%. The purified L-AI from B. stearothermophilus IAM 11001 was characterised and shown to be a good candidate for potential application in D-tagatose production. Copyright (c) 2010 Society of Chemical Industry.

Pan-Arctic modelling of net ecosystem exchange of CO2

PubMed Central

Shaver, G. R.; Rastetter, E. B.; Salmon, V.; Street, L. E.; van de Weg, M. J.; Rocha, A.; van Wijk, M. T.; Williams, M.

2013-01-01

Net ecosystem exchange (NEE) of C varies greatly among Arctic ecosystems. Here, we show that approximately 75 per cent of this variation can be accounted for in a single regression model that predicts NEE as a function of leaf area index (LAI), air temperature and photosynthetically active radiation (PAR). The model was developed in concert with a survey of the light response of NEE in Arctic and subarctic tundras in Alaska, Greenland, Svalbard and Sweden. Model parametrizations based on data collected in one part of the Arctic can be used to predict NEE in other parts of the Arctic with accuracy similar to that of predictions based on data collected in the same site where NEE is predicted. The principal requirement for the dataset is that it should contain a sufficiently wide range of measurements of NEE at both high and low values of LAI, air temperature and PAR, to properly constrain the estimates of model parameters. Canopy N content can also be substituted for leaf area in predicting NEE, with equal or greater accuracy, but substitution of soil temperature for air temperature does not improve predictions. Overall, the results suggest a remarkable convergence in regulation of NEE in diverse ecosystem types throughout the Arctic. PMID:23836790

Evaluation of modelled methane emissions over northern peatland sites

NASA Astrophysics Data System (ADS)

Gao, Yao; Burke, Eleanor; Chadburn, Sarah; Raivonen, Maarit; Susiluoto, Jouni; Vesala, Timo; Aurela, Mika; Lohila, Annalea; Aalto, Tuula

2017-04-01

Methane (CH4) is a powerful greenhouse gas, with approximately 34 times the global warming potential of carbon dioxide (CO2) over a century time horizon (IPCC, 2013). The strong sensitivity of methane emissions to environmental factors has led to concerns about potential positive feedbacks to climate change. Evaluation of the ability of the process-based land surface models of earth system models (ESMs) in simulating CH4 emission over peatland is needed for more precise future predictions. In this study, two peatland sites of poor and rich soil nutrient conditions, in southern and northern Finland respectively, are adopted. The measured CH4 fluxes at the two sites are used to evaluate the CH4 emissions simulated by the land surface model (JULES) of the UK Earth System model and by the Helsinki peatland methane emission model (HIMMELI), which is developed at Finnish Meteorological Institute and Helsinki University. In JULES, CH4 flux is simply related to soil temperature, wetland fraction and effective substrate availability. However, HIMMELI has detailed descriptions of microbial and transport processes for simulating CH4 flux. The seasonal dynamics of CH4 fluxes at the two sites are relatively well captured by both models, but model biases exist. Simulated CH4 flux is sensitive to water table depth (WTD) at both models. However, the simulated WTD is limited to be below ground in JULES. It is also important to have the annual cycle of LAI correct when coupling JULES with HIMMELI.

Estimating primary productivity of tropical oil palm in Malaysia using remote sensing technique and ancillary data

NASA Astrophysics Data System (ADS)

Kanniah, K. D.; Tan, K. P.; Cracknell, A. P.

2014-10-01

The amount of carbon sequestration by vegetation can be estimated using vegetation productivity. At present, there is a knowledge gap in oil palm net primary productivity (NPP) at a regional scale. Therefore, in this study NPP of oil palm trees in Peninsular Malaysia was estimated using remote sensing based light use efficiency (LUE) model with inputs from local meteorological data, upscaled leaf area index/fractional photosynthetically active radiation (LAI/fPAR) derived using UK-DMC 2 satellite data and a constant maximum LUE value from the literature. NPP values estimated from the model was then compared and validated with NPP estimated using allometric equations developed by Corley and Tinker (2003), Henson (2003) and Syahrinudin (2005) with diameter at breast height, age and the height of the oil palm trees collected from three estates in Peninsular Malaysia. Results of this study show that oil palm NPP derived using a light use efficiency model increases with respect to the age of oil palm trees, and it stabilises after ten years old. The mean value of oil palm NPP at 118 plots as derived using the LUE model is 968.72 g C m-2 year-1 and this is 188% - 273% higher than the NPP derived from the allometric equations. The estimated oil palm NPP of young oil palm trees is lower compared to mature oil palm trees (<10 years old), as young oil palm trees contribute to lower oil palm LAI and therefore fPAR, which is an important variable in the LUE model. In contrast, it is noted that oil palm NPP decreases with respect to the age of oil palm trees as estimated using the allomeric equations. It was found in this study that LUE models could not capture NPP variation of oil palm trees if LAI/fPAR is used. On the other hand, tree height and DBH are found to be important variables that can capture changes in oil palm NPP as a function of age.

Isoprene and monoterpene emissions in south-east Australia: comparison of a multi-layer canopy model with MEGAN and with atmospheric observations

NASA Astrophysics Data System (ADS)

Emmerson, Kathryn M.; Cope, Martin E.; Galbally, Ian E.; Lee, Sunhee; Nelson, Peter F.

2018-05-01

One of the key challenges in atmospheric chemistry is to reduce the uncertainty of biogenic volatile organic compound (BVOC) emission estimates from vegetation to the atmosphere. In Australia, eucalypt trees are a primary source of biogenic emissions, but their contribution to Australian air sheds is poorly quantified. The Model of Emissions of Gases and Aerosols from Nature (MEGAN) has performed poorly against Australian isoprene and monoterpene observations. Finding reasons for the MEGAN discrepancies and strengthening our understanding of biogenic emissions in this region is our focus. We compare MEGAN to the locally produced Australian Biogenic Canopy and Grass Emissions Model (ABCGEM), to identify the uncertainties associated with the emission estimates and the data requirements necessary to improve isoprene and monoterpene emissions estimates for the application of MEGAN in Australia. Previously unpublished, ABCGEM is applied as an online biogenic emissions inventory to model BVOCs in the air shed overlaying Sydney, Australia. The two models use the same meteorological inputs and chemical mechanism, but independent inputs of leaf area index (LAI), plant functional type (PFT) and emission factors. We find that LAI, a proxy for leaf biomass, has a small role in spatial, temporal and inter-model biogenic emission variability, particularly in urban areas for ABCGEM. After removing LAI as the source of the differences, we found large differences in the emission activity function for monoterpenes. In MEGAN monoterpenes are partially light dependent, reducing their dependence on temperature. In ABCGEM monoterpenes are not light dependent, meaning they continue to be emitted at high rates during hot summer days, and at night. When the light dependence of monoterpenes is switched off in MEGAN, night-time emissions increase by 90-100 % improving the comparison with observations, suggesting the possibility that monoterpenes emitted from Australian vegetation may not be as light dependent as vegetation globally. Targeted measurements of emissions from in situ Australian vegetation, particularly of the light dependence issue are critical to improving MEGAN for one of the world's major biogenic emitting regions.

LEAF AREA INDEX (LAI) CHANGE DETECTION ON LOBLOLLY PINE FOREST STANDS WITH COMPLETE UNDERSTORY REMOVAL

EPA Science Inventory

The confounding effect of understory vegetation contributions to satellite derived
estimates of leaf area index (LAI) was investigated on two loblolly pine (Pinus taeda) forest stands located in the southeastern United States. Previous studies have shown that understory can a...

Daily mapping of Landsat-like LAI and correlation to grape yield

USDA-ARS?s Scientific Manuscript database

Wine grape quality and quantity are affected by vine growing condition during some critical growing stages. In this paper, MODIS and Landsat were used to map daily LAI in the two Grape Remote sensing Atmospheric Profiling and Evapotranspiration eXperiment (GRAPEX) experiment fields near Lodi, Califo...

LEAF AREA INDEX (LAI) CHANGE DETECTION ANALYSIS ON LOBLOLLY PINE (PINUS TAEDA) FOLLOWING COMPLETE UNDERSTORY REMOVAL

EPA Science Inventory

The confounding effect of understory vegetation contributions to satellite-derived estimates of leaf area index (LAI) was investigated on two loblolly pine (Pinus taeda) forest stands located in Virginia and North Carolina. In order to separate NDVI contributions of the dominantc...

LEAF AREA INDEX (LAI) CHANGE DETECTION ON LOBLOLLY PINE FOREST STANDS WITH COMPLETE UNDERSTORY REMOVAL

EPA Science Inventory

The confounding effect of understory vegetation contributions to satellite derived estimates of leaf area index (LAI) was investigated on two loblolly pine forest stands located in the southeastern United States. Previous studies have shown that understory can account from 0-40%...

ANALYSIS OF YEAR 2002 SEASONAL FOREST DYNAMICS USING TIME SERIES IN SITU LAI MEASUREMENTS AND MODIS LAI SATELLITE PRODUCTS

EPA Science Inventory

Multitemporal satellite images are the standard basis for regional-scale land-cover (LC) change detection. However, embedded in the data are the confounding effects of vegetation dynamics (phenology). As photosynthetic vegetation progresses through its annual cycle, the spectral ...

LINKING IN SITU TIME SERIES FOREST CANOPY LAI AND PHENOLOGY METRICS WITH MODIS AND LANDSAT NDVI AND LAI PRODUCTS

EPA Science Inventory

The subject of this presentation is forest vegetation dynamics as observed by the TERRA spacecraft's Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat Thematic Mapper, and complimentary in situ time series measurements of forest canopy metrics related to Leaf Area...

Assessment of crop growth and soil water modules in SWAT2000 using extensive field experiment data in an irrigation district of the Yellow River Basin

USGS Publications Warehouse

Luo, Y.; He, C.; Sophocleous, M.; Yin, Z.; Hongrui, R.; Ouyang, Z.

2008-01-01

SWAT, a physically-based, hydrological model simulates crop growth, soil water and groundwater movement, and transport of sediment and nutrients at both the process and watershed scales. While the different versions of SWAT have been widely used throughout the world for agricultural and water resources applications, little has been done to test the performance, variability, and transferability of the parameters in the crop growth, soil water, and groundwater modules in an integrated way with multiple sets of field experimental data at the process scale. Using an multiple years of field experimental data of winter wheat (Triticum aestivum L.) in the irrigation district of the Yellow River Basin, this paper assesses the performance of the plant-soil-groundwater modules and the variability and transferability of SWAT2000. Comparison of the simulated results by SWAT to the observations showed that SWAT performed quite unsatisfactorily in LAI predictions during the senescence stage, in yield predictions, and in soil-water estimation under dry soil-profile conditions. The unsatisfactory performance in LAI prediction might be attributed to over-simplified senescence modeling; in yield prediction to the improper computation of the harvest index; and in soil water under dry conditions to the exclusion of groundwater evaporation from the soil water balance in SWAT. In this paper, improvements in crop growth, soil water, and groundwater modules in SWAT were implemented. The saturated soil profile was coupled to the oscillating groundwater table. A variable evaporation coefficient taking into account soil water deficit index, groundwater depth, and crop root depth was used to replace the fixed coefficient in computing groundwater evaporation. The soil water balance included the groundwater evaporation. The modifications improved simulations of crop evapotranspiration and biomass as well as soil water dynamics under dry soil-profile conditions. The evaluation shows that the crop growth and soil water components of SWAT could be further refined to better simulate the hydrology of agricultural watersheds. ?? 2008 Elsevier B.V. All rights reserved.

BOREAS RSS-7 Regional LAI and FPAR Images From 10-Day AVHRR-LAC Composites

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Nickeson, Jaime (Editor); Chen, Jing; Cihlar, Josef

2000-01-01

The BOReal Ecosystem-Atmosphere Study Remote Sensing Science (BOREAS RSS-7) team collected various data sets to develop and validate an algorithm to allow the retrieval of the spatial distribution of Leaf Area Index (LAI) from remotely sensed images. Advanced Very High Resolution Radiometer (AVHRR) level-4c 10-day composite Normalized Difference Vegetation Index (NDVI) images produced at CCRS were used to produce images of LAI and the Fraction of Photosynthetically Active Radiation (FPAR) absorbed by plant canopies for the three summer IFCs in 1994 across the BOREAS region. The algorithms were developed based on ground measurements and Landsat Thematic Mapper (TM) images. The data are stored in binary image format files.

The influence of sugarcane crop development on rainfall interception losses

NASA Astrophysics Data System (ADS)

Fernandes, Rafael Pires; Silva, Robson Willians da Costa; Salemi, Luiz Felippe; Andrade, Tatiana Morgan Berteli de; Moraes, Jorge Marcos de; Dijk, Albert I. J. M. Van; Martinelli, Luiz Antonio

2017-08-01

The expansion of sugarcane plantations in Brazil has raised concerns regarding its hydrological impacts. One of these impacts is related to rainfall interception, which can be expected to vary in response to substantial changes in canopy structure throughout the cropping cycle. We collected field measurements to determine interception losses and interpreted the observations using an adapted Gash model during different stages of a sugarcane ratoon cropping cycle. Cumulative gross rainfall (PG), throughfall (TF) and stemflow (SF) were measured biweekly, along with vegetation structure measurements of leaf area index (LAI) and plant height. For the first 300 days after the first harvest, the cumulative PG of 1095 mm was partitioned into 635 mm TF (58%) and 263 mm SF (24%). The inferred interception loss (IL) was 263 mm (24%). There was a gradual and clear increase in IL from 3% to 46% while partitioning between TF and SF also changed during ratoon regrowth. After model parameter optimisation, observed IL was simulated satisfactorily. Model estimates suggested that evaporation from the saturated canopy is the main IL pathway, followed by evaporation after storms. Plant architecture, LAI and meteorological conditions during the cropping cycle appeared the main factors determining IL.

Predicting future land cover change and its impact on streamflow and sediment load in a trans-boundary river basin

NASA Astrophysics Data System (ADS)

Wang, Jie; Wang, Hao; Ning, Shaowei; Hiroshi, Ishidaira

2018-06-01

Sediment load can provide very important perspective on erosion of river basin. The changes of human-induced vegetation cover, such as deforestation or afforestation, affect sediment yield process of a catchment. We have already evaluated that climate change and land cover change changed the historical streamflow and sediment yield, and land cover change is the main factor in Red river basin. But future streamflow and sediment yield changes under potential future land cover change scenario still have not been evaluated. For this purpose, future scenario of land cover change is developed based on historical land cover changes and land change model (LCM). In addition, future leaf area index (LAI) is simulated by ecological model (Biome-BGC) based on future land cover scenario. Then future scenarios of land cover change and LAI are used to drive hydrological model and new sediment rating curve. The results of this research provide information that decision-makers need in order to promote water resources planning efforts. Besides that, this study also contributes a basic framework for assessing climate change impacts on streamflow and sediment yield that can be applied in the other basins around the world.

Generating Vegetation Leaf Area Index Earth System Data Record from Multiple Sensors. Part 1; Theory

NASA Technical Reports Server (NTRS)

Ganguly, Sangram; Schull, Mitchell A.; Samanta, Arindam; Shabanov, Nikolay V.; Milesi, Cristina; Nemani, Ramakrishna R.; Knyazikhin, Yuri; Myneni, Ranga B.

2008-01-01

The generation of multi-decade long Earth System Data Records (ESDRs) of Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) from remote sensing measurements of multiple sensors is key to monitoring long-term changes in vegetation due to natural and anthropogenic influences. Challenges in developing such ESDRs include problems in remote sensing science (modeling of variability in global vegetation, scaling, atmospheric correction) and sensor hardware (differences in spatial resolution, spectral bands, calibration, and information content). In this paper, we develop a physically based approach for deriving LAI and FPAR products from the Advanced Very High Resolution Radiometer (AVHRR) data that are of comparable quality to the Moderate resolution Imaging Spectroradiometer (MODIS) LAI and FPAR products, thus realizing the objective of producing a long (multi-decadal) time series of these products. The approach is based on the radiative transfer theory of canopy spectral invariants which facilitates parameterization of the canopy spectral bidirectional reflectance factor (BRF). The methodology permits decoupling of the structural and radiometric components and obeys the energy conservation law. The approach is applicable to any optical sensor, however, it requires selection of sensor-specific values of configurable parameters, namely, the single scattering albedo and data uncertainty. According to the theory of spectral invariants, the single scattering albedo is a function of the spatial scale, and thus, accounts for the variation in BRF with sensor spatial resolution. Likewise, the single scattering albedo accounts for the variation in spectral BRF with sensor bandwidths. The second adjustable parameter is data uncertainty, which accounts for varying information content of the remote sensing measurements, i.e., Normalized Difference Vegetation Index (NDVI, low information content), vs. spectral BRF (higher information content). Implementation of this approach indicates good consistency in LAI values retrieved from NDVI (AVHRRmode) and spectral BRF (MODIS-mode). Specific details of the implementation and evaluation of the derived products are detailed in the second part of this two-paper series.

Measuring fraction of intercepted photosynthetically active radiation with a ceptometer: the importance of adopting a universal methodological approach

USDA-ARS?s Scientific Manuscript database

It is desirable to be able to predict above ground biomass production indirectly, without extensive sampling or destructive harvesting. Leaf area index (LAI) is the amount of leaf surface area per ground area and is an important parameter in ecophysiology. As LAI increases, the photosynthetically ...

Seasonal leaf dynamics across a tree density gradient in a Brazilian savanna.

Treesearch

William A. Hoffmann; Edson Rangel da Silva; Gustavo C. Machado; Sandra Bucci; Fabian G. Scholz; Guillermo Goldstein; Frederick C. Meinzer

2005-01-01

Interactions between trees and grasses that influence leaf area index (LAI) have important consequences for savanna ecosystem processes through their controls on water, carbon, and energy fluxes as well as fire regimes. We measured LAI, of the groundlayer (herbaceous and woody plants 1-m tall), in the Brazilian...

Challenges in Diagnosis and Management of Serotonin Syndrome in A Patient with Schizophrenia Treated with A LAI Antipsychotic

DTIC Science & Technology

2017-05-20

Syndrome in A Patient With Schizophrenia Treated with A LAJ Antipsychotic presented at/published to 2017 American Psychiatric Association Annual Meeting...OR PRESENTED: Challenges in Diagnosis and Management of Serotonin Syndrome in a Patient With Schizophrenia Treated With a LAI Antipsychotic 7

Chemical improvement of chitosan-modified beads for the immobilization of Enterococcus faecium DBFIQ E36 L-arabinose isomerase through multipoint covalent attachment approach.

PubMed

Manzo, Ricardo M; de Sousa, Marylane; Fenoglio, Cecilia L; Gonçalves, Luciana Rocha Barro; Mammarella, Enrique J

2015-10-01

D-tagatose is produced from D-galactose by the enzyme L-arabinose isomerase (L-AI) in a commercially viable bioprocess. An active and stable biocatalyst was obtained by modifying chitosan gel structure through reaction with TNBS, D-fructose or DMF, among others. This led to a significant improvement in L-AI immobilization via multipoint covalent attachment approach. Synthetized derivatives were compared with commercial supports such as Eupergit(®) C250L and glyoxal-agarose. The best chitosan derivative for L-AI immobilization was achieved by reacting 4 % (w/v) D-fructose with 3 % (w/v) chitosan at 50 °C for 4 h. When compared to the free enzyme, the glutaraldehyde-activated chitosan biocatalyst showed an apparent activity of 88.4 U g (gel) (-1) with a 211-fold stabilization factor while the glyoxal-agarose biocatalyst gave an apparent activity of 161.8 U g (gel) (-1) with an 85-fold stabilization factor. Hence, chitosan derivatives were comparable to commercial resins, thus becoming a viable low-cost strategy to obtain high active L-AI insolubilized derivatives.

Massive open online courses in health sciences from Latin American institutions: A need for improvement?

PubMed

Culquichicón, Carlos; Helguero-Santin, Luis M; Labán-Seminario, L Max; Cardona-Ospina, Jaime A; Aboshady, Omar A; Correa, Ricardo

2017-01-01

Background: Massive open online courses (MOOCs) have undergone exponential growth over the past few years, offering free and worldwide access to high-quality education. We identified the characteristics of MOOCs in the health sciences offered by Latin American institutions (LAIs). Methods: We screened the eight leading MOOCs platforms to gather their list of offerings. The MOOCs were classified by region and subject. Then, we obtained the following information: Scopus H-index for each institution and course instructor, QS World University Ranking® 2015/16 of LAI, and official language of the course. Results: Our search identified 4170 MOOCs worldwide. From them, 205 MOOCs were offered by LAIs, and six MOOCs were health sciences related. Most of these courses (n = 115) were offered through Coursera. One health science MOOC was taught by three instructors, of which only one was registered in Scopus (H-index = 0). The remaining five health science MOOCs had solely one instructor (H-index = 4 [0-17]). The Latin American country with the highest participation was Brazil (n = 11). Conclusion: The contribution of LAI to MOOCs in the health sciences is low.

Automated In-Situ Laser Scanner for Monitoring Forest Leaf Area Index

PubMed Central

Culvenor, Darius S.; Newnham, Glenn J.; Mellor, Andrew; Sims, Neil C.; Haywood, Andrew

2014-01-01

An automated laser rangefinding instrument was developed to characterize overstorey and understorey vegetation dynamics over time. Design criteria were based on information needs within the statewide forest monitoring program in Victoria, Australia. The ground-based monitoring instrument captures the key vegetation structural information needed to overcome ambiguity in the estimation of forest Leaf Area Index (LAI) from satellite sensors. The scanning lidar instrument was developed primarily from low cost, commercially accessible components. While the 635 nm wavelength lidar is not ideally suited to vegetation studies, there was an acceptable trade-off between cost and performance. Tests demonstrated reliable range estimates to live foliage up to a distance of 60 m during night-time operation. Given the instrument's scan angle of 57.5 degrees zenith, the instrument is an effective tool for monitoring LAI in forest canopies up to a height of 30 m. An 18 month field trial of three co-located instruments showed consistent seasonal trends and mean LAI of between 1.32 to 1.56 and a temporal LAI variation of 8 to 17% relative to the mean. PMID:25196006

Analysis on the vegetation phenology of tropical seasonal rain forest in South America

NASA Astrophysics Data System (ADS)

Liang, B.; Chen, X.

2016-12-01

Using Global Land Surface Satellite (GLASS) LAI data during 1982 to 2003, we analyzed spatial and temporal variations of vegetation phenology in the tropical seasonal rain forest of South America. Several methods were used to fit seasonal LAI curves and extract start (SOS) and end (EOS) of the growing season. The results show that Fourier function can most effectively fit LAI curves, and yearly RMSEs for differences between observed and fitted LAI values are less than 0.01. The SOS ranged from 250 to 350 days of year, and occurred earlier in west than in east. Contrarily, the EOS were between 120 and 180 days of year, and appeared earlier in east than in west. Thus, the growing season was longer in west than in east. With regard to linear trends, SOS shows a significant advancement at 7% of pixels and a significant delay at 13% of pixels, whereas EOS advanced significantly at 16% of pixels and was delayed significantly at 18% of pixels. Preseason precipitation is the main influence factor of SOS and EOS in the tropical seasonal rain forest of South America.

Evaluation of different gridded rainfall datasets for rainfed wheat yield prediction in an arid environment

NASA Astrophysics Data System (ADS)

Lashkari, A.; Salehnia, N.; Asadi, S.; Paymard, P.; Zare, H.; Bannayan, M.

2018-05-01

The accuracy of daily output of satellite and reanalysis data is quite crucial for crop yield prediction. This study has evaluated the performance of APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation), PERSIANN (Rainfall Estimation from Remotely Sensed Information using Artificial Neural Networks), TRMM (Tropical Rainfall Measuring Mission), and AgMERRA (The Modern-Era Retrospective Analysis for Research and Applications) precipitation products to apply as input data for CSM-CERES-Wheat crop growth simulation model to predict rainfed wheat yield. Daily precipitation output from various sources for 7 years (2000-2007) was obtained and compared with corresponding ground-observed precipitation data for 16 ground stations across the northeast of Iran. Comparisons of ground-observed daily precipitation with corresponding data recorded by different sources of datasets showed a root mean square error (RMSE) of less than 3.5 for all data. AgMERRA and APHRODITE showed the highest correlation (0.68 and 0.87) and index of agreement (d) values (0.79 and 0.89) with ground-observed data. When daily precipitation data were aggregated over periods of 10 days, the RMSE values, r, and d values increased (30, 0.8, and 0.7) for AgMERRA, APHRODITE, PERSIANN, and TRMM precipitation data sources. The simulations of rainfed wheat leaf area index (LAI) and dry matter using various precipitation data, coupled with solar radiation and temperature data from observed ones, illustrated typical LAI and dry matter shape across all stations. The average values of LAImax were 0.78, 0.77, 0.74, 0.70, and 0.69 using PERSIANN, AgMERRA, ground-observed precipitation data, APHRODITE, and TRMM. Rainfed wheat grain yield simulated by using AgMERRA and APHRODITE daily precipitation data was highly correlated (r 2 ≥ 70) with those simulated using observed precipitation data. Therefore, gridded data have high potential to be used to supply lack of data and gaps in ground-observed precipitation data.

Improving land surface parameter retrieval by integrating plant traits priors in the MULTIPLY data assimilation platform

NASA Astrophysics Data System (ADS)

Corbin, A. E.; Timmermans, J.; Hauser, L.; Bodegom, P. V.; Soudzilovskaia, N. A.

2017-12-01

There is a growing demand for accurate land surface parameterization from remote sensing (RS) observations. This demand has not been satisfied, because most estimation schemes apply 1) a single-sensor single-scale approach, and 2) require specific key-variables to be `guessed'. This is because of the relevant observational information required to accurately retrieve parameters of interest. Consequently, many schemes assume specific variables to be constant or not present; subsequently leading to more uncertainty. In this aspect, the MULTIscale SENTINEL land surface information retrieval Platform (MULTIPLY) was created. MULTIPLY couples a variety of RS sources with Radiative Transfer Models (RTM) over varying spectral ranges using data-assimilation to estimate geophysical parameters. In addition, MULTIPLY also uses prior information about the land surface to constrain the retrieval problem. This research aims to improve the retrieval of plant biophysical parameters through the use of priors of biophysical parameters/plant traits. Of particular interest are traits (physical, morphological or chemical trait) affecting individual performance and fitness of species. Plant traits that are able to be retrieved via RS and with RTMs include traits such as leaf-pigments, leaf water, LAI, phenols, C/N, etc. In-situ data for plant traits that are retrievable via RS techniques were collected for a meta-analysis from databases such as TRY, Ecosis, and individual collaborators. Of particular interest are the following traits: chlorophyll, carotenoids, anthocyanins, phenols, leaf water, and LAI. ANOVA statistics were generated for each traits according to species, plant functional groups (such as evergreens, grasses, etc.), and the trait itself. Afterwards, traits were also compared using covariance matrices. Using these as priors, MULTIPLY was is used to retrieve several plant traits in two validation sites in the Netherlands (Speulderbos) and in Finland (Sodankylä). Initial comparisons show significant improved results over non-a priori based retrievals.

Topic Models in Information Retrieval

DTIC Science & Technology

2007-08-01

Information Processing Systems, Cambridge, MA, MIT Press, 2004. Brown, P.F., Della Pietra, V.J., deSouza, P.V., Lai, J.C. and Mercer, R.L., Class-based...2003. http://www.wkap.nl/prod/b/1-4020-1216-0. Croft, W.B., Lucia , T.J., Cringean, J., and Willett, P., Retrieving Documents By Plausible Inference

Estimation of big sagebrush leaf area index with terrestrial laser scanning

USDA-ARS?s Scientific Manuscript database

Accurate monitoring and quantification of the structure and function of semiarid ecosystems is necessary to improve carbon and water flux models that help describe how these systems will respond in the future. The leaf area index (LAI, m2 m-2) is an important indicator of energy, water, and carbon e...

[Characteristics and numerical simulation of surface albedo in temperate desert steppe in Inner Mongolia].

PubMed

Yang, Fu-lin; Zhou, Guang-sheng; Zhang, Feng; Wang, Feng-yu; Bao, Fang; Ping, Xiao-yan

2009-12-01

Based on the meteorological and biological observation data from the temperate desert steppe ecosystem research station in Sunitezuoqi of Inner Mongolia during growth season (from May 1st to October 15th, 2008), the diurnal and seasonal characteristics of surface albedo in the steppe were analyzed, with related model constructed. In the steppe, the diurnal variation of surface albedo was mainly affected by solar altitude, being higher just after sunrise and before sunset and lower in midday. During growth season, the surface albedo was from 0.20 to 0.34, with an average of 0.25, and was higher in May, decreased in June, kept relatively stable from July to September, and increased in October. This seasonal variation was related to the phenology of canopy leaf, and affected by precipitation process. Soil water content (SWC) and leaf area index (LAI) were the key factors affecting the surface albedo. A model for the surface albedo responding to SWC and LAI was developed, which showed a good performance in consistent between simulated and observed surface albedo.

VitiCanopy: A Free Computer App to Estimate Canopy Vigor and Porosity for Grapevine

PubMed Central

De Bei, Roberta; Fuentes, Sigfredo; Gilliham, Matthew; Tyerman, Steve; Edwards, Everard; Bianchini, Nicolò; Smith, Jason; Collins, Cassandra

2016-01-01

Leaf area index (LAI) and plant area index (PAI) are common and important biophysical parameters used to estimate agronomical variables such as canopy growth, light interception and water requirements of plants and trees. LAI can be either measured directly using destructive methods or indirectly using dedicated and expensive instrumentation, both of which require a high level of know-how to operate equipment, handle data and interpret results. Recently, a novel smartphone and tablet PC application, VitiCanopy, has been developed by a group of researchers from the University of Adelaide and the University of Melbourne, to estimate grapevine canopy size (LAI and PAI), canopy porosity, canopy cover and clumping index. VitiCanopy uses the front in-built camera and GPS capabilities of smartphones and tablet PCs to automatically implement image analysis algorithms on upward-looking digital images of canopies and calculates relevant canopy architecture parameters. Results from the use of VitiCanopy on grapevines correlated well with traditional methods to measure/estimate LAI and PAI. Like other indirect methods, VitiCanopy does not distinguish between leaf and non-leaf material but it was demonstrated that the non-leaf material could be extracted from the results, if needed, to increase accuracy. VitiCanopy is an accurate, user-friendly and free alternative to current techniques used by scientists and viticultural practitioners to assess the dynamics of LAI, PAI and canopy architecture in vineyards, and has the potential to be adapted for use on other plants. PMID:27120600

Predictive factors of overall functioning improvement in patients with chronic schizophrenia and schizoaffective disorder treated with paliperidone palmitate and aripiprazole monohydrate.

PubMed

Girardi, Paolo; Del Casale, Antonio; Rapinesi, Chiara; Kotzalidis, Georgios D; Splendori, Francesca; Verzura, Claudio; Trovini, Giada; Sorice, Serena; Carrus, Dario; Mancinelli, Iginia; Comparelli, Anna; De Filippis, Sergio; Francomano, Antonio; Ballerini, Andrea; Marcellusi, Andrea; Mennini, Francesco S; Ducci, Giuseppe; Sani, Gabriele; Pompili, Maurizio; Brugnoli, Roberto

2018-05-01

Long-acting injectable (LAI) antipsychotics can improve medication adherence and reduce hospitalisation rates compared with oral treatments. Paliperidone palmitate (PAL) and aripiprazole monohydrate (ARI) LAI treatments were associated with improvements in global functioning in patients with schizophrenia. The objective of this study was to assess the predictive factors of better overall functioning in patients with chronic schizophrenia and schizoaffective disorder treated with PAL and ARI. Enrolled were 143 (97 males, 46 females, mean age 38.24 years, SD = 12.65) patients with a diagnosis of schizophrenia or schizoaffective disorder, whom we allocated in two groups (PAL and ARI treatments). We assessed global functioning, amount of oral medications, adherence to oral treatment, and number of hospitalisations before LAI introduction and at assessment time point. Longer treatment time with LAIs (p < .001), lower number of oral drugs (p < .001), and hospitalisations (p = .002) before LAI introduction, and shorter duration of illness (p = .038) predicted better Global Assessment of Functioning scores in the whole sample (R 2  = 0.337). Early administration and longer duration of ARI or PAL treatments could play a significant role in improving global functioning of patients with schizophrenia and schizoaffective disorder. Better improvement in functioning could be achieved with ARI in young individuals with recent illness onset and PAL in patients at risk for recurrent hospitalisations. Copyright © 2018 John Wiley & Sons, Ltd.

VitiCanopy: A Free Computer App to Estimate Canopy Vigor and Porosity for Grapevine.

PubMed

De Bei, Roberta; Fuentes, Sigfredo; Gilliham, Matthew; Tyerman, Steve; Edwards, Everard; Bianchini, Nicolò; Smith, Jason; Collins, Cassandra

2016-04-23

Leaf area index (LAI) and plant area index (PAI) are common and important biophysical parameters used to estimate agronomical variables such as canopy growth, light interception and water requirements of plants and trees. LAI can be either measured directly using destructive methods or indirectly using dedicated and expensive instrumentation, both of which require a high level of know-how to operate equipment, handle data and interpret results. Recently, a novel smartphone and tablet PC application, VitiCanopy, has been developed by a group of researchers from the University of Adelaide and the University of Melbourne, to estimate grapevine canopy size (LAI and PAI), canopy porosity, canopy cover and clumping index. VitiCanopy uses the front in-built camera and GPS capabilities of smartphones and tablet PCs to automatically implement image analysis algorithms on upward-looking digital images of canopies and calculates relevant canopy architecture parameters. Results from the use of VitiCanopy on grapevines correlated well with traditional methods to measure/estimate LAI and PAI. Like other indirect methods, VitiCanopy does not distinguish between leaf and non-leaf material but it was demonstrated that the non-leaf material could be extracted from the results, if needed, to increase accuracy. VitiCanopy is an accurate, user-friendly and free alternative to current techniques used by scientists and viticultural practitioners to assess the dynamics of LAI, PAI and canopy architecture in vineyards, and has the potential to be adapted for use on other plants.

A Bayesian Alternative for Multi-objective Ecohydrological Model Specification

NASA Astrophysics Data System (ADS)

Tang, Y.; Marshall, L. A.; Sharma, A.; Ajami, H.

2015-12-01

Process-based ecohydrological models combine the study of hydrological, physical, biogeochemical and ecological processes of the catchments, which are usually more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov Chain Monte Carlo (MCMC) techniques. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological framework. In our study, a formal Bayesian approach is implemented in an ecohydrological model which combines a hydrological model (HyMOD) and a dynamic vegetation model (DVM). Simulations focused on one objective likelihood (Streamflow/LAI) and multi-objective likelihoods (Streamflow and LAI) with different weights are compared. Uniform, weakly informative and strongly informative prior distributions are used in different simulations. The Kullback-leibler divergence (KLD) is used to measure the dis(similarity) between different priors and corresponding posterior distributions to examine the parameter sensitivity. Results show that different prior distributions can strongly influence posterior distributions for parameters, especially when the available data is limited or parameters are insensitive to the available data. We demonstrate differences in optimized parameters and uncertainty limits in different cases based on multi-objective likelihoods vs. single objective likelihoods. We also demonstrate the importance of appropriately defining the weights of objectives in multi-objective calibration according to different data types.

Photon Recollision Probability: a Useful Concept for Cross Scale Consistency Check between Leaf Area Index and Foliage Clumping Products

NASA Astrophysics Data System (ADS)

Pisek, J.

2017-12-01

Clumping index (CI) is the measure of foliage aggregation relative to a random distribution of leaves in space. CI is an important factor for the correct quantification of true leaf area index (LAI). Global and regional scale CI maps have been generated from various multi-angle sensors based on an empirical relationship with the normalized difference between hotspot and darkspot (NDHD) index (Chen et al., 2005). Ryu et al. (2011) suggested that accurate calculation of radiative transfer in a canopy, important for controlling gross primary productivity (GPP) and evapotranspiration (ET) (Baldocchi and Harley, 1995), should be possible by integrating CI with incoming solar irradiance and LAI from MODIS land and atmosphere products. It should be noted that MODIS LAI/FPAR product uses internal non-empirical, stochastic equations for parameterization of foliage clumping. This raises a question if integration of the MODIS LAI product with empirically-based CI maps does not introduce any inconsistencies. Here, the consistency is examined independently through the `recollision probability theory' or `p-theory' (Knyazikhin et al., 1998) along with raw LAI-2000/2200 Plant Canopy Analyzer (PCA) data from > 30 sites, surveyed across a range of vegetation types. The theory predicts that the amount of radiation scattered by a canopy should depend only on the wavelength and the spectrally invariant canopy structural parameter p. The parameter p is linked to the foliage clumping (Stenberg et al., 2016). Results indicate that integration of the MODIS LAI product with empirically-based CI maps is feasible. Importantly, for the first time it is shown that it is possible to obtain p values for any location solely from Earth Observation data. This is very relevant for future applications of photon recollision probability concept for global and local monitoring of vegetation using Earth Observation data.

Remote canopy hemispherical image collection system

NASA Astrophysics Data System (ADS)

Wan, Xuefen; Liu, Bingyu; Yang, Yi; Han, Fang; Cui, Jian

2016-11-01

Canopies are major part of plant photosynthesis and have distinct architectural elements such as tree crowns, whorls, branches, shoots, etc. By measuring canopy structural parameters, the solar radiation interception, photosynthesis effects and the spatio-temporal distribution of solar radiation under the canopy can be evaluated. Among canopy structure parameters, Leaf Area Index (LAI) is the key one. Leaf area index is a crucial variable in agronomic and environmental studies, because of its importance for estimating the amount of radiation intercepted by the canopy and the crop water requirements. The LAI can be achieved by hemispheric images which are obtained below the canopy with high accuracy and effectiveness. But existing hemispheric images canopy-LAI measurement technique is based on digital SLR camera with a fisheye lens. Users need to collect hemispheric image manually. The SLR camera with fisheye lens is not suit for long-term canopy-LAI outdoor measurement too. And the high cost of SLR limits its capacity. In recent years, with the development of embedded system and image processing technology, low cost remote canopy hemispheric image acquisition technology is becoming possible. In this paper, we present a remote hemispheric canopy image acquisition system with in-field/host configuration. In-field node based on imbed platform, low cost image sensor and fisheye lens is designed to achieve hemispherical image of plant canopy at distance with low cost. Solar radiation and temperature/humidity data, which are important for evaluating image data validation, are obtained for invalid hemispherical image elimination and node maintenance too. Host computer interacts with in-field node by 3G network. The hemispherical image calibration and super resolution are used to improve image quality in host computer. Results show that the remote canopy image collection system can make low cost remote canopy image acquisition for LAI effectively. It will be a potential technology candidate for low-cost remote canopy hemispherical image collection to measure canopy LAI.

Acute effects of muscle vibration on sensorimotor integration.

PubMed

Lapole, Thomas; Tindel, Jérémy

2015-02-05

Projections from the somesthetic cortex are believed to be involved in the modulation of motor cortical excitability by muscle vibration. The aim of the present pilot study was to analyse the effects of a vibration intervention on short-latency afferent inhibition (SAI), long-latency afferent inhibition (LAI), and afferent facilitation (AF), three intracortical mechanisms reflecting sensorimotor integration. Abductor pollicis brevis (APB) SAI, AF and LAI were investigated on 10 subjects by conditioning test transcranial magnetic stimulation pulses with median nerve electrical stimulation at inter-stimuli intervals in the range 15-25 ms, 25-60 ms, and 100-200 ms, respectively. Test motor evoked potentials (MEPs) were compared to unconditioned MEPs. Measurements were performed before and just after 15 min of vibration applied to the muscle belly of APB at a frequency of 80 Hz. SAI and LAI responses were significantly reduced compared to unconditioned test MEPs (P=0.039 and P<0.001, respectively). AF MEP amplitude was greater than SAI and LAI one (P=0.009 and P=0.004, respectively), but not different from test MEP (P=0.511). There was no significant main effect of vibration (P=0.905). However, 4 subjects were clearly identified as responders. Their mean vibration-induced increase was 324 ± 195% in APB SAI MEP amplitude, and 158 ± 53% and 319 ± 80% in AF and LAI, respectively. Significant differences in SAI, AF and LAI vibration-induced changes were found for responders when compared to non-responders (P=0.019, P=0.038, and P=0.01, respectively). A single session of APB vibration may increase sensorimotor integration, via decreased inhibition and increased facilitation. However, such results were not observed for all subjects, suggesting that other factors (such as attention to the sensory inputs) may have played a role. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

[Green space vegetation quantity in workshop area of Wuhan Iron and Steel Company].

PubMed

Chen, Fang; Zhou, Zhixiang; Wang, Pengcheng; Li, Haifang; Zhong, Yingfei

2006-04-01

Aimed at the complex community structure and higher fragmentation of urban green space, and based on the investigation of synusia structure and its coverage, this paper studied the vegetation quantity of ornamental green space in the workshop area of Wuhan Iron and Steel Company, with the help of GIS. The results showed that different life forms of ornamental plants in this area had a greater difference in their single leaf area and leaf area index (LAI), and the LAI was not only depended on single leaf area, but also governed by the shape of tree crown and the intensive degree of branches and leaves. The total vegetation quantity was 1 694.2 hm2, with the average LAI being 7.75, and the vegetation quantity of arbor-shrub-herb and arbor-shrub communities accounted for 79.7% and 92.3% of the total, respectively, reflecting that the green space structure was dominated by arbor species and by arbor-shrub-herb and arbor-shrub community types. Single layer-structured lawn had a less percentage, while the vegetation quantity of herb synusia accounted for 22.9% of the total, suggesting an afforestation characteristic of "making use of every bit of space" in the workshop area. The vegetation quantity of urban ornamental green space depended on the area of green space, its synusia structure, and the LAI and coverage of ornamental plants. In enlarging urban green space, ornamental plant species with high LAI should be selected, and community structure should be improved to have a higher vegetation quantity in urban area. To quantify the vegetation quantity of urban ornamental green space more accurately, synusia should be taken as the unit to measure the LAI of typical species, and the synusia structure and its coverage of different community types should be investigated with the help of remote sensing images and GIS.

the Role of Species, Structure, and Biochemical Traits in the Spatial Distribution of a Woodland Community

NASA Astrophysics Data System (ADS)

Adeline, K.; Ustin, S.; Roth, K. L.; Huesca Martinez, M.; Schaaf, C.; Baldocchi, D. D.; Gastellu-Etchegorry, J. P.

2015-12-01

The assessment of canopy biochemical diversity is critical for monitoring ecological and physiological functioning and for mapping vegetation change dynamics in relation to environmental resources. For example in oak woodland savannas, these dynamics are mainly driven by water constraints. Inversion using radiative transfer theory is one method for estimating canopy biochemistry. However, this approach generally only considers relatively simple scenarios to model the canopy due to the difficulty in encompassing stand heterogeneity with spatial and temporal consistency. In this research, we compared 3 modeling strategies for estimating canopy biochemistry variables (i.e. chlorophyll, carotenoids, water, dry matter) by coupling of the PROSPECT (leaf level) and DART (canopy level) models : i) a simple forest representation made of ellipsoid trees, and two representations taking into account the tree species and structural composition, and the landscape spatial pattern, using (ii) geometric tree crown shapes and iii) detailed tree crown and wood structure retrieved from terrestrial lidar acquisitions. AVIRIS 18m remote sensing data are up-scaled to simulate HyspIRI 30m images. Both spatial resolutions are validated by measurements acquired during 2013-2014 field campaigns (cover/tree inventory, LAI, leaf sampling, optical measures). The results outline the trade-off between accurate and abstract canopy modeling for inversion purposes and may provide perspectives to assess the impact of the California drought with multi-temporal monitoring of canopy biochemistry traits.

Modelling the potential role of forest thinning in maintaining water supplies under a changing climate across the conterminous United States

Treesearch

Ge Sun; Peter V. Caldwell; Steven G. McNulty

2015-01-01

The goal of this study was to test the sensitivity of water yield to forest thinning and other forest management/disturbances and climate across the conterminous United States (CONUS). Leaf area index (LAI) was selected as a key parameter linking changes in forest ecosystem structure and functions. We used the Water Supply Stress Index model to examine water yield...

Hydrologic models for land-atmosphere retrospective studies of the use of LANDSAT and AVHRR data

NASA Technical Reports Server (NTRS)

Duchon, Claude E.; Williams, T. H. Lee; Nicks, Arlin D.

1988-01-01

The use of a Geographic Information System (GIS) and LANDSAT analysis in conjunction with the Simulator for Water Resources on a Rural Basin (SWRRB) hydrologic model to examine the water balance on the Little Washita River basin is discussed. LANDSAT analysis was used to divide the basin into eight non-contiguous land covers or subareas: rangeland, grazed range, winter wheat, alfalfa/pasture, bare soil, water, woodland, and impervious land (roads, quarry). The use of a geographic information system allowed for the calculation of SWRRB model parameters in each subarea. Four data sets were constructed in order to compare SWRRB estimates of hydrologic processes using two methods of maximum LAI and two methods of watershed subdivision. Maximum LAI was determined from a continental scale map, which provided a value of 4.5 for the entire basin, and from its association with the type of land-cover (eight values). The two methods of watershed subdivision were determined according to drainage subbasin (four) and the eight land-covers. These data sets were used with the SWRRB model to obtain daily hydrologic estimates for 1985. The results of the one year analysis lead to the conclusion that the greater homogeneity of a land-cover subdivision provides better water yield estimates than those based on a drainage properties subdivision.

Diversity in plant hydraulic traits explains seasonal and inter-annual variations of vegetation dynamics in seasonally dry tropical forests.

PubMed

Xu, Xiangtao; Medvigy, David; Powers, Jennifer S; Becknell, Justin M; Guan, Kaiyu

2016-10-01

We assessed whether diversity in plant hydraulic traits can explain the observed diversity in plant responses to water stress in seasonally dry tropical forests (SDTFs). The Ecosystem Demography model 2 (ED2) was updated with a trait-driven mechanistic plant hydraulic module, as well as novel drought-phenology and plant water stress schemes. Four plant functional types were parameterized on the basis of meta-analysis of plant hydraulic traits. Simulations from both the original and the updated ED2 were evaluated against 5 yr of field data from a Costa Rican SDTF site and remote-sensing data over Central America. The updated model generated realistic plant hydraulic dynamics, such as leaf water potential and stem sap flow. Compared with the original ED2, predictions from our novel trait-driven model matched better with observed growth, phenology and their variations among functional groups. Most notably, the original ED2 produced unrealistically small leaf area index (LAI) and underestimated cumulative leaf litter. Both of these biases were corrected by the updated model. The updated model was also better able to simulate spatial patterns of LAI dynamics in Central America. Plant hydraulic traits are intercorrelated in SDTFs. Mechanistic incorporation of plant hydraulic traits is necessary for the simulation of spatiotemporal patterns of vegetation dynamics in SDTFs in vegetation models. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

The secreted L-arabinose isomerase displays anti-hyperglycemic effects in mice.

PubMed

Rhimi, Moez; Bermudez-Humaran, Luis G; Huang, Yuan; Boudebbouze, Samira; Gaci, Nadia; Garnier, Alexandrine; Gratadoux, Jean-Jacques; Mkaouar, Héla; Langella, Philippe; Maguin, Emmanuelle

2015-12-21

The L-arabinose isomerase is an intracellular enzyme which converts L-arabinose into L-ribulose in living systems and D-galactose into D-tagatose in industrial processes and at industrial scales. D-tagatose is a natural ketohexose with potential uses in pharmaceutical and food industries. The D-galactose isomerization reaction is thermodynamically equilibrated, and leads to secondary subproducts at high pH. Therefore, an attractive L-arabinose isomerase should be thermoactive and acidotolerant with high catalytic efficiency. While many reports focused on the set out of a low cost process for the industrial production of D-tagatose, these procedures remain costly. When compared to intracellular enzymes, the production of extracellular ones constitutes an interesting strategy to increase the suitability of the biocatalysts. The L-arabinose isomerase (L-AI) from Lactobacillus sakei was expressed in Lactococcus lactis in fusion with the signal peptide of usp45 (SP(Usp45)). The L-AI protein and activity were detected only in the supernatant of the induced cultures of the recombinant L. lactis demonstrating the secretion in the medium of the intracellular L. sakei L-AI in an active form. Moreover, we showed an improvement in the enzyme secretion using either (1) L. lactis strains deficient for their two major proteases, ClpP and HtrA, or (2) an enhancer of protein secretion in L. lactis fused to the recombinant L-AI with the SP(Usp45). Th L-AI enzyme secreted by the recombinant L. lactis strains or produced intracellularly in E. coli, showed the same functional properties than the native enzyme. Furthermore, when mice are fed with the L. lactis strain secreting the L-AI and galactose, tagatose was produced in vivo and reduced the glycemia index. We report for the first time the secretion of the intracellular L-arabinose isomerase in the supernatant of food grade L. lactis cultures with hardly display other secreted proteins. The secreted L-AI originated from the food grade L. sakei 23 K was active and showed the same catalytic and structural properties as the intracellular enzyme. The L. lactis strains secreting the L-arabinose isomerase has the ability to produce D-tagatose in vivo and conferred an anti-hyperglycemic effect to mice.

Policy and programmatic considerations for introducing a longer-acting injectable contraceptive: perspectives of stakeholders from Kenya and Rwanda.

PubMed

McKenna, Kevin; Arcara, Jennet; Rademacher, Kate H; Mackenzie, Caroline; Ngabo, Fidele; Munyambanza, Emmanuel; Wesson, Jennifer; Tolley, Elizabeth E

2014-10-15

More than 40 million women use injectable contraceptives to prevent pregnancy, and most current or previous injectable users report being satisfied with the method. However, while women may find injectables acceptable, they may not always find them accessible due to stock-outs and difficulties with returning to the clinic for reinjections. FHI 360 is spearheading efforts to develop a longer-acting injectable (LAI) contraceptive that could provide at least 6 months of protection against pregnancy. This article addresses systems-level considerations for the introduction of a new LAI. We conducted qualitative case studies in Kenya and Rwanda-two countries that have high levels of injectable use but with different service delivery contexts. Between June and September 2012, we conducted in-depth interviews with 27 service providers and 19 policy makers and program implementers focusing on 4 themes: systems-level barriers and facilitators to delivering LAI services; process for introducing an LAI; LAI distribution approaches; and potential LAI characteristics. We also obtained electronic feedback from 28 international family planning opinion leaders. Respondents indicated strong interest in an LAI and thought it would appeal to existing injectable users as well as new family planning clients, both for spacing and for limiting births. Providers appreciated the potential for a lighter workload due to fewer follow-up visits, but they were concerned that fewer visits would also decrease their ability to help women manage side effects. The providers also appreciated the 1-month grace period for follow-up LAI injections; some seemed unaware of the latest international guidance that had increased the grace period from 2 weeks to 4 weeks for the currently available 3-month injectable. The majority of policy makers and program implementers were supportive of letting community health workers provide the method, but many nurses and midwives in Kenya had reservations about the approach. At the policy level, respondents indicated that obtaining regulatory approvals before introducing the new method could be costly and time-consuming. Manufacturing and procurement decisions could also affect cost and availability. Successful introduction of a potential longer-acting injectable may be enhanced by considering broader systemic issues, including managing cost to the health system and users, expanding access through community-based distribution, and training providers on the latest service delivery guidelines. © McKenna et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly cited. To view a copy of the license, visit http://creativecommons.org/licenses/by/3.0/. When linking to this article, please use the following permanent link: http://dx.doi.org/10.9745/GHSP-D-14-00106.

Potential impacts of climate change and adaptation strategies for sunflower in Pakistan.

PubMed

Awais, Muhammad; Wajid, Aftab; Saleem, Muhammad Farrukh; Nasim, Wajid; Ahmad, Ashfaq; Raza, Muhammad Aown Sammar; Bashir, Muhammad Usman; Mubeen, Muhammad; Hammad, Hafiz Mohkum; Habib Ur Rahman, Muhammad; Saeed, Umer; Arshad, Muhammad Naveed; Hussain, Jamshad

2018-05-01

Growth, development, and economic yield of agricultural crops rely on moisture, temperature, light, and carbon dioxide concentration. However, the amount of these parameters is varying with time due to climate change. Climate change is factual and ongoing so, first principle of agronomy should be to identify climate change potential impacts and adaptation measures to manage the susceptibilities of agricultural sector. Crop models have ability to predict the crop's yield under changing climatic conditions. We used OILCROP-SUN model to simulate the influence of elevated temperature and CO 2 on crop growth duration, maximum leaf area index (LAI), total dry matter (TDM), and achene yield of sunflower under semi-arid conditions of Pakistan (Faisalabad, Punjab). The model was calibrated and validated with the experimental data of 2012 and 2013, respectively. The simulation results showed that phenological events of sunflower were not changed at higher concentration of CO 2 (430 and 550 ppm). However LAI, achene yield, and TDM increased by 0.24, 2.41, and 4.67% at 430 ppm and by 0.48, 3.09, and 9.87% at 550 ppm, respectively. Increased temperature (1 and 2 °C) reduced the sunflower duration to remain green that finally led to less LAI, achene yield, and TDM as compared to present conditions. However, the drastic effects of increased temperature on sunflower were reduced to some extent at 550 ppm CO 2 concentration. Evaluation of different adaptation options revealed that 21 days earlier (as compared to current sowing date) planting of sunflower crop with increased plant population (83,333 plants ha -1 ) could reduce the yield losses due to climate change. Flowering is the most critical stage of sunflower to water scarcity. We recommended skipping second irrigation or 10% (337.5 mm) less irrigation water application to conserve moisture under possible water scarce conditions of 2025 and 2050.

Estimating wheat and maize daily evapotranspiration using artificial neural network

NASA Astrophysics Data System (ADS)

Abrishami, Nazanin; Sepaskhah, Ali Reza; Shahrokhnia, Mohammad Hossein

2018-02-01

In this research, artificial neural network (ANN) is used for estimating wheat and maize daily standard evapotranspiration. Ten ANN models with different structures were designed for each crop. Daily climatic data [maximum temperature (T max), minimum temperature (T min), average temperature (T ave), maximum relative humidity (RHmax), minimum relative humidity (RHmin), average relative humidity (RHave), wind speed (U 2), sunshine hours (n), net radiation (Rn)], leaf area index (LAI), and plant height (h) were used as inputs. For five structures of ten, the evapotranspiration (ETC) values calculated by ETC = ET0 × K C equation (ET0 from Penman-Monteith equation and K C from FAO-56, ANNC) were used as outputs, and for the other five structures, the ETC values measured by weighing lysimeter (ANNM) were used as outputs. In all structures, a feed forward multiple-layer network with one or two hidden layers and sigmoid transfer function and BR or LM training algorithm was used. Favorite network was selected based on various statistical criteria. The results showed the suitable capability and acceptable accuracy of ANNs, particularly those having two hidden layers in their structure in estimating the daily evapotranspiration. Best model for estimation of maize daily evapotranspiration is «M»ANN1 C (8-4-2-1), with T max, T min, RHmax, RHmin, U 2, n, LAI, and h as input data and LM training rule and its statistical parameters (NRMSE, d, and R2) are 0.178, 0.980, and 0.982, respectively. Best model for estimation of wheat daily evapotranspiration is «W»ANN5 C (5-2-3-1), with T max, T min, Rn, LAI, and h as input data and LM training rule, its statistical parameters (NRMSE, d, and R 2) are 0.108, 0.987, and 0.981 respectively. In addition, if the calculated ETC used as the output of the network for both wheat and maize, higher accurate estimation was obtained. Therefore, ANN is suitable method for estimating evapotranspiration of wheat and maize.

Linking Land Surface Phenology and Growth Limiting Factor Shifts over the Past 30 Years

NASA Astrophysics Data System (ADS)

Garonna, I.; Schenkel, D.; de Jong, R.; Schaepman, M. E.

2015-12-01

The study of global vegetation dynamics contributes to a better understanding of global change drivers and how these affect ecosystems and ecological diversity. Land-surface phenology (LSP) is a key response and feedback of vegetation to the climate system, and hence a parameter that needs to be accurately represented in terrestrial biosphere models [1]. However, the effects of climatic changes on LSP depend on the relative importance of climatic constraints in specific regions - which are not well understood at global scale. In this study, we analyzed a Phenology Reanalysis dataset [2] to evaluate shifts in three climatic drivers of phenology at global scale and over the last 30 years (1982-2012): incoming radiation, evaporative demand and minimum temperature. As a first step, we compared LAI as modeled from these three factors (LAIre) to remotely sensed observations of LSP (LAI3g, [3]) over the same time period. As a second step, we examined temporal trends in the climatic constraints at Start- and End- of the Growing Season. There was good agreement between phenology metrics as derived form LAI3g and LAIre over the last 30 years - thus providing confidence in the climatic constraints underlying the modeled data. Our analysis reveals inter-annual variation in the relative importance of the three climatic factors in limiting vegetation growth at Start- and End- of the Growing Season over the last 30 years. High northern latitudes, as well as northern Europe and central Asia, appear to have undergone significant changes in dominance between the three controls. We also find that evaporative demand has become increasingly limiting for growth in many parts of the world, in particular in South America and eastern Asia. [1] Richardson, A.D. et al. Global Change Biology 18, 566-584 (2012). [2] Stöckli, R. et al. J. Geophys. Res 116, G03020 (2011). [3] Zhu, Z. et al. Remote Sensing 5, 927-948 (2013).

Effects of Spatial N nutrient mobility relevant to plants, soils and microtopograhy on plant growth and soil organic matter accumulation by using coupled CLM-PFLOTRAN biogeochemical model in an Area in NGEE-Arctic Intensive Study Sites, Barrow, AK.

NASA Astrophysics Data System (ADS)

Yuan, F.; Thornton, P. E.; Tang, G.; Xu, X.; Kumar, J.; Iversen, C. M.; Bisht, G.; Hammond, G. E.; Mills, R. T.; Wullschleger, S. D.

2015-12-01

At fine-scale spatially-explicit reactive-transport (RT) and hydrological coupled modeling for likely soil nutrient N transport mechanisms driven by gradients, soil properties and micro-topography is critical to spatial distribution of plants and thus soil organic matter stocks accumulation or changes. In this study we successfully carried out a fully coupled fine-scale CLM-PFLOTRAN soil biogeochemical (BGC) RT model simulation on Titan at 2.5mx2.5m resolution for the Area C of 100mx100m in the NGEE-Arctic Intensive Study Sites, Barrow, AK. The Area spatially varies in terms of plant function types (PFT) and soil thermal-hydraulic properties associated with locally polygonal landscape features. The spatially explicit CLM-PFLOTRAN coupled RT model allows soil N nutrient mobility driven either by diffusion or by advection or both. The modeling experiments are conducted with three soil nutrient N (NH4+ and NO3-) mobility mechanisms within the CLM-PFLOTAN: no transport, diffusion only, and diffusion and advection in 3-D soils. It shows that CLM-PFLOTRAN model simulated higher SOM C density in both lower troughs and neighbored areas when transport mechanism allowed, compared to no-transport, although with similar ranges (about 0.1~20 kgC m-3). It also simulates slightly higher LAI (0.16~0.84 vs. 0.11~0.85) in growing season, especially in lower troughs and neighbored regions. It's likely because CLM-PFLOTRAN can explicitly simulate transport of nutrients and others both vertically and laterally. So it can more mechanically mimic plant root N extract caused relatively low concentration in root zone and thus allow transport from surrounding high N concentration regions. The lateral mobility also implies that N nutrient can transport from initially high-production columns to the neighbored low-production area where then production could be improved. The results suggest that taking account of locally mobility of soil N nutrients may be critical to plant growth and thus long-term soil organic carbon stocks in this polygonal coastal tundra ecosystem at fine scale. It also implies that regional or global scale modelings should consider vertical transport (2D) due to shallow soil root zones, for which a feature in CLM-PFLOTRAN is available as well.

Simultaneous inversion of multiple land surface parameters from MODIS optical-thermal observations

NASA Astrophysics Data System (ADS)

Ma, Han; Liang, Shunlin; Xiao, Zhiqiang; Shi, Hanyu

2017-06-01

Land surface parameters from remote sensing observations are critical in monitoring and modeling of global climate change and biogeochemical cycles. Current methods for estimating land surface variables usually focus on individual parameters separately even from the same satellite observations, resulting in inconsistent products. Moreover, no efforts have been made to generate global products from integrated observations from the optical to Thermal InfraRed (TIR) spectrum. Particularly, Middle InfraRed (MIR) observations have received little attention due to the complexity of the radiometric signal, which contains both reflected and emitted radiation. In this paper, we propose a unified algorithm for simultaneously retrieving six land surface parameters - Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), land surface albedo, Land Surface Emissivity (LSE), Land Surface Temperature (LST), and Upwelling Longwave radiation (LWUP) by exploiting MODIS visible-to-TIR observations. We incorporate a unified physical radiative transfer model into a data assimilation framework. The MODIS visible-to-TIR time series datasets include the daily surface reflectance product and MIR-to-TIR surface radiance, which are atmospherically corrected from the MODIS data using the Moderate Resolution Transmittance program (MODTRAN, ver. 5.0). LAI was first estimated using a data assimilation method that combines MODIS daily reflectance data and a LAI phenology model, and then the LAI was input to the unified radiative transfer model to simulate spectral surface reflectance and surface emissivity for calculating surface broadband albedo and emissivity, and FAPAR. LST was estimated from the MIR-TIR surface radiance data and the simulated emissivity, using an iterative optimization procedure. Lastly, LWUP was estimated using the LST and surface emissivity. The retrieved six parameters were extensively validated across six representative sites with different biome types, and compared with MODIS, GLASS, and GlobAlbedo land surface products. The results demonstrate that the unified inversion algorithm can retrieve temporally complete and physically consistent land surface parameters, and provides more accurate estimates of surface albedo, LST, and LWUP than existing products, with R2 values of 0.93 and 0.62, RMSE of 0.029 and 0.037, and BIAS values of 0.016 and 0.012 for the retrieved and MODIS albedo products, respectively, compared with field albedo measurements; R2 values of 0.95 and 0.93, RMSE of 2.7 and 4.2 K, and BIAS values of -0.6 and -2.7 K for the retrieved and MODIS LST products, respectively, compared with field LST measurements; and R2 values of 0.93 and 0.94, RMSE of 18.2 and 22.8 W/m2, and BIAS values of -2.7 and -14.6 W/m2 for the retrieved and MODIS LWUP products, respectively, compared with field LWUP measurements.

Golden Girl: Mary Lai Reflects as She Marks Her 50th Anniversary.

ERIC Educational Resources Information Center

Iwanowski, Jay

1996-01-01

The career and administrative style of Mary M. Lai, who celebrates her 50th year as chief financial officer at Long Island University (New York), are discussed. Her perspectives on change in higher education and in the institution during that time, the administrator's role, current challenges for financial officers, and the college environment as…