Science.gov

Sample records for aboveground biomass estimates

  1. MODIS Based Estimation of Forest Aboveground Biomass in China

    PubMed Central

    Sun, Yan; Wang, Tao; Zeng, Zhenzhong; Piao, Shilong

    2015-01-01

    Accurate estimation of forest biomass C stock is essential to understand carbon cycles. However, current estimates of Chinese forest biomass are mostly based on inventory-based timber volumes and empirical conversion factors at the provincial scale, which could introduce large uncertainties in forest biomass estimation. Here we provide a data-driven estimate of Chinese forest aboveground biomass from 2001 to 2013 at a spatial resolution of 1 km by integrating a recently reviewed plot-level ground-measured forest aboveground biomass database with geospatial information from 1-km Moderate-Resolution Imaging Spectroradiometer (MODIS) dataset in a machine learning algorithm (the model tree ensemble, MTE). We show that Chinese forest aboveground biomass is 8.56 Pg C, which is mainly contributed by evergreen needle-leaf forests and deciduous broadleaf forests. The mean forest aboveground biomass density is 56.1 Mg C ha−1, with high values observed in temperate humid regions. The responses of forest aboveground biomass density to mean annual temperature are closely tied to water conditions; that is, negative responses dominate regions with mean annual precipitation less than 1300 mm y−1 and positive responses prevail in regions with mean annual precipitation higher than 2800 mm y−1. During the 2000s, the forests in China sequestered C by 61.9 Tg C y−1, and this C sink is mainly distributed in north China and may be attributed to warming climate, rising CO2 concentration, N deposition, and growth of young forests. PMID:26115195

  2. MODIS Based Estimation of Forest Aboveground Biomass in China.

    PubMed

    Yin, Guodong; Zhang, Yuan; Sun, Yan; Wang, Tao; Zeng, Zhenzhong; Piao, Shilong

    2015-01-01

    Accurate estimation of forest biomass C stock is essential to understand carbon cycles. However, current estimates of Chinese forest biomass are mostly based on inventory-based timber volumes and empirical conversion factors at the provincial scale, which could introduce large uncertainties in forest biomass estimation. Here we provide a data-driven estimate of Chinese forest aboveground biomass from 2001 to 2013 at a spatial resolution of 1 km by integrating a recently reviewed plot-level ground-measured forest aboveground biomass database with geospatial information from 1-km Moderate-Resolution Imaging Spectroradiometer (MODIS) dataset in a machine learning algorithm (the model tree ensemble, MTE). We show that Chinese forest aboveground biomass is 8.56 Pg C, which is mainly contributed by evergreen needle-leaf forests and deciduous broadleaf forests. The mean forest aboveground biomass density is 56.1 Mg C ha-1, with high values observed in temperate humid regions. The responses of forest aboveground biomass density to mean annual temperature are closely tied to water conditions; that is, negative responses dominate regions with mean annual precipitation less than 1300 mm y-1 and positive responses prevail in regions with mean annual precipitation higher than 2800 mm y-1. During the 2000s, the forests in China sequestered C by 61.9 Tg C y-1, and this C sink is mainly distributed in north China and may be attributed to warming climate, rising CO2 concentration, N deposition, and growth of young forests. PMID:26115195

  3. Evaluating lidar point densities for effective estimation of aboveground biomass

    USGS Publications Warehouse

    Wu, Zhuoting; Dye, Dennis G.; Stoker, Jason; Vogel, John M.; Velasco, Miguel G.; Middleton, Barry R.

    2016-01-01

    The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) was recently established to provide airborne lidar data coverage on a national scale. As part of a broader research effort of the USGS to develop an effective remote sensing-based methodology for the creation of an operational biomass Essential Climate Variable (Biomass ECV) data product, we evaluated the performance of airborne lidar data at various pulse densities against Landsat 8 satellite imagery in estimating above ground biomass for forests and woodlands in a study area in east-central Arizona, U.S. High point density airborne lidar data, were randomly sampled to produce five lidar datasets with reduced densities ranging from 0.5 to 8 point(s)/m2, corresponding to the point density range of 3DEP to provide national lidar coverage over time. Lidar-derived aboveground biomass estimate errors showed an overall decreasing trend as lidar point density increased from 0.5 to 8 points/m2. Landsat 8-based aboveground biomass estimates produced errors larger than the lowest lidar point density of 0.5 point/m2, and therefore Landsat 8 observations alone were ineffective relative to airborne lidar for generating a Biomass ECV product, at least for the forest and woodland vegetation types of the Southwestern U.S. While a national Biomass ECV product with optimal accuracy could potentially be achieved with 3DEP data at 8 points/m2, our results indicate that even lower density lidar data could be sufficient to provide a national Biomass ECV product with accuracies significantly higher than that from Landsat observations alone.

  4. Wavelet analysis for aboveground biomass estimate in temperate deciduous forests

    NASA Astrophysics Data System (ADS)

    Wei, Xiao-Fang

    2008-10-01

    The ever-increasing concentration of anthropogenic greenhouse gases (CO2, CH4, and CFCs) has been identified as a likely (greater than 90% confidence) cause of the observed increase of global mean temperatures since the mid-20th century (IPCC, 2007). The effect of human-induced climate change could be unprecedented and far-reaching. Carbon sequestration into trees and forests is an effective and inexpensive way for mitigating the CO2 level in the atmosphere. Hence, accurate measurement of biomass will be of great importance to global carbon cycle and climate change. This study performed a wavelet-based forest aboveground biomass estimation approach in a temperate deciduous forest, the Hoosier National Forest, in Indiana. Wavelet analysis, specifically two-dimensional discrete wavelet transform (DWT) was applied to ASTER images to obtain wavelet coefficients (WCs), which were correlated with forest inventory data using multiple linear regression analysis to investigate the relationship. Different mother wavelets and level of decomposition were tested. Moreover, vegetation indices, RATIO, normalized difference vegetation index (NDVI), and principal component analyses (PCA) were computed and correlated with field biomass measurements. The results indicate that wavelet coefficients correlate better with field biomass data than vegetation indices. For level one decomposition, the correlation coefficients are 0.3 to 0.5, while 0.1-0.3 for vegetation indices; for level two decomposition, the overall R value increased by 0.2, and for level three, the R value can be increased to 0.6-0.7. Meanwhile, tree per acre and basal area were also examined and correlated with field measurements. This study demonstrates that wavelet-based biomass estimation could be a very promising approach for solving the uncertainty between reflectance value from satellite images and forest biomass and therefore providing better biomass estimation; however, further research is needed for identifying

  5. Stratified aboveground forest biomass estimation by remote sensing data

    NASA Astrophysics Data System (ADS)

    Latifi, Hooman; Fassnacht, Fabian E.; Hartig, Florian; Berger, Christian; Hernández, Jaime; Corvalán, Patricio; Koch, Barbara

    2015-06-01

    Remote sensing-assisted estimates of aboveground forest biomass are essential for modeling carbon budgets. It has been suggested that estimates can be improved by building species- or strata-specific biomass models. However, few studies have attempted a systematic analysis of the benefits of such stratification, especially in combination with other factors such as sensor type, statistical prediction method and sampling design of the reference inventory data. We addressed this topic by analyzing the impact of stratifying forest data into three classes (broadleaved, coniferous and mixed forest). We compare predictive accuracy (a) between the strata (b) to a case without stratification for a set of pre-selected predictors from airborne LiDAR and hyperspectral data obtained in a managed mixed forest site in southwestern Germany. We used 5 commonly applied algorithms for biomass predictions on bootstrapped subsamples of the data to obtain cross validated RMSE and r2 diagnostics. Those values were analyzed in a factorial design by an analysis of variance (ANOVA) to rank the relative importance of each factor. Selected models were used for wall-to-wall mapping of biomass estimates and their associated uncertainty. The results revealed marginal advantages for the strata-specific prediction models over the unstratified ones, which were more obvious on the wall-to-wall mapped area-based predictions. Yet further tests are necessary to establish the generality of these results. Input data type and statistical prediction method are concluded to remain the two most crucial factors for the quality of remote sensing-assisted biomass models.

  6. Developing a generalized allometric equation for aboveground biomass estimation

    NASA Astrophysics Data System (ADS)

    Xu, Q.; Balamuta, J. J.; Greenberg, J. A.; Li, B.; Man, A.; Xu, Z.

    2015-12-01

    A key potential uncertainty in estimating carbon stocks across multiple scales stems from the use of empirically calibrated allometric equations, which estimate aboveground biomass (AGB) from plant characteristics such as diameter at breast height (DBH) and/or height (H). The equations themselves contain significant and, at times, poorly characterized errors. Species-specific equations may be missing. Plant responses to their local biophysical environment may lead to spatially varying allometric relationships. The structural predictor may be difficult or impossible to measure accurately, particularly when derived from remote sensing data. All of these issues may lead to significant and spatially varying uncertainties in the estimation of AGB that are unexplored in the literature. We sought to quantify the errors in predicting AGB at the tree and plot level for vegetation plots in California. To accomplish this, we derived a generalized allometric equation (GAE) which we used to model the AGB on a full set of tree information such as DBH, H, taxonomy, and biophysical environment. The GAE was derived using published allometric equations in the GlobAllomeTree database. The equations were sparse in details about the error since authors provide the coefficient of determination (R2) and the sample size. A more realistic simulation of tree AGB should also contain the noise that was not captured by the allometric equation. We derived an empirically corrected variance estimate for the amount of noise to represent the errors in the real biomass. Also, we accounted for the hierarchical relationship between different species by treating each taxonomic level as a covariate nested within a higher taxonomic level (e.g. species < genus). This approach provides estimation under incomplete tree information (e.g. missing species) or blurred information (e.g. conjecture of species), plus the biophysical environment. The GAE allowed us to quantify contribution of each different

  7. A study on estimation of aboveground wet biomass based on the microwave vegetation indices

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Vegetation biomass is an important parameter in the carbon cycle study. In this paper, a new technique to estimate aboveground vegetation wet biomass based on the Microwave Vegetation Indices (MVIs), which are computed through the observed brightness temperature of AMSR-E/Aqua under two adjacent fre...

  8. Estimating forest and woodland aboveground biomass using active and passive remote sensing

    USGS Publications Warehouse

    Wu, Zhuoting; Dye, Dennis G.; Vogel, John M.; Middleton, Barry R.

    2016-01-01

    Aboveground biomass was estimated from active and passive remote sensing sources, including airborne lidar and Landsat-8 satellites, in an eastern Arizona (USA) study area comprised of forest and woodland ecosystems. Compared to field measurements, airborne lidar enabled direct estimation of individual tree height with a slope of 0.98 (R2 = 0.98). At the plot-level, lidar-derived height and intensity metrics provided the most robust estimate for aboveground biomass, producing dominant species-based aboveground models with errors ranging from 4 to 14Mg ha –1 across all woodland and forest species. Landsat-8 imagery produced dominant species-based aboveground biomass models with errors ranging from 10 to 28 Mg ha –1. Thus, airborne lidar allowed for estimates for fine-scale aboveground biomass mapping with low uncertainty, while Landsat-8 seems best suited for broader spatial scale products such as a national biomass essential climate variable (ECV) based on land cover types for the United States.

  9. Range vegetation type mapping and above-ground green biomass estimations using multispectral imagery. [Wyoming

    NASA Technical Reports Server (NTRS)

    Houston, R. S. (Principal Investigator); Gordon, R. C.

    1974-01-01

    The author has identified the following significant results. Range vegetation types have been successfully mapped on a portion of the 68,000 acre study site located west of Baggs, Wyoming, using ERTS-1 imagery. These types have been ascertained from field transects over a five year period. Comparable studies will be made with EREP imagery. Above-ground biomass estimation studies are being conducted utilizing double sampling techniques on two similar study sites. Information obtained will be correlated with percent relative reflectance measurements obtained on the ground which will be related to image brightness levels. This will provide an estimate of above-ground green biomass with multispectral imagery.

  10. Are Inventory Based and Remotely Sensed Above-Ground Biomass Estimates Consistent?

    PubMed Central

    Hill, Timothy C.; Williams, Mathew; Bloom, A. Anthony; Mitchard, Edward T. A.; Ryan, Casey M.

    2013-01-01

    Carbon emissions resulting from deforestation and forest degradation are poorly known at local, national and global scales. In part, this lack of knowledge results from uncertain above-ground biomass estimates. It is generally assumed that using more sophisticated methods of estimating above-ground biomass, which make use of remote sensing, will improve accuracy. We examine this assumption by calculating, and then comparing, above-ground biomass area density (AGBD) estimates from studies with differing levels of methodological sophistication. We consider estimates based on information from nine different studies at the scale of Africa, Mozambique and a 1160 km2 study area within Mozambique. The true AGBD is not known for these scales and so accuracy cannot be determined. Instead we consider the overall precision of estimates by grouping different studies. Since an the accuracy of an estimate cannot exceed its precision, this approach provides an upper limit on the overall accuracy of the group. This reveals poor precision at all scales, even between studies that are based on conceptually similar approaches. Mean AGBD estimates for Africa vary from 19.9 to 44.3 Mg ha−1, for Mozambique from 12.7 to 68.3 Mg ha−1, and for the 1160 km2 study area estimates range from 35.6 to 102.4 Mg ha−1. The original uncertainty estimates for each study, when available, are generally small in comparison with the differences between mean biomass estimates of different studies. We find that increasing methodological sophistication does not appear to result in improved precision of AGBD estimates, and moreover, inadequate estimates of uncertainty obscure any improvements in accuracy. Therefore, despite the clear advantages of remote sensing, there is a need to improve remotely sensed AGBD estimates if they are to provide accurate information on above-ground biomass. In particular, more robust and comprehensive uncertainty estimates are needed. PMID:24069275

  11. Allometric Equations for Aboveground and Belowground Biomass Estimations in an Evergreen Forest in Vietnam

    PubMed Central

    Nam, Vu Thanh; van Kuijk, Marijke; Anten, Niels P. R.

    2016-01-01

    Allometric regression models are widely used to estimate tropical forest biomass, but balancing model accuracy with efficiency of implementation remains a major challenge. In addition, while numerous models exist for aboveground mass, very few exist for roots. We developed allometric equations for aboveground biomass (AGB) and root biomass (RB) based on 300 (of 45 species) and 40 (of 25 species) sample trees respectively, in an evergreen forest in Vietnam. The biomass estimations from these local models were compared to regional and pan-tropical models. For AGB we also compared local models that distinguish functional types to an aggregated model, to assess the degree of specificity needed in local models. Besides diameter at breast height (DBH) and tree height (H), wood density (WD) was found to be an important parameter in AGB models. Existing pan-tropical models resulted in up to 27% higher estimates of AGB, and overestimated RB by nearly 150%, indicating the greater accuracy of local models at the plot level. Our functional group aggregated local model which combined data for all species, was as accurate in estimating AGB as functional type specific models, indicating that a local aggregated model is the best choice for predicting plot level AGB in tropical forests. Finally our study presents the first allometric biomass models for aboveground and root biomass in forests in Vietnam. PMID:27309718

  12. Allometric Equations for Aboveground and Belowground Biomass Estimations in an Evergreen Forest in Vietnam.

    PubMed

    Nam, Vu Thanh; van Kuijk, Marijke; Anten, Niels P R

    2016-01-01

    Allometric regression models are widely used to estimate tropical forest biomass, but balancing model accuracy with efficiency of implementation remains a major challenge. In addition, while numerous models exist for aboveground mass, very few exist for roots. We developed allometric equations for aboveground biomass (AGB) and root biomass (RB) based on 300 (of 45 species) and 40 (of 25 species) sample trees respectively, in an evergreen forest in Vietnam. The biomass estimations from these local models were compared to regional and pan-tropical models. For AGB we also compared local models that distinguish functional types to an aggregated model, to assess the degree of specificity needed in local models. Besides diameter at breast height (DBH) and tree height (H), wood density (WD) was found to be an important parameter in AGB models. Existing pan-tropical models resulted in up to 27% higher estimates of AGB, and overestimated RB by nearly 150%, indicating the greater accuracy of local models at the plot level. Our functional group aggregated local model which combined data for all species, was as accurate in estimating AGB as functional type specific models, indicating that a local aggregated model is the best choice for predicting plot level AGB in tropical forests. Finally our study presents the first allometric biomass models for aboveground and root biomass in forests in Vietnam. PMID:27309718

  13. Estimating above-ground carbon biomass in a newly restored coastal plain wetland using remote sensing.

    PubMed

    Riegel, Joseph B; Bernhardt, Emily; Swenson, Jennifer

    2013-01-01

    Developing accurate but inexpensive methods for estimating above-ground carbon biomass is an important technical challenge that must be overcome before a carbon offset market can be successfully implemented in the United States. Previous studies have shown that LiDAR (light detection and ranging) is well-suited for modeling above-ground biomass in mature forests; however, there has been little previous research on the ability of LiDAR to model above-ground biomass in areas with young, aggrading vegetation. This study compared the abilities of discrete-return LiDAR and high resolution optical imagery to model above-ground carbon biomass at a young restored forested wetland site in eastern North Carolina. We found that the optical imagery model explained more of the observed variation in carbon biomass than the LiDAR model (adj-R(2) values of 0.34 and 0.18 respectively; root mean squared errors of 0.14 Mg C/ha and 0.17 Mg C/ha respectively). Optical imagery was also better able to predict high and low biomass extremes than the LiDAR model. Combining both the optical and LiDAR improved upon the optical model but only marginally (adj-R(2) of 0.37). These results suggest that the ability of discrete-return LiDAR to model above-ground biomass may be rather limited in areas with young, small trees and that high spatial resolution optical imagery may be the better tool in such areas. PMID:23840837

  14. Estimating Above-Ground Carbon Biomass in a Newly Restored Coastal Plain Wetland Using Remote Sensing

    PubMed Central

    Riegel, Joseph B.; Bernhardt, Emily; Swenson, Jennifer

    2013-01-01

    Developing accurate but inexpensive methods for estimating above-ground carbon biomass is an important technical challenge that must be overcome before a carbon offset market can be successfully implemented in the United States. Previous studies have shown that LiDAR (light detection and ranging) is well-suited for modeling above-ground biomass in mature forests; however, there has been little previous research on the ability of LiDAR to model above-ground biomass in areas with young, aggrading vegetation. This study compared the abilities of discrete-return LiDAR and high resolution optical imagery to model above-ground carbon biomass at a young restored forested wetland site in eastern North Carolina. We found that the optical imagery model explained more of the observed variation in carbon biomass than the LiDAR model (adj-R2 values of 0.34 and 0.18 respectively; root mean squared errors of 0.14 Mg C/ha and 0.17 Mg C/ha respectively). Optical imagery was also better able to predict high and low biomass extremes than the LiDAR model. Combining both the optical and LiDAR improved upon the optical model but only marginally (adj-R2 of 0.37). These results suggest that the ability of discrete-return LiDAR to model above-ground biomass may be rather limited in areas with young, small trees and that high spatial resolution optical imagery may be the better tool in such areas. PMID:23840837

  15. Improved allometric models to estimate the aboveground biomass of tropical trees.

    PubMed

    Chave, Jérôme; Réjou-Méchain, Maxime; Búrquez, Alberto; Chidumayo, Emmanuel; Colgan, Matthew S; Delitti, Welington B C; Duque, Alvaro; Eid, Tron; Fearnside, Philip M; Goodman, Rosa C; Henry, Matieu; Martínez-Yrízar, Angelina; Mugasha, Wilson A; Muller-Landau, Helene C; Mencuccini, Maurizio; Nelson, Bruce W; Ngomanda, Alfred; Nogueira, Euler M; Ortiz-Malavassi, Edgar; Pélissier, Raphaël; Ploton, Pierre; Ryan, Casey M; Saldarriaga, Juan G; Vieilledent, Ghislain

    2014-10-01

    Terrestrial carbon stock mapping is important for the successful implementation of climate change mitigation policies. Its accuracy depends on the availability of reliable allometric models to infer oven-dry aboveground biomass of trees from census data. The degree of uncertainty associated with previously published pantropical aboveground biomass allometries is large. We analyzed a global database of directly harvested trees at 58 sites, spanning a wide range of climatic conditions and vegetation types (4004 trees ≥ 5 cm trunk diameter). When trunk diameter, total tree height, and wood specific gravity were included in the aboveground biomass model as covariates, a single model was found to hold across tropical vegetation types, with no detectable effect of region or environmental factors. The mean percent bias and variance of this model was only slightly higher than that of locally fitted models. Wood specific gravity was an important predictor of aboveground biomass, especially when including a much broader range of vegetation types than previous studies. The generic tree diameter-height relationship depended linearly on a bioclimatic stress variable E, which compounds indices of temperature variability, precipitation variability, and drought intensity. For cases in which total tree height is unavailable for aboveground biomass estimation, a pantropical model incorporating wood density, trunk diameter, and the variable E outperformed previously published models without height. However, to minimize bias, the development of locally derived diameter-height relationships is advised whenever possible. Both new allometric models should contribute to improve the accuracy of biomass assessment protocols in tropical vegetation types, and to advancing our understanding of architectural and evolutionary constraints on woody plant development. PMID:24817483

  16. Estimating aboveground biomass for broadleaf woody plants and young conifers in Sierra Nevada, California forests.

    USGS Publications Warehouse

    McGinnis, Thomas W.; Shook, Christine D.; Keeley, Jon E.

    2010-01-01

    Quantification of biomass is fundamental to a wide range of research and natural resource management goals. An accurate estimation of plant biomass is essential to predict potential fire behavior, calculate carbon sequestration for global climate change research, assess critical wildlife habitat, and so forth. Reliable allometric equations from simple field measurements are necessary for efficient evaluation of plant biomass. However, allometric equations are not available for many common woody plant taxa in the Sierra Nevada. In this report, we present more than 200 regression equations for the Sierra Nevada western slope that relate crown diameter, plant height, crown volume, stem diameter, and both crown diameter and height to the dry weight of foliage, branches, and entire aboveground biomass. Destructive sampling methods resulted in regression equations that accurately predict biomass from one or two simple, nondestructive field measurements. The tables presented here will allow researchers and natural resource managers to easily choose the best equations to fit their biomass assessment needs.

  17. Estimating aboveground biomass for broadleaf woody plants and young conifers in Sierra Nevada, California, forests

    USGS Publications Warehouse

    McGinnis, T.W.; Shook, C.D.; Keeley, J.E.

    2010-01-01

    Quantification of biomass is fundamental to a wide range of research and natural resource management goals. An accurate estimation of plant biomass is essential to predict potential fire behavior, calculate carbon sequestration for global climate change research, assess critical wildlife habitat, and so forth. Reliable allometric equations from simple field measurements are necessary for efficient evaluation of plant biomass. However, allometric equations are not available for many common woody plant taxa in the Sierra Nevada. In this report, we present more than 200 regression equations for the Sierra Nevada western slope that relate crown diameter, plant height, crown volume, stem diameter, and both crown diameter and height to the dry weight of foliage, branches, and entire aboveground biomass. Destructive sampling methods resulted in regression equations that accurately predict biomass from one or two simple, nondestructive field measurements. The tables presented here will allow researchers and natural resource managers to easily choose the best equations to fit their biomass assessment needs.

  18. Estimating aboveground biomass of broadleaved woody plants in the understory of Florida Keys pine forests

    USGS Publications Warehouse

    Sah, J.P.; Ross, M.S.; Koptur, S.; Snyder, J.R.

    2004-01-01

    Species-specific allometric equations that provide estimates of biomass from measured plant attributes are currently unavailable for shrubs common to South Florida pine rocklands, where fire plays an important part in shaping the structure and function of ecosystems. We developed equations to estimate total aboveground biomass and fine fuel of 10 common hardwood species in the shrub layer of pine forests of the lower Florida Keys. Many equations that related biomass categories to crown area and height were significant (p < 0.05), but the form and variables comprising the best model varied among species. We applied the best-fit regression models to structural information from the shrub stratum in 18 plots on Big Pine Key, the most extensive pine forest in the Keys. Estimates based on species-specific equations indicated clearly that total aboveground shrub biomass and shrub fine fuel increased with time since last fire, but the relationships were non-linear. The relative proportion of biomass constituted by the major species also varied with stand age. Estimates based on mixed-species regressions differed slightly from estimates based on species-specific models, but the former could provide useful approximations in similar forests where species-specific regressions are not yet available. ?? 2004 Elsevier B.V. All rights reserved.

  19. Assessing general relationships between aboveground biomass and vegetation structure parameters for improved carbon estimate from lidar remote sensing

    NASA Astrophysics Data System (ADS)

    Ni-Meister, Wenge; Lee, Shihyan; Strahler, Alan H.; Woodcock, Curtis E.; Schaaf, Crystal; Yao, Tian; Ranson, K. Jon; Sun, Guoqing; Blair, J. Bryan

    2010-06-01

    Lidar-based aboveground biomass is derived based on the empirical relationship between lidar-measured vegetation height and aboveground biomass, often leading to large uncertainties of aboveground biomass estimates at large scales. This study investigates whether the use of any additional lidar-derived vegetation structure parameters besides height improves aboveground biomass estimation. The analysis uses data collected in the field and with the Laser Vegetation Imaging Sensor (LVIS), and the Echidna® validation instrument (EVI), a ground-based hemispherical-scanning lidar data in New England in 2003 and 2007. Our field data analysis shows that using wood volume (approximated by the product of basal area and top 10% tree height) and vegetation type (conifer/softwood or deciduous/hardwood forests, providing wood density) has the potential to improve aboveground biomass estimates at large scales. This result is comparable to previous individual-tree based analyses. Our LVIS data analysis indicates that structure parameters that combine height and gap fraction, such as RH100*cover and RH50*cover, are closely related to wood volume and thus biomass particularly for conifer forests. RH100*cover and RH50*cover perform similarly or even better than RH50, a good biomass predictor found in previous study. This study shows that the use of structure parameters that combine height and gap fraction (rather than height alone) improves the aboveground biomass estimate, and that the fusion of lidar and optical remote sensing (to provide vegetation type) will provide better aboveground biomass estimates than using lidar alone. Our ground lidar analysis shows that EVI provides good estimates of wood volume, and thus accurate estimates of aboveground biomass particularly at the stand level.

  20. Assessing General Relationships Between Above-Ground Biomass and Vegetation Structure Parameters for Improved Carbon Estimate from Lidar Remote Sensing

    NASA Astrophysics Data System (ADS)

    Ni-Meister, W.; Lee, S.; Strahler, A. H.; Woodcock, C. E.; Schaaf, C.; Yao, T.; Ranson, J.; Sun, G.; Blair, J. B.

    2009-12-01

    Lidar remote sensing uses vegetation height to estimate large-scale above-ground biomass. However, lidar height and biomass relationships are empirical and thus often lead to large uncertainties in above-ground biomass estimates. This study uses vegetation structure measurements from field: an airborne lidar (Laser Vegetation Imaging Sensor, LVIS)) and a full wave form ground-based lidar (Echidna® validation instrument, EVI) collected in the New England region in 2003 and 2007, to investigate using additional vegetation structure parameters besides height for improved above-ground biomass estimation from lidar. Our field data analysis shows that using woody volume (approximated by the product of basal area and top 10% tree height) and vegetation type (conifer/softwood or deciduous/hardwood forests, providing wood density) has the potential to improve above-ground biomass estimates at large scale. This result is comparable to previous work by Chave et al. (2005), which focused on individual trees. However this study uses a slightly different approach, and our woody volume is estimated differently from Chave et al. (2005). Previous studies found that RH50 is a good predictor of above-ground biomass (Drake et al., 2002; 2003). Our LVIS data analysis shows that structure parameters that combine height and gap fraction, such as RH100*cover and RH50*cover, perform similarly or even better than RH50. We also found that the close relationship of RH100*cover and RH50*cover with woody volume explains why they are good predictors of above-ground biomass. RH50 is highly related to RH100*cover, and this explains why RH50 is a better predictor of biomass than RH100. This study shows that using structure parameters combining height and gap fraction improve above-ground biomass estimate compared to height alone, and fusion of lidar and optical remote sensing (to provide vegetation type) will provide better above-ground biomass estimates than lidar alone. Ground lidar analysis

  1. Mid-Term Status of the Forest Dragon III: Data Collection and Regional Aboveground Biomass Estimation

    NASA Astrophysics Data System (ADS)

    Pang, Yong; Li, Zengyuan; Liu, Luxia; Lu, Hao; Jia, Wen; Liu, Qingwang; Tian, Xin; Zhang, Ruiying; Shmullius, Christiana

    2014-11-01

    In the 1st two years of Forest Dragon 3 project, Chinese groups engaged in following activities: 1) field measurements and airborne campaigns for forest map validation, 2) regional forest aboveground biomass (AGB) estimation algorithm development and map generation. The AGB estimation by fusion multisensor fusion was investigated. Two campaigns consist of in-situ observation, airborne flight and spaceborne measurements were designed and implemented in the Heilongjiang Province and Yunnan Province of China. The Heilongjiang Province is located in Northeast China and has typical temperate forest. The Yunnan Province is located in Southwest China and contains multiple forest types including tropical forest. By using these observation data from different scales, multi-source satellite data were used to estimate spatial explicit AGB for Da Xinganling study area.

  2. Total aboveground biomass (TAGB) estimation using IFSAR: speckle noise effect on TAGB in tropical forest

    NASA Astrophysics Data System (ADS)

    Misbari, S.; Hashim, M.

    2014-02-01

    Total Aboveground Biomass (TAGB) estimation is critically important to enhance understanding of dynamics of carbon fluxes between atmosphere and terrestrial ecosystem. For humid tropical forest, it is a challenging task for researchers due to complex canopy structure and predominant cloud cover. Optical sensors are only able to sense canopy crown. In contrast, radar technology is able to sense sub-canopy structure of the forest with penetration ability through the cloud for precise biomass estimation with validation from field data including diameter at breast height (DBH) of trees. This study is concerned about estimation of TAGB through the utilization of Interferometry Synthetic Aperture Radar (IFSAR). Based on this study, it is found that the stand parameters such as DBH and backscattered on IFSAR image has high correlation, R2=0.6411. The most suitable model for TAGB estimation on IFSAR is Chave Model with R2=0.9139. This study analyzes the impact brought by speckle noises on IFSAR image. It is found that filtering process has improves TAGB estimation about +30% using several filtering schemes especially Gamma filter for 11×11 window size. Using field data obtained from a primary tropical forest at Gerik, Perak, TAGBestimation can be validated and the assessment has been carried out.

  3. Estimating aboveground biomass in interior Alaska with Landsat data and field measurements

    USGS Publications Warehouse

    Ji, Lei; Wylie, Bruce K.; Nossov, Dana R.; Peterson, Birgit; Waldrop, Mark P.; McFarland, Jack W.; Rover, Jennifer R.; Hollingsworth, Teresa N.

    2012-01-01

    Terrestrial plant biomass is a key biophysical parameter required for understanding ecological systems in Alaska. An accurate estimation of biomass at a regional scale provides an important data input for ecological modeling in this region. In this study, we created an aboveground biomass (AGB) map at 30-m resolution for the Yukon Flats ecoregion of interior Alaska using Landsat data and field measurements. Tree, shrub, and herbaceous AGB data in both live and dead forms were collected in summers and autumns of 2009 and 2010. Using the Landsat-derived spectral variables and the field AGB data, we generated a regression model and applied this model to map AGB for the ecoregion. A 3-fold cross-validation indicated that the AGB estimates had a mean absolute error of 21.8 Mg/ha and a mean bias error of 5.2 Mg/ha. Additionally, we validated the mapping results using an airborne lidar dataset acquired for a portion of the ecoregion. We found a significant relationship between the lidar-derived canopy height and the Landsat-derived AGB (R2 = 0.40). The AGB map showed that 90% of the ecoregion had AGB values ranging from 10 Mg/ha to 134 Mg/ha. Vegetation types and fires were the primary factors controlling the spatial AGB patterns in this ecoregion.

  4. Estimating Yellow Starthistle (Centaurea solstitialis) Leaf Area Index and Aboveground Biomass with the Use of Hyperspectral Data

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral remote-sensed data were obtained via a Compact Airborne Spectrographic Imager-II (CASI-II) and used to estimate leaf-area index (LAI) and aboveground biomass of a highly invasive weed species, yellow starthistle (YST). In parallel, 34 ground-based field plots were used to measure abov...

  5. Estimating leaf area index and aboveground biomass of an invasive weed (yellow starthistle, Centaurea solstitalis L.) using airborne hyperspectral data

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Hyperspectral remote sensed data was obtained via a Compact Airborne Spectrographic Imager (CASI) and used to estimate leaf area index (LAI) and aboveground biomass of a highly invasive weed species, yellow starthistle (Centaurea solstitialis L.). In parallel, 34 ground-based field plots were used t...

  6. Use of Radar to Estimate Above-Ground Biomass in Disturbed Tropical Landscapes

    NASA Technical Reports Server (NTRS)

    Houghton, R. A.

    1998-01-01

    The overall purpose of this work was to evaluate the use of satellite radar in distinguishing, first, different cover classes in tropical landscapes and, second, cover classes with different amounts of above-ground biomass. The work focused on Ama7onian forests around Paragominas, Para, Brazil where extensive ground data had been obtained through previous field work.

  7. Estimating grassland aboveground biomass using multitemporal MODIS data in the West Songnen Plain, China

    NASA Astrophysics Data System (ADS)

    Li, Fei; Jiang, Lei; Wang, Xufeng; Zhang, Xiaoqiang; Zheng, Jiajia; Zhao, Qianjun

    2013-01-01

    The West Songnen Plain is an ecologically fragile area. The grasslands on the plain have been seriously degraded over the past five decades and this process is continuing. The reliable estimation of grassland aboveground biomass (AGB) provides scientific data for determining the livestock stocking rate on rangeland. AGB is also of considerable significance for biodiversity and environmental protection in this region. Remote sensing is the most effective way to estimate grassland AGB on a regional scale. Multitemporal, remotely sensed data were used for grassland AGB estimation with statistical models and an artificial neural network (ANN), and the accuracy of estimation for these methods was compared. The results demonstrate that the use of multi-temporal remotely sensed data has advantages for grassland AGB estimation, whether with statistical models or ANN methods, compared with single-temporal remotely sensed data, although the ANN had a higher accuracy of estimation for grassland AGB. Finally, the grassland AGB on the Songnen Plain was estimated with the ANN using multitemporal MODIS data. The spatial distribution pattern of grassland AGB showed large variations, and grassland productivity was generally low. The maximum green weight of the grassland AGB was 927.22 g/m2 and was mainly distributed on the northeast of the West Songnen Plain. The minimum green weight of the grassland AGB was 194.82 g/m2 and was mainly distributed on the central and southwestern West Songnen Plain. Most of the areas had medium- and low-yielding grasses. The significant increases of population and livestock number were the primary and direct reasons for the decrease in grassland quality. This study will contribute to policy making for the control of grazing and for biodiversity and environmental protection.

  8. Optimal Atmospheric Correction for Above-Ground Forest Biomass Estimation with the ETM+ Remote Sensor.

    PubMed

    Nguyen, Hieu Cong; Jung, Jaehoon; Lee, Jungbin; Choi, Sung-Uk; Hong, Suk-Young; Heo, Joon

    2015-01-01

    The reflectance of the Earth's surface is significantly influenced by atmospheric conditions such as water vapor content and aerosols. Particularly, the absorption and scattering effects become stronger when the target features are non-bright objects, such as in aqueous or vegetated areas. For any remote-sensing approach, atmospheric correction is thus required to minimize those effects and to convert digital number (DN) values to surface reflectance. The main aim of this study was to test the three most popular atmospheric correction models, namely (1) Dark Object Subtraction (DOS); (2) Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) and (3) the Second Simulation of Satellite Signal in the Solar Spectrum (6S) and compare them with Top of Atmospheric (TOA) reflectance. By using the k-Nearest Neighbor (kNN) algorithm, a series of experiments were conducted for above-ground forest biomass (AGB) estimations of the Gongju and Sejong region of South Korea, in order to check the effectiveness of atmospheric correction methods for Landsat ETM+. Overall, in the forest biomass estimation, the 6S model showed the bestRMSE's, followed by FLAASH, DOS and TOA. In addition, a significant improvement of RMSE by 6S was found with images when the study site had higher total water vapor and temperature levels. Moreover, we also tested the sensitivity of the atmospheric correction methods to each of the Landsat ETM+ bands. The results confirmed that 6S dominates the other methods, especially in the infrared wavelengths covering the pivotal bands for forest applications. Finally, we suggest that the 6S model, integrating water vapor and aerosol optical depth derived from MODIS products, is better suited for AGB estimation based on optical remote-sensing data, especially when using satellite images acquired in the summer during full canopy development. PMID:26263996

  9. Optimal Atmospheric Correction for Above-Ground Forest Biomass Estimation with the ETM+ Remote Sensor

    PubMed Central

    Nguyen, Hieu Cong; Jung, Jaehoon; Lee, Jungbin; Choi, Sung-Uk; Hong, Suk-Young; Heo, Joon

    2015-01-01

    The reflectance of the Earth’s surface is significantly influenced by atmospheric conditions such as water vapor content and aerosols. Particularly, the absorption and scattering effects become stronger when the target features are non-bright objects, such as in aqueous or vegetated areas. For any remote-sensing approach, atmospheric correction is thus required to minimize those effects and to convert digital number (DN) values to surface reflectance. The main aim of this study was to test the three most popular atmospheric correction models, namely (1) Dark Object Subtraction (DOS); (2) Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) and (3) the Second Simulation of Satellite Signal in the Solar Spectrum (6S) and compare them with Top of Atmospheric (TOA) reflectance. By using the k-Nearest Neighbor (kNN) algorithm, a series of experiments were conducted for above-ground forest biomass (AGB) estimations of the Gongju and Sejong region of South Korea, in order to check the effectiveness of atmospheric correction methods for Landsat ETM+. Overall, in the forest biomass estimation, the 6S model showed the bestRMSE’s, followed by FLAASH, DOS and TOA. In addition, a significant improvement of RMSE by 6S was found with images when the study site had higher total water vapor and temperature levels. Moreover, we also tested the sensitivity of the atmospheric correction methods to each of the Landsat ETM+ bands. The results confirmed that 6S dominates the other methods, especially in the infrared wavelengths covering the pivotal bands for forest applications. Finally, we suggest that the 6S model, integrating water vapor and aerosol optical depth derived from MODIS products, is better suited for AGB estimation based on optical remote-sensing data, especially when using satellite images acquired in the summer during full canopy development. PMID:26263996

  10. Estimation of aboveground biomass in Mediterranean forests by statistical modelling of ASTER fraction images

    NASA Astrophysics Data System (ADS)

    Fernández-Manso, O.; Fernández-Manso, A.; Quintano, C.

    2014-09-01

    Aboveground biomass (AGB) estimation from optical satellite data is usually based on regression models of original or synthetic bands. To overcome the poor relation between AGB and spectral bands due to mixed-pixels when a medium spatial resolution sensor is considered, we propose to base the AGB estimation on fraction images from Linear Spectral Mixture Analysis (LSMA). Our study area is a managed Mediterranean pine woodland (Pinus pinaster Ait.) in central Spain. A total of 1033 circular field plots were used to estimate AGB from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) optical data. We applied Pearson correlation statistics and stepwise multiple regression to identify suitable predictors from the set of variables of original bands, fraction imagery, Normalized Difference Vegetation Index and Tasselled Cap components. Four linear models and one nonlinear model were tested. A linear combination of ASTER band 2 (red, 0.630-0.690 μm), band 8 (short wave infrared 5, 2.295-2.365 μm) and green vegetation fraction (from LSMA) was the best AGB predictor (Radj2=0.632, the root-mean-squared error of estimated AGB was 13.3 Mg ha-1 (or 37.7%), resulting from cross-validation), rather than other combinations of the above cited independent variables. Results indicated that using ASTER fraction images in regression models improves the AGB estimation in Mediterranean pine forests. The spatial distribution of the estimated AGB, based on a multiple linear regression model, may be used as baseline information for forest managers in future studies, such as quantifying the regional carbon budget, fuel accumulation or monitoring of management practices.

  11. Quantification of uncertainty in aboveground biomass estimates derived from small-footprint LiDAR data

    NASA Astrophysics Data System (ADS)

    Xu, Q.; Greenberg, J. A.; Li, B.; Ramirez, C.; Balamuta, J. J.; Evans, K.; Man, A.; Xu, Z.

    2015-12-01

    A promising approach to determining aboveground biomass (AGB) in forests comes through the use of individual tree crown delineation (ITCD) techniques applied to small-footprint LiDAR data. These techniques, when combined with allometric equations, can produce per-tree estimates of AGB. At this scale, AGB estimates can be quantified in a manner similar to how ground-based forest inventories are produced. However, these approaches have significant uncertainties that are rarely described in full. Allometric equations are often based on species-specific diameter-at-breast height (DBH) relationships, but neither DBH nor species can be reliably determined using remote sensing analysis. Furthermore, many approaches to ITCD only delineate trees appearing in the upper canopy so subcanopy trees are often missing from the inventories. In this research, we performed a propagation-of-error analysis to determine the spatially varying uncertainties in AGB estimates at the individual plant and stand level for a large collection of LiDAR acquisitions covering a large portion of California. Furthermore, we determined the relative contribution of various aspects of the analysis towards the uncertainty, including errors in the ITCD results, the allometric equations, the taxonomic designation, and the local biophysical environment. Watershed segmentation was used to obtain the preliminary crown segments. Lidar points within the preliminary segments were extracted to form profiling data of the segments, and then mode detection algorithms were applied to identify the tree number and tree heights within each segment. As part of this analysis, we derived novel "remote sensing aware" allometric equations and their uncertainties based on three-dimensional morphological metrics that can be accurately derived from LiDAR data.

  12. Estimation of aboveground woody biomass using HJ-1 and Radarsat-2 data for deciduous forests in Daxing'anling, China

    NASA Astrophysics Data System (ADS)

    Liu, Qian; Yang, Le; Liu, Qinhuo; Li, Jing

    2014-11-01

    Accurate estimation of forest aboveground biomass is important for global carbon budgets and ecosystem change studies. Most algorithms for regional or global aboveground biomass estimation using optical and microwave remote sensing data are based on empirical regression and non-parametric training methods, which require large amount of ground measurements for training and are lacking of explicit interaction mechanisms between electromagnetic wave and vegetation. In this study, we proposed an optical/microwave synergy method based on a coherent polarimetric SAR model to estimate woody biomass. The study area is sparse deciduous forest dominated by birch with understory of shrubs and herbs in Daxing'anling, China. HJ-1, Radarsat-2 images, and field LAI were collected during May to August in 2013, tree biophysical parameters were measured at the field campaign during August to September in 2012. The effects of understory and wet ground were evaluated by introducing the NDVI derived from HJ-1 image and rain rate. Field measured LAI was used as an input to the SAR model to define the scattering and attenuation of the green canopy to the total backscatter. Finally, an logarithmic equation between the backscatter coefficient of direct forest scattering mechanism and woody biomass was generated (R2=0.582). The retrieval results were validated with the ground biomass measurements (RMSE=29.01ton/ha). The results indicated the synergy of optical and microwave remote sensing data based on SAR model has the potential to improve the accuracy of woody biomass estimation.

  13. Investigating Appropriate Sampling Design for Estimating Above-Ground Biomass in Bruneian Lowland Mixed Dipterocarp Forest

    NASA Astrophysics Data System (ADS)

    Lee, S.; Lee, D.; Abu Salim, K.; Yun, H. M.; Han, S.; Lee, W. K.; Davies, S. J.; Son, Y.

    2014-12-01

    Mixed tropical forest structure is highly heterogeneous unlike plantation or mixed temperate forest structure, and therefore, different sampling approaches are required. However, the appropriate sampling design for estimating the above-ground biomass (AGB) in Bruneian lowland mixed dipterocarp forest (MDF) has not yet been fully clarified. The aim of this study was to provide supportive information in sampling design for Bruneian forest carbon inventory. The study site was located at Kuala Belalong lowland MDF, which is part of the Ulu Tembulong National Park, Brunei Darussalam. Six 60 m × 60 m quadrats were established, separated by a distance of approximately 100 m and each was subdivided into quadrats of 10 m × 10 m, at an elevation between 200 and 300 m above sea level. At each plot all free-standing trees with diameter at breast height (dbh) ≥ 1 cm were measured. The AGB for all trees with dbh ≥ 10 cm was estimated by allometric models. In order to analyze changes in the diameter-dependent parameters used for estimating the AGB, different quadrat areas, ranging from 10 m × 10 m to 60 m × 60 m, were used across the study area, starting at the South-West end and moving towards the North-East end. The derived result was as follows: (a) Big trees (dbh ≥ 70 cm) with sparse distribution have remarkable contribution to the total AGB in Bruneian lowland MDF, and therefore, special consideration is required when estimating the AGB of big trees. Stem number of trees with dbh ≥ 70 cm comprised only 2.7% of all trees with dbh ≥ 10 cm, but 38.5% of the total AGB. (b) For estimating the AGB of big trees at the given acceptable limit of precision (p), it is more efficient to use large quadrats than to use small quadrats, because the total sampling area decreases with the former. Our result showed that 239 20 m × 20 m quadrats (9.6 ha in total) were required, while 15 60 m × 60 m quadrats (5.4 ha in total) were required when estimating the AGB of the trees

  14. A RAPID NON-DESTRUCTIVE METHOD FOR ESTIMATING ABOVEGROUND BIOMASS OF SALT MARSH GRASSES

    EPA Science Inventory

    Understanding the primary productivity of salt marshes requires accurate estimates of biomass. Unfortunately, these estimates vary enough within and among salt marshes to require large numbers of replicates if the averages are to be statistically meaningful. Large numbers of repl...

  15. Estimating aboveground biomass in the boreal forests of the Yukon River Basin, Alaska

    NASA Astrophysics Data System (ADS)

    Ji, L.; Wylie, B. K.; Nossov, D.; Peterson, B.; Waldrop, M. P.; McFarland, J.; Alexander, H. D.; Mack, M. C.; Rover, J. A.; Chen, X.

    2011-12-01

    Quantification of aboveground biomass (AGB) in Alaska's boreal forests is essential to accurately evaluate terrestrial carbon stocks and dynamics in northern high-latitude ecosystems. However, regional AGB datasets with spatially detailed information (<500 m) are not available for this extensive and remote area. Our goal was to map AGB at 30-m resolution for the boreal forests in the Yukon River Basin of Alaska using recent Landsat data and ground measurements. We collected field data in the Yukon River Basin from 2008 to 2010. Ground measurements included diameter at breast height (DBH) or basal diameter (BD) for live and dead trees and shrubs (>1 m tall), which were converted to plot-level AGB using allometric equations. We acquired Landsat Enhanced Thematic Mapper Plus (ETM+) images from the Web Enabled Landsat Data (WELD) that provides multi-date composites of top-of-atmosphere reflectance and brightness temperature for Alaska. From the WELD images, we generated a three-year (2008 - 2010) image composite for the Yukon River Basin using a series of compositing criteria including non-saturation, non-cloudiness, maximal normalize difference vegetation index (NDVI), and maximal brightness temperature. Airborne lidar datasets were acquired for two sub-regions in the central basin in 2009, which were converted to vegetation height datasets using the bare-earth digital surface model (DSM) and the first-return DSM. We created a multiple regression model in which the response variable was the field-observed AGB and the predictor variables were Landsat-derived reflectance, brightness temperature, and spectral vegetation indices including NDVI, soil adjusted vegetation index (SAVI), enhanced vegetation index (EVI), normalized difference infrared index (NDII), and normalized difference water index (NDWI). Principal component analysis was incorporated in the regression model to remedy the multicollinearity problems caused by high correlations between predictor variables

  16. [Estimating individual tree aboveground biomass of the mid-subtropical forest using airborne LiDAR technology].

    PubMed

    Liu, Feng; Tan, Chang; Lei, Pi-Feng

    2014-11-01

    Taking Wugang forest farm in Xuefeng Mountain as the research object, using the airborne light detection and ranging (LiDAR) data under leaf-on condition and field data of concomitant plots, this paper assessed the ability of using LiDAR technology to estimate aboveground biomass of the mid-subtropical forest. A semi-automated individual tree LiDAR cloud point segmentation was obtained by using condition random fields and optimization methods. Spatial structure, waveform characteristics and topography were calculated as LiDAR metrics from the segmented objects. Then statistical models between aboveground biomass from field data and these LiDAR metrics were built. The individual tree recognition rates were 93%, 86% and 60% for coniferous, broadleaf and mixed forests, respectively. The adjusted coefficients of determination (R(2)adj) and the root mean squared errors (RMSE) for the three types of forest were 0.83, 0.81 and 0.74, and 28.22, 29.79 and 32.31 t · hm(-2), respectively. The estimation capability of model based on canopy geometric volume, tree percentile height, slope and waveform characteristics was much better than that of traditional regression model based on tree height. Therefore, LiDAR metrics from individual tree could facilitate better performance in biomass estimation. PMID:25898621

  17. Rapid assessment of above-ground biomass of Giant Reed using visibility estimates

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A method for the rapid estimation of biomass and density of giant reed (Arundo donax L.) was developed using estimates of visibility as a predictive tool. Visibility estimates were derived by capturing digital images of a 0.25 m2 polystyrene whiteboard placed a set distance (1m) from the edge of gia...

  18. Using multi-frequency radar and discrete-return LiDAR measurements to estimate above-ground biomass and biomass components in a coastal temperate forest

    NASA Astrophysics Data System (ADS)

    Tsui, Olivier W.; Coops, Nicholas C.; Wulder, Michael A.; Marshall, Peter L.; McCardle, Adrian

    2012-04-01

    Height measurements from small-footprint discrete-return LiDAR and backscatter coefficients from C- and L-band radar were used independently and in combination to estimate above-ground component and total biomass for a coniferous temperate forest, located on Vancouver Island, British Columbia, Canada. Reference biomass data were obtained from plot-level data and used for comparison against the LiDAR and radar-based biomass models. For the LiDAR-only model, height metrics such as mean first return height and percentiles (e.g., 10th and 90th) of first returns correlated best to total above-ground and stem biomass. While percent of first returns above 2 m and percentiles (75th and 90th) of first returns height metrics correlated best to crown biomass. A comparison between above-ground components and total biomass indicate that stem biomass displayed the highest relationship with the LiDAR measurements while crown biomass showed the lowest relationship with relative root mean squared error ranging from 16% to 22%, respectively. Alternatively, the radar-only models indicated that for C-band radar, a combination of HH and VV backscatter demonstrated the most significant correlation with forest biomass compared to coherence based models with a relative root mean squared error of 53%. For L-band radar, a combination of HH and HV backscatter showed the most significant correlation compared to coherence based models with a relative root mean squared error of 44%. Exploring a mixture of C- and L-band backscatter and coherence based models revealed that a combination of C-HV and L-HV coherence magnitudes provided the best radar relationship with forest biomass with a relative root mean squared error of 35%. Also for all radar-based models, L- and C-band backscatter and coherence magnitudes were poorly correlated with individual biomass components when compared to total above-ground biomass. The addition of C- and L-band backscatter and coherence variables to the Li

  19. Estimating above-ground biomass on mountain meadows and pastures through remote sensing

    NASA Astrophysics Data System (ADS)

    Barrachina, M.; Cristóbal, J.; Tulla, A. F.

    2015-06-01

    Extensive stock-breeding systems developed in mountain areas like the Pyrenees are crucial for local farming economies and depend largely on above-ground biomass (AGB) in the form of grass produced on meadows and pastureland. In this study, a multiple linear regression analysis technique based on in-situ biomass collection and vegetation and wetness indices derived from Landsat-5 TM data is successfully applied in a mountainous Pyrenees area to model AGB. Temporal thoroughness of the data is ensured by using a large series of images. Results of on-site AGB collection show the importance for AGB models to capture the high interannual and intraseasonal variability that results from both meteorological conditions and farming practices. AGB models yield best results at midsummer and end of summer before mowing operations by farmers, with a mean R2, RMSE and PE for 2008 and 2009 midsummer of 0.76, 95 g m-2 and 27%, respectively; and with a mean R2, RMSE and PE for 2008 and 2009 end of summer of 0.74, 128 g m-2 and 36%, respectively. Although vegetation indices are a priori more related with biomass production, wetness indices play an important role in modeling AGB, being statistically selected more frequently (more than 50%) than other traditional vegetation indexes (around 27%) such as NDVI. This suggests that middle infrared bands are crucial descriptors of AGB. The methodology applied in this work compares favorably with other works in the literature, yielding better results than those works in mountain areas, owing to the ability of the proposed methodology to capture natural and anthropogenic variations in AGB which are the key to increasing AGB modeling accuracy.

  20. Above-ground biomass estimation of tuberous bulrush ( Bolboschoenus planiculmis) in mudflats using remotely sensed multispectral image

    NASA Astrophysics Data System (ADS)

    Kim, Ji Yoon; Im, Ran-Young; Do, Yuno; Kim, Gu-Yeon; Joo, Gea-Jae

    2016-03-01

    We present a multivariate regression approach for mapping the spatial distribution of above-ground biomass (AGB) of B. planiculmis using field data and coincident moderate spatial resolution satellite imagery. A total of 232 ground sample plots were used to estimate the biomass distribution in the Nakdong River estuary. Field data were overlain and correlated with digital values from an atmospherically corrected multispectral image (Landsat 8). The AGB distribution was derived using empirical models trained with field-measured AGB data. The final regression model for AGB estimation was composed using the OLI3, OLI4, and OLI7 spectral bands. The Pearson correlation between the observed and predicted biomass was significant (R = 0.84, p < 0.0001). OLI3 made the largest contribution to the final model (relative coefficient value: 53.4%) and revealed a negative relationship with the AGB biomass. The total distribution area of B. planiculmis was 1,922,979 m2. Based on the model estimation, the total AGB had a dry weight (DW) of approximately 298.2 tons. The distribution of high biomass stands (> 200 kg DW/900 m2) constituted approximately 23.91% of the total vegetated area. Our findings suggest the expandability of remotely sensed products to understand the distribution pattern of estuarine plant productivity at the landscape level.

  1. Spatially explicit estimation of aboveground boreal forest biomass in the Yukon River Basin, Alaska

    USGS Publications Warehouse

    Ji, Lei; Wylie, Bruce K.; Brown, Dana R. N.; Peterson, Birgit; Alexander, Heather D.; Mack, Michelle C.; Rover, Jennifer R.; Waldrop, Mark P.; McFarland, Jack W.; Chen, Xuexia; Pastick, Neal J.

    2015-01-01

    Quantification of aboveground biomass (AGB) in Alaska’s boreal forest is essential to the accurate evaluation of terrestrial carbon stocks and dynamics in northern high-latitude ecosystems. Our goal was to map AGB at 30 m resolution for the boreal forest in the Yukon River Basin of Alaska using Landsat data and ground measurements. We acquired Landsat images to generate a 3-year (2008–2010) composite of top-of-atmosphere reflectance for six bands as well as the brightness temperature (BT). We constructed a multiple regression model using field-observed AGB and Landsat-derived reflectance, BT, and vegetation indices. A basin-wide boreal forest AGB map at 30 m resolution was generated by applying the regression model to the Landsat composite. The fivefold cross-validation with field measurements had a mean absolute error (MAE) of 25.7 Mg ha−1 (relative MAE 47.5%) and a mean bias error (MBE) of 4.3 Mg ha−1(relative MBE 7.9%). The boreal forest AGB product was compared with lidar-based vegetation height data; the comparison indicated that there was a significant correlation between the two data sets.

  2. Aboveground Biomass Estimation in a Tidal Brackish Marsh Using Simulated Thematic Mapper Spectral Data

    NASA Technical Reports Server (NTRS)

    Hardisky, M.; Klemas, V.

    1984-01-01

    Spectral radiance data were collected from the ground and from a low altitude aircraft in an attempt to gain some insight into the potential utility of actual Thematic Mapper data for biomass estimation in wetland plant communities. No attempt was made to distinguish individual plant species within brackish marsh plant associations. Rather, it was decided to lump plant species with similar canopy morphologies and then estimate from spectral radiance data the biomass of the group. The rationale for such an approach is that plants with a similar morphology will produce a similar reflecting or absorping surface (i.e., canopy) for incoming electromagnetic radiation. Variations in observed reflectance from different plant communities with a similar canopy morphology are more likely to be a result of biomass differences than a result of differences in canopy architecture. If the hypothesis that plants with a similar morphology exhibit similar reflectance characteristics is true, then biomass can be estimated based on a model for the dominant plant morphology within a plant association and the need for species discrimination has effectively been eliminated.

  3. Lidar Estimation of Aboveground Biomass in a Tropical Coastal Forest of Gabon

    NASA Astrophysics Data System (ADS)

    Meyer, V.; Saatchi, S. S.; Poulsen, J.; Clark, C.; Lewis, S.; White, L.

    2012-12-01

    Estimation of tropical forest carbon stocks is a critical yet challenging problem from both ground surveys and remote sensing measurements. However, with its increasing importance in global climate mitigation and carbon cycle assessment, there is a need to develop new techniques to measure forest carbon stocks at landscape scales. Progresses have been made in terms of above ground biomass (AGB) monitoring techniques using ground measurements, with the development of tree allometry techniques. Besides, studies have shown that new remote sensing technologies such as Lidar can give accurate information on tree height and forest structure at a landscape level and can be very useful to estimate AGB. This study examines the ability of small footprint Lidar to estimate above ground biomass in Mondah forest, Gabon. Mondah forest is a coastal tropical forest that is partially flooded and includes areas of mangrove. Its mean annual temperature is 18.8C and mean annual precipitation is 2631mm/yr. Its proximity to the capital of Gabon, Libreville, makes it particularly subject to environmental pressure. The analysis is based on small footprint Lidar waveform information and relative height (RH) metrics that correspond to the percentiles of energy of the signal (25%, 50%, 75% and 100%). AGB estimation is calibrated with ground measurements. Ground-estimated AGB is calculated using allometric equations based on tree diameter, wood density and tree height. Lidar-derived AGB is calculated using a linear regression model between the four Lidar RH metrics and ground-estimated AGB and using available models developed in other tropical regions that use one height metric, average wood density, and tree stocking number. We present uncertainty of different approaches and discuss the universality of lidar biomass estimation models in tropical forests.

  4. Exploring the possibility of estimating the aboveground biomass of Vallisneria spiralis L. using Landsat TM image in Dahuchi, Jiangxi Province, China

    NASA Astrophysics Data System (ADS)

    Wu, Guofeng; de Leeuw, Jan; Skidmore, Andrew K.; Prins, Herbert H. T.; Liu, Yaolin

    2005-10-01

    The provision of food to breeding and migrating waterfowl is one of the major functions of submerged aquatic vegetation in shallow lakes. Vallisneria spiralis L. is a submerged aquatic plant species widely distributed within Jiangxi Poyang Lake National Nature Reserve, China. More than 95% of the world population of the endangered Siberian crane as well as significant numbers of Bewick's swan and swan goose over winter in this area, while foraging on the tubers of Vallisneria. The objective of this paper was to explore the possibility of estimating the aboveground biomass of Vallisneria in Dahuchi Lake using Landsat TM image. The relations between aboveground biomass and the bands of a Landsat TM image and their derived variables were investigated using uni- and multivariate linear and non-linear regression models. The results revealed significant but very weak relations between aboveground biomass and the remotely sensed variables. Hence Landsat TM imagery offered little potential to predict aboveground biomass of Vallisneria in this particular region. Possible reasons which could have caused these results were discussed, including: 1) the possible influence of suspended matter in the water; 2) the less accurate field sampling; 3) the limitations of spatial and spectral resolutions of Landsat TM image; 4) the methods used are not appropriate; 5) the homogeneously spatial distribution of aboveground biomass. We propose considering two alternative methods to improve the estimation of aboveground biomass of Vallisneria. First of all, results might be improved while combining alternative data sources (hyperspectral or high spatial resolution images) with innovative methods and more accurate sampling data; Secondly we propose assessing aboveground biomass while using productivity simulation models of submerged aquatic vegetation integrated with geographic information system (GIS) and remote sensing.

  5. Estimating Forest Aboveground Biomass by Combining Optical and SAR Data: A Case Study in Genhe, Inner Mongolia, China

    PubMed Central

    Shao, Zhenfeng; Zhang, Linjing

    2016-01-01

    Estimation of forest aboveground biomass is critical for regional carbon policies and sustainable forest management. Passive optical remote sensing and active microwave remote sensing both play an important role in the monitoring of forest biomass. However, optical spectral reflectance is saturated in relatively dense vegetation areas, and microwave backscattering is significantly influenced by the underlying soil when the vegetation coverage is low. Both of these conditions decrease the estimation accuracy of forest biomass. A new optical and microwave integrated vegetation index (VI) was proposed based on observations from both field experiments and satellite (Landsat 8 Operational Land Imager (OLI) and RADARSAT-2) data. According to the difference in interaction between the multispectral reflectance and microwave backscattering signatures with biomass, the combined VI (COVI) was designed using the weighted optical optimized soil-adjusted vegetation index (OSAVI) and microwave horizontally transmitted and vertically received signal (HV) to overcome the disadvantages of both data types. The performance of the COVI was evaluated by comparison with those of the sole optical data, Synthetic Aperture Radar (SAR) data, and the simple combination of independent optical and SAR variables. The most accurate performance was obtained by the models based on the COVI and optical and microwave optimal variables excluding OSAVI and HV, in combination with a random forest algorithm and the largest number of reference samples. The results also revealed that the predictive accuracy depended highly on the statistical method and the number of sample units. The validation indicated that this integrated method of determining the new VI is a good synergistic way to combine both optical and microwave information for the accurate estimation of forest biomass. PMID:27338378

  6. Estimating Forest Aboveground Biomass by Combining Optical and SAR Data: A Case Study in Genhe, Inner Mongolia, China.

    PubMed

    Shao, Zhenfeng; Zhang, Linjing

    2016-01-01

    Estimation of forest aboveground biomass is critical for regional carbon policies and sustainable forest management. Passive optical remote sensing and active microwave remote sensing both play an important role in the monitoring of forest biomass. However, optical spectral reflectance is saturated in relatively dense vegetation areas, and microwave backscattering is significantly influenced by the underlying soil when the vegetation coverage is low. Both of these conditions decrease the estimation accuracy of forest biomass. A new optical and microwave integrated vegetation index (VI) was proposed based on observations from both field experiments and satellite (Landsat 8 Operational Land Imager (OLI) and RADARSAT-2) data. According to the difference in interaction between the multispectral reflectance and microwave backscattering signatures with biomass, the combined VI (COVI) was designed using the weighted optical optimized soil-adjusted vegetation index (OSAVI) and microwave horizontally transmitted and vertically received signal (HV) to overcome the disadvantages of both data types. The performance of the COVI was evaluated by comparison with those of the sole optical data, Synthetic Aperture Radar (SAR) data, and the simple combination of independent optical and SAR variables. The most accurate performance was obtained by the models based on the COVI and optical and microwave optimal variables excluding OSAVI and HV, in combination with a random forest algorithm and the largest number of reference samples. The results also revealed that the predictive accuracy depended highly on the statistical method and the number of sample units. The validation indicated that this integrated method of determining the new VI is a good synergistic way to combine both optical and microwave information for the accurate estimation of forest biomass. PMID:27338378

  7. Impacts of Sample Design on Estimation of Aboveground Biomass: Implications for the Assimilation of Lidar and Forest Inventory Data

    NASA Astrophysics Data System (ADS)

    Duffy, P.; Keller, M. M.; Morton, D. C.; Schimel, D.

    2015-12-01

    The availability of lidar data that can be used to characterize forest structure and estimate aboveground biomass (AGB) is rapidly increasing. When lidar data are considered in conjunction with forest inventory data to estimate AGB, the order of acquisition for these data products may impact the quality of the resulting estimates. In this work, we address this question in the context of uncertainty reduction with respect to estimation of AGB in a degraded forest in Paragominas, Brazil. We have developed a simulation framework that quantitatively assesses the uncertainty associated with estimation of AGB for different sampling strategies that combine forest inventory and lidar data. We utilize a Bayesian hierarchical modeling (BHM) data assimilation framework to combine information from the forest inventory and lidar data products into a higher order data product of AGB. Spatially explicit realizations of AGB are generated under different sampling strategies. Sampling strategies are assessed using the distributional properties of the assimilated higher order data product in the context of uncertainty reduction. We consider both spatially explicit maps of uncertainty as well as the standard deviation of the posterior predictive distributions of AGB as endpoints for the quantification of uncertainty. This framework allows for the explicit characterization of important sources of uncertainty. Our results show that a significant reduction in the uncertainty associated with estimation of AGB can be realized when design optimization is utilized in this context.

  8. Improving estimation of tree carbon stocks by harvesting aboveground woody biomass within airborne LiDAR flight areas

    NASA Astrophysics Data System (ADS)

    Colgan, M.; Asner, G. P.; Swemmer, A. M.

    2011-12-01

    The accurate estimation of carbon stored in a tree is essential to accounting for the carbon emissions due to deforestation and degradation. Airborne LiDAR (Light Detection and Ranging) has been successful in estimating aboveground carbon density (ACD) by correlating airborne metrics, such as canopy height, to field-estimated biomass. This latter step is reliant on field allometry which is applied to forest inventory quantities, such as stem diameter and height, to predict the biomass of a given tree stem. Constructing such allometry is expensive, time consuming, and requires destructive sampling. Consequently, the sample sizes used to construct such allometry are often small, and the largest tree sampled is often much smaller than the largest in the forest population. The uncertainty resulting from these sampling errors can lead to severe biases when the allometry is applied to stems larger than those harvested to construct the allometry, which is then subsequently propagated to airborne ACD estimates. The Kruger National Park (KNP) mission of maintaining biodiversity coincides with preserving ecosystem carbon stocks. However, one hurdle to accurately quantifying carbon density in savannas is that small stems are typically harvested to construct woody biomass allometry, yet they are not representative of Kruger's distribution of biomass. Consequently, these equations inadequately capture large tree variation in sapwood/hardwood composition, root/shoot/leaf allocation, branch fall, and stem rot. This study eliminates the "middleman" of field allometry by directly measuring, or harvesting, tree biomass within the extent of airborne LiDAR. This enables comparisons of field and airborne ACD estimates, and also enables creation of new airborne algorithms to estimate biomass at the scale of individual trees. A field campaign was conducted at Pompey Silica Mine 5km outside Kruger National Park, South Africa, in Mar-Aug 2010 to harvest and weigh tree mass. Since

  9. A Comparison of Two Above-Ground Biomass Estimation Techniques Integrating Satellite-Based Remotely Sensed Data and Ground Data for Tropical and Semiarid Forests in Puerto Rico

    EPA Science Inventory

    Two above-ground forest biomass estimation techniques were evaluated for the United States Territory of Puerto Rico using predictor variables acquired from satellite based remotely sensed data and ground data from the U.S. Department of Agriculture Forest Inventory Analysis (FIA)...

  10. Reducing Uncertainties in Satellite-derived Forest Aboveground Biomass Estimates using a High Resolution Forest Cover Map

    NASA Astrophysics Data System (ADS)

    Zhang, G.; Ganguly, S.; Nemani, R. R.; Milesi, C.; Basu, S.; Kumar, U.

    2014-12-01

    Several studies to date have provided an extensive knowledge base for estimating forest aboveground biomass (AGB) and recent advances in space-based modeling of the 3-D canopy structure, combined with canopy reflectance measured by passive optical sensors and radar backscatter, are providing improved satellite-derived AGB density mapping for large scale carbon monitoring applications. A key limitation in forest AGB estimation from remote sensing, however, is the large uncertainty in forest cover estimates from the coarse-to-medium resolution satellite-derived land cover maps (present resolution is limited to 30-m of the USGS NLCD Program). The uncertainties in forest cover estimates at the Landsat scale result in high uncertainties for AGB estimation, predominantly in heterogeneous forest and urban landscapes. We have successfully developed an approach using a machine learning algorithm and High-Performance-Computing with NAIP air-borne imagery data for mapping tree cover at 1-m over California and Maryland. In a comparison with high resolution LiDAR data available over selected regions in the two states, we found our results to be promising both in terms of accuracy as well as our ability to scale nationally. The generated 1-m forest cover map will be aggregated to the Landsat spatial grid to demonstrate differences in AGB estimates (pixel-level AGB density, total AGB at aggregated scales like ecoregions and counties) when using a native 30-m forest cover map versus a 30-m map derived from a higher resolution dataset. The process will also be complemented with a LiDAR derived AGB estimate at the 30-m scale to aid in true validation.

  11. Using LiDAR to Estimate Total Aboveground Biomass of Redwood Stands in the Jackson Demonstration State Forest, Mendocino, California

    NASA Astrophysics Data System (ADS)

    Rao, M.; Vuong, H.

    2013-12-01

    The overall objective of this study is to develop a method for estimating total aboveground biomass of redwood stands in Jackson Demonstration State Forest, Mendocino, California using airborne LiDAR data. LiDAR data owing to its vertical and horizontal accuracy are increasingly being used to characterize landscape features including ground surface elevation and canopy height. These LiDAR-derived metrics involving structural signatures at higher precision and accuracy can help better understand ecological processes at various spatial scales. Our study is focused on two major species of the forest: redwood (Sequoia semperirens [D.Don] Engl.) and Douglas-fir (Pseudotsuga mensiezii [Mirb.] Franco). Specifically, the objectives included linear regression models fitting tree diameter at breast height (dbh) to LiDAR derived height for each species. From 23 random points on the study area, field measurement (dbh and tree coordinate) were collected for more than 500 trees of Redwood and Douglas-fir over 0.2 ha- plots. The USFS-FUSION application software along with its LiDAR Data Viewer (LDV) were used to to extract Canopy Height Model (CHM) from which tree heights would be derived. Based on the LiDAR derived height and ground based dbh, a linear regression model was developed to predict dbh. The predicted dbh was used to estimate the biomass at the single tree level using Jenkin's formula (Jenkin et al 2003). The linear regression models were able to explain 65% of the variability associated with Redwood's dbh and 80% of that associated with Douglas-fir's dbh.

  12. Sensitivity of Above-Ground Biomass Estimates to Height-Diameter Modelling in Mixed-Species West African Woodlands

    PubMed Central

    Aynekulu, Ermias; Pitkänen, Sari; Packalen, Petteri

    2016-01-01

    It has been suggested that above-ground biomass (AGB) inventories should include tree height (H), in addition to diameter (D). As H is a difficult variable to measure, H-D models are commonly used to predict H. We tested a number of approaches for H-D modelling, including additive terms which increased the complexity of the model, and observed how differences in tree-level predictions of H propagated to plot-level AGB estimations. We were especially interested in detecting whether the choice of method can lead to bias. The compared approaches listed in the order of increasing complexity were: (B0) AGB estimations from D-only; (B1) involving also H obtained from a fixed-effects H-D model; (B2) involving also species; (B3) including also between-plot variability as random effects; and (B4) involving multilevel nested random effects for grouping plots in clusters. In light of the results, the modelling approach affected the AGB estimation significantly in some cases, although differences were negligible for some of the alternatives. The most important differences were found between including H or not in the AGB estimation. We observed that AGB predictions without H information were very sensitive to the environmental stress parameter (E), which can induce a critical bias. Regarding the H-D modelling, the most relevant effect was found when species was included as an additive term. We presented a two-step methodology, which succeeded in identifying the species for which the general H-D relation was relevant to modify. Based on the results, our final choice was the single-level mixed-effects model (B3), which accounts for the species but also for the plot random effects reflecting site-specific factors such as soil properties and degree of disturbance. PMID:27367857

  13. Sensitivity of Above-Ground Biomass Estimates to Height-Diameter Modelling in Mixed-Species West African Woodlands.

    PubMed

    Valbuena, Rubén; Heiskanen, Janne; Aynekulu, Ermias; Pitkänen, Sari; Packalen, Petteri

    2016-01-01

    It has been suggested that above-ground biomass (AGB) inventories should include tree height (H), in addition to diameter (D). As H is a difficult variable to measure, H-D models are commonly used to predict H. We tested a number of approaches for H-D modelling, including additive terms which increased the complexity of the model, and observed how differences in tree-level predictions of H propagated to plot-level AGB estimations. We were especially interested in detecting whether the choice of method can lead to bias. The compared approaches listed in the order of increasing complexity were: (B0) AGB estimations from D-only; (B1) involving also H obtained from a fixed-effects H-D model; (B2) involving also species; (B3) including also between-plot variability as random effects; and (B4) involving multilevel nested random effects for grouping plots in clusters. In light of the results, the modelling approach affected the AGB estimation significantly in some cases, although differences were negligible for some of the alternatives. The most important differences were found between including H or not in the AGB estimation. We observed that AGB predictions without H information were very sensitive to the environmental stress parameter (E), which can induce a critical bias. Regarding the H-D modelling, the most relevant effect was found when species was included as an additive term. We presented a two-step methodology, which succeeded in identifying the species for which the general H-D relation was relevant to modify. Based on the results, our final choice was the single-level mixed-effects model (B3), which accounts for the species but also for the plot random effects reflecting site-specific factors such as soil properties and degree of disturbance. PMID:27367857

  14. Adjusting lidar-derived digital terrain models in coastal marshes based on estimated aboveground biomass density

    SciTech Connect

    Medeiros, Stephen; Hagen, Scott; Weishampel, John; Angelo, James

    2015-03-25

    Digital elevation models (DEMs) derived from airborne lidar are traditionally unreliable in coastal salt marshes due to the inability of the laser to penetrate the dense grasses and reach the underlying soil. To that end, we present a novel processing methodology that uses ASTER Band 2 (visible red), an interferometric SAR (IfSAR) digital surface model, and lidar-derived canopy height to classify biomass density using both a three-class scheme (high, medium and low) and a two-class scheme (high and low). Elevation adjustments associated with these classes using both median and quartile approaches were applied to adjust lidar-derived elevation values closer to true bare earth elevation. The performance of the method was tested on 229 elevation points in the lower Apalachicola River Marsh. The two-class quartile-based adjusted DEM produced the best results, reducing the RMS error in elevation from 0.65 m to 0.40 m, a 38% improvement. The raw mean errors for the lidar DEM and the adjusted DEM were 0.61 ± 0.24 m and 0.32 ± 0.24 m, respectively, thereby reducing the high bias by approximately 49%.

  15. Adjusting lidar-derived digital terrain models in coastal marshes based on estimated aboveground biomass density

    DOE PAGESBeta

    Medeiros, Stephen; Hagen, Scott; Weishampel, John; Angelo, James

    2015-03-25

    Digital elevation models (DEMs) derived from airborne lidar are traditionally unreliable in coastal salt marshes due to the inability of the laser to penetrate the dense grasses and reach the underlying soil. To that end, we present a novel processing methodology that uses ASTER Band 2 (visible red), an interferometric SAR (IfSAR) digital surface model, and lidar-derived canopy height to classify biomass density using both a three-class scheme (high, medium and low) and a two-class scheme (high and low). Elevation adjustments associated with these classes using both median and quartile approaches were applied to adjust lidar-derived elevation values closer tomore » true bare earth elevation. The performance of the method was tested on 229 elevation points in the lower Apalachicola River Marsh. The two-class quartile-based adjusted DEM produced the best results, reducing the RMS error in elevation from 0.65 m to 0.40 m, a 38% improvement. The raw mean errors for the lidar DEM and the adjusted DEM were 0.61 ± 0.24 m and 0.32 ± 0.24 m, respectively, thereby reducing the high bias by approximately 49%.« less

  16. Spatial distribution of forest aboveground biomass in China: estimation through combination of spaceborne lidar, optical imagery, and forest inventory data

    NASA Astrophysics Data System (ADS)

    Xue, B. L.; Su, Y.; Guo, Q.; Hu, T.; Alvarez, O.; Tao, S.; Fang, J.

    2015-12-01

    The global forest ecosystem, which acts as a large carbon sink, plays an important role in modeling the global carbon balance. An accurate estimation of the total forest carbon stock in the aboveground biomass (AGB) is therefore necessary to improve our understanding of carbon dynamics, especially against the background of global climate change. The forest area of China is among the top five globally. However, because of limitations in forest AGB mapping methods and the availability of ground inventory data, there is still a lack in nationwide wall-to-wall forest AGB estimation map for China. In this study, we collected over 8000 ground inventory data from the literature, and developed an AGB mapping method using a combination of these ground inventory data, Geoscience Laser Altimeter System (GLAS)/Ice, Cloud, and Land Elevation Satellite (ICESat) data, optical imagery, climate surfaces, and topographic data. An uncertainty field model was introduced into the forest AGB mapping model to minimize the influence of plot locality uncertainty. Our nationwide wall-to-wall forest AGB mapping results show that the forest AGB density in China is 120 Mg/ha on average, with a standard deviation of 61 Mg/ha. Evaluation with an independent ground inventory dataset showed that our proposed method can accurately map wall-to-wall forest AGB across a large landscape. The coefficient of determination (R2) and root-mean-square error between our predicted results and the validation dataset were 0.75 and 42.39 Mg/ha, respectively. This new method and the resulting nationwide wall-to-wall AGB map will help to improve the accuracy of carbon dynamic predictions in China.

  17. Estimation of Regional Forest Aboveground Biomass Combining Icesat-Glas Waveforms and HJ-1A/HSI Hyperspectral Imageries

    NASA Astrophysics Data System (ADS)

    Xing, Yanqiu; Qiu, Sai; Ding, Jianhua; Tian, Jing

    2016-06-01

    Estimation of forest aboveground biomass (AGB) is a critical challenge for understanding the global carbon cycle because it dominates the dynamics of the terrestrial carbon cycle. Light Detection and Ranging (LiDAR) system has a unique capability for estimating accurately forest canopy height, which has a direct relationship and can provide better understanding to the forest AGB. The Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud, and land Elevation Satellite (ICESat) is the first polarorbiting LiDAR instrument for global observations of Earth, and it has been widely used for extracting forest AGB with footprints of nominally 70 m in diameter on the earth's surface. However, the GLAS footprints are discrete geographically, and thus it has been restricted to produce the regional full coverage of forest AGB. To overcome the limit of discontinuity, the Hyper Spectral Imager (HSI) of HJ-1A with 115 bands was combined with GLAS waveforms to predict the regional forest AGB in the study. Corresponding with the field investigation in Wangqing of Changbai Mountain, China, the GLAS waveform metrics were derived and employed to establish the AGB model, which was used further for estimating the AGB within GLAS footprints. For HSI imagery, the Minimum Noise Fraction (MNF) method was used to decrease noise and reduce the dimensionality of spectral bands, and consequently the first three of MNF were able to offer almost 98% spectral information and qualified to regress with the GLAS estimated AGB. Afterwards, the support vector regression (SVR) method was employed in the study to establish the relationship between GLAS estimated AGB and three of HSI MNF (i.e. MNF1, MNF2 and MNF3), and accordingly the full covered regional forest AGB map was produced. The results showed that the adj.R2 and RMSE of SVR-AGB models were 0.75 and 4.68 t hm-2 for broadleaf forests, 0.73 and 5.39 t hm-2 for coniferous forests and 0.71 and 6.15 t hm-2 for mixed forests respectively. The

  18. A comparison of two above-ground biomass estimation techniques integrating satellite-based remotely sensed data and ground data for tropical and semiarid forests in Puerto Rico

    NASA Astrophysics Data System (ADS)

    Iiames, J. S.; Riegel, J.; Lunetta, R.

    2013-12-01

    Two above-ground forest biomass estimation techniques were evaluated for the United States Territory of Puerto Rico using predictor variables acquired from satellite based remotely sensed data and ground data from the U.S. Department of Agriculture Forest Inventory Analysis (FIA) program. The U.S. Environmental Protection Agency (EPA) estimated above-ground forest biomass implementing methodology first posited by the Woods Hole Research Center developed for conterminous United States (National Biomass and Carbon Dataset [NBCD2000]). For EPA's effort, spatial predictor layers for above-ground biomass estimation included derived products from the U.S. Geologic Survey (USGS) National Land Cover Dataset 2001 (NLCD) (landcover and canopy density), the USGS Gap Analysis Program (forest type classification), the USGS National Elevation Dataset, and the NASA Shuttle Radar Topography Mission (tree heights). In contrast, the U.S. Forest Service (USFS) biomass product integrated FIA ground-based data with a suite of geospatial predictor variables including: (1) the Moderate Resolution Imaging Spectrometer (MODIS)-derived image composites and percent tree cover; (2) NLCD land cover proportions; (3) topographic variables; (4) monthly and annual climate parameters; and (5) other ancillary variables. Correlations between both data sets were made at variable watershed scales to test level of agreement. Notice: This work is done in support of EPA's Sustainable Healthy Communities Research Program. The U.S EPA 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.

  19. A cross-scale remote sensing approach to estimate tree cover and aboveground biomass in pinyon-juniper woodlands of the Colorado Plateau, USA

    NASA Astrophysics Data System (ADS)

    Huang, C.; Asner, G.; Martin, R.; Barger, N.; Neff, J.

    2007-12-01

    Vegetation dominated by pinyon pines and junipers (pinyon-juniper [P-J] woodlands) is one of the largest vegetation types in the North America. P-J woodlands maintain the highest level of woody biomass compared to other major dryland ecosystems. However, distributions of tree cover and biomass in the P-J woodlands of the Colorado Plateau have not been well studied. Here we developed a synoptic remote sensing approach to scale up pinyon pine and juniper cover and biomass field observations from plot to regional levels using fractional photosynthetic vegetation cover (PV) derived from airborne imaging spectroscopy and Landsat satellite data. Our results demonstrated strong correlations (p < 0.001) between field and airborne tree canopy cover estimates (r2 = 0.92), and between airborne and satellite canopy cover estimates (r2 = 0.61). Field data also indicated that P-J aboveground biomass can be estimated from canopy cover using a unified allometric equation (r2 = 0.69, p < 0.001). Using these multi-scale, cover-biomass relationships, we developed high-resolution, regional-scale maps of P-J cover and biomass for the western Colorado Plateau. The mean (± standard deviation) P-J cover was 27.4 (± 9.9)%, and the mean aboveground woody carbon (C) converted from biomass was 5.2 (± 2.0)MgC/ha. Combining our data with the southwest Regional Gap Analysis Program vegetation map, we estimated that total contemporary woody C storage for the entire Colorado Plateau P-J woodlands (113,600 km2) is 59 TgC. Our results facilitate further investigation of the processes controlling carbon stocks and fluxes across this large region, which forms a key component of the North American Carbon Program (NACP).

  20. Assessing aboveground tropical forest biomass using Google Earth canopy images.

    PubMed

    Ploton, Pierre; Pélissier, Raphaël; Proisy, Christophe; Flavenot, Théo; Barbier, Nicolas; Rai, S N; Couteron, Pierre

    2012-04-01

    Reducing Emissions from Deforestation and Forest Degradation (REDD) in efforts to combat climate change requires participating countries to periodically assess their forest resources on a national scale. Such a process is particularly challenging in the tropics because of technical difficulties related to large aboveground forest biomass stocks, restricted availability of affordable, appropriate remote-sensing images, and a lack of accurate forest inventory data. In this paper, we apply the Fourier-based FOTO method of canopy texture analysis to Google Earth's very-high-resolution images of the wet evergreen forests in the Western Ghats of India in order to (1) assess the predictive power of the method on aboveground biomass of tropical forests, (2) test the merits of free Google Earth images relative to their native commercial IKONOS counterparts and (3) highlight further research needs for affordable, accurate regional aboveground biomass estimations. We used the FOTO method to ordinate Fourier spectra of 1436 square canopy images (125 x 125 m) with respect to a canopy grain texture gradient (i.e., a combination of size distribution and spatial pattern of tree crowns), benchmarked against virtual canopy scenes simulated from a set of known forest structure parameters and a 3-D light interception model. We then used 15 1-ha ground plots to demonstrate that both texture gradients provided by Google Earth and IKONOS images strongly correlated with field-observed stand structure parameters such as the density of large trees, total basal area, and aboveground biomass estimated from a regional allometric model. Our results highlight the great potential of the FOTO method applied to Google Earth data for biomass retrieval because the texture-biomass relationship is only subject to 15% relative error, on average, and does not show obvious saturation trends at large biomass values. We also provide the first reliable map of tropical forest aboveground biomass predicted

  1. Above-ground biomass and carbon estimates of Shorea robusta and Tectona grandis forests using QuadPOL ALOS PALSAR data

    NASA Astrophysics Data System (ADS)

    Behera, M. D.; Tripathi, P.; Mishra, B.; Kumar, Shashi; Chitale, V. S.; Behera, Soumit K.

    2016-01-01

    Mechanisms to mitigate climate change in tropical countries such as India require information on forest structural components i.e., biomass and carbon for conservation steps to be implemented successfully. The present study focuses on investigating the potential use of a one time, QuadPOL ALOS PALSAR L-band 25 m data to estimate above-ground biomass (AGB) using a water cloud model (WCM) in a wildlife sanctuary in India. A significant correlation was obtained between the SAR-derived backscatter coefficient (σ°) and the field measured AGB, with the maximum coefficient of determination for cross-polarized (HV) σ° for Shorea robusta, and the weakest correlation was observed with co-polarized (HH) σ° for Tectona grandis forests. The biomass of S. robusta and that of T. grandis were estimated on the basis of field-measured data at 444.7 ± 170.4 Mg/ha and 451 ± 179.4 Mg/ha respectively. The mean biomass values estimated using the WCM varied between 562 and 660 Mg/ha for S. robusta; between 590 and 710 Mg/ha for T. grandis using various polarized data. Our results highlighted the efficacy of one time, fully polarized PALSAR data for biomass and carbon estimate in a dense forest.

  2. Characterizing uncertainties of the national-scale forest gross aboveground biomass (AGB) loss estimate: a case study of the Democratic Republic of the Congo

    NASA Astrophysics Data System (ADS)

    Tyukavina, A.; Stehman, S.; Potapov, P.; Turubanova, S.; Baccini, A.; Goetz, S. J.; Laporte, N. T.; Houghton, R. A.; Hansen, M.

    2013-12-01

    Modern remote sensing techniques enable the mapping and monitoring of aboveground biomass (AGB) carbon stocks without relying on extensive in situ measurements. The Democratic Republic of the Congo (DRC) is among the countries where a national forest inventory (NFI) has yet to be established due to a lack of infrastructure and political instability. We demonstrate a method for producing national-scale gross AGB loss estimates and quantifying uncertainty of the estimates using remotely sensed-derived forest cover loss and biomass carbon density data. Forest cover type and loss were characterized using published Landsat-based data sets and related to LIDAR-derived biomass data from the Geoscience Laser Altimeter System (GLAS). We produced two gross AGB loss estimates for the DRC for the last decade (2000-2010): a conservative estimate accounting for classification errors in the 60-m resolution FACET forest cover change product, and a maximal estimate that also took into consideration omitted change at the 30m spatial resolution. Omitted disturbances were largely related to smallholder agriculture, the detection of which is scale-dependent. The use of LIDAR data as a substitute for NFI data to estimate AGB loss based on Landsat-derived activity data was demonstrated. Comparisons of our forest cover loss and AGB estimates with published studies raise the issue of scale in forest cover change mapping and its impact on carbon stock change estimation using remotely sensed data.

  3. Estimation of Aboveground Biomass in Alpine Forests: A Semi-Empirical Approach Considering Canopy Transparency Derived from Airborne LiDAR Data

    PubMed Central

    Jochem, Andreas; Hollaus, Markus; Rutzinger, Martin; Höfle, Bernhard

    2011-01-01

    In this study, a semi-empirical model that was originally developed for stem volume estimation is used for aboveground biomass (AGB) estimation of a spruce dominated alpine forest. The reference AGB of the available sample plots is calculated from forest inventory data by means of biomass expansion factors. Furthermore, the semi-empirical model is extended by three different canopy transparency parameters derived from airborne LiDAR data. These parameters have not been considered for stem volume estimation until now and are introduced in order to investigate the behavior of the model concerning AGB estimation. The developed additional input parameters are based on the assumption that transparency of vegetation can bemeasured by determining the penetration of the laser beams through the canopy. These parameters are calculated for every single point within the 3D point cloud in order to consider the varying properties of the vegetation in an appropriate way. Exploratory Data Analysis (EDA) is performed to evaluate the influence of the additional LiDAR derived canopy transparency parameters for AGB estimation. The study is carried out in a 560 km2 alpine area in Austria, where reference forest inventory data and LiDAR data are available. The investigations show that the introduction of the canopy transparency parameters does not change the results significantly according to R2 (R2 = 0.70 to R2 = 0.71) in comparison to the results derived from, the semi-empirical model, which was originally developed for stem volume estimation. PMID:22346577

  4. Age-related and stand-wise estimates of carbon stocks and sequestration in the aboveground coarse wood biomass of wetland forests in the northern Pantanal, Brazil

    NASA Astrophysics Data System (ADS)

    Schöngart, J.; Arieira, J.; Felfili Fortes, C.; Cezarine de Arruda, E.; Nunes da Cunha, C.

    2011-11-01

    In this study we use allometric models combined with tree ring analysis to estimate carbon stocks and sequestration in the aboveground coarse wood biomass (AGWB) of wetland forests in the Pantanal, located in central South America. In four 1-ha plots in stands characterized by the pioneer tree species Vochysia divergens Pohl (Vochysiaceae) forest inventories (trees ≥10 cm diameter at breast height, D) have been performed and converted to estimates of AGWB by two allometric models using three independent parameters (D, tree height H and wood density ρ). We perform a propagation of measurement errors to estimate uncertainties in the estimates of AGWB. Carbon stocks of AGWB vary from 7.8 ± 1.5 to 97.2 ± 14.4 Mg C ha-1 between the four stands. From models relating tree ages determined by dendrochronological techniques to C-stocks in AGWB we derived estimates for C-sequestration which differs from 0.50 ± 0.03 to 3.34 ± 0.31 Mg C ha-1 yr-1. Maps based on geostatistic techniques indicate the heterogeneous spatial distribution of tree ages and C-stocks of the four studied stands. This distribution is the result of forest dynamics due to the colonizing and retreating of V. divergens and other species associated with pluriannual wet and dry episodes in the Pantanal, respectively. Such information is essential for the management of the cultural landscape of the Pantanal wetlands.

  5. Uncertainty Analysis in Large Area Aboveground Biomass Mapping

    NASA Astrophysics Data System (ADS)

    Baccini, A.; Carvalho, L.; Dubayah, R.; Goetz, S. J.; Friedl, M. A.

    2011-12-01

    Satellite and aircraft-based remote sensing observations are being more frequently used to generate spatially explicit estimates of aboveground carbon stock of forest ecosystems. Because deforestation and forest degradation account for circa 10% of anthropogenic carbon emissions to the atmosphere, policy mechanisms are increasingly recognized as a low-cost mitigation option to reduce carbon emission. They are, however, contingent upon the capacity to accurately measures carbon stored in the forests. Here we examine the sources of uncertainty and error propagation in generating maps of aboveground biomass. We focus on characterizing uncertainties associated with maps at the pixel and spatially aggregated national scales. We pursue three strategies to describe the error and uncertainty properties of aboveground biomass maps, including: (1) model-based assessment using confidence intervals derived from linear regression methods; (2) data-mining algorithms such as regression trees and ensembles of these; (3) empirical assessments using independently collected data sets.. The latter effort explores error propagation using field data acquired within satellite-based lidar (GLAS) acquisitions versus alternative in situ methods that rely upon field measurements that have not been systematically collected for this purpose (e.g. from forest inventory data sets). A key goal of our effort is to provide multi-level characterizations that provide both pixel and biome-level estimates of uncertainties at different scales.

  6. Distribution of Aboveground Live Biomass in the Amazon Basin

    NASA Technical Reports Server (NTRS)

    Saatchi, S. S.; Houghton, R. A.; DosSantos Alvala, R. C.; Soares, J. V.; Yu, Y.

    2007-01-01

    The amount and spatial distribution of forest biomass in the Amazon basin is a major source of uncertainty in estimating the flux of carbon released from land-cover and land-use change. Direct measurements of aboveground live biomass (AGLB) are limited to small areas of forest inventory plots and site-specific allometric equations that cannot be readily generalized for the entire basin. Furthermore, there is no spaceborne remote sensing instrument that can measure tropical forest biomass directly. To determine the spatial distribution of forest biomass of the Amazon basin, we report a method based on remote sensing metrics representing various forest structural parameters and environmental variables, and more than 500 plot measurements of forest biomass distributed over the basin. A decision tree approach was used to develop the spatial distribution of AGLB for seven distinct biomass classes of lowland old-growth forests with more than 80% accuracy. AGLB for other vegetation types, such as the woody and herbaceous savanna and secondary forests, was directly estimated with a regression based on satellite data. Results show that AGLB is highest in Central Amazonia and in regions to the east and north, including the Guyanas. Biomass is generally above 300Mgha(sup 1) here except in areas of intense logging or open floodplains. In Western Amazonia, from the lowlands of Peru, Ecuador, and Colombia to the Andean mountains, biomass ranges from 150 to 300Mgha(sup 1). Most transitional and seasonal forests at the southern and northwestern edges of the basin have biomass ranging from 100 to 200Mgha(sup 1). The AGLB distribution has a significant correlation with the length of the dry season. We estimate that the total carbon in forest biomass of the Amazon basin, including the dead and below ground biomass, is 86 PgC with +/- 20% uncertainty.

  7. Estimating aboveground forest biomass carbon and fire consumption in the U.S. Utah High Plateaus using data from the Forest Inventory and Analysis program, Landsat, and LANDFIRE

    USGS Publications Warehouse

    Chen, X.; Liu, S.; Zhu, Z.; Vogelmann, J.; Li, Z.; Ohlen, D.

    2011-01-01

    The concentrations of CO2 and other greenhouse gases in the atmosphere have been increasing and greatly affecting global climate and socio-economic systems. Actively growing forests are generally considered to be a major carbon sink, but forest wildfires lead to large releases of biomass carbon into the atmosphere. Aboveground forest biomass carbon (AFBC), an important ecological indicator, and fireinduced carbon emissions at regional scales are highly relevant to forest sustainable management and climate change. It is challenging to accurately estimate the spatial distribution of AFBC across large areas because of the spatial heterogeneity of forest cover types and canopy structure. In this study, Forest Inventory and Analysis (FIA) data, Landsat, and Landscape Fire and Resource Management Planning Tools Project (LANDFIRE) data were integrated in a regression tree model for estimating AFBC at a 30-m resolution in the Utah High Plateaus. AFBC were calculated from 225 FIA field plots and used as the dependent variable in the model. Of these plots, 10% were held out for model evaluation with stratified random sampling, and the other 90% were used as training data to develop the regression tree model. Independent variable layers included Landsat imagery and the derived spectral indicators, digital elevation model (DEM) data and derivatives, biophysical gradient data, existing vegetation cover type and vegetation structure. The cross-validation correlation coefficient (r value) was 0.81 for the training model. Independent validation using withheld plot data was similar with r value of 0.82. This validated regression tree model was applied to map AFBC in the Utah High Plateaus and then combined with burn severity information to estimate loss of AFBC in the Longston fire of Zion National Park in 2001. The final dataset represented 24 forest cover types for a 4 million ha forested area. We estimated a total of 353 Tg AFBC with an average of 87 MgC/ha in the Utah High

  8. Modeling aboveground biomass of Tamarix ramosissima in the Arkansas River Basin of Southeastern Colorado, USA

    USGS Publications Warehouse

    Evangelista, P.; Kumar, S.; Stohlgren, T.J.; Crall, A.W.; Newman, G.J.

    2007-01-01

    Predictive models of aboveground biomass of nonnative Tamarix ramosissima of various sizes were developed using destructive sampling techniques on 50 individuals and four 100-m2 plots. Each sample was measured for average height (m) of stems and canopy area (m2) prior to cutting, drying, and weighing. Five competing regression models (P < 0.05) were developed to estimate aboveground biomass of T. ramosissima using average height and/or canopy area measurements and were evaluated using Akaike's Information Criterion corrected for small sample size (AICc). Our best model (AICc = -148.69, ??AICc = 0) successfully predicted T. ramosissima aboveground biomass (R2 = 0.97) and used average height and canopy area as predictors. Our 2nd-best model, using the same predictors, was also successful in predicting aboveground biomass (R2 = 0.97, AICc = -131.71, ??AICc = 16.98). A 3rd model demonstrated high correlation between only aboveground biomass and canopy area (R2 = 0.95), while 2 additional models found high correlations between aboveground biomass and average height measurements only (R2 = 0.90 and 0.70, respectively). These models illustrate how simple field measurements, such as height and canopy area, can be used in allometric relationships to accurately predict aboveground biomass of T. ramosissima. Although a correction factor may be necessary for predictions at larger scales, the models presented will prove useful for many research and management initiatives.

  9. Influence of tree size, taxonomy, and edaphic conditions on heart rot in mixed-dipterocarp Bornean rainforests: implications for aboveground biomass estimates

    NASA Astrophysics Data System (ADS)

    Heineman, K. D.; Russo, S. E.; Baillie, I. C.; Mamit, J. D.; Chai, P. P.-K.; Chai, L.; Hindley, E. W.; Lau, B.-T.; Tan, S.; Ashton, P. S.

    2015-05-01

    Fungal decay of heartwood creates hollows and areas of reduced wood density within the stems of living trees known as heart rot. Although heart rot is acknowledged as a source of error in forest aboveground biomass estimates, there are few datasets available to evaluate the environmental controls over heart rot infection and severity in tropical forests. Using legacy and recent data from drilled, felled, and cored stems in mixed dipterocarp forests in Sarawak, Malaysian Borneo, we quantified the frequency and severity of heart rot, and used generalized linear mixed effect models to characterize the association of heart rot with tree size, wood density, taxonomy, and edaphic conditions. Heart rot was detected in 55% of felled stems > 30 cm DBH, while the detection frequency was lower for stems of the same size evaluated by non-destructive drilling (45%) and coring (23%) methods. Heart rot severity, defined as the percent stem volume lost in infected stems, ranged widely from 0.1-82.8%. Tree taxonomy explained the greatest proportion of variance in heart rot frequency and severity among the fixed and random effects evaluated in our models. Heart rot frequency, but not severity, increased sharply with tree diameter, ranging from 56% infection across all datasets in stems > 50 cm DBH to 11% in trees 10-30 cm DBH. The frequency and severity of heart rot increased significantly in soils with low pH and cation concentrations in topsoil, and heart rot was more common in tree species associated with dystrophic sandy soils than with nutrient-rich clays. When scaled to forest stands, the percent of stem biomass lost to heart rot varied significantly with soil properties, and we estimate that 7% of the forest biomass is in some stage of heart rot decay. This study demonstrates not only that heart rot is a significant source of error in forest carbon estimates, but also that it strongly covaries with soil resources, underscoring the need to account for edaphic variation in

  10. Evaluation of stem rot in 339 Bornean tree species: implications of size, taxonomy, and soil-related variation for aboveground biomass estimates

    NASA Astrophysics Data System (ADS)

    Heineman, K. D.; Russo, S. E.; Baillie, I. C.; Mamit, J. D.; Chai, P. P.-K.; Chai, L.; Hindley, E. W.; Lau, B.-T.; Tan, S.; Ashton, P. S.

    2015-10-01

    Fungal decay of heart wood creates hollows and areas of reduced wood density within the stems of living trees known as stem rot. Although stem rot is acknowledged as a source of error in forest aboveground biomass (AGB) estimates, there are few data sets available to evaluate the controls over stem rot infection and severity in tropical forests. Using legacy and recent data from 3180 drilled, felled, and cored stems in mixed dipterocarp forests in Sarawak, Malaysian Borneo, we quantified the frequency and severity of stem rot in a total of 339 tree species, and related variation in stem rot with tree size, wood density, taxonomy, and species' soil association, as well as edaphic conditions. Predicted stem rot frequency for a 50 cm tree was 53 % of felled, 39 % of drilled, and 28 % of cored stems, demonstrating differences among methods in rot detection ability. The percent stem volume infected by rot, or stem rot severity, ranged widely among trees with stem rot infection (0.1-82.8 %) and averaged 9 % across all trees felled. Tree taxonomy explained the greatest proportion of variance in both stem rot frequency and severity among the predictors evaluated in our models. Stem rot frequency, but not severity, increased sharply with tree diameter, ranging from 13 % in trees 10-30 cm DBH to 54 % in stems ≥ 50 cm DBH across all data sets. The frequency of stem rot increased significantly in soils with low pH and cation concentrations in topsoil, and stem rot was more common in tree species associated with dystrophic sandy soils than with nutrient-rich clays. When scaled to forest stands, the maximum percent of stem biomass lost to stem rot varied significantly with soil properties, and we estimate that stem rot reduces total forest AGB estimates by up to 7 % relative to what would be predicted assuming all stems are composed strictly of intact wood. This study demonstrates not only that stem rot is likely to be a significant source of error in forest AGB estimation

  11. [Spatial distribution of aboveground biomass of shrubs in Tianlaochi catchment of the Qilian Mountains].

    PubMed

    Liang, Bei; Di, Li; Zhao, Chuan-Yan; Peng, Shou-Zhang; Peng, Huan-Hua; Wang, Chao

    2014-02-01

    This study estimated the spatial distribution of the aboveground biomass of shrubs in the Tianlaochi catchment of Qilian Mountains based on the field survey and remote sensing data. A relationship model of the aboveground biomass and its feasibly measured factors (i. e. , canopy perimeter and plant height) was built. The land use was classified by object-oriented technique with the high resolution image (GeoEye-1) of the study area, and the distribution of shrub coverage was extracted. Then the total aboveground biomass of shrubs in the study area was estimated by the relationship model with the distribution of shrub coverage. The results showed that the aboveground biomass of shrubs in the study area was 1.8 x 10(3) t and the aboveground biomass per unit area was 1598.45 kg x m(-2). The distribution of shrubs mainly was at altitudes of 3000-3700 m, and the aboveground biomass of shrubs on the sunny slope (1.15 x 10(3) t) was higher than that on the shady slope (0.65 x 10(3) t). PMID:24830234

  12. [Aboveground architecture and biomass distribution of Quercus variabilis].

    PubMed

    Yu, Bi-yun; Zhang, Wen-hui; Hu, Xiao-jing; Shen, Jia-peng; Zhen, Xue-yuan; Yang, Xiao-zhou

    2015-08-01

    The aboveground architecture, biomass and its allocation, and the relationship between architecture and biomass of Quercus variabilis of different diameter classes in Shangluo, south slope of Qinling Mountains were researched. The results showed that differences existed in the aboveground architecture and biomass allocation of Q. variabilis of different diameter classes. With the increase of diameter class, tree height, DBH, and crown width increased gradually. The average decline rate of each diameter class increased firstly then decreased. Q. variabilis overall bifurcation ratio and stepwise bifurcation ratio increased then declined. The specific leaf areas of Q. variabilis of all different diameter classes at vertical direction were 0.02-0.03, and the larger values of leaf mass ratio, LAI and leaf area ratio at vertical direction in diameter level I , II, III appeared in the middle and upper trunk, while in diameter level IV, V, VI, they appeared in the central trunk, with the increase of diameter class, there appeared two peaks in vertical direction, which located in the lower and upper trunk. The trunk biomass accounted for 71.8%-88.4% of Q. variabilis aboveground biomass, while the branch biomass accounted for 5.8%-19.6%, and the leaf biomass accounted for 4.2%-8.6%. With the increase of diameter class, stem biomass proportion of Q. variabilis decreased firstly then increased, while the branch and leaf biomass proportion showed a trend that increased at first then decreased, and then increased again. The aboveground biomass of Q. variabilis was significantly positively correlated to tree height, DBH, crown width and stepwise bifurcation ratio (R2:1), and positively related to the overall bifurcation ratio and stepwise bifurcation ratio (R3:2), but there was no significant correlation. Trunk biomass and total biomass aboveground were negatively related to the trunk decline rate, while branch biomass and leaf biomass were positively related to trunk decline

  13. Estimation of Aboveground Biomass Change for Tropical Deciduous Forest in Bago Yoma, Myanmar between year 2000 and 2014 using Landsat Images and Ground Measurements

    NASA Astrophysics Data System (ADS)

    Kim, H. S.; Wynn, K. Z.; Ryu, Y.

    2015-12-01

    Even with recently increased awareness of the environmental conservation, the degradation of tropical forests are still one of the major sources of global carbon emission. Especially in Myanmar, the pressure to develop natural forest is growing rapidly after the change from socialism to capitalism in 2010. As the initial step of the forest conservation, the aboveground biomass(AGB) of South Zarmani Reserved Forest in Bago Yoma region were estimated using Landsat 8 OLI after the evaluation with 100 sample plot measurements. Multiple linear regression (MLR) model of band values and their principal component analysis (PCA) model were developed to estimate the AGB using the spectral reflectance from Landsat images and elevation as the input variables. The MLR model had r2 = 0.43, RMSE = 60.2 tons/ha, relative RMSE = 70.1%, Bias = -9.1 tons/ha, Bias (%) = -10.6%, and p < 0.0001, while the PCA model showed r2 = 0.45, RMSE = 55.1 tons/ha, relative RMSE = 64.1%, Bias = -8.3 tons/ha, Bias (%) = -9.7%, and p < 0.0001. The AGB maps of the study area were generated based on both MLR and PCA models. The estimated mean AGB values were 74.74±22.3 tons/ha and 73.04±17.6 tons/ha and the total AGB of the study area are about 5.7 and 5.6 million tons from MLR and PCA, respectively. Then, Landsat 7 ETM+ image acquired on 2000 was also used to compare the changing of AGB between year 2000 and 2014. The estimated mean AGB value generated from the Landsat 7 ETM+ image was 78.9±16.9 tons/ha, which is substantially decreased about 7.5% compared to year 2014. The reduction of AGB increased with closeness to village, however AGB in distant areas showed steady increases. In conclusion, we were able to generate solid regression models from Landsat 8 OLI image after ground truth and two regression models gave us very similar AGB estimation (less than 2%) of the study area. We were also able to estimate the changing of AGB from year 2000 to 2014 of South Zarmani Reserved Forest, Bago Yoma

  14. Above-ground biomass of mangrove species. I. Analysis of models

    NASA Astrophysics Data System (ADS)

    Soares, Mário Luiz Gomes; Schaeffer-Novelli, Yara

    2005-10-01

    This study analyzes the above-ground biomass of Rhizophora mangle and Laguncularia racemosa located in the mangroves of Bertioga (SP) and Guaratiba (RJ), Southeast Brazil. Its purpose is to determine the best regression model to estimate the total above-ground biomass and compartment (leaves, reproductive parts, twigs, branches, trunk and prop roots) biomass, indirectly. To do this, we used structural measurements such as height, diameter at breast-height (DBH), and crown area. A combination of regression types with several compositions of independent variables generated 2.272 models that were later tested. Subsequent analysis of the models indicated that the biomass of reproductive parts, branches, and prop roots yielded great variability, probably because of environmental factors and seasonality (in the case of reproductive parts). It also indicated the superiority of multiple regression to estimate above-ground biomass as it allows researchers to consider several aspects that affect above-ground biomass, specially the influence of environmental factors. This fact has been attested to the models that estimated the biomass of crown compartments.

  15. Estimating Mangrove Canopy Height and Above-Ground Biomass in Everglades National Park with Airbone LiDAR and TanDEM-X Data.

    NASA Astrophysics Data System (ADS)

    Feliciano, E. A.; Wdowinski, S.; Potts, M. D.; Fatoyinbo, T. E.; Lee, S. K.

    2014-12-01

    The coastal mangroves forests of Everglades National Park (ENP) are well protected from development. Nevertheless, climate change, hurricanes and other anthropogenic disturbances have affected these intertidal ecosystems. Understanding and monitoring forest structural parameters such as canopy height and above-ground biomass (AGB) are important for the establishment of an historical database for past, present and future ecosystem comparison. Forest canopy height has a well understood and directly proportional correlation with AGB. It is possible to derive it using (1) airborne LiDAR/Laser Scanning (ALS) or (2) space-borne radar systems such as Shuttle Radar Topography Mission (SRTM) and TanDEM-X (TDX). A previous study of the mangrove canopy height and AGB in the ENP was conducted a decade ago based on ALS data acquired in 2004 in conjunction with SRTM data, which were acquired in 2000 (Simard et al. 2006). In this study we estimated canopy height and AGB using an ALS dataset acquired in 2012 and TDX data acquired during the years 2012-2014. The ALS dataset was acquired along a 16.5 x 1.5 km swath of mangrove forest with variable canopy height. The sampled areas were representative of mangrove stature and structure in the whole ENP. Analysis of the ALS dataset showed that mangrove canopy height can reach up to ~25 meters close to the coastal ENP waters. Additionally, by comparing our ALS results with those of a previous study by Simard et al. (2006) we identified areas where mangrove height changes greater than ± 3 meters occurred. To expand the study area to the full ENP mangrove ecosystem we processed single-polarization TDX data to obtain a Digital Canopy Model (DCM) that represents the mangrove canopy height. In order to obtain the true canopy height we calibrated the TDX phase center height with ALS true canopy height. Preliminary results of a corrected single-polarized (HH) TDX scene show that mangrove canopy height can reach up to ~25 meters in the western

  16. Investigating the robustness of the new Landsat-8 Operational Land Imager derived texture metrics in estimating plantation forest aboveground biomass in resource constrained areas

    NASA Astrophysics Data System (ADS)

    Dube, Timothy; Mutanga, Onisimo

    2015-10-01

    The successful launch of the 30-m Landsat-8 Operational Land Imager (OLI) pushbroom sensor offers a new primary data source necessary for aboveground biomass (AGB) estimation, especially in resource-limited environments. In this work, the strength and performance of Landsat-8 OLI image derived texture metrics (i.e. texture measures and texture ratios) in estimating plantation forest species AGB was investigated. It was hypothesized that the sensor's pushbroom design, coupled with the presence of refined spectral properties, enhanced radiometric resolution (i.e. from 8 bits to 12 bits) and improved signal-to-noise ratio have the potential to provide detailed spectral information necessary for significantly strengthening AGB estimation in medium-density forest canopies. The relationship between image texture metrics and measurements of forest attributes can be used to help characterize complex forests, and enhance fine vegetation biophysical properties, a difficult challenge when using spectral vegetation indices especially in closed canopies. This study examines the prospects of using Landsat-8 OLI sensor derived texture metrics for estimating AGB for three medium-density plantation forest species in KwaZulu Natal, South Africa. In order to achieve this objective, three unique data pre-processing techniques were tested (analysis I: Landsat-8 OLI raw spectral-bands vs. raw texture bands; analysis II: Landsat-8 OLI raw spectral-band ratios vs. texture band ratios and analysis III: Landsat-8 OLI derived vegetation indices vs. texture band ratios). The landsat-8 OLI derived texture parameters were examined for robustness in estimating AGB using linear regression, stepwise-multiple linear regression and stochastic gradient boosting regression models. The results of this study demonstrated that all texture parameters particularly band texture ratios calculated using a 3 × 3 window size, could enhance AGB estimation when compared to simple spectral reflectance, simple

  17. Comparing the above-ground component biomass estimates of western junipers using airborne and full-waveform terrestrial laser scanning data

    NASA Astrophysics Data System (ADS)

    Shrestha, R.; Glenn, N. F.; Spaete, L.; Hardegree, S. P.

    2012-12-01

    With the rapid expansion into shrub steppe and grassland ecosystems over the last century, western juniper (Juniperus occidentalis var. occidentalis Hook) is becoming a major component of the regional carbon pool in the Intermountain West. Understanding how biomass is allocated across individual tree components is necessary to understand the uncertainties in biomass estimates and more accurately quantify biomass and carbon dynamics in these ecosystems. Estimates of component biomass are also important for canopy fuel load assessment and predicting rangeland fire behavior. Airborne LiDAR can capture vegetation structure over larger scales, but the high crown penetration and sampling density of terrestrial laser scanner (TLS) instruments can better capture tree components. In this study, we assessed the ability of airborne LiDAR to estimate biomass of tree components of western juniper with validation data from field measured tees and a full-waveform TLS. Sixteen juniper trees (height range 1.5-10 m) were randomly selected using a double sampling strategy from different height classes in the Reynolds Creek Experimental Watershed in the Owyhee Mountains, southwestern Idaho, USA. Each tree was scanned with a full-waveform TLS, and the dry biomass of each component (foliage, branches and main stem) were measured by destructive harvesting of the trees. We compare the allometric relationships of biomass estimates of the tree components obtained from field-measured trees and TLS-based estimates with the estimates from discrete-return airborne-LiDAR based estimates.

  18. Soil nutrients affect spatial patterns of aboveground biomass and emergent tree density in southwestern Borneo.

    PubMed

    Paoli, Gary D; Curran, Lisa M; Slik, J W F

    2008-03-01

    Studies on the relationship between soil fertility and aboveground biomass in lowland tropical forests have yielded conflicting results, reporting positive, negative and no effect of soil nutrients on aboveground biomass. Here, we quantify the impact of soil variation on the stand structure of mature Bornean forest throughout a lowland watershed (8-196 m a.s.l.) with uniform climate and heterogeneous soils. Categorical and bivariate methods were used to quantify the effects of (1) parent material differing in nutrient content (alluvium > sedimentary > granite) and (2) 27 soil parameters on tree density, size distribution, basal area and aboveground biomass. Trees > or =10 cm (diameter at breast height, dbh) were enumerated in 30 (0.16 ha) plots (sample area = 4.8 ha). Six soil samples (0-20 cm) per plot were analyzed for physiochemical properties. Aboveground biomass was estimated using allometric equations. Across all plots, stem density averaged 521 +/- 13 stems ha(-1), basal area 39.6 +/- 1.4 m(2) ha(-1) and aboveground biomass 518 +/- 28 Mg ha(-1) (mean +/- SE). Adjusted forest-wide aboveground biomass to account for apparent overestimation of large tree density (based on 69 0.3-ha transects; sample area = 20.7 ha) was 430 +/- 25 Mg ha(-1). Stand structure did not vary significantly among substrates, but it did show a clear trend toward larger stature on nutrient-rich alluvium, with a higher density and larger maximum size of emergent trees. Across all plots, surface soil phosphorus (P), potassium, magnesium and percentage sand content were significantly related to stem density and/or aboveground biomass (R (Pearson) = 0.368-0.416). In multiple linear regression, extractable P and percentage sand combined explained 31% of the aboveground biomass variance. Regression analyses on size classes showed that the abundance of emergent trees >120 cm dbh was positively related to soil P and exchangeable bases, whereas trees 60-90 cm dbh were negatively related to these

  19. Remote sensing of aboveground biomass and annual net aerial primary productivity in tidal wetlands

    SciTech Connect

    Hardisky, M.A.

    1983-01-01

    A technique was investigated for estimating biomass and net aerial primary productivity (NAPP) in Delaware tidal marshes from spectral data, describing marsh vegetation canopies. Spectral radiance data were collected with hand-held radiometers from the ground and from low altitude aircraft. Spectral wavebands corresponding to Landsat 4 thematic mapper bands 3, 4 and 5 and multispectral scanner bands 5 and 7 were employed. Spectral data, expressed as index values, were substituted into simple regression models to nondestructively compute total aboveground biomass. Dead biomass, salt crystals on plant leaves and soil background reflectance, all attenuated the spectral radiance index values. A large spectral contribution from any one of these canopy components caused an underestimate of live biomass. Biomass and annual NAPP of a S. alterniflora dominated salt marsh was estimated by traditional harvesting techniques and from ground-gathered spectral radiance data. The live and dead standing crop biomass estimates computed from spectral data were usually not significantly different from harvest biomass estimates. Spectral estimates of NAPP were usually within 10% of NAPP estimates calculated from harvest data. August live standing crop biomass estimates computed from ground-gathered spectral data for a tidal brackish marsh were generally within 10% of harvest estimates. Live biomass estimates computed from spectral data gathered from a low altitude aircraft were equally similar to harvest biomass estimates. The remote sensing technique holds much promise for rapid and accurate estimates of biomass and NAPP in tidal marshes.

  20. The impact of integrating WorldView-2 sensor and environmental variables in estimating plantation forest species aboveground biomass and carbon stocks in uMgeni Catchment, South Africa

    NASA Astrophysics Data System (ADS)

    Dube, Timothy; Mutanga, Onisimo

    2016-09-01

    Reliable and accurate mapping and extraction of key forest indicators of ecosystem development and health, such as aboveground biomass (AGB) and aboveground carbon stocks (AGCS) is critical in understanding forests contribution to the local, regional and global carbon cycle. This information is critical in assessing forest contribution towards ecosystem functioning and services, as well as their conservation status. This work aimed at assessing the applicability of the high resolution 8-band WorldView-2 multispectral dataset together with environmental variables in quantifying AGB and aboveground carbon stocks for three forest plantation species i.e. Eucalyptus dunii (ED), Eucalyptus grandis (EG) and Pinus taeda (PT) in uMgeni Catchment, South Africa. Specifically, the strength of the Worldview-2 sensor in terms of its improved imaging agilities is examined as an independent dataset and in conjunction with selected environmental variables. The results have demonstrated that the integration of high resolution 8-band Worldview-2 multispectral data with environmental variables provide improved AGB and AGCS estimates, when compared to the use of spectral data as an independent dataset. The use of integrated datasets yielded a high R2 value of 0.88 and RMSEs of 10.05 t ha-1 and 5.03 t C ha-1 for E. dunii AGB and carbon stocks; whereas the use of spectral data as an independent dataset yielded slightly weaker results, producing an R2 value of 0.73 and an RMSE of 18.57 t ha-1 and 09.29 t C ha-1. Similarly, high accurate results (R2 value of 0.73 and RMSE values of 27.30 t ha-1 and 13.65 t C ha-1) were observed from the estimation of inter-species AGB and carbon stocks. Overall, the findings of this work have shown that the integration of new generation multispectral datasets with environmental variables provide a robust toolset required for the accurate and reliable retrieval of forest aboveground biomass and carbon stocks in densely forested terrestrial ecosystems.

  1. Comparing the Accuracy of Aboveground Biomass Estimates and Forest Structure Metrics at Large Footprint Level: Satellite Waveform Lidar vs. Discrete-Return Airborne Lidar

    NASA Astrophysics Data System (ADS)

    Popescu, S. C.; Zhao, K.; Gatziolis, D.

    2009-12-01

    The overall goal of this paper was to compare biomass estimates and forest structure metrics obtained by processing ICESat waveform data and spatially coincident discrete-return airborne lidar data over varied terrain conditions. For mostly flat-terrain conditions, we investigated lidar data over forests in east Texas, which are characteristics for most of the south-eastern United States, thus allowing terrain to be largely excluded as a source of error. For sloped-terrain, we used lidar data over forests in Oregon. With biomass estimates derived from waveform height metrics, we also compared ground elevation measurements and canopy parameters. Specific objectives were to: compare ground elevations and canopy height parameters derived from ICESat and airborne lidar; (2) investigate above ground biomass estimates; (3) develop a 3D ray-tracing model to simulate lidar waveforms over forests with sloped terrain; and (4) test model performance with real lidar data over terrain with varied topography. Over flat terrain, results indicated a very strong correlation for terrain elevations between ICESat and airborne lidar, with R-square values of 0.98 and sub-meter RMSE. ICESat height variables were able to explain 80% of the variance associated with the reference biomass derived from airborne lidar, with an RMSE of 37.7 Mg/ha. Most of the models for height metrics had R-square values above 0.9. Results from ongoing investigations for sloped-terrain are expected to establish practical procedures for improving analysis of waveform data in vegetation studies.

  2. Aboveground biomass estimation using SAR-optical (Lidar, RapidEye) and field inventory datasets in Skukuza, Kruger National Park in South Africa

    NASA Astrophysics Data System (ADS)

    Onyango Odipo, Victor; Hüttich, Christian; Luck, Wolfgang; Schmullius, Christiane

    2015-04-01

    African savanna covers approximately two-thirds of sub-saharan Africa, playing important roles as a carbon pool, habitat for mankind and wildlife, source of livelihood, an important tropical climate modifier, among other ecological roles. Sub-saharan Africa alone accounts for 25% of the tropical aboveground carbon stock (193 Gt C). Global and national level AGB estimates rely on extrapolations with regression models from few field inventories, leading in some cases, up to 100% uncertainty. Remote sensing has proven to provide reliable vegetation structural mapping, given the high spatial and temporal resolution allowing datasets to be availed in areas where ground based inventories are infeasible due to time and financial constraints. The availability of freely accessible optical remotely-sensed datasets has made this feat attainable. However, the heterogeneity of tropical savannas (co-existence of trees and grasses), coupled with erratic rainfall events and atmospheric clouds and aerosol in the tropics has made it difficult to extract biophysical properties of the savannas by solely using optical datasets. This has necessitated an assessment of synergies between active and passive remotely sensed datasets to benefit from the complementarities. In this study we assess the extent to which multi-level sub-centimeter Unmanned Aerial Vehicle (UAV) Lidar, high resolution RapidEye and microwave (ALOS PALSAR L-band and Sentinel-1 C-band) remotely sensed datasets can be used together with tree census datasets to estimate AGB within the complex southern Africa savanna ecosystem. A random forest (RF) regression model is produced which relates the Lidar canopy-height metrics (CHM) with both synthetic aperture radar (SAR) and high resolution RapidEye datasets. As a validation, we compare our results with both national and global level ABG estimates.

  3. Study on forest above-ground biomass synergy inversion from GLAS and HJ-1 data

    NASA Astrophysics Data System (ADS)

    Fang, Zhou; Cao, Chunxiang; Ji, Wei; Xu, Min; Chen, Wei

    2012-10-01

    The need exists to develop a systematic approach to inventory and monitor global forests, both for carbon stock evaluation and for land use change analysis. The use of freely available satellite-based data for carbon stock estimation mitigates both the cost and the spatial limitations of field-based techniques. Spaceborne lidar data have been demonstrated as useful for forest aboveground biomass (AGB) estimation over a wide range of biomass values and forest types. However, the application of these data is limited because of their spatially discrete nature. Spaceborne multispectral sensors have been used extensively to estimate AGB, but these methods have been demonstrated as inappropriate for forest structure characterization in high-biomass mature forests. This study uses an integration of ICESat Geospatial Laser Altimeter System (GLAS) lidar and HJ-1 satellites data to develop methods to estimate AGB in an area of Qilian Mountains, Northwest China. Considering the study area belongs to mountainous terrain, the difficulties of this article are how to extract canopy height from GLAS waveform metrics. Combining with HJ-1 data and ground survey data of the study area, we establish forest biomass estimation model for the GLAS data based on BP neural network model. In order to estimate AGB, the training sample data includes the canopy height extracted from GLAS, LAI, vegetation coverage and several kinds of vegetation indices from HJ-1 data. The results of forest aboveground biomass are very close to the fields measured results, and are consistent with land cover data in the spatial distribution.

  4. Shifts in Aboveground Biomass Allocation Patterns of Dominant Shrub Species across a Strong Environmental Gradient

    PubMed Central

    Kumordzi, Bright B.; Gundale, Michael J.; Nilsson, Marie-Charlotte; Wardle, David A.

    2016-01-01

    Most plant biomass allocation studies have focused on allocation to shoots versus roots, and little is known about drivers of allocation for aboveground plant organs. We explored the drivers of within-and between-species variation of aboveground biomass allocation across a strong environmental resource gradient, i.e., a long-term chronosequence of 30 forested islands in northern Sweden across which soil fertility and plant productivity declines while light availability increases. For each of the three coexisting dominant understory dwarf shrub species on each island, we estimated the fraction of the total aboveground biomass produced year of sampling that was allocated to sexual reproduction (i.e., fruits), leaves and stems for each of two growing seasons, to determine how biomass allocation responded to the chronosequence at both the within-species and whole community levels. Against expectations, within-species allocation to fruits was least on less fertile islands, and allocation to leaves at the whole community level was greatest on intermediate islands. Consistent with expectations, different coexisting species showed contrasting allocation patterns, with the species that was best adapted for more fertile conditions allocating the most to vegetative organs, and with its allocation pattern showing the strongest response to the gradient. Our study suggests that co-existing dominant plant species can display highly contrasting biomass allocations to different aboveground organs within and across species in response to limiting environmental resources within the same plant community. Such knowledge is important for understanding how community assembly, trait spectra, and ecological processes driven by the plant community vary across environmental gradients and among contrasting ecosystems. PMID:27270445

  5. Shifts in Aboveground Biomass Allocation Patterns of Dominant Shrub Species across a Strong Environmental Gradient.

    PubMed

    Kumordzi, Bright B; Gundale, Michael J; Nilsson, Marie-Charlotte; Wardle, David A

    2016-01-01

    Most plant biomass allocation studies have focused on allocation to shoots versus roots, and little is known about drivers of allocation for aboveground plant organs. We explored the drivers of within-and between-species variation of aboveground biomass allocation across a strong environmental resource gradient, i.e., a long-term chronosequence of 30 forested islands in northern Sweden across which soil fertility and plant productivity declines while light availability increases. For each of the three coexisting dominant understory dwarf shrub species on each island, we estimated the fraction of the total aboveground biomass produced year of sampling that was allocated to sexual reproduction (i.e., fruits), leaves and stems for each of two growing seasons, to determine how biomass allocation responded to the chronosequence at both the within-species and whole community levels. Against expectations, within-species allocation to fruits was least on less fertile islands, and allocation to leaves at the whole community level was greatest on intermediate islands. Consistent with expectations, different coexisting species showed contrasting allocation patterns, with the species that was best adapted for more fertile conditions allocating the most to vegetative organs, and with its allocation pattern showing the strongest response to the gradient. Our study suggests that co-existing dominant plant species can display highly contrasting biomass allocations to different aboveground organs within and across species in response to limiting environmental resources within the same plant community. Such knowledge is important for understanding how community assembly, trait spectra, and ecological processes driven by the plant community vary across environmental gradients and among contrasting ecosystems. PMID:27270445

  6. Pasture burning in Amazonia: Dynamics of residual biomass and the storage and release of aboveground carbon

    NASA Astrophysics Data System (ADS)

    Barbosa, Reinaldo Imbrózio; Fearnside, Philip M.

    1996-11-01

    Aboveground biomass in cattle pasture converted from tropical dense forest was studied both before and after reburning in Brazilian Amazonia. In a 7-year-old pasture studied in Apiaú, Roraima, the aboveground dry weight of biomass (live plus dead) exposed to burning consisted of 96.3 t ha-1 of original forest remains, 6.2 t ha-1 of secondary successional vegetation (woody invaders in the pasture), and 8.0 t ha-1 of pasture grass (carbon contents 48.2%, 45.4%, and 42.2%, respectively). In terms of carbon, burning efficiencies for these three categories were 13.2%, 66.7% and 94.6%, respectively. Net charcoal formation was 0.35 t C ha-1 or 0.63% of the carbon exposed to the reburn, while the total accumulated since conversion (including the initial burn) is estimated at 2.3 t ha-1 (1.82% of the predeforestation aboveground biomass carbon stock). The dynamics of the original forest remains were represented in simulations that included parameters such as charcoal formation, burning efficiency and carbon concentration in different biomass components. Releases from initial burning of the cleared forest (44.0 t C ha-1) plus releases over the course of the succeeding decade through combustion (12.5 t C ha-1) and decay (51.5 t C ha-1) total 92% of the original forest biomass carbon (126 t C ha-1). Of biomass carbon remaining after the initial burn (84.3 t C ha-1), 76.0% is released: 61.1% through decay and 14.9% through combustion in reburns, while 1.2% is net conversion to charcoal in the reburns. These results indicate an amount of charcoal accumulation that is smaller than some carbon calculations have assumed, therefore suggesting a greater impact on global warming from conversion of forest to pasture.

  7. Mapping Aboveground Biomass in the Amazon Basin: Exploring Sensors, Scales, and Strategies for Optimal Data Linkage

    NASA Astrophysics Data System (ADS)

    Walker, W. S.; Baccini, A.

    2013-05-01

    encompassing the state of Acre Brazil. Through a comprehensive comparison involving nearly 50 separate analyses, we assess accuracy in aboveground biomass estimates with respect to varying (a) satellite data inputs, (b) image spatial scales, (c) and field/image data linkage strategies. Our results confirm the utility of both ALOS/PALSAR and Landsat data for the provision of accurate estimates of aboveground biomass, with accuracy increasing markedly with increasing spectral resolution, decreasing spatial resolution, and as the spatial mismatches between field and image data sources are minimized.

  8. Aboveground total and green biomass of dryland shrub derived from terrestrial laser scanning

    NASA Astrophysics Data System (ADS)

    Olsoy, Peter J.; Glenn, Nancy F.; Clark, Patrick E.; Derryberry, DeWayne R.

    2014-02-01

    Sagebrush (Artemisia tridentata), a dominant shrub species in the sagebrush-steppe ecosystem of the western US, is declining from its historical distribution due to feedbacks between climate and land use change, fire, and invasive species. Quantifying aboveground biomass of sagebrush is important for assessing carbon storage and monitoring the presence and distribution of this rapidly changing dryland ecosystem. Models of shrub canopy volume, derived from terrestrial laser scanning (TLS) point clouds, were used to accurately estimate aboveground sagebrush biomass. Ninety-one sagebrush plants were scanned and sampled across three study sites in the Great Basin, USA. Half of the plants were scanned and destructively sampled in the spring (n = 46), while the other half were scanned again in the fall before destructive sampling (n = 45). The latter set of sagebrush plants was scanned during both spring and fall to further test the ability of the TLS to quantify seasonal changes in green biomass. Sagebrush biomass was estimated using both a voxel and a 3-D convex hull approach applied to TLS point cloud data. The 3-D convex hull model estimated total and green biomass more accurately (R2 = 0.92 and R2 = 0.83, respectively) than the voxel-based method (R2 = 0.86 and R2 = 0.73, respectively). Seasonal differences in TLS-predicted green biomass were detected at two of the sites (p < 0.001 and p = 0.029), elucidating the amount of ephemeral leaf loss in the face of summer drought. The methods presented herein are directly transferable to other dryland shrubs, and implementation of the convex hull model with similar sagebrush species is straightforward.

  9. Regional Mapping, Modelling, and Monitoring of Tree Aboveground Biomass Carbon

    NASA Astrophysics Data System (ADS)

    Hudak, Andrew

    2016-04-01

    Airborne lidar collections are preferred for mapping aboveground biomass carbon (AGBC), while historical Landsat imagery are preferred for monitoring decadal scale forest cover change. Our modelling approach tracks AGBC change regionally using Landsat time series metrics; training areas are defined by airborne lidar extents within which AGBC is accurately mapped with high confidence. Geospatial topographic and climate layers are also included in the predictive model. Validation is accomplished using systematically sampled Forest Inventory and Analysis (FIA) plot data that have been independently collected, processed and summarized at the county level. Our goal is to demonstrate that spatially and temporally aggregated annual AGBC map predictions show no bias when compared to annual county-level summaries across the Northwest USA. A prominent source of bias is trees outside forest; much of the more arid portions of our study area meet the FIA definition of non-forest because the tree cover does not exceed their minimum tree cover threshold. We employ detailed tree cover maps derived from high-resolution aerial imagery to extend our AGBC predictions into non-forest areas. We also employ Landsat-derived annual disturbance maps into our mapped AGBC predictions prior to aggregation and validation.

  10. Aboveground Live Forest Biomass Map for the US From Satellite Imagery and Inventory Data

    NASA Astrophysics Data System (ADS)

    Helmer, E.; Blackard, J.; Finco, M.; Holden, G.; Hoppus, M.; Jacobs, D.; Lister, A.; Moisen, G.; Nelson, M.; Riemann, R.; Ruefenacht, B.; Salajanu, D.; Weyermann, D.; Winterberger, K.; Czaplewski, R.; Tymcio, R.; Brandeis, T.

    2004-12-01

    A gridded map of aboveground live forest biomass for the conterminous U.S., Alaska and Puerto Rico with a 250-m cell size resulted from integrating plot-level biomass estimates, from USDA Forest Service (USFS) nation-wide forest inventory data, with satellite imagery and ancillary geospatial data. The image and other predictor layers included MOD09 8-Day surface reflectance imagery (1) from the Moderate Resolution Imaging Spectroradiometer, MODIS-derived proportional tree cover (2), Landsat image-derived proportional land cover (3-4), climate averages (5-6) and topographic variables (7). By state or mapping zone (8), plot-based aboveground live forest biomass estimates generally fell within 5 percent of map-based estimates, and the map provided previously unavailable spatial detail. Here we describe the inventory data, the modeling approach, and the error maps. We secondly compare estimates of U.S. forest carbon storage in live woody biomass from this map with other estimates. We also critically evaluate the modeling process and spatial scaling issues. (1)Vermote EF, Vermueulen A. 1999. MOD09 ATBD, Univ. of Maryland, College Park, 107 pp. (2) Hansen M, DeFries R, et al. 2003. GLCF, Univ. of Maryland, College Park (3) Vogelmann JE, Howard S, et al. 2001. Photogramm Eng Rem S 67:650 (4) Helmer E, Ramos O, et al. 2002. Caribbean J Sci 38:165 (5) Daly C, Kittel T, et al. 2000. 12th AMS Conf on Applied Climatology, Amer Meteorol Soc, Asheville (6) Daly C, Helmer E, et al. 2003. Intl J Climatology 23:1359 (7) Gesch D, Oimoen M, et al. 2002. Photogramm Eng Rem S 68:5 (8) Homer C, Huang C, et al. 2004. Photogramm Eng Rem S 70:829

  11. Mapping aboveground forest biomass combining dendrometric data and spectral signature of forest species

    NASA Astrophysics Data System (ADS)

    Avocat, H.; Tourneux, F.

    2013-12-01

    Accurate measures and explicit spatial representations of forest biomass compose an important aspect to model the forest productivity and crops, and to implement sustainable forest management. Several methods have been developed to estimate and to map forest biomass, combining point-sources measurements of biophysical variables such as diameter-at-breast height (DBH), tree height, crown size, crown length, crown volume and remote sensing data (spectral vegetation index values). In this study, we propose a new method for aboveground biomass (AGB) mapping of forests and isolated trees. This method is tested on a 1100 km2 area located in the eastern France. In contrast to most of studies, our model is not calibrated using field plot measurements or point-source inventory data. The primary goal of this model is to propose an accessible and reproducible method for AGB mapping of temperate forests, by combining standard biomass values coming from bibliography and remotely sensed data. This method relies on three steps. (1) The first step consists of produce a map of wooded areas including small woods and isolated trees, and to identify the major forest stands. To do this, we use an unsupervised classification of a Landsat 7 ETM+ image. Results are compared and improved with various land cover data. (2) The second step consists of extract the normalized difference vegetation index (NDVI) values of main forest stands. (3) Finally, these values are combined with standard AGB values provided by bibliography, to calibrate four AGB estimation models of different forest types (broadleaves, coniferous, coppices, and mixed stands). This method provides a map of aboveground biomass for forests and isolated trees with a 30 meters spatial resolution. Results demonstrate that 71 % of AGB values for hardwoods vary between 143 and 363 t.ha-1, i.e. × 1 standard deviation around the average. For coniferous stands, most of values of AGB range from 167 to 256 t.ha-1.

  12. Estimating Above-Ground Biomass Within the Footprint of an Eddy-Covariance Flux Tower: Continuous LiDAR Based Estimates Compared With Discrete Inventory and Disturbance History Based Stratification Boundaries

    NASA Astrophysics Data System (ADS)

    Ferster, C. J.; Trofymow, J. A.; Coops, N. C.; Chen, B.; Black, T. A.

    2008-12-01

    Eddy-covariance (EC) flux towers provide data about carbon (C) exchange between land and the atmosphere at an ecosystem scale. However, important research questions need to be addressed when placing EC flux towers in complex heterogeneous forest landscapes, such as the coastal forests of Western Canada. Recently available footprint analysis, which describes the contribution function and catchment area where EC flux is being measured, can be used to relate EC flux tower measurements with the biological structure and carbon stock distributions of complex forest landscapes. In this study, above ground biomass is estimated near an EC flux tower using two approaches. In the first approach, a remote sensing based surface representing above ground biomass was estimated using small footprint, discrete return, light detection and ranging (LiDAR) data. Plot level LiDAR metrics were supplemented with metrics calculated using individual tree detection. A multiple regression model was developed to estimate above ground biomass using ground plot and LiDAR data, and then the model was applied across the EC flux footprint area to estimate the spatial distribution of above ground biomass. In the second approach, line boundaries from forest inventory, disturbance history, and site series were used to delineate discrete stratification units and the measured groundplot data assigned to the various strata. Within the heterogeneous tower footprint area, footprint weighting allows us to compare and contrast above ground biomass estimates from these two approaches. Using this methodology we then plan to compare, for the same period, ground-based measurements of ecosystem C stock changes with accumulative EC measured net ecosystem C flux.

  13. Landscape Patterns of Wood Density and Aboveground Biomass Along a Tropical Elevation Gradient in Costa Rica

    NASA Astrophysics Data System (ADS)

    Robinson, C. M.

    2015-12-01

    This research sought to understand how tree wood density and taxonomic diversity relate to topography and three-dimensional vegetation structure in the tropical montane forest of Braulio Carrillo National Park in Costa Rica. The study utilized forest inventory and botanical data from twenty 1-ha plots ranging from 55 m to 2800 m above sea level and remote sensing data from an airborne lidar sensor (NASA's Land, Vegetation, and Ice Sensor [LVIS]) to quantify variations in forest structure. There is growing evidence that ecosystem structure may help to control the functional variations across landscapes. This study relates patterns of tree functional wood density and alpha diversity to three-dimensional structure using remote sensing observations of forest structure. We were able to test the effect of the gradient on wood density measured from collected tree cores and on the subsequent aboveground biomass estimations. We sought to determine if there was a significant pattern of wood density across the altitudinal gradient, which has implications for conservation of both ecosystem services and biodiversity. We also wanted to determine how many random individuals could be sampled to accurately estimate aboveground biomass in a one-hectare plot. Our results indicate that there is a strong relationship between LVIS-derived forest 3D-structure and alpha diversity, likely controlled by variations in abiotic factors and topography along the elevation. Using spatial analysis with the aid of remote sensing data, we found patterns along the environmental gradients defining species composition and forest structure. Wood density values were found to vary significantly from database values for the same species. This variation in tree growth has repercussions on overall forest structure, and subsequent carbon estimates extrapolated from field measurements. Because these wood density values are directly tied to biomass estimates, it is possible that carbon storage has been

  14. Modeling aboveground tree woody biomass using national-scale allometric methods and airborne lidar

    NASA Astrophysics Data System (ADS)

    Chen, Qi

    2015-08-01

    Estimating tree aboveground biomass (AGB) and carbon (C) stocks using remote sensing is a critical component for understanding the global C cycle and mitigating climate change. However, the importance of allometry for remote sensing of AGB has not been recognized until recently. The overarching goals of this study are to understand the differences and relationships among three national-scale allometric methods (CRM, Jenkins, and the regional models) of the Forest Inventory and Analysis (FIA) program in the U.S. and to examine the impacts of using alternative allometry on the fitting statistics of remote sensing-based woody AGB models. Airborne lidar data from three study sites in the Pacific Northwest, USA were used to predict woody AGB estimated from the different allometric methods. It was found that the CRM and Jenkins estimates of woody AGB are related via the CRM adjustment factor. In terms of lidar-biomass modeling, CRM had the smallest model errors, while the Jenkins method had the largest ones and the regional method was between. The best model fitting from CRM is attributed to its inclusion of tree height in calculating merchantable stem volume and the strong dependence of non-merchantable stem biomass on merchantable stem biomass. This study also argues that it is important to characterize the allometric model errors for gaining a complete understanding of the remotely-sensed AGB prediction errors.

  15. Ecological studies on the revegetation process of surface coal mined areas in North Dakota. 6. Relationship between cover and aboveground biomass. Final report Aug 75-Jun 82

    SciTech Connect

    Schimmelpfennig, D.K.

    1982-06-01

    Assessment of revegetation success on mined lands is a difficult, time consuming task and has been the subject of a number of controversies. Present regulations require that both plant cover and aboveground plant biomass be measured for use in making that assessment. Of these two variables, biomass is the most time consuming to measure and requires destructive sampling, a most undesirable, requirement on fragile, recently revegetated areas. A study was done to evaluate the predictability of aboveground biomass production on revegetated mined sites and adjacent native prairies using plant cover estimates made with the point frame method. A positive, statistically significant correlation was demonstrated between plant cover and aboveground biomass regardless of the community type, species composition, diversity or level of biomass production. However, the latter did have their effects on the relationship and must be accounted for in any predictive equations.

  16. Plot-level aboveground woody biomass modeling using canopy height and auxiliary remote sensing data in a heterogeneous savanna

    NASA Astrophysics Data System (ADS)

    Gwenzi, David; Lefsky, Michael Andrew

    2016-01-01

    Remote sensing studies aiming at assessing woody biomass have demonstrated a strong relationship between canopy height and plot-level aboveground biomass, but most of these studies focused on closed canopy forests. To date, a few studies have examined the strength and reliability of this relationship using large footprint lidar in savannas. Furthermore, there have been few studies of appropriate methods for the comparison of models that relate aboveground biomass to canopy height metrics without consideration of variation in species composition (generic models) to models developed for individual species composition or vegetation types. We developed generic models using the classical least-squares regression modeling approach to relate selected canopy height metrics to aboveground woody biomass in a savanna landscape. Hierarchical Bayesian analysis (HBA) was then used to explore the implications of using generic or composition-specific models. Our study used the estimates of aboveground biomass from field data, canopy height estimates from airborne discrete return lidar, and a proxy for canopy cover (the Normalized Difference Vegetation Index) from Landsat 5 Thematic Mapper data, collected from the oak savannas of Tejon Ranch Conservancy in Kern County, California. Models were developed and analyzed using estimates of canopy height and aboveground biomass calculated at the level of 50-m diameter plots, comparable with footprint diameter of existing large footprint spaceborne lidar data. The two generic models that incorporated canopy cover proxies performed better than one model that did not use canopy cover information. From the HBA, we found out that for all models both the intercept and slope had interspecific variability. The valley oak dominated plots consistently had higher slopes and intercepts, whereas the plots dominated by blue oaks had the lowest. However, the intercept and slope values of the composition-specific models did not differ much from the

  17. Quantification of aboveground forest biomass using Quickbird imagery, topographic variables, and field data

    NASA Astrophysics Data System (ADS)

    Zhou, Jing-Jing; Zhao, Zhong; Zhao, Qingxia; Zhao, Jun; Wang, Haize

    2013-01-01

    Optical remote sensing is the most widely used method for obtaining forest biomass information. This research investigated the potential of using topographical and high-resolution optical data from Quickbird for measurement of black locust plantation aboveground biomass (AGB) grown in the hill-gully region of the Loess Plateau. Three different processing techniques, including spectral vegetation indices (SVIs), texture, and topography were evaluated, both individually and combined. Simple linear regression and stepwise multiple-linear regression models were developed to describe the relationship between image parameters obtained using these approaches and field measurements. SVI and topography-based approaches did not yield reliable AGB estimates, accounting for at best 23 and 19% of the observed variation in AGB. Texture-based methods were better, explaining up to 70% of the observed variation. A combination of SVIs, texture, and topography yielded an even better R value of 0.74 with the lowest root mean square error (17.21 t/ha) and bias (-1.85 t/ha). The results suggest that texture information from high-resolution optical data was more effective than SVIs and topography to estimate AGB. The performance of AGB estimation can be improved by adding SVIs and topography results to texture data; the best results can be obtained using a combination of these three data types.

  18. Structure, Aboveground Biomass, and Soil Characterization of Avicennia marina in Eastern Mangrove Lagoon National Park, Abu Dhabi

    NASA Astrophysics Data System (ADS)

    Alsumaiti, Tareefa Saad Sultan

    Mangrove forests are national treasures of the United Arab Emirates (UAE) and other arid countries with limited forested areas. Mangroves form a crucial part of the coastal ecosystem and provide numerous benefits to society, economy, and especially the environment. Mangrove trees, specifically Avicennia marina, are studied in their native habitat in order to characterize their population structure, aboveground biomass, and soil properties. This study focused on Eastern Mangrove Lagoon National Park in Abu Dhabi, which was the first mangrove protected area to be designated in UAE. In situ measurements were collected to estimate Avicennia marina status, mortality rate (%), height (m), crown spread (m), stem number, diameter at breast height (cm), basal area (m), and aboveground biomass (t ha-1 ). Small-footprint aerial light detection and ranging (LIDAR) data acquired by UAE were processed to characterize mangrove canopy height and aboveground biomass density. This included extraction of LIDAR-derived height percentile statistics, segmentation of the forest into structurally homogenous units, and development of regression relationships between in situ reference and remote sensing data using a machine learning approach. An in situ soil survey was conducted to examine the soils' physical and chemical properties, fertility status, and organic matter. The data of soil survey were used to create soil maps to evaluate key characteristics of soils, and their influence on Avicennia marina in Eastern Mangrove Lagoon National Park. The results of this study provide new insights into Avicennia marina canopy population, structure, aboveground biomass, and soil properties in Abu Dhabi, as data in such arid environments is lacking. This valuable information can help in managing and preserving this unique ecosystem.

  19. Demographic controls of aboveground forest biomass across North America.

    PubMed

    Vanderwel, Mark C; Zeng, Hongcheng; Caspersen, John P; Kunstler, Georges; Lichstein, Jeremy W

    2016-04-01

    Ecologists have limited understanding of how geographic variation in forest biomass arises from differences in growth and mortality at continental to global scales. Using forest inventories from across North America, we partitioned continental-scale variation in biomass growth and mortality rates of 49 tree species groups into (1) species-independent spatial effects and (2) inherent differences in demographic performance among species. Spatial factors that were separable from species composition explained 83% and 51% of the respective variation in growth and mortality. Moderate additional variation in mortality (26%) was attributable to differences in species composition. Age-dependent biomass models showed that variation in forest biomass can be explained primarily by spatial gradients in growth that were unrelated to species composition. Species-dependent patterns of mortality explained additional variation in biomass, with forests supporting less biomass when dominated by species that are highly susceptible to competition (e.g. Populus spp.) or to biotic disturbances (e.g. Abies balsamea). PMID:26913575

  20. Mapping 2002-2012 Aboveground Biomass Carbon from LiDAR and Landsat Time Series across Northern Idaho, USA

    NASA Astrophysics Data System (ADS)

    Hudak, A. T.; Fekety, P.; Falkowski, M. J.; Kennedy, R. E.; Crookston, N.; Smith, A. M.

    2015-12-01

    The heavy investment by public and private land management entities in commercial off-the-shelf airborne lidar provides an optimum basis for a Carbon Monitoring System due to the known sensitivity of lidar to vegetation canopy structure. The ability to accurately map aboveground carbon pools from lidar and collocated field plot data has been demonstrated in many studies. Our goal is to upscale this biomass information, mapped at 30 m resolution, to the regional level using wall-to-wall, multi-temporal Landsat imagery. We use the LandTrendr approach to transform Landsat time series into annual maps of Brightness, Greenness, and Wetness along with annual change estimates of these same tasseled cap indices. These, along with ancillary layers of canopy height (e.g., GLAS-derived), topography (e.g., insolation), and climate (e.g., mean annual precipitation) are used to predict 2002-2012 aboveground carbon annually across the northern half of Idaho, USA. Ecoregion-specific models are developed to impute aboveground biomass and forest type beneath a forest/non-forest mask. Annual maps are then summarized at the county-level and compared to publically available Forest Inventory and Analysis estimates for Monitoring, Reporting and Verification.

  1. Final Harvest of Above-Ground Biomass and Allometric Analysis of the Aspen FACE Experiment

    SciTech Connect

    Mark E. Kubiske

    2013-04-15

    The Aspen FACE experiment, located at the US Forest Service Harshaw Research Facility in Oneida County, Wisconsin, exposes the intact canopies of model trembling aspen forests to increased concentrations of atmospheric CO2 and O3. The first full year of treatments was 1998 and final year of elevated CO2 and O3 treatments is scheduled for 2009. This proposal is to conduct an intensive, analytical harvest of the above-ground parts of 24 trees from each of the 12, 30 m diameter treatment plots (total of 288 trees) during June, July & August 2009. This above-ground harvest will be carefully coordinated with the below-ground harvest proposed by D.F. Karnosky et al. (2008 proposal to DOE). We propose to dissect harvested trees according to annual height growth increment and organ (main stem, branch orders, and leaves) for calculation of above-ground biomass production and allometric comparisons among aspen clones, species, and treatments. Additionally, we will collect fine root samples for DNA fingerprinting to quantify biomass production of individual aspen clones. This work will produce a thorough characterization of above-ground tree and stand growth and allocation above ground, and, in conjunction with the below ground harvest, total tree and stand biomass production, allocation, and allometry.

  2. Quantifying variation in forest disturbance, and its effects on aboveground biomass dynamics, across the eastern United States

    PubMed Central

    Vanderwel, Mark C; Coomes, David A; Purves, Drew W

    2013-01-01

    The role of tree mortality in the global carbon balance is complicated by strong spatial and temporal heterogeneity that arises from the stochastic nature of carbon loss through disturbance. Characterizing spatio-temporal variation in mortality (including disturbance) and its effects on forest and carbon dynamics is thus essential to understanding the current global forest carbon sink, and to predicting how it will change in future. We analyzed forest inventory data from the eastern United States to estimate plot-level variation in mortality (relative to a long-term background rate for individual trees) for nine distinct forest regions. Disturbances that produced at least a fourfold increase in tree mortality over an approximately 5 year interval were observed in 1–5% of plots in each forest region. The frequency of disturbance was lowest in the northeast, and increased southwards along the Atlantic and Gulf coasts as fire and hurricane disturbances became progressively more common. Across the central and northern parts of the region, natural disturbances appeared to reflect a diffuse combination of wind, insects, disease, and ice storms. By linking estimated covariation in tree growth and mortality over time with a data-constrained forest dynamics model, we simulated the implications of stochastic variation in mortality for long-term aboveground biomass changes across the eastern United States. A geographic gradient in disturbance frequency induced notable differences in biomass dynamics between the least- and most-disturbed regions, with variation in mortality causing the latter to undergo considerably stronger fluctuations in aboveground stand biomass over time. Moreover, regional simulations showed that a given long-term increase in mean mortality rates would support greater aboveground biomass when expressed through disturbance effects compared with background mortality, particularly for early-successional species. The effects of increased tree mortality on

  3. Topographic Variation in Aboveground Biomass in a Subtropical Evergreen Broad-Leaved Forest in China

    PubMed Central

    Lin, Dunmei; Lai, Jiangshan; Muller-Landau, Helene C.; Mi, Xiangcheng; Ma, Keping

    2012-01-01

    The subtropical forest biome occupies about 25% of China, with species diversity only next to tropical forests. Despite the recognized importance of subtropical forest in regional carbon storage and cycling, uncertainties remain regarding the carbon storage of subtropical forests, and few studies have quantified within-site variation of biomass, making it difficult to evaluate the role of these forests in the global and regional carbon cycles. Using data for a 24-ha census plot in east China, we quantify aboveground biomass, characterize its spatial variation among different habitats, and analyse species relative contribution to the total aboveground biomass of different habitats. The average aboveground biomass was 223.0 Mg ha−1 (bootstrapped 95% confidence intervals [217.6, 228.5]) and varied substantially among four topographically defined habitats, from 180.6 Mg ha−1 (bootstrapped 95% CI [167.1, 195.0]) in the upper ridge to 245.9 Mg ha−1 (bootstrapped 95% CI [238.3, 253.8]) in the lower ridge, with upper and lower valley intermediate. In consistent with our expectation, individual species contributed differently to the total aboveground biomass of different habitats, reflecting significant species habitat associations. Different species show differently in habitat preference in terms of biomass contribution. These patterns may be the consequences of ecological strategies difference among different species. Results from this study enhance our ability to evaluate the role of subtropical forests in the regional carbon cycle and provide valuable information to guide the protection and management of subtropical broad-leaved forest for carbon sequestration and carbon storage. PMID:23118961

  4. Aboveground Biomass Monitoring over Siberian Boreal Forest Using Radar Remote Sensing Data

    NASA Astrophysics Data System (ADS)

    Stelmaszczuk-Gorska, M. A.; Thiel, C. J.; Schmullius, C.

    2014-12-01

    Aboveground biomass (AGB) plays an essential role in ecosystem research, global cycles, and is of vital importance in climate studies. AGB accumulated in the forests is of special monitoring interest as it contains the most of biomass comparing with other land biomes. The largest of the land biomes is boreal forest, which has a substantial carbon accumulation capability; carbon stock estimated to be 272 +/-23 Pg C (32%) [1]. Russian's forests are of particular concern, due to the largest source of uncertainty in global carbon stock calculations [1], and old inventory data that have not been updated in the last 25 years [2]. In this research new empirical models for AGB estimation are proposed. Using radar L-band data for AGB retrieval and optical data for an update of in situ data the processing scheme was developed. The approach was trained and validated in the Asian part of the boreal forest, in southern Russian Central Siberia; two Siberian Federal Districts: Krasnoyarsk Kray and Irkutsk Oblast. Together the training and testing forest territories cover an area of approximately 3,500 km2. ALOS PALSAR L-band single (HH - horizontal transmitted and received) and dual (HH and HV - horizontal transmitted, horizontal and vertical received) polarizations in Single Look Complex format (SLC) were used to calculate backscattering coefficient in gamma nought and coherence. In total more than 150 images acquired between 2006 and 2011 were available. The data were obtained through the ALOS Kyoto and Carbon Initiative Project (K&C). The data were used to calibrate a randomForest algorithm. Additionally, a simple linear and multiple-regression approach was used. The uncertainty of the AGB estimation at pixel and stand level were calculated approximately as 35% by validation against an independent dataset. The previous studies employing ALOS PALSAR data over boreal forests reported uncertainty of 39.4% using randomForest approach [2] or 42.8% using semi-empirical approach [3].

  5. Topographically mediated controls on aboveground biomass across a mediterranean-type landscape

    NASA Astrophysics Data System (ADS)

    Dahlin, K.; Asner, G. P.; Field, C. B.

    2009-12-01

    Aboveground biomass accumulation is a useful metric for evaluating habitat restoration and ecosystem services projects, in addition to being a robust measure of carbon sequestration. However, at the landscape scale non-anthropogenic controls on biomass accumulation are poorly understood. In this study we combined field measurements, high resolution data from the NASA JPL Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and the Carnegie Airborne Observatory (CAO) airborne light detection and ranging (lidar) system to create a comprehensive map of aboveground biomass across a patchy mediterranean-type landscape (Jasper Ridge Biological Preserve, Stanford, CA). Candidate explanatory variables (e.g. slope, elevation, incident solar radiation) were developed using a geologic map and a digital elevation model derived from the lidar data. Finally, candidate variables were tested, and a model was produced to predict aboveground biomass from environmental data. Though many of the explanatory variables have only indirect effects on plant growth, the model permits inferences to be made about the relative importance of light, water, temperature, and edaphic characteristics on carbon accumulation in mediterranean-type systems.

  6. Aboveground biomass and carbon stocks modelling using non-linear regression model

    NASA Astrophysics Data System (ADS)

    Ain Mohd Zaki, Nurul; Abd Latif, Zulkiflee; Nazip Suratman, Mohd; Zainee Zainal, Mohd

    2016-06-01

    Aboveground biomass (AGB) is an important source of uncertainty in the carbon estimation for the tropical forest due to the variation biodiversity of species and the complex structure of tropical rain forest. Nevertheless, the tropical rainforest holds the most extensive forest in the world with the vast diversity of tree with layered canopies. With the usage of optical sensor integrate with empirical models is a common way to assess the AGB. Using the regression, the linkage between remote sensing and a biophysical parameter of the forest may be made. Therefore, this paper exemplifies the accuracy of non-linear regression equation of quadratic function to estimate the AGB and carbon stocks for the tropical lowland Dipterocarp forest of Ayer Hitam forest reserve, Selangor. The main aim of this investigation is to obtain the relationship between biophysical parameter field plots with the remotely-sensed data using nonlinear regression model. The result showed that there is a good relationship between crown projection area (CPA) and carbon stocks (CS) with Pearson Correlation (p < 0.01), the coefficient of correlation (r) is 0.671. The study concluded that the integration of Worldview-3 imagery with the canopy height model (CHM) raster based LiDAR were useful in order to quantify the AGB and carbon stocks for a larger sample area of the lowland Dipterocarp forest.

  7. Impacts of Tree Height-Dbh Allometry on Lidar-Based Tree Aboveground Biomass Modeling

    NASA Astrophysics Data System (ADS)

    Fang, R.

    2016-06-01

    Lidar has been widely used in tree aboveground biomass (AGB) estimation at plot or stand levels. Lidar-based AGB models are usually constructed with the ground AGB reference as the response variable and lidar canopy indices as predictor variables. Tree diameter at breast height (dbh) is the major variable of most allometric models for estimating reference AGB. However, lidar measurements are mainly related to tree vertical structure. Therefore, tree height-dbh allometric model residuals are expected to have a large impact on lidar-based AGB model performance. This study attempts to investigate sensitivity of lidar-based AGB model to the decreasing strength of height-dbh relationship using a Monte Carlo simulation approach. Striking decrease in R2 and increase in relative RMSE were found in lidar-based AGB model, as the variance of height-dbh model residuals grew. I, therefore, concluded that individual tree height-dbh model residuals fundamentally introduce errors to lidar-AGB models.

  8. QUANTIFYING FOREST ABOVEGROUND CARBON POOLS AND FLUXES USING MULTI-TEMPORAL LIDAR A report on field monitoring, remote sensing MMV, GIS integration, and modeling results for forestry field validation test to quantify aboveground tree biomass and carbon

    SciTech Connect

    Lee Spangler; Lee A. Vierling; Eva K. Stand; Andrew T. Hudak; Jan U.H. Eitel; Sebastian Martinuzzi

    2012-04-01

    Sound policy recommendations relating to the role of forest management in mitigating atmospheric carbon dioxide (CO{sub 2}) depend upon establishing accurate methodologies for quantifying forest carbon pools for large tracts of land that can be dynamically updated over time. Light Detection and Ranging (LiDAR) remote sensing is a promising technology for achieving accurate estimates of aboveground biomass and thereby carbon pools; however, not much is known about the accuracy of estimating biomass change and carbon flux from repeat LiDAR acquisitions containing different data sampling characteristics. In this study, discrete return airborne LiDAR data was collected in 2003 and 2009 across {approx}20,000 hectares (ha) of an actively managed, mixed conifer forest landscape in northern Idaho, USA. Forest inventory plots, established via a random stratified sampling design, were established and sampled in 2003 and 2009. The Random Forest machine learning algorithm was used to establish statistical relationships between inventory data and forest structural metrics derived from the LiDAR acquisitions. Aboveground biomass maps were created for the study area based on statistical relationships developed at the plot level. Over this 6-year period, we found that the mean increase in biomass due to forest growth across the non-harvested portions of the study area was 4.8 metric ton/hectare (Mg/ha). In these non-harvested areas, we found a significant difference in biomass increase among forest successional stages, with a higher biomass increase in mature and old forest compared to stand initiation and young forest. Approximately 20% of the landscape had been disturbed by harvest activities during the six-year time period, representing a biomass loss of >70 Mg/ha in these areas. During the study period, these harvest activities outweighed growth at the landscape scale, resulting in an overall loss in aboveground carbon at this site. The 30-fold increase in sampling density

  9. Allometric Scaling and Resource Limitations Model of Total Aboveground Biomass in Forest Stands: Site-scale Test of Model

    NASA Astrophysics Data System (ADS)

    CHOI, S.; Shi, Y.; Ni, X.; Simard, M.; Myneni, R. B.

    2013-12-01

    Sparseness in in-situ observations has precluded the spatially explicit and accurate mapping of forest biomass. The need for large-scale maps has raised various approaches implementing conjugations between forest biomass and geospatial predictors such as climate, forest type, soil property, and topography. Despite the improved modeling techniques (e.g., machine learning and spatial statistics), a common limitation is that biophysical mechanisms governing tree growth are neglected in these black-box type models. The absence of a priori knowledge may lead to false interpretation of modeled results or unexplainable shifts in outputs due to the inconsistent training samples or study sites. Here, we present a gray-box approach combining known biophysical processes and geospatial predictors through parametric optimizations (inversion of reference measures). Total aboveground biomass in forest stands is estimated by incorporating the Forest Inventory and Analysis (FIA) and Parameter-elevation Regressions on Independent Slopes Model (PRISM). Two main premises of this research are: (a) The Allometric Scaling and Resource Limitations (ASRL) theory can provide a relationship between tree geometry and local resource availability constrained by environmental conditions; and (b) The zeroth order theory (size-frequency distribution) can expand individual tree allometry into total aboveground biomass at the forest stand level. In addition to the FIA estimates, two reference maps from the National Biomass and Carbon Dataset (NBCD) and U.S. Forest Service (USFS) were produced to evaluate the model. This research focuses on a site-scale test of the biomass model to explore the robustness of predictors, and to potentially improve models using additional geospatial predictors such as climatic variables, vegetation indices, soil properties, and lidar-/radar-derived altimetry products (or existing forest canopy height maps). As results, the optimized ASRL estimates satisfactorily

  10. Mapping aboveground woody biomass using forest inventory, remote sensing and geostatistical techniques.

    PubMed

    Yadav, Bechu K V; Nandy, S

    2015-05-01

    Mapping forest biomass is fundamental for estimating CO₂ emissions, and planning and monitoring of forests and ecosystem productivity. The present study attempted to map aboveground woody biomass (AGWB) integrating forest inventory, remote sensing and geostatistical techniques, viz., direct radiometric relationships (DRR), k-nearest neighbours (k-NN) and cokriging (CoK) and to evaluate their accuracy. A part of the Timli Forest Range of Kalsi Soil and Water Conservation Division, Uttarakhand, India was selected for the present study. Stratified random sampling was used to collect biophysical data from 36 sample plots of 0.1 ha (31.62 m × 31.62 m) size. Species-specific volumetric equations were used for calculating volume and multiplied by specific gravity to get biomass. Three forest-type density classes, viz. 10-40, 40-70 and >70% of Shorea robusta forest and four non-forest classes were delineated using on-screen visual interpretation of IRS P6 LISS-III data of December 2012. The volume in different strata of forest-type density ranged from 189.84 to 484.36 m(3) ha(-1). The total growing stock of the forest was found to be 2,024,652.88 m(3). The AGWB ranged from 143 to 421 Mgha(-1). Spectral bands and vegetation indices were used as independent variables and biomass as dependent variable for DRR, k-NN and CoK. After validation and comparison, k-NN method of Mahalanobis distance (root mean square error (RMSE) = 42.25 Mgha(-1)) was found to be the best method followed by fuzzy distance and Euclidean distance with RMSE of 44.23 and 45.13 Mgha(-1) respectively. DRR was found to be the least accurate method with RMSE of 67.17 Mgha(-1). The study highlighted the potential of integrating of forest inventory, remote sensing and geostatistical techniques for forest biomass mapping. PMID:25930205

  11. Temporal variability in aboveground plant biomass decreases as spatial variability increases.

    PubMed

    McGranahan, Devan Allen; Hovick, Torre J; Elmore, R Dwayne; Engle, David M; Fuhlendorf, Samuel D; Winter, Stephen L; Miller, James R; Debinski, Diane M

    2016-03-01

    Ecological theory predicts that diversity decreases variability in ecosystem function. We predict that, at the landscape scale, spatial variability created by a mosaic of contrasting patches that differ in time since disturbance will decrease temporal variability in aboveground plant biomass. Using data from a multi-year study of seven grazed tallgrass prairie landscapes, each experimentally managed for one to eight patches, we show that increased spatial variability driven by spatially patchy fire and herbivory reduces temporal variability in aboveground plant biomass. This pattern is associated with statistical evidence for the portfolio effect and a positive relationship between temporal variability and functional group synchrony as predicted by metacommunity variability theory. As disturbance from fire and grazing interact to create a shifting mosaic of spatially heterogeneous patches within a landscape, temporal variability in aboveground plant biomass can be dampened. These results suggest that spatially heterogeneous disturbance regimes contribute to a portfolio of ecosystem functions provided by biodiversity, including wildlife habitat, fuel, and forage. We discuss how spatial patterns of disturbance drive variability within and among patches. PMID:27197382

  12. Above-ground biomass and structure of 260 African tropical forests

    PubMed Central

    Lewis, Simon L.; Sonké, Bonaventure; Sunderland, Terry; Begne, Serge K.; Lopez-Gonzalez, Gabriela; van der Heijden, Geertje M. F.; Phillips, Oliver L.; Affum-Baffoe, Kofi; Baker, Timothy R.; Banin, Lindsay; Bastin, Jean-François; Beeckman, Hans; Boeckx, Pascal; Bogaert, Jan; De Cannière, Charles; Chezeaux, Eric; Clark, Connie J.; Collins, Murray; Djagbletey, Gloria; Djuikouo, Marie Noël K.; Droissart, Vincent; Doucet, Jean-Louis; Ewango, Cornielle E. N.; Fauset, Sophie; Feldpausch, Ted R.; Foli, Ernest G.; Gillet, Jean-François; Hamilton, Alan C.; Harris, David J.; Hart, Terese B.; de Haulleville, Thales; Hladik, Annette; Hufkens, Koen; Huygens, Dries; Jeanmart, Philippe; Jeffery, Kathryn J.; Kearsley, Elizabeth; Leal, Miguel E.; Lloyd, Jon; Lovett, Jon C.; Makana, Jean-Remy; Malhi, Yadvinder; Marshall, Andrew R.; Ojo, Lucas; Peh, Kelvin S.-H.; Pickavance, Georgia; Poulsen, John R.; Reitsma, Jan M.; Sheil, Douglas; Simo, Murielle; Steppe, Kathy; Taedoumg, Hermann E.; Talbot, Joey; Taplin, James R. D.; Taylor, David; Thomas, Sean C.; Toirambe, Benjamin; Verbeeck, Hans; Vleminckx, Jason; White, Lee J. T.; Willcock, Simon; Woell, Hannsjorg; Zemagho, Lise

    2013-01-01

    We report above-ground biomass (AGB), basal area, stem density and wood mass density estimates from 260 sample plots (mean size: 1.2 ha) in intact closed-canopy tropical forests across 12 African countries. Mean AGB is 395.7 Mg dry mass ha−1 (95% CI: 14.3), substantially higher than Amazonian values, with the Congo Basin and contiguous forest region attaining AGB values (429 Mg ha−1) similar to those of Bornean forests, and significantly greater than East or West African forests. AGB therefore appears generally higher in palaeo- compared with neotropical forests. However, mean stem density is low (426 ± 11 stems ha−1 greater than or equal to 100 mm diameter) compared with both Amazonian and Bornean forests (cf. approx. 600) and is the signature structural feature of African tropical forests. While spatial autocorrelation complicates analyses, AGB shows a positive relationship with rainfall in the driest nine months of the year, and an opposite association with the wettest three months of the year; a negative relationship with temperature; positive relationship with clay-rich soils; and negative relationships with C : N ratio (suggesting a positive soil phosphorus–AGB relationship), and soil fertility computed as the sum of base cations. The results indicate that AGB is mediated by both climate and soils, and suggest that the AGB of African closed-canopy tropical forests may be particularly sensitive to future precipitation and temperature changes. PMID:23878327

  13. Latitudinal Characteristics of Below- and Above-ground Biomass of Typha: a Modelling Approach

    PubMed Central

    ASAEDA, TAKASHI; HAI, DINH NGOC; MANATUNGE, JAGATH; WILLIAMS, DAVID; ROBERTS, JANE

    2005-01-01

    • Background and Aims The latitudinal differences in the growth characteristics of Typha are largely unknown, although a number of studies have pointed out the effects of climate on the growth and productivity of Typha. Therefore, a dynamic growth model was developed for Typha to examine the effects of latitudinal changes in temperature and radiation on partitioning of the total biomass during the growing season into rhizomes, roots, flowering and vegetative shoots, and inflorescences. • Methods After validating the model with data from growth studies of Typha found in past literature, it was used to investigate the dynamics of above- and below-ground biomasses at three latitudes: 30°, 40° and 50°. • Key Results Regardless of the initial rhizome biomass, both above- and below-ground biomass values converged to a latitude-specific equilibrium produced by the balance between the total production and respiration and mortality losses. Above-ground biomass was high from 10° to 35° latitude with sufficient radiation, despite high metabolic losses; however, it decreased markedly at higher latitudes due to a low photosynthetic rate. Below-ground biomass, on the other hand, increased with latitude up to 40° due to decreasing metabolic losses, and then markedly decreased at higher latitudes. Above-ground biomass was enhanced with an increasing number of cohorts regardless of latitude. However, although more cohorts resulted in a larger below-ground biomass at low latitudes, the largest below-ground biomass was provided by a smaller number of cohorts at high latitudes. This difference is due to low production rates of late-season cohorts in high latitudes, compared with consumption for shooting and establishing foliage. • Conclusions The model could be used to predict the potential growth of Typha in given conditions over a wide range of latitudes and is useful for practical applications such as wetland management or wastewater treatment systems using Typha

  14. Simulation results of aboveground woody biomass and leaf litterfall for African tropical forest with a global terrestrial model

    NASA Astrophysics Data System (ADS)

    De Weirdt, Marjolein; Maignan, Fabienne; Peylin, Philippe; Poulter, Benjamin; Moreau, Inès; Ciais, Philippe; Defourny, Pierre; Steppe, Kathy; Verbeeck, Hans

    2014-05-01

    The response of tropical forest vegetation to global climate change could be central to predictions of future levels of atmospheric carbon dioxide. Tropical forests are believed to annually process approximately six times as much carbon via photosynthesis and respiration as humans emit from fossil fuel use. Of all tropical forests worldwide, the role of African tropical forest is not very well known and both the quantity as well as the dynamics of tropical forest carbon stocks and fluxes are very poorly quantified components of the global carbon cycle. Furthermore, African tropical forest spatial carbon stocks patterns as measured in the field are not as well represented by the global biogeochemical models as they are for temperate forests. In this study, a first simulation for the African tropical forest with the process based global terrestrial ecosystem model ORCHIDEE was done. In this work, ORCHIDEE included deep soils, seasonal leaf litterfall and phosphorus availability mechanisms for tropical evergreen forests included. The ORCHIDEE model run outputs are evaluated against reported field inventories, investigating seasonal variations in leaf litterfall and spatial variation in aboveground woody biomass. A comparison between modeled and measured leaf litterfall was made at a semi-deciduous Equatorial rainforest site in the Republic of Congo at the Biosphere reserve Dimonika south of Gabon. Also, simulated woody aboveground biomass was compared against site-level field inventories and satellite-based estimates based on a combination of MODIS imagery with field inventory data from Uganda, DRC and Cameroon. First comparison results seem promising and show that the radiation driven leaf litterfall model results correspond well with the field inventories and that the mean of the modelled aboveground woody biomass matches the available field inventory observations but there is still a need for more ground data to evaluate the model outcome over a large region like

  15. Spectroscopic Determination of Aboveground Biomass in Grasslands Using Spectral Transformations, Support Vector Machine and Partial Least Squares Regression

    PubMed Central

    Marabel, Miguel; Alvarez-Taboada, Flor

    2013-01-01

    Aboveground biomass (AGB) is one of the strategic biophysical variables of interest in vegetation studies. The main objective of this study was to evaluate the Support Vector Machine (SVM) and Partial Least Squares Regression (PLSR) for estimating the AGB of grasslands from field spectrometer data and to find out which data pre-processing approach was the most suitable. The most accurate model to predict the total AGB involved PLSR and the Maximum Band Depth index derived from the continuum removed reflectance in the absorption features between 916–1,120 nm and 1,079–1,297 nm (R2 = 0.939, RMSE = 7.120 g/m2). Regarding the green fraction of the AGB, the Area Over the Minimum index derived from the continuum removed spectra provided the most accurate model overall (R2 = 0.939, RMSE = 3.172 g/m2). Identifying the appropriate absorption features was proved to be crucial to improve the performance of PLSR to estimate the total and green aboveground biomass, by using the indices derived from those spectral regions. Ordinary Least Square Regression could be used as a surrogate for the PLSR approach with the Area Over the Minimum index as the independent variable, although the resulting model would not be as accurate. PMID:23925082

  16. A hyperspectral imaging system for an accurate prediction of the above-ground biomass of individual rice plants.

    PubMed

    Feng, Hui; Jiang, Ni; Huang, Chenglong; Fang, Wei; Yang, Wanneng; Chen, Guoxing; Xiong, Lizhong; Liu, Qian

    2013-09-01

    Biomass is an important component of the plant phenomics, and the existing methods for biomass estimation for individual plants are either destructive or lack accuracy. In this study, a hyperspectral imaging system was developed for the accurate prediction of the above-ground biomass of individual rice plants in the visible and near-infrared spectral region. First, the structure of the system and the influence of various parameters on the camera acquisition speed were established. Then the system was used to image 152 rice plants, which selected from the rice mini-core collection, in two stages, the tillering to elongation (T-E) stage and the booting to heading (B-H) stage. Several variables were extracted from the images. Following, linear stepwise regression analysis and 5-fold cross-validation were used to select effective variables for model construction and test the stability of the model, respectively. For the T-E stage, the R(2) value was 0.940 for the fresh weight (FW) and 0.935 for the dry weight (DW). For the B-H stage, the R(2) value was 0.891 for the FW and 0.783 for the DW. Moreover, estimations of the biomass using visible light images were also calculated. These comparisons showed that hyperspectral imaging performed better than the visible light imaging. Therefore, this study provides not only a stable hyperspectral imaging platform but also an accurate and nondestructive method for the prediction of biomass for individual rice plants. PMID:24089866

  17. Modelling Growth and Partitioning of Annual Above-Ground Vegetative and Reproductive Biomass of Grapevine

    NASA Astrophysics Data System (ADS)

    Meggio, Franco; Vendrame, Nadia; Maniero, Giovanni; Pitacco, Andrea

    2014-05-01

    In the current climate change scenarios, both agriculture and forestry inherently may act as carbon sinks and consequently can play a key role in limiting global warming. An urgent need exists to understand which land uses and land resource types have the greatest potential to mitigate greenhouse gas (GHG) emissions contributing to global change. A common believe is that agricultural fields cannot be net carbon sinks due to many technical inputs and repeated disturbances of upper soil layers that all contribute to a substantial loss both of the old and newly-synthesized organic matter. Perennial tree crops (vineyards and orchards), however, can behave differently: they grow a permanent woody structure, stand undisturbed in the same field for decades, originate a woody pruning debris, and are often grass-covered. In this context, reliable methods for quantifying and modelling emissions and carbon sequestration are required. Carbon stock changes are calculated by multiplying the difference in oven dry weight of biomass increments and losses with the appropriate carbon fraction. These data are relatively scant, and more information is needed on vineyard management practices and how they impact vineyard C sequestration and GHG emissions in order to generate an accurate vineyard GHG footprint. During the last decades, research efforts have been made for estimating the vineyard carbon budget and its allocation pattern since it is crucial to better understand how grapevines control the distribution of acquired resources in response to variation in environmental growth conditions and agronomic practices. The objective of the present study was to model and compare the dynamics of current year's above-ground biomass among four grapevine varieties. Trials were carried out over three growing seasons in field conditions. The non-linear extra-sums-of-squares method demonstrated to be a feasible way of growth models comparison to statistically assess significant differences among

  18. Interactions between herbivory and warming in aboveground biomass production of arctic vegetation

    PubMed Central

    Pedersen, Christian; Post, Eric

    2008-01-01

    Background Many studies investigating the ecosystem effects of global climate change have focused on arctic ecosystems because the Arctic is expected to undergo the earliest and most pronounced changes in response to increasing global temperatures, and arctic ecosystems are considerably limited by low temperatures and permafrost. In these nutrient limited systems, a warmer climate is expected to increase plant biomass production, primarily through increases in shrubs over graminoids and forbs. But, the influence of vertebrate and invertebrate herbivores has been largely absent in studies investigating the effects of vegetation responses to climate change, despite the fact that herbivory can have a major influence on plant community composition, biomass and nutrient cycling. Here, we present results from a multi-annual field experiment investigating the effects of vertebrate herbivory on plant biomass response to simulated climate warming in arctic Greenland. Results The results after four years of treatments did not give any clear evidence of increased biomass of shrubs in response climate warming. Nor did our study indicate that vertebrate grazing mediated any increased domination of shrubs over other functional plant groups in response to warming. However, our results indicate an important role of insect outbreaks on aboveground biomass. Intense caterpillar foraging from a two-year outbreak of the moth Eurois occulta during two growing seasons may have concealed any treatment effects. However, there was some evidence suggesting that vertebrate herbivores constrain the biomass production of shrubs over graminoids and forbs. Conclusion Although inconclusive, our results were likely constrained by the overwhelming influence of an unexpected caterpillar outbreak on aboveground biomass. It is likely that the role of large vertebrate herbivores in vegetation response to warming will become more evident as this experiment proceeds and the plant community recovers from

  19. Plant diversity and functional groups affect Si and Ca pools in aboveground biomass of grassland systems.

    PubMed

    Schaller, Jörg; Roscher, Christiane; Hillebrand, Helmut; Weigelt, Alexandra; Oelmann, Yvonne; Wilcke, Wolfgang; Ebeling, Anne; Weisser, Wolfgang W

    2016-09-01

    Plant diversity is an important driver of nitrogen and phosphorus stocks in aboveground plant biomass of grassland ecosystems, but plant diversity effects on other elements also important for plant growth are less understood. We tested whether plant species richness, functional group richness or the presence/absence of particular plant functional groups influences the Si and Ca concentrations (mmol g(-1)) and stocks (mmol m(-2)) in aboveground plant biomass in a large grassland biodiversity experiment (Jena Experiment). In the experiment including 60 temperate grassland species, plant diversity was manipulated as sown species richness (1, 2, 4, 8, 16) and richness and identity of plant functional groups (1-4; grasses, small herbs, tall herbs, legumes). We found positive species richness effects on Si as well as Ca stocks that were attributable to increased biomass production. The presence of particular functional groups was the most important factor explaining variation in aboveground Si and Ca stocks (mmol m(-2)). Grass presence increased the Si stocks by 140 % and legume presence increased the Ca stock by 230 %. Both the presence of specific plant functional groups and species diversity altered Si and Ca stocks, whereas Si and Ca concentration were affected mostly by the presence of specific plant functional groups. However, we found a negative effect of species diversity on Si and Ca accumulation, by calculating the deviation between mixtures and mixture biomass proportions, but in monoculture concentrations. These changes may in turn affect ecosystem processes such as plant litter decomposition and nutrient cycling in grasslands. PMID:27164912

  20. Multi- and hyperspectral remote-sensing retrieval of floodplain-forest aboveground biomass using machine learning

    NASA Astrophysics Data System (ADS)

    Filippi, A. M.; Guneralp, I.; Randall, J.

    2014-12-01

    Forests within dynamic floodplain landscapes, such as meandering-river landscapes, are composed of uneven-aged trees and entail high spatial variability, which results from intersecting hydrological, fluvial, and ecological processes. Floodplain forests are an important carbon sink relative to other terrestrial ecosystems and thus serve a critical role in the global carbon cycle. Accurate, quantitative aboveground biomass (AGB) retrieval within floodplain forests is urgently needed for improved carbon-pool estimates in such areas and enhanced process understanding of river-floodplain biomorphodynamics. We perform remote AGB retrieval for a meander-bend bottomland hardwood forest, based on utilization of stochastic gradient boosting (SGB), multivariate adaptive regression splines (MARS), and Cubist algorithms and multi- and hyperspectral image-based data sets. For multispectral experiments, we use 30-m and 10-m image bands (Landsat 7 ETM+ and SPOT 5, respectively) and ancillary input vectors; for hyperspectral-based experiments, we use 30-m Hyperion bands and other input variables. Results indicate that for both the multispectral and hyperspectral experimental trials, SGB- and MARS-derived AGB are significantly more accurate than Cubist estimates. (Cubist is used for U.S. national-scale forest biomass mapping.) For the multispectral results, across all data-experiments and algorithms, at 10-m spatial resolution, SGB gives the most accurate estimates (RMSE = 22.49 tonnes/ha; coefficient of determination (R2) = 0.96) when geomorphometric data are also included. For 30-m multispectral data trials, MARS performs the best (RMSE = 29.2 tonnes/ha; R2 = 0.94) when image-derived data are also incorporated. For the hyperspectral experiments, the most accurate MARS- and SGB-based retrievals have R2 of 0.97 and 0.95, respectively; the most accurate Cubist AGB retrieval has R2 of 0.85. MARS and SGB AGB are not significantly different though for the hyperspectral experiments. The

  1. [Vegetation above-ground biomass and its affecting factors in water/wind erosion crisscross region on Loess Plateau].

    PubMed

    Wang, Jian-guo; Fan, Jun; Wang, Quan-jiu; Wang, Li

    2011-03-01

    Field investigations were conducted in Liudaogou small watershed in late September 2009 to study the differences of vegetation above-ground biomass, soil moisture content, and soil nutrient contents under different land use patterns, aimed to approach the vegetation above-ground biomass level and related affecting factors in typical small watershed in water/wind erosion crisscross region on Loess Plateau. The above-ground dry biomass of the main vegetations in Liudaogou was 177-2207 g x m(-2), and that in corn field, millet field, abandoned farmland, artificial grassland, natural grassland, and shrub land was 2097-2207, 518-775, 248-578, 280-545, 177-396, and 372-680 g x m(-2), respectively. The mean soil moisture content in 0-100 layer was the highest (14.2%) in farmlands and the lowest (10.9%) in shrub land. The coefficient of variation of soil moisture content was the greatest (26. 7% ) in abandoned farmland, indicating the strong spatial heterogeneity of soil moisture in this kind of farmland. The mean soil water storage was in the order of farmland > artificial grassland > natural grassland > shrub land. Soil dry layer was observed in alfalfa and caragana lands. There was a significant positive correlation (r = 0.639, P < 0.05) between above-ground dry biomass and 0-100 cm soil water storage, and also, a very significant positive correlation between above-ground fresh biomass and vegetation height. The above-ground biomass of the higher vegetations could potentially better control the wind and water erosion in the water/wind erosion crisscross region. Vegetation above-ground biomass was highly correlated with soil moisture and nutrient contents, but had no significant correlations with elevation, slope gradient, slope aspect, and soil bulk density. PMID:21657007

  2. Statewide Mapping of Aboveground Biomass by Integrating Airborne Lidar Data and National Forestry Inventory Plots

    NASA Astrophysics Data System (ADS)

    Chen, Q.; McRoberts, R. E.

    2015-12-01

    The freely available airborne lidar data at the sub-national level in the United States provide unprecedented opportunities for mapping large-area yet accurate information about vegetation structure, biomass, and carbon. However, the challenge of processing massive lidar data and extracting useful information is huge. This study is to conduct a statewide mapping study of aboveground biomass (AGB) by integrating airborne lidar data and FIA (Forest Inventory and Analysis) plot data for the whole state of Minnesota. We will share our experience and lessons in issues including 1) automatic generation of Digital Terrain Model from point cloud, 2) classification of vegetation returns, 3) calculation of AGB from FIA plots using different allometric models, 4) statistical modeling of AGB by integrating with FIA plots, and 5) assessing the uncertainty of mapped AGB.

  3. Testing the generality of above-ground biomass allometry across plant functional types at the continent scale.

    PubMed

    Paul, Keryn I; Roxburgh, Stephen H; Chave, Jerome; England, Jacqueline R; Zerihun, Ayalsew; Specht, Alison; Lewis, Tom; Bennett, Lauren T; Baker, Thomas G; Adams, Mark A; Huxtable, Dan; Montagu, Kelvin D; Falster, Daniel S; Feller, Mike; Sochacki, Stan; Ritson, Peter; Bastin, Gary; Bartle, John; Wildy, Dan; Hobbs, Trevor; Larmour, John; Waterworth, Rob; Stewart, Hugh T L; Jonson, Justin; Forrester, David I; Applegate, Grahame; Mendham, Daniel; Bradford, Matt; O'Grady, Anthony; Green, Daryl; Sudmeyer, Rob; Rance, Stan J; Turner, John; Barton, Craig; Wenk, Elizabeth H; Grove, Tim; Attiwill, Peter M; Pinkard, Elizabeth; Butler, Don; Brooksbank, Kim; Spencer, Beren; Snowdon, Peter; O'Brien, Nick; Battaglia, Michael; Cameron, David M; Hamilton, Steve; McAuthur, Geoff; Sinclair, Jenny

    2016-06-01

    Accurate ground-based estimation of the carbon stored in terrestrial ecosystems is critical to quantifying the global carbon budget. Allometric models provide cost-effective methods for biomass prediction. But do such models vary with ecoregion or plant functional type? We compiled 15 054 measurements of individual tree or shrub biomass from across Australia to examine the generality of allometric models for above-ground biomass prediction. This provided a robust case study because Australia includes ecoregions ranging from arid shrublands to tropical rainforests, and has a rich history of biomass research, particularly in planted forests. Regardless of ecoregion, for five broad categories of plant functional type (shrubs; multistemmed trees; trees of the genus Eucalyptus and closely related genera; other trees of high wood density; and other trees of low wood density), relationships between biomass and stem diameter were generic. Simple power-law models explained 84-95% of the variation in biomass, with little improvement in model performance when other plant variables (height, bole wood density), or site characteristics (climate, age, management) were included. Predictions of stand-based biomass from allometric models of varying levels of generalization (species-specific, plant functional type) were validated using whole-plot harvest data from 17 contrasting stands (range: 9-356 Mg ha(-1) ). Losses in efficiency of prediction were <1% if generalized models were used in place of species-specific models. Furthermore, application of generalized multispecies models did not introduce significant bias in biomass prediction in 92% of the 53 species tested. Further, overall efficiency of stand-level biomass prediction was 99%, with a mean absolute prediction error of only 13%. Hence, for cost-effective prediction of biomass across a wide range of stands, we recommend use of generic allometric models based on plant functional types. Development of new species

  4. Spatial effects of aboveground biomass on soil ecological parameters and trace gas fluxes in a savannah ecosystem of Mount Kilimanjaro

    NASA Astrophysics Data System (ADS)

    Becker, Joscha; Gütlein, Adrian; Sierra Cornejo, Natalia; Kiese, Ralf; Hertel, Dietrich; Kuzyakov, Yakov

    2015-04-01

    The savannah biome is a hotspot for biodiversity and wildlife conservation in Africa and recently got in the focus of research on carbon sequestration. Savannah ecosystems are under strong pressure from climate and land-use change, especially around populous areas like the Mt. Kilimanjaro region. Savannah vegetation in this area consists of grassland with isolated trees and is therefore characterized by high spatial variation of canopy cover, aboveground biomass and root structure. Canopy structure is known to affect microclimate, throughfall and evapotranspiration and thereby controls soil moisture conditions. Consequently, the canopy structure is a major regulator for soil ecological parameters and soil-atmospheric trace gas exchange (CO2, N2O, CH4) in water limited environments. The spatial distribution of these parameters and the connection between above and belowground processes are important to understand and predict ecosystem changes and estimate its vulnerability. Our objective was to determine trends and changes of soil parameters and relate their spatial variability to the vegetation structure. We chose three trees from each of the two most dominant species (Acacia nilotica and Balanites aegyptiaca) in our research area. For each tree, we selected transects with nine sampling points of the same relative distances to the stem. Distances were calculated in relation to the crown radius. At these each sampling point a soil core was taken and separated in 0-10 cm and 10-30 cm depth. We measured soil carbon (C) and nitrogen (N) storage, microbial biomass carbon C and N, soil respiration as well as root biomass and -density, soil temperature and soil water content. Each tree was characterized by crown spread, leaf area index and basal area. Preliminary results show that C and N stocks decreased about 50% with depth independently of distance to the tree. Soil water content under the tree crown increased with depth while it decreased under grass cover. Microbial

  5. Carbon dynamics in aboveground biomass of co-dominant plant species: related rather to leaf life span than to species

    NASA Astrophysics Data System (ADS)

    Ostler, Ulrike; Schleip, Inga; Lattanzi, Fernando A.; Schnyder, Hans

    2016-04-01

    This study investigates the role of individual organisms in whole ecosystem carbon (C) fluxes. It is currently unknown if different plant community members share the same or different kinetics of C pools in aboveground biomass, thereby adding (or not) variability to the first steps in ecosystem C cycling. We assessed the residence times in metabolic and non-metabolic (or structural) C pools and the allocation pattern of assimilated C in aboveground plant parts of four co-existing, co-dominant species from different functional groups in a temperate grassland community. For this purpose continuous, 14-16 day long 13CO2/12CO2-labeling experiments were performed in Sept. 2006, May 2007 and Sept. 2007, and the tracer kinetics were analysed with compartmental modeling. In all experimental periods, the species shared vastly similar residence times in metabolic C (5-8 d). In contrast, the residence times in non-metabolic C ranged from 20 to 58 d (except one outlier) and the fraction of fixed C allocated to the non-metabolic pool from 7 to 45%. These variations in non-metabolic C kinetics were not systematically associated with species or experimental periods, but exhibited close relationships with (independent estimates of) leaf life span, particularly in the grasses. This adds new meaning to leaf life span as a functional trait in the leaf and plant economics spectrum and its implication for C cycle studies in grassland and also forest systems. As the four co-dominant species accounted for ~80% of total community shoot biomass, we should also expect that the observed similarities in pool kinetics and allocation will scale up to similar relationships at the community level.

  6. Using satellite radar backscatter to predict above-ground woody biomass: A consistent relationship across four different African landscapes

    NASA Astrophysics Data System (ADS)

    Mitchard, E. T. A.; Saatchi, S. S.; Woodhouse, I. H.; Nangendo, G.; Ribeiro, N. S.; Williams, M.; Ryan, C. M.; Lewis, S. L.; Feldpausch, T. R.; Meir, P.

    2009-12-01

    Regional-scale above-ground biomass (AGB) estimates of tropical savannas and woodlands are highly uncertain, despite their global importance for ecosystems services and as carbon stores. In response, we collated field inventory data from 253 plots at four study sites in Cameroon, Uganda and Mozambique, and examined the relationships between field-measured AGB and cross-polarized radar backscatter values derived from ALOS PALSAR, an L-band satellite sensor. The relationships were highly significant, similar among sites, and displayed high prediction accuracies up to 150 Mg ha-1 (±˜20%). AGB predictions for any given site obtained using equations derived from data from only the other three sites generated only small increases in error. The results suggest that a widely applicable general relationship exists between AGB and L-band backscatter for lower-biomass tropical woody vegetation. This relationship allows regional-scale AGB estimation, required for example by planned REDD (Reducing Emissions from Deforestation and Degradation) schemes.

  7. Biomass Estimation of Dry Tropical Woody Species at Juvenile Stage

    PubMed Central

    Chaturvedi, R. K.; Raghubanshi, A. S.; Singh, J. S.

    2012-01-01

    Accurate characterization of biomass in different forest components is important to estimate their contribution to total carbon stock. Due to lack of allometric equations for biomass estimation of woody species at juvenile stage, the carbon stored in this forest component is ignored. We harvested 47 woody species at juvenile stage in a dry tropical forest and developed regression models for the estimation of above-ground biomass (AGB). The models including wood-specific gravity (ρ) exhibited higher R2 than those without ρ. The model consisting of ρ, stem diameter (D), and height (H) not only exhibited the highest R2 value but also had the lowest standard error of estimate. We suggest that ρ-based regression model is a viable option for nondestructive estimation of biomass of forest trees at juvenile stage. PMID:22448139

  8. Aboveground Biomass and Dynamics of Forest Attributes using LiDAR Data and Vegetation Model

    NASA Astrophysics Data System (ADS)

    V V L, P. A.

    2015-12-01

    In recent years, biomass estimation for tropical forests has received much attention because of the fact that regional biomass is considered to be a critical input to climate change. Biomass almost determines the potential carbon emission that could be released to the atmosphere due to deforestation or conservation to non-forest land use. Thus, accurate biomass estimation is necessary for better understating of deforestation impacts on global warming and environmental degradation. In this context, forest stand height inclusion in biomass estimation plays a major role in reducing the uncertainty in the estimation of biomass. The improvement in the accuracy in biomass shall also help in meeting the MRV objectives of REDD+. Along with the precise estimate of biomass, it is also important to emphasize the role of vegetation models that will most likely become an important tool for assessing the effects of climate change on potential vegetation dynamics and terrestrial carbon storage and for managing terrestrial ecosystem sustainability. Remote sensing is an efficient way to estimate forest parameters in large area, especially at regional scale where field data is limited. LIDAR (Light Detection And Ranging) provides accurate information on the vertical structure of forests. We estimated average tree canopy heights and AGB from GLAS waveform parameters by using a multi-regression linear model in forested area of Madhya Pradesh (area-3,08,245 km2), India. The derived heights from ICESat-GLAS were correlated with field measured tree canopy heights for 60 plots. Results have shown a significant correlation of R2= 74% for top canopy heights and R2= 57% for stand biomass. The total biomass estimation 320.17 Mt and canopy heights are generated by using random forest algorithm. These canopy heights and biomass maps were used in vegetation models to predict the changes biophysical/physiological characteristics of forest according to the changing climate. In our study we have

  9. 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.

  10. Ability of LANDSAT-8 Oli Derived Texture Metrics in Estimating Aboveground Carbon Stocks of Coppice Oak Forests

    NASA Astrophysics Data System (ADS)

    Safari, A.; Sohrabi, H.

    2016-06-01

    The role of forests as a reservoir for carbon has prompted the need for timely and reliable estimation of aboveground carbon stocks. Since measurement of aboveground carbon stocks of forests is a destructive, costly and time-consuming activity, aerial and satellite remote sensing techniques have gained many attentions in this field. Despite the fact that using aerial data for predicting aboveground carbon stocks has been proved as a highly accurate method, there are challenges related to high acquisition costs, small area coverage, and limited availability of these data. These challenges are more critical for non-commercial forests located in low-income countries. Landsat program provides repetitive acquisition of high-resolution multispectral data, which are freely available. The aim of this study was to assess the potential of multispectral Landsat 8 Operational Land Imager (OLI) derived texture metrics in quantifying aboveground carbon stocks of coppice Oak forests in Zagros Mountains, Iran. We used four different window sizes (3×3, 5×5, 7×7, and 9×9), and four different offsets ([0,1], [1,1], [1,0], and [1,-1]) to derive nine texture metrics (angular second moment, contrast, correlation, dissimilar, entropy, homogeneity, inverse difference, mean, and variance) from four bands (blue, green, red, and infrared). Totally, 124 sample plots in two different forests were measured and carbon was calculated using species-specific allometric models. Stepwise regression analysis was applied to estimate biomass from derived metrics. Results showed that, in general, larger size of window for deriving texture metrics resulted models with better fitting parameters. In addition, the correlation of the spectral bands for deriving texture metrics in regression models was ranked as b4>b3>b2>b5. The best offset was [1,-1]. Amongst the different metrics, mean and entropy were entered in most of the regression models. Overall, different models based on derived texture metrics

  11. Testing the sensitivity of terrestrial carbon models using remotely sensed biomass estimates

    NASA Astrophysics Data System (ADS)

    Hashimoto, H.; Saatchi, S. S.; Meyer, V.; Milesi, C.; Wang, W.; Ganguly, S.; Zhang, G.; Nemani, R. R.

    2010-12-01

    There is a large uncertainty in carbon allocation and biomass accumulation in forest ecosystems. With the recent availability of remotely sensed biomass estimates, we now can test some of the hypotheses commonly implemented in various ecosystem models. We used biomass estimates derived by integrating MODIS, GLAS and PALSAR data to verify above-ground biomass estimates simulated by a number of ecosystem models (CASA, BIOME-BGC, BEAMS, LPJ). This study extends the hierarchical framework (Wang et al., 2010) for diagnosing ecosystem models by incorporating independent estimates of biomass for testing and calibrating respiration, carbon allocation, turn-over algorithms or parameters.

  12. Wildfires in bamboo-dominated Amazonian forest: impacts on above-ground biomass and biodiversity.

    PubMed

    Barlow, Jos; Silveira, Juliana M; Mestre, Luiz A M; Andrade, Rafael B; Camacho D'Andrea, Gabriela; Louzada, Julio; Vaz-de-Mello, Fernando Z; Numata, Izaya; Lacau, Sébastien; Cochrane, Mark A

    2012-01-01

    Fire has become an increasingly important disturbance event in south-western Amazonia. We conducted the first assessment of the ecological impacts of these wildfires in 2008, sampling forest structure and biodiversity along twelve 500 m transects in the Chico Mendes Extractive Reserve, Acre, Brazil. Six transects were placed in unburned forests and six were in forests that burned during a series of forest fires that occurred from August to October 2005. Normalized Burn Ratio (NBR) calculations, based on Landsat reflectance data, indicate that all transects were similar prior to the fires. We sampled understorey and canopy vegetation, birds using both mist nets and point counts, coprophagous dung beetles and the leaf-litter ant fauna. Fire had limited influence upon either faunal or floral species richness or community structure responses, and stems <10 cm DBH were the only group to show highly significant (p = 0.001) community turnover in burned forests. Mean aboveground live biomass was statistically indistinguishable in the unburned and burned plots, although there was a significant increase in the total abundance of dead stems in burned plots. Comparisons with previous studies suggest that wildfires had much less effect upon forest structure and biodiversity in these south-western Amazonian forests than in central and eastern Amazonia, where most fire research has been undertaken to date. We discuss potential reasons for the apparent greater resilience of our study plots to wildfire, examining the role of fire intensity, bamboo dominance, background rates of disturbance, landscape and soil conditions. PMID:22428035

  13. Comment on 'A first map of tropical Africa's above-ground biomass derived from satellite imagery'

    NASA Astrophysics Data System (ADS)

    Mitchard, E. T. A.; Saatchi, S. S.; Lewis, S. L.; Feldpausch, T. R.; Gerard, F. F.; Woodhouse, I. H.; Meir, P.

    2011-10-01

    We present a critical evaluation of the above-ground biomass (AGB) map of Africa published in this journal by Baccini et al (2008 Environ. Res. Lett. 3 045011). We first test their map against an independent dataset of 1154 scientific inventory plots from 16 African countries, and find only weak correspondence between our field plots and the AGB value given for the surrounding 1 km pixel by Baccini et al. Separating our field data using a continental landcover classification suggests that the Baccini et al map underestimates the AGB of forests and woodlands, while overestimating the AGB of savannas and grasslands. Secondly, we compare their map to 216 000 × 0.25 ha spaceborne LiDAR footprints. A comparison between Lorey's height (basal-area-weighted average height) derived from the LiDAR data for 1 km pixels containing at least five LiDAR footprints again does not support the hypothesis that the Baccini et al map is accurate, and suggests that it significantly underestimates the AGB of higher AGB areas. We conclude that this is due to the unsuitability of some of the field data used by Baccini et al to create their map, and overfitting in their model, resulting in low accuracies outside the small areas from which their field data are drawn.

  14. Wildfires in Bamboo-Dominated Amazonian Forest: Impacts on Above-Ground Biomass and Biodiversity

    PubMed Central

    Barlow, Jos; Silveira, Juliana M.; Mestre, Luiz A. M.; Andrade, Rafael B.; Camacho D'Andrea, Gabriela; Louzada, Julio; Vaz-de-Mello, Fernando Z.; Numata, Izaya; Lacau, Sébastien; Cochrane, Mark A.

    2012-01-01

    Fire has become an increasingly important disturbance event in south-western Amazonia. We conducted the first assessment of the ecological impacts of these wildfires in 2008, sampling forest structure and biodiversity along twelve 500 m transects in the Chico Mendes Extractive Reserve, Acre, Brazil. Six transects were placed in unburned forests and six were in forests that burned during a series of forest fires that occurred from August to October 2005. Normalized Burn Ratio (NBR) calculations, based on Landsat reflectance data, indicate that all transects were similar prior to the fires. We sampled understorey and canopy vegetation, birds using both mist nets and point counts, coprophagous dung beetles and the leaf-litter ant fauna. Fire had limited influence upon either faunal or floral species richness or community structure responses, and stems <10 cm DBH were the only group to show highly significant (p = 0.001) community turnover in burned forests. Mean aboveground live biomass was statistically indistinguishable in the unburned and burned plots, although there was a significant increase in the total abundance of dead stems in burned plots. Comparisons with previous studies suggest that wildfires had much less effect upon forest structure and biodiversity in these south-western Amazonian forests than in central and eastern Amazonia, where most fire research has been undertaken to date. We discuss potential reasons for the apparent greater resilience of our study plots to wildfire, examining the role of fire intensity, bamboo dominance, background rates of disturbance, landscape and soil conditions. PMID:22428035

  15. Response of Plant Height, Species Richness and Aboveground Biomass to Flooding Gradient along Vegetation Zones in Floodplain Wetlands, Northeast China

    PubMed Central

    Lou, Yanjing; Pan, Yanwen; Gao, Chuanyu; Jiang, Ming; Lu, Xianguo; Xu, Y. Jun

    2016-01-01

    Flooding regime changes resulting from natural and human activity have been projected to affect wetland plant community structures and functions. It is therefore important to conduct investigations across a range of flooding gradients to assess the impact of flooding depth on wetland vegetation. We conducted this study to identify the pattern of plant height, species richness and aboveground biomass variation along the flooding gradient in floodplain wetlands located in Northeast China. We found that the response of dominant species height to the flooding gradient depends on specific species, i.e., a quadratic response for Carex lasiocarpa, a negative correlation for Calamagrostis angustifolia, and no response for Carex appendiculata. Species richness showed an intermediate effect along the vegetation zone from marsh to wet meadow while aboveground biomass increased. When the communities were analysed separately, only the water table depth had significant impact on species richness for two Carex communities and no variable for C. angustifolia community, while height of dominant species influenced aboveground biomass. When the three above-mentioned communities were grouped together, variations in species richness were mainly determined by community type, water table depth and community mean height, while variations in aboveground biomass were driven by community type and the height of dominant species. These findings indicate that if habitat drying of these herbaceous wetlands in this region continues, then two Carex marshes would be replaced gradually by C. angustifolia wet meadow in the near future. This will lead to a reduction in biodiversity and an increase in productivity and carbon budget. Meanwhile, functional traits must be considered, and should be a focus of attention in future studies on the species diversity and ecosystem function in this region. PMID:27097325

  16. Response of Plant Height, Species Richness and Aboveground Biomass to Flooding Gradient along Vegetation Zones in Floodplain Wetlands, Northeast China.

    PubMed

    Lou, Yanjing; Pan, Yanwen; Gao, Chuanyu; Jiang, Ming; Lu, Xianguo; Xu, Y Jun

    2016-01-01

    Flooding regime changes resulting from natural and human activity have been projected to affect wetland plant community structures and functions. It is therefore important to conduct investigations across a range of flooding gradients to assess the impact of flooding depth on wetland vegetation. We conducted this study to identify the pattern of plant height, species richness and aboveground biomass variation along the flooding gradient in floodplain wetlands located in Northeast China. We found that the response of dominant species height to the flooding gradient depends on specific species, i.e., a quadratic response for Carex lasiocarpa, a negative correlation for Calamagrostis angustifolia, and no response for Carex appendiculata. Species richness showed an intermediate effect along the vegetation zone from marsh to wet meadow while aboveground biomass increased. When the communities were analysed separately, only the water table depth had significant impact on species richness for two Carex communities and no variable for C. angustifolia community, while height of dominant species influenced aboveground biomass. When the three above-mentioned communities were grouped together, variations in species richness were mainly determined by community type, water table depth and community mean height, while variations in aboveground biomass were driven by community type and the height of dominant species. These findings indicate that if habitat drying of these herbaceous wetlands in this region continues, then two Carex marshes would be replaced gradually by C. angustifolia wet meadow in the near future. This will lead to a reduction in biodiversity and an increase in productivity and carbon budget. Meanwhile, functional traits must be considered, and should be a focus of attention in future studies on the species diversity and ecosystem function in this region. PMID:27097325

  17. Seeing the Forest through the Trees: Citizen Scientists Provide Critical Data to Refine Aboveground Carbon Estimates in Restored Riparian Forests

    NASA Astrophysics Data System (ADS)

    Viers, J. H.

    2013-12-01

    Integrating citizen scientists into ecological informatics research can be difficult due to limited opportunities for meaningful engagement given vast data streams. This is particularly true for analysis of remotely sensed data, which are increasingly being used to quantify ecosystem services over space and time, and to understand how land uses deliver differing values to humans and thus inform choices about future human actions. Carbon storage and sequestration are such ecosystem services, and recent environmental policy advances in California (i.e., AB 32) have resulted in a nascent carbon market that is helping fuel the restoration of riparian forests in agricultural landscapes. Methods to inventory and monitor aboveground carbon for market accounting are increasingly relying on hyperspatial remotely sensed data, particularly the use of light detection and ranging (LiDAR) technologies, to estimate biomass. Because airborne discrete return LiDAR can inexpensively capture vegetation structural differences at high spatial resolution (< 1 m) over large areas (> 1000 ha), its use is rapidly increasing, resulting in vast stores of point cloud and derived surface raster data. While established algorithms can quantify forest canopy structure efficiently, the highly complex nature of native riparian forests can result in highly uncertain estimates of biomass due to differences in composition (e.g., species richness, age class) and structure (e.g., stem density). This study presents the comparative results of standing carbon estimates refined with field data collected by citizen scientists at three different sites, each capturing a range of agricultural, remnant forest, and restored forest cover types. These citizen science data resolve uncertainty in composition and structure, and improve allometric scaling models of biomass and thus estimates of aboveground carbon. Results indicate that agricultural land and horticulturally restored riparian forests store similar

  18. Grazing effects on aboveground primary production and root biomass of early-seral, mid-seral, and undisturbed semiarid grassland

    USGS Publications Warehouse

    Milchunas, D.G.; Vandever, M.W.

    2013-01-01

    Annual/perennial and tall/short plant species differentially dominate early to late successional shortgrass steppe communities. Plant species can have different ratios of above-/below-ground biomass distributions and this can be modified by precipitation and grazing. We compared grazing effects on aboveground production and root biomass in early- and mid-seral fields and undisturbed shortgrass steppe. Production averaged across four years and grazed and ungrazed treatments were 246, 134, and 102 g m−2 yr−1 for the early-, mid-seral, and native sites, respectively, while root biomass averaged 358, 560, and 981 g m−2, respectively. Early- and mid-seral communities provided complimentary forage supplies but at the cost of root biomass. Grazing increased, decreased, or had no effect on aboveground production in early-, mid-seral, and native communities, and had no effect on roots in any. Grazing had some negative effects on early spring forage species, but not in the annual dominated early-seral community. Dominant species increased with grazing in native communities with a long evolutionary history of grazing by large herbivores, but had no effects on the same species in mid-seral communities. Effects of grazing in native communities in a region cannot necessarily be used to predict effects at other seral stages.

  19. Tropical Africa: Land use, biomass, and carbon estimates for 1980

    SciTech Connect

    Brown, S.; Gaston, G.; Daniels, R.C.

    1996-06-01

    This document describes the contents of a digital database containing maximum potential aboveground biomass, land use, and estimated biomass and carbon data for 1980 and describes a methodology that may be used to extend this data set to 1990 and beyond based on population and land cover data. The biomass data and carbon estimates are for woody vegetation in Tropical Africa. These data were collected to reduce the uncertainty associated with the possible magnitude of historical releases of carbon from land use change. Tropical Africa is defined here as encompassing 22.7 x 10{sup 6} km{sup 2} of the earth`s land surface and includes those countries that for the most part are located in Tropical Africa. Countries bordering the Mediterranean Sea and in southern Africa (i.e., Egypt, Libya, Tunisia, Algeria, Morocco, South Africa, Lesotho, Swaziland, and Western Sahara) have maximum potential biomass and land cover information but do not have biomass or carbon estimate. The database was developed using the GRID module in the ARC/INFO{sup TM} geographic information system. Source data were obtained from the Food and Agriculture Organization (FAO), the U.S. National Geophysical Data Center, and a limited number of biomass-carbon density case studies. These data were used to derive the maximum potential and actual (ca. 1980) aboveground biomass-carbon values at regional and country levels. The land-use data provided were derived from a vegetation map originally produced for the FAO by the International Institute of Vegetation Mapping, Toulouse, France.

  20. The Spatial Distribution of Forest Biomass in the Brazilian Amazon: A Comparison of Estimates

    NASA Technical Reports Server (NTRS)

    Houghton, R. A.; Lawrence, J. L.; Hackler, J. L.; Brown, S.

    2001-01-01

    The amount of carbon released to the atmosphere as a result of deforestation is determined, in part, by the amount of carbon held in the biomass of the forests converted to other uses. Uncertainty in forest biomass is responsible for much of the uncertainty in current estimates of the flux of carbon from land-use change. We compared several estimates of forest biomass for the Brazilian Amazon, based on spatial interpolations of direct measurements, relationships to climatic variables, and remote sensing data. We asked three questions. First, do the methods yield similar estimates? Second, do they yield similar spatial patterns of distribution of biomass? And, third, what factors need most attention if we are to predict more accurately the distribution of forest biomass over large areas? Amazonian forests (including dead and below-ground biomass) vary by more than a factor of two, from a low of 39 PgC to a high of 93 PgC. Furthermore, the estimates disagree as to the regions of high and low biomass. The lack of agreement among estimates confirms the need for reliable determination of aboveground biomass over large areas. Potential methods include direct measurement of biomass through forest inventories with improved allometric regression equations, dynamic modeling of forest recovery following observed stand-replacing disturbances (the approach used in this research), and estimation of aboveground biomass from airborne or satellite-based instruments sensitive to the vertical structure plant canopies.

  1. Improving Aboveground Carbon Estimates in Dryland Ecosystems with Airborne LiDAR and Satellite Laser Altimetry

    NASA Astrophysics Data System (ADS)

    Glenn, N. F.; Shrestha, R.; Li, A.; Spaete, L.

    2014-12-01

    Numerous studies have demonstrated the utility of ground and airborne LiDAR data to quantify ecosystem structure. In addition, data from satellite-based laser altimetry (e.g. ICESat's GLAS instrument) have been used to estimate vegetation heights, aboveground carbon, and topography in forested areas. With the upcoming ICESAT-2 satellite scheduled to launch in 2017, we have the potential to map vegetation characteristics and dynamics in other ecosystems, including semiarid and low-height ecosystems, at global and regional scales. The ICESat-2 satellite will include the Advanced Topographic Laser Altimeter System (ATLAS) with a configuration of 6 laser beams with 532 nm wavelength and photon counting detectors. We will demonstrate the potential of ICESat-2 to provide estimates of vegetation structure and topography in a dryland ecosystem by simulating the configuration of the ATLAS mission. We will also examine how airborne LiDAR can be used together with ICESat-2 and other satellite data to achieve estimates of aboveground carbon. We will explore how these data may be used for future monitoring and quantification of spatial and temporal changes in aboveground carbon and topography.

  2. The response of tundra plant biomass, above-ground production, nitrogen, and CO{sub 2} flux to experimental warming

    SciTech Connect

    Hobbie, S.E.; Chapin, F.S. III

    1998-07-01

    The authors manipulated air temperature in tussock tundra near Toolik Lake, Alaska, and determined the consequences for total plant biomass, aboveground net primary production (ANPP), ecosystem nitrogen (N) pools and N uptake, and ecosystem CO{sub 2} flux. After 3.5 growing seasons, in situ plastic greenhouses that raised air temperature during the growing season had little effect on total biomass, N content, or growing-season N uptake of the major plant and soil pools. Similarly, vascular ANPP and net ecosystem CO{sub 2} exchange did not change with warming, although net primary production of mosses decreased with warming. Such general lack of response supports the hypothesis that productivity in tundra is constrained by the indirect effects of cold temperatures rather than by cold growing-season temperatures per se. Despite no effect on net ecosystem CO{sub 2} flux, air warming stimulated early-season gross photosynthesis (GP) and ecosystem respiration (ER) throughout the growing season. This increased carbon turnover was probably associated with species-level responses to increased air temperature. Warming increased the aboveground biomass of the overstory shrub, dwarf birch (Betula nana), and caused a significant net redistribution of N from the understory evergreen shrub, Vaccinium vitis-idaea, to B. nana, despite no effects on soil temperature, total plant N, or N availability.

  3. Spatial distributions of forest aboveground biomass and landscape dynamics associated with conservation status and ownership in New England, USA

    NASA Astrophysics Data System (ADS)

    Zheng, D.; Heath, L. S.; Ducey, M. J.

    2009-05-01

    We combined remote sensing derived forest aboveground biomass (AGB) estimation and the Conservation Biology Institute/World Wildlife Fund Protected Area Database using GIS techniques and spatial pattern analysis to illustrate how different conservation status and ownership could affect the landscape dynamics and spatial distributions of AGB in New England states, USA. The AGB means between all pairs of protection status and ownership categories were significantly different (P < 0.05). The highest mean AGB was observed in the protected public lands (156 Mg/ha), 44% higher than the lowest AGB mean (108 Mg/ha) observed in private regulated lands (privately owned but under the regulatory control by a state agency), or 30% higher than that in privately owned lands on average (120 Mg/ha). Seventy-seven percent of the regional forests with AGB > 200 Mg/ha, totaling about 9,300 km2, were located outside protected areas and were concentrated in western MA, southern VT, southwestern NH, and northwestern CT. The fragmentation rate in protected public lands between 1992 and 2001 was the least with greater rates were observed in privately regulated and non-regulated lands. Changing rates for the 4 representative fragmentation indices (patch density (PD), edge density (ED), landscape shape index (LSI), and mean patch size (MPS)) ranged from 1% in MPS to 6% in PD in protected public lands during the 9-year period whereas the mean changing rates ranged from 21% in LSI to 32% in PD in private lands. Thus, ownership and conservation status appears to have a strong impact on the dynamic changes of landscape structures in the region. These results indicate that if maintenance and enhancement of relatively unfragmented, high-AGB forest is a goal, expansion of protected areas appears to be an important management strategy.

  4. Model Effects on GLAS-Based Regional Estimates of Forest Biomass and Carbon

    NASA Technical Reports Server (NTRS)

    Nelson, Ross

    2008-01-01

    ICESat/GLAS waveform data are used to estimate biomass and carbon on a 1.27 million sq km study area. the Province of Quebec, Canada, below treeline. The same input data sets and sampling design are used in conjunction with four different predictive models to estimate total aboveground dry forest biomass and forest carbon. The four models include nonstratified and stratified versions of a multiple linear model where either biomass or (square root of) biomass serves as the dependent variable. The use of different models in Quebec introduces differences in Provincial biomass estimates of up to 0.35 Gt (range 4.942+/-0.28 Gt to 5.29+/-0.36 Gt). The results suggest that if different predictive models are used to estimate regional carbon stocks in different epochs, e.g., y2005, y2015, one might mistakenly infer an apparent aboveground carbon "change" of, in this case, 0.18 Gt, or approximately 7% of the aboveground carbon in Quebec, due solely to the use of different predictive models. These findings argue for model consistency in future, LiDAR-based carbon monitoring programs. Regional biomass estimates from the four GLAS models are compared to ground estimates derived from an extensive network of 16,814 ground plots located in southern Quebec. Stratified models proved to be more accurate and precise than either of the two nonstratified models tested.

  5. [Optimized Spectral Indices Based Estimation of Forage Grass Biomass].

    PubMed

    An, Hai-bo; Li, Fei; Zhao, Meng-li; Liu, Ya-jun

    2015-11-01

    As an important indicator of forage production, aboveground biomass will directly illustrate the growth of forage grass. Therefore, Real-time monitoring biomass of forage grass play a crucial role in performing suitable grazing and management in artificial and natural grassland. However, traditional sampling and measuring are time-consuming and labor-intensive. Recently, development of hyperspectral remote sensing provides the feasibility in timely and nondestructive deriving biomass of forage grass. In the present study, the main objectives were to explore the robustness of published and optimized spectral indices in estimating biomass of forage grass in natural and artificial pasture. The natural pasture with four grazing density (control, light grazing, moderate grazing and high grazing) was designed in desert steppe, and different forage cultivars with different N rate were conducted in artificial forage fields in Inner Mongolia. The canopy reflectance and biomass in each plot were measured during critical stages. The result showed that, due to the influence in canopy structure and biomass, the canopy reflectance have a great difference in different type of forage grass. The best performing spectral index varied in different species of forage grass with different treatments (R² = 0.00-0.69). The predictive ability of spectral indices decreased under low biomass of desert steppe, while red band based spectral indices lost sensitivity under moderate-high biomass of forage maize. When band combinations of simple ratio and normalized difference spectral indices were optimized in combined datasets of natural and artificial grassland, optimized spectral indices significant increased predictive ability and the model between biomass and optimized spectral indices had the highest R² (R² = 0.72) compared to published spectral indices. Sensitive analysis further confirmed that the optimized index had the lowest noise equivalent and were the best performing index in

  6. Predictive modeling of hazardous waste landfill total above-ground biomass using passive optical and LIDAR remotely sensed data

    NASA Astrophysics Data System (ADS)

    Hadley, Brian Christopher

    This dissertation assessed remotely sensed data and geospatial modeling technique(s) to map the spatial distribution of total above-ground biomass present on the surface of the Savannah River National Laboratory's (SRNL) Mixed Waste Management Facility (MWMF) hazardous waste landfill. Ordinary least squares (OLS) regression, regression kriging, and tree-structured regression were employed to model the empirical relationship between in-situ measured Bahia (Paspalum notatum Flugge) and Centipede [Eremochloa ophiuroides (Munro) Hack.] grass biomass against an assortment of explanatory variables extracted from fine spatial resolution passive optical and LIDAR remotely sensed data. Explanatory variables included: (1) discrete channels of visible, near-infrared (NIR), and short-wave infrared (SWIR) reflectance, (2) spectral vegetation indices (SVI), (3) spectral mixture analysis (SMA) modeled fractions, (4) narrow-band derivative-based vegetation indices, and (5) LIDAR derived topographic variables (i.e. elevation, slope, and aspect). Results showed that a linear combination of the first- (1DZ_DGVI), second- (2DZ_DGVI), and third-derivative of green vegetation indices (3DZ_DGVI) calculated from hyperspectral data recorded over the 400--960 nm wavelengths of the electromagnetic spectrum explained the largest percentage of statistical variation (R2 = 0.5184) in the total above-ground biomass measurements. In general, the topographic variables did not correlate well with the MWMF biomass data, accounting for less than five percent of the statistical variation. It was concluded that tree-structured regression represented the optimum geospatial modeling technique due to a combination of model performance and efficiency/flexibility factors.

  7. Secondary Forest Age and Tropical Forest Biomass Estimation Using TM

    NASA Technical Reports Server (NTRS)

    Nelson, R. F.; Kimes, D. S.; Salas, W. A.; Routhier, M.

    1999-01-01

    The age of secondary forests in the Amazon will become more critical with respect to the estimation of biomass and carbon budgets as tropical forest conversion continues. Multitemporal Thematic Mapper data were used to develop land cover histories for a 33,000 Square kM area near Ariquemes, Rondonia over a 7 year period from 1989-1995. The age of the secondary forest, a surrogate for the amount of biomass (or carbon) stored above-ground, was found to be unimportant in terms of biomass budget error rates in a forested TM scene which had undergone a 20% conversion to nonforest/agricultural cover types. In such a situation, the 80% of the scene still covered by primary forest accounted for over 98% of the scene biomass. The difference between secondary forest biomass estimates developed with and without age information were inconsequential relative to the estimate of biomass for the entire scene. However, in futuristic scenarios where all of the primary forest has been converted to agriculture and secondary forest (55% and 42% respectively), the ability to age secondary forest becomes critical. Depending on biomass accumulation rate assumptions, scene biomass budget errors on the order of -10% to +30% are likely if the age of the secondary forests are not taken into account. Single-date TM imagery cannot be used to accurately age secondary forests into single-year classes. A neural network utilizing TM band 2 and three TM spectral-texture measures (bands 3 and 5) predicted secondary forest age over a range of 0-7 years with an RMSE of 1.59 years and an R(Squared) (sub actual vs predicted) = 0.37. A proposal is made, based on a literature review, to use satellite imagery to identify general secondary forest age groups which, within group, exhibit relatively constant biomass accumulation rates.

  8. Hand-held spectral radiometer to estimate gramineous biomass. [with interfaced pocket calculator solution

    NASA Technical Reports Server (NTRS)

    Pearson, R. L.; Miller, L. D.; Tucker, C. J.

    1976-01-01

    A simple hand-held instrument has been designed and constructed to nondestructively estimate above-ground gramineous biomass using radiometric measurements. The prototype unit consists of a modified two-channel digital radiometer interfaced to a pocket calculator. A digital interface was constructed to join electronically and control the radiometer and calculator to enable the radiometer-calculator system to solve a linear conversion solution from radiometric units to estimated biomass. This instrument has been used to estimate radiometrically gramineous biomass in a more efficient fashion with a high degree of accuracy.

  9. National-scale estimation of gross forest aboveground carbon loss: a case study of the Democratic Republic of the Congo

    NASA Astrophysics Data System (ADS)

    Tyukavina, A.; Stehman, S. V.; Potapov, P. V.; Turubanova, S. A.; Baccini, A.; Goetz, S. J.; Laporte, N. T.; Houghton, R. A.; Hansen, M. C.

    2013-12-01

    Recent advances in remote sensing enable the mapping and monitoring of carbon stocks without relying on extensive in situ measurements. The Democratic Republic of the Congo (DRC) is among the countries where national forest inventories (NFI) are either non-existent or out of date. Here we demonstrate a method for estimating national-scale gross forest aboveground carbon (AGC) loss and associated uncertainties using remotely sensed-derived forest cover loss and biomass carbon density data. Lidar data were used as a surrogate for NFI plot measurements to estimate carbon stocks and AGC loss based on forest type and activity data derived using time-series multispectral imagery. Specifically, DRC forest type and loss from the FACET (Forêts d’Afrique Centrale Evaluées par Télédétection) product, created using Landsat data, were related to carbon data derived from the Geoscience Laser Altimeter System (GLAS). Validation data for FACET forest area loss were created at a 30-m spatial resolution and compared to the 60-m spatial resolution FACET map. We produced two gross AGC loss estimates for the DRC for the last decade (2000-2010): a map-scale estimate (53.3 ± 9.8 Tg C yr-1) accounting for whole-pixel classification errors in the 60-m resolution FACET forest cover change product, and a sub-grid estimate (72.1 ± 12.7 Tg C yr-1) that took into account 60-m cells that experienced partial forest loss. Our sub-grid forest cover and AGC loss estimates, which included smaller-scale forest disturbances, exceed published assessments. Results raise the issue of scale in forest cover change mapping and validation, and subsequent impacts on remotely sensed carbon stock change estimation, particularly for smallholder dominated systems such as the DRC.

  10. Evaluating land use and aboveground biomass dynamics in an oil palm-dominated landscape in Borneo using optical remote sensing

    NASA Astrophysics Data System (ADS)

    Singh, Minerva; Malhi, Yadvinder; Bhagwat, Shonil

    2014-01-01

    The focus of this study is to assess the efficacy of using optical remote sensing (RS) in evaluating disparities in forest composition and aboveground biomass (AGB). The research was carried out in the East Sabah region, Malaysia, which constitutes a disturbance gradient ranging from pristine old growth forests to forests that have experienced varying levels of disturbances. Additionally, a significant proportion of the area consists of oil palm plantations. In accordance with local laws, riparian forest (RF) zones have been retained within oil palm plantations and other forest types. The RS imagery was used to assess forest stand structure and AGB. Band reflectance, vegetation indicators, and gray-level co-occurrence matrix (GLCM) consistency features were used as predictor variables in regression analysis. Results indicate that the spectral variables were limited in their effectiveness in differentiating between forest types and in calculating biomass. However, GLCM based variables illustrated strong correlations with the forest stand structures as well as with the biomass of the various forest types in the study area. The present study provides new insights into the efficacy of texture examination methods in differentiating between various land-use types (including small, isolated forest zones such as RFs) as well as their AGB stocks.

  11. Evaluation of non-destructive methods for estimating biomass in marshes of the upper Texas, USA coast

    USGS Publications Warehouse

    Whitbeck, M.; Grace, J.B.

    2006-01-01

    The estimation of aboveground biomass is important in the management of natural resources. Direct measurements by clipping, drying, and weighing of herbaceous vegetation are time-consuming and costly. Therefore, non-destructive methods for efficiently and accurately estimating biomass are of interest. We compared two non-destructive methods, visual obstruction and light penetration, for estimating aboveground biomass in marshes of the upper Texas, USA coast. Visual obstruction was estimated using the Robel pole method, which primarily measures the density and height of the canopy. Light penetration through the canopy was measured using a Decagon light wand, with readings taken above the vegetation and at the ground surface. Clip plots were also taken to provide direct estimates of total aboveground biomass. Regression relationships between estimated and clipped biomass were significant using both methods. However, the light penetration method was much more strongly correlated with clipped biomass under these conditions (R2 value 0.65 compared to 0.35 for the visual obstruction approach). The primary difference between the two methods in this situation was the ability of the light-penetration method to account for variations in plant litter. These results indicate that light-penetration measurements may be better for estimating biomass in marshes when plant litter is an important component. We advise that, in all cases, investigators should calibrate their methods against clip plots to evaluate applicability to their situation. ?? 2006, The Society of Wetland Scientists.

  12. Changes in aboveground biomass and nutrient content on Walker Branch Watershed from 1967 to 1983

    SciTech Connect

    Johnson, D.W.; Henderson, G.S.; Harris, W.F.

    1987-01-01

    The increment of forest biomass and nutrient content on Walker Branch Watershed, Tennessee, from 1967 to 1983 was interrupted by two insect outbreaks. An outbreak of the southern pine beetle in the early 1970s and an outbreak of the hickory borer in the late 1970s to early 1980s killed a number of shortleaf pine (Pinus echinata) and hickory (Carya spp.), respectively. Yellow-poplar (Liriodendron tulipifera) growth increased over this 16-year period, especially in response to the mortality of shortleaf pine. The net result of these events was little change in total biomass but a substantial shift in species composition (from pine to yellow-poplar) in the Pine forest type over this period. No species has yet responded to the mortality of hickory. Due to the shift in species composition in the Pine type, calcium and magnesium accumulation rates in biomass increased but foliage biomass decreased over the inventory period. There was little change in foliage biomass or nutrient content in other forest types, despite hickory mortality, since mortality occurred primarily among large trees having low foliage-to-woody-biomass ratios. The insect attacks, combined with apparently natural self-thinning, caused a large increase in standing dead biomass and in nutrient return via tree fall. This increased rate of return will substantially alter forest floor nutrient content and availability, especially with regard to calcium (where the calcium content of standing dead currently equals forest floor calcium content) and nitrogen (where inputs of woody litter will substantially alter carbon to nitrogen ratios). 23 refs., 7 figs., 2 tabs.

  13. Response of aboveground biomass and diversity to nitrogen addition along a degradation gradient in the Inner Mongolian steppe, China

    PubMed Central

    Xu, Xiaotian; Liu, Hongyan; Song, Zhaoliang; Wang, Wei; Hu, Guozheng; Qi, Zhaohuan

    2015-01-01

    Although nitrogen addition and recovery from degradation can both promote production of grassland biomass, these two factors have rarely been investigated in combination. In this study, we established a field experiment with six N-treatment (CK, 10, 20, 30, 40, 50 g N m−2 yr−1) on five fields with different degradation levels in the Inner Mongolian steppe of China from 2011–2013. Our observations showed that while the external nitrogen increased the aboveground biomass in all five grasslands, the magnitude of the effects differed with the severity of degradation. Fields with a higher level of degradation tended to have a higher saturation value (20 g N m−2 yr−1) than those with a lower degradation level ( < 10 g N m−2 yr−1). After three years of experimentation, species richness showed little change across degradation levels. Among the four functional groups of grasses, sedges, forbs and legumes, grasses shared the most similar response patterns with those of the whole community, demonstrating the predominant role that they play in the restoration of grassland under a stimulus of nitrogen addition. PMID:26194184

  14. Biotic and abiotic controls on the distribution of tropical forest aboveground biomass

    NASA Astrophysics Data System (ADS)

    Saatchi, S. S.; Schimel, D.; Keller, M. M.; Chambers, J. Q.; Dubayah, R.; Duffy, P.; Yu, Y.; Robinson, C. M.; Chowdhury, D.; Yang, Y.

    2013-12-01

    AUTHOR: Sassan Saatchi1,2, Yan Yang2, Diya Chowdhury2, Yifan Yu2, Chelsea Robinson2, David Schimel1, Paul Duffy3, Michael Keller4, Ralph Dubayah5, Jeffery Chambers6 1. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA USA 2. Institute of Environment and Sustainability, University of California, Los Angeles, CA, USA 3. Neptune and Company, Inc. Denver, CO, USA 4. International Institute of Tropical Forestry & International Programs, USDA Forest Service, Campinas, Brazil 5. Department of Geography, University of Maryland, College Park, MD, USA 6. Department of Geography, University of California, Berkeley, CA, USA ABSTRACT BODY: In recent years, climate change policies and scientific research created a widespread interest in quantify the carbon stock and changes of global tropical forests extending from forest patches to national and regional scales. Using a combination of inventory data from field plots and forest structure from spaceborne Lidar data, we examine the main controls on the distribution of tropical forest biomass. Here, we concentrate on environmental and landscape variables (precipitation, temperature, topography, and soil), and biotic variables such as functional traits (density of large trees, and wood specific gravity). The analysis is performed using global bioclimatic variables for precipitation and temperature, SRTM data for topographical variables (elevation and ruggedness), and global harmonized soil data for soil type and texture. For biotic variables, we use the GLAS Lidar data to quantify the distribution of large trees, a combined field and remote sensing data for distribution of tree wood specific gravity. The results show that climate variables such as precipitation of dry season can explain the heterogeneity of forest biomass over the landscape but cannot predict the biomass variability significantly and particularly for high biomass forests. Topography such as elevation and ruggedness along with temperature can

  15. Estimates of US biomass energy consumption 1992

    SciTech Connect

    Not Available

    1994-05-06

    This report is the seventh in a series of publications developed by the Energy Information Administration (EIA) to quantify the biomass-derived primary energy used by the US economy. It presents estimates of 1991 and 1992 consumption. The objective of this report is to provide updated estimates of biomass energy consumption for use by Congress, Federal and State agencies, biomass producers and end-use sectors, and the public at large.

  16. Estimates of US biomass energy consumption 1992

    NASA Astrophysics Data System (ADS)

    1994-05-01

    This report is the seventh in a series of publications developed by the Energy Information Administration (EIA) to quantify the biomass-derived primary energy used by the U.S. economy. It presents estimates of 1991 and 1992 consumption. The objective of this report is to provide updated estimates of biomass energy consumption for use by Congress, Federal and State agencies, biomass producers and end-use sectors, and the public at large.

  17. Error propagation and scaling for tropical forest biomass estimates.

    PubMed Central

    Chave, Jerome; Condit, Richard; Aguilar, Salomon; Hernandez, Andres; Lao, Suzanne; Perez, Rolando

    2004-01-01

    The above-ground biomass (AGB) of tropical forests is a crucial variable for ecologists, biogeochemists, foresters and policymakers. Tree inventories are an efficient way of assessing forest carbon stocks and emissions to the atmosphere during deforestation. To make correct inferences about long-term changes in biomass stocks, it is essential to know the uncertainty associated with AGB estimates, yet this uncertainty is rarely evaluated carefully. Here, we quantify four types of uncertainty that could lead to statistical error in AGB estimates: (i) error due to tree measurement; (ii) error due to the choice of an allometric model relating AGB to other tree dimensions; (iii) sampling uncertainty, related to the size of the study plot; (iv) representativeness of a network of small plots across a vast forest landscape. In previous studies, these sources of error were reported but rarely integrated into a consistent framework. We estimate all four terms in a 50 hectare (ha, where 1 ha = 10(4) m2) plot on Barro Colorado Island, Panama, and in a network of 1 ha plots scattered across central Panama. We find that the most important source of error is currently related to the choice of the allometric model. More work should be devoted to improving the predictive power of allometric models for biomass. PMID:15212093

  18. ESTIMATES OF BIOMASS DENSITY FOR TROPICAL FORESTS

    EPA Science Inventory

    An accurate estimation of the biomass density in forests is a necessary step in understanding the global carbon cycle and production of other atmospheric trace gases from biomass burning. n this paper the authors summarize the various approaches that have developed for estimating...

  19. Efficacy of generic allometric equations for estimating biomass: a test in Japanese natural forests.

    PubMed

    Ishihara, Masae I; Utsugi, Hajime; Tanouchi, Hiroyuki; Aiba, Masahiro; Kurokawa, Hiroko; Onoda, Yusuke; Nagano, Masahiro; Umehara, Toru; Ando, Makoto; Miyata, Rie; Hiura, Tsutom

    2015-07-01

    Accurate estimation of tree and forest biomass is key to evaluating forest ecosystem functions and the global carbon cycle. Allometric equations that estimate tree biomass from a set of predictors, such as stem diameter and tree height, are commonly used. Most allometric equations are site specific, usually developed from a small number of trees harvested in a small area, and are either species specific or ignore interspecific differences in allometry. Due to lack of site-specific allometries, local equations are often applied to sites for which they were not originally developed (foreign sites), sometimes leading to large errors in biomass estimates. In this study, we developed generic allometric equations for aboveground biomass and component (stem, branch, leaf, and root) biomass using large, compiled data sets of 1203 harvested trees belonging to 102 species (60 deciduous angiosperm, 32 evergreen angiosperm, and 10 evergreen gymnosperm species) from 70 boreal, temperate, and subtropical natural forests in Japan. The best generic equations provided better biomass estimates than did local equations that were applied to foreign sites. The best generic equations included explanatory variables that represent interspecific differences in allometry in addition to stem diameter, reducing error by 4-12% compared to the generic equations that did not include the interspecific difference. Different explanatory variables were selected for different components. For aboveground and stem biomass, the best generic equations had species-specific wood specific gravity as an explanatory variable. For branch, leaf, and root biomass, the best equations had functional types (deciduous angiosperm, evergreen angiosperm, and evergreen gymnosperm) instead of functional traits (wood specific gravity or leaf mass per area), suggesting importance of other traits in addition to these traits, such as canopy and root architecture. Inclusion of tree height in addition to stem diameter improved

  20. Recovery of aboveground plant biomass and productivity after fire in mesic and dry black spruce forests of interior Alaska

    USGS Publications Warehouse

    Mack, M.C.; Treseder, K.K.; Manies, K.L.; Harden, J.W.; Schuur, E.A.G.; Vogel, J.G.; Randerson, J.T.; Chapin, F. S., III

    2008-01-01

    Plant biomass accumulation and productivity are important determinants of ecosystem carbon (C) balance during post-fire succession. In boreal black spruce (Picea mariana) forests near Delta Junction, Alaska, we quantified aboveground plant biomass and net primary productivity (ANPP) for 4 years after a 1999 wildfire in a well-drained (dry) site, and also across a dry and a moderately well-drained (mesic) chronosequence of sites that varied in time since fire (2 to ???116 years). Four years after fire, total biomass at the 1999 burn site had increased exponentially to 160 ?? 21 g m-2 (mean ?? 1SE) and vascular ANPP had recovered to 138 ?? 32 g m-2 y -1, which was not different than that of a nearby unburned stand (160 ?? 48 g m-2 y-1) that had similar pre-fire stand structure and understory composition. Production in the young site was dominated by re-sprouting graminoids, whereas production in the unburned site was dominated by black spruce. On the dry and mesic chronosequences, total biomass pools, including overstory and understory vascular and non-vascular plants, and lichens, increased logarithmically (dry) or linearly (mesic) with increasing site age, reaching a maximum of 2469 ?? 180 (dry) and 4008 ?? 233 g m-2 (mesic) in mature stands. Biomass differences were primarily due to higher tree density in the mesic sites because mass per tree was similar between sites. ANPP of vascular and non-vascular plants increased linearly over time in the mesic chronosequence to 335 ?? 68 g m-2 y -1 in the mature site, but in the dry chronosequence it peaked at 410 ?? 43 g m-2 y-1 in a 15-year-old stand dominated by deciduous trees and shrubs. Key factors regulating biomass accumulation and production in these ecosystems appear to be the abundance and composition of re-sprouting species early in succession, the abundance of deciduous trees and shrubs in intermediate aged stands, and the density of black spruce across all stand ages. A better understanding of the controls

  1. Lasting effects of climate disturbance on perennial grassland above-ground biomass production under two cutting frequencies.

    PubMed

    Zwicke, Marine; Alessio, Giorgio A; Thiery, Lionel; Falcimagne, Robert; Baumont, René; Rossignol, Nicolas; Soussana, Jean-François; Picon-Cochard, Catherine

    2013-11-01

    Climate extremes can ultimately reshape grassland services such as forage production and change plant functional type composition. This 3-year field research studied resistance to dehydration and recovery after rehydration of plant community and plant functional types in an upland perennial grassland subjected to climate and cutting frequency (Cut+, Cut-) disturbances by measuring green tissue percentage and above-ground biomass production (ANPP). In year 1, a climate disturbance gradient was applied by co-manipulating temperature and precipitation. Four treatments were considered: control and warming-drought climatic treatment, with or without extreme summer event. In year 2, control and warming-drought treatments were maintained without extreme. In year 3, all treatments received ambient climatic conditions. We found that the grassland community was very sensitive to dehydration during the summer extreme: aerial senescence reached 80% when cumulated climatic water balance fell to -156 mm and biomass declined by 78% at the end of summer. In autumn, canopy greenness and biomass totally recovered in control but not in the warming-drought treatment. However ANPP decreased under both climatic treatments, but the effect was stronger on Cut+ (-24%) than Cut- (-15%). This decline was not compensated by the presence of three functional types because they were negatively affected by the climatic treatments, suggesting an absence of buffering effect on grassland production. In the following 2 years, lasting effects of climate disturbance on ANPP were observable. The unexpected stressful conditions of year 3 induced a decline in grassland production in the Cut+ control treatment. The fact that this treatment cumulated higher (45%) N export over the 3 years suggests that N plays a key role in ANPP stability. As ANPP in this mesic perennial grassland did not show engineering resilience, long-term experimental manipulation is needed. Infrequent mowing appears more

  2. Field note: comparative efficacy of a woody evapotranspiration landfill cover following the removal of aboveground biomass.

    PubMed

    Schnabel, William; Munk, Jens; Byrd, Amanda

    2015-01-01

    Woody vegetation cultivated for moisture management on evapotranspiration (ET) landfill covers could potentially serve a secondary function as a biomass crop. However, research is required to evaluate the extent to which trees could be harvested from ET covers without significantly impacting their moisture management function. This study investigated the drainage through a six-year-old, primarily poplar/cottonwood ET test cover for a period of one year following the harvest of all woody biomass exceeding a height of 30 cm above ground surface. Results were compared to previously reported drainage observed during the years leading up to the coppice event. In the first year following coppice, the ET cover was found to be 93% effective at redirecting moisture during the spring/summer season, and 95% effective during the subsequent fall/winter season. This was slightly lower than the 95% and 100% efficacy observed in the spring/summer and fall/winter seasons, respectively, during the final measured year prior to coppice. However, the post-coppice efficacy was higher than the efficacy observed during the first three years following establishment of the cover. While additional longer-term studies are recommended, this project demonstrated that woody ET covers could potentially produce harvestable biomass while still effectively managing aerial moisture. PMID:25254294

  3. Pathways of Leymus chinensis Individual Aboveground Biomass Decline in Natural Semiarid Grassland Induced by Overgrazing: A Study at the Plant Functional Trait Scale

    PubMed Central

    Wang, Zhen; Wu, Xinhong; Li, Xinle; Hu, Jing; Shi, Hongxiao; Guo, Fenghui; Zhang, Yong; Hou, Xiangyang

    2015-01-01

    Natural grassland productivity, which is based on an individual plant’s aboveground biomass (AB) and its interaction with herbivores, can obviously affect terrestrial ecosystem services and the grassland’s agricultural production. As plant traits have been linked to both AB and ecosystem success, they may provide a useful approach to understand the changes in individual plants and grassland productivity in response to grazing on a generic level. Unfortunately, the current lack of studies on how plant traits affect AB affected by herbivores leaves a major gap in our understanding of the mechanism of grassland productivity decline. This study, therefore, aims to analyze the paths of overgrazing-induced decline in the individual AB of Leymus chinensis (the dominant species of meadow-steppe grassland in northern China) on a plant functional trait scale. Using a paired-sampling approach, we compared the differences in the functional traits of L. chinensis in long-term grazing-excluded and experimental grazing grassland plots over a continuous period of approximately 20 years (located in meadow steppe lands in Hailar, Inner Mongolia, China). We found a highly significant decline in the individual height and biomass (leaf, stem, and the whole plant) of L. chinensis as a result of overgrazing. Biomass allocation and leaf mass per unit area were significantly affected by the variation in individual size. Grazing clearly enhanced the sensitivity of the leaf-to-stem biomass ratio in response to variation in individual size. Moreover, using a method of standardized major axis estimation, we found that the biomass in the leaves, stems, and the plant as a whole had highly significant allometric scaling with various functional traits. Also, the slopes of the allometric equations of these relationships were significantly altered by grazing. Therefore, a clear implication of this is that grazing promotes an asymmetrical response of different plant functional traits to variation

  4. Pathways of Leymus chinensis Individual Aboveground Biomass Decline in Natural Semiarid Grassland Induced by Overgrazing: A Study at the Plant Functional Trait Scale.

    PubMed

    Li, Xiliang; Liu, Zhiying; Wang, Zhen; Wu, Xinhong; Li, Xinle; Hu, Jing; Shi, Hongxiao; Guo, Fenghui; Zhang, Yong; Hou, Xiangyang

    2015-01-01

    Natural grassland productivity, which is based on an individual plant's aboveground biomass (AB) and its interaction with herbivores, can obviously affect terrestrial ecosystem services and the grassland's agricultural production. As plant traits have been linked to both AB and ecosystem success, they may provide a useful approach to understand the changes in individual plants and grassland productivity in response to grazing on a generic level. Unfortunately, the current lack of studies on how plant traits affect AB affected by herbivores leaves a major gap in our understanding of the mechanism of grassland productivity decline. This study, therefore, aims to analyze the paths of overgrazing-induced decline in the individual AB of Leymus chinensis (the dominant species of meadow-steppe grassland in northern China) on a plant functional trait scale. Using a paired-sampling approach, we compared the differences in the functional traits of L. chinensis in long-term grazing-excluded and experimental grazing grassland plots over a continuous period of approximately 20 years (located in meadow steppe lands in Hailar, Inner Mongolia, China). We found a highly significant decline in the individual height and biomass (leaf, stem, and the whole plant) of L. chinensis as a result of overgrazing. Biomass allocation and leaf mass per unit area were significantly affected by the variation in individual size. Grazing clearly enhanced the sensitivity of the leaf-to-stem biomass ratio in response to variation in individual size. Moreover, using a method of standardized major axis estimation, we found that the biomass in the leaves, stems, and the plant as a whole had highly significant allometric scaling with various functional traits. Also, the slopes of the allometric equations of these relationships were significantly altered by grazing. Therefore, a clear implication of this is that grazing promotes an asymmetrical response of different plant functional traits to variation in

  5. Changes in composition, structure and aboveground biomass over seventy-six years (1930-2006) in the Black Rock Forest, Hudson Highlands, southeastern New York State.

    PubMed

    Schuster, W S F; Griffin, K L; Roth, H; Turnbull, M H; Whitehead, D; Tissue, D T

    2008-04-01

    We sought to quantify changes in tree species composition, forest structure and aboveground forest biomass (AGB) over 76 years (1930-2006) in the deciduous Black Rock Forest in southeastern New York, USA. We used data from periodic forest inventories, published floras and a set of eight long-term plots, along with species-specific allometric equations to estimate AGB and carbon content. Between the early 1930s and 2000, three species were extirpated from the forest (American elm (Ulmus americana L.), paper birch (Betula papyrifera Marsh.) and black spruce (Picea mariana (nigra) (Mill.) BSP)) and seven species invaded the forest (non-natives tree-of-heaven (Ailanthus altissima (Mill.) Swingle) and white poplar (Populus alba L.) and native, generally southerly distributed, southern catalpa (Catalpa bignonioides Walt.), cockspur hawthorn (Crataegus crus-galli L.), red mulberry (Morus rubra L.), eastern cottonwood (Populus deltoides Bartr.) and slippery elm (Ulmus rubra Muhl.)). Forest canopy was dominated by red oak and chestnut oak, but the understory tree community changed substantially from mixed oak-maple to red maple-black birch. Density decreased from an average of 1500 to 735 trees ha(-1), whereas basal area doubled from less than 15 m(2) ha(-1) to almost 30 m(2) ha(-1) by 2000. Forest-wide mean AGB from inventory data increased from about 71 Mg ha(-1) in 1930 to about 145 Mg ha(-1) in 1985, and mean AGB on the long-term plots increased from 75 Mg ha(-1) in 1936 to 218 Mg ha(-1) in 1998. Over 76 years, red oak (Quercus rubra L.) canopy trees stored carbon at about twice the rate of similar-sized canopy trees of other species. However, there has been a significant loss of live tree biomass as a result of canopy tree mortality since 1999. Important constraints on long-term biomass increment have included insect outbreaks and droughts. PMID:18244941

  6. Linking Carbon Fluxes with Remotely-Sensed Vegetation Indices for Leaf Area and Aboveground Biomass Through Footprint Climatology

    NASA Astrophysics Data System (ADS)

    Wayson, C.; Clark, K.; Hollinger, D. Y.; Skowronski, N.; Schmid, H. E.

    2010-12-01

    A major challenge of bottom-up scaling is that in-situ flux observations are spatially limited. Thus, to achieve valid regional exchange rates, models are used to interpolate and extrapolate to the vegetational/spatial domain covered by these observations. To parameterize these models from flux data, efforts must be made to select data that best represents the region being modeled as well as linking the fluxes to remotely-sensed data products that can be produced from site to regional scales. Because most long-term flux stations are not in spatially extensive, homogeneous locations, this requirement is often a challenge. However, this requirement can be met by selecting observation periods whose flux footprints are statistically representative of the type of ecosystem identified in the model. The flux footprint function indicates the time-varying surface “field-of-view” (or spatial sampling window) of an eddy-flux sensor, oriented mostly in upwind direction. For each observation period, the modeled flux footprint window is overlain with a high-resolution vegetation index map to determine a footprint-weighted vegetation index for which the observation is representative. Using flux-footprint analysis to link fluxes to models using just an enhanced vegetation index (EVI) map shows a positive trend between EVI and eddy covariance measured fluxes, but the link is not strong. Leaf area is linked with carbon (C) uptake, but forests tend to maximize leaf area, as determined through remote sensing, early on with forests having similar leaf areas across a wide range of ages. Adding another remotely-sensed dataset, aboveground biomass map (AGB), helps capture the processes of lower productivity rates (as biomass increases per unit of leaf area there is a decline, due to the forest ageing) and the C losses due to respiration, both heterotrophic and autotrophic (linked to live and detrital biomass pools). Adding biomass from LIDAR and a combined EVI-biomass layer to examine

  7. Diversity and Above-Ground Biomass Patterns of Vascular Flora Induced by Flooding in the Drawdown Area of China's Three Gorges Reservoir

    PubMed Central

    Wang, Qiang; Yuan, Xingzhong; Willison, J.H.Martin; Zhang, Yuewei; Liu, Hong

    2014-01-01

    Hydrological alternation can dramatically influence riparian environments and shape riparian vegetation zonation. However, it was difficult to predict the status in the drawdown area of the Three Gorges Reservoir (TGR), because the hydrological regime created by the dam involves both short periods of summer flooding and long-term winter impoundment for half a year. In order to examine the effects of hydrological alternation on plant diversity and biomass in the drawdown area of TGR, twelve sites distributed along the length of the drawdown area of TGR were chosen to explore the lateral pattern of plant diversity and above-ground biomass at the ends of growing seasons in 2009 and 2010. We recorded 175 vascular plant species in 2009 and 127 in 2010, indicating that a significant loss of vascular flora in the drawdown area of TGR resulted from the new hydrological regimes. Cynodon dactylon and Cyperus rotundus had high tolerance to short periods of summer flooding and long-term winter flooding. Almost half of the remnant species were annuals. Species richness, Shannon-Wiener Index and above-ground biomass of vegetation exhibited an increasing pattern along the elevation gradient, being greater at higher elevations subjected to lower submergence stress. Plant diversity, above-ground biomass and species distribution were significantly influenced by the duration of submergence relative to elevation in both summer and previous winter. Several million tonnes of vegetation would be accumulated on the drawdown area of TGR in every summer and some adverse environmental problems may be introduced when it was submerged in winter. We conclude that vascular flora biodiversity in the drawdown area of TGR has dramatically declined after the impoundment to full capacity. The new hydrological condition, characterized by long-term winter flooding and short periods of summer flooding, determined vegetation biodiversity and above-ground biomass patterns along the elevation gradient in

  8. Utilizing national agriculture imagery program data to estimate tree cover and biomass of pinyon and juniper woodlands

    Technology Transfer Automated Retrieval System (TEKTRAN)

    With the encroachment of pinyon (Pinus ssp.) and juniper (Juniperus ssp.) (P-J) woodlands into sagebrush steppe communities, there is an increasing interest in rapid, accurate, and inexpensive quantification methods to estimate tree canopy cover and aboveground biomass over large landscapes. The o...

  9. Model Effects on GLAS-Based Regional Estimates of Forest Biomass and Carbon

    NASA Technical Reports Server (NTRS)

    Nelson, Ross F.

    2010-01-01

    Ice, Cloud, and land Elevation Satellite (ICESat) / Geosciences Laser Altimeter System (GLAS) waveform data are used to estimate biomass and carbon on a 1.27 X 10(exp 6) square km study area in the Province of Quebec, Canada, below the tree line. The same input datasets and sampling design are used in conjunction with four different predictive models to estimate total aboveground dry forest biomass and forest carbon. The four models include non-stratified and stratified versions of a multiple linear model where either biomass or (biomass)(exp 0.5) serves as the dependent variable. The use of different models in Quebec introduces differences in Provincial dry biomass estimates of up to 0.35 G, with a range of 4.94 +/- 0.28 Gt to 5.29 +/-0.36 Gt. The differences among model estimates are statistically non-significant, however, and the results demonstrate the degree to which carbon estimates vary strictly as a function of the model used to estimate regional biomass. Results also indicate that GLAS measurements become problematic with respect to height and biomass retrievals in the boreal forest when biomass values fall below 20 t/ha and when GLAS 75th percentile heights fall below 7 m.

  10. UNCERTAINTIES IN COUNTRYWIDE FOREST BIOMASS ESTIMATES

    EPA Science Inventory

    Country-wide estimates of forest biomass are the major driver for estimating and understanding carbon pools and flux, a critical component of global change research. mportant determinants in making these estimates include the areal extend of forested lands and their associated bi...

  11. The evaluation of different forest structural indices to predict the stand aboveground biomass of even-aged Scotch pine (Pinus sylvestris L.) forests in Kunduz, Northern Turkey.

    PubMed

    Ercanli, İlker; Kahriman, Aydın

    2015-03-01

    We assessed the effect of stand structural diversity, including the Shannon, improved Shannon, Simpson, McIntosh, Margelef, and Berger-Parker indices, on stand aboveground biomass (AGB) and developed statistical prediction models for the stand AGB values, including stand structural diversity indices and some stand attributes. The AGB prediction model, including only stand attributes, accounted for 85 % of the total variance in AGB (R (2)) with an Akaike's information criterion (AIC) of 807.2407, Bayesian information criterion (BIC) of 809.5397, Schwarz Bayesian criterion (SBC) of 818.0426, and root mean square error (RMSE) of 38.529 Mg. After inclusion of the stand structural diversity into the model structure, considerable improvement was observed in statistical accuracy, including 97.5 % of the total variance in AGB, with an AIC of 614.1819, BIC of 617.1242, SBC of 633.0853, and RMSE of 15.8153 Mg. The predictive fitting results indicate that some indices describing the stand structural diversity can be employed as significant independent variables to predict the AGB production of the Scotch pine stand. Further, including the stand diversity indices in the AGB prediction model with the stand attributes provided important predictive contributions in estimating the total variance in AGB. PMID:25663395

  12. 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

    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.

  13. Tropical Africa: Land Use, Biomass, and Carbon Estimates for 1980 (NDP-055)

    SciTech Connect

    Brown, S.

    2002-04-16

    This document describes the contents of a digital database containing maximum potential aboveground biomass, land use, and estimated biomass and carbon data for 1980. The biomass data and carbon estimates are associated with woody vegetation in Tropical Africa. These data were collected to reduce the uncertainty associated with estimating historical releases of carbon from land use change. Tropical Africa is defined here as encompassing 22.7 x 10{sup 6} km{sup 2} of the earth's land surface and is comprised of countries that are located in tropical Africa (Angola, Botswana, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Congo, Benin, Equatorial Guinea, Ethiopia, Djibouti, Gabon, Gambia, Ghana, Guinea, Ivory Coast, Kenya, Liberia, Madagascar, Malawi, Mali, Mauritania, Mozambique, Namibia, Niger, Nigeria, Guinea-Bissau, Zimbabwe (Rhodesia), Rwanda, Senegal, Sierra Leone, Somalia, Sudan, Tanzania, Togo, Uganda, Burkina Faso (Upper Volta), Zaire, and Zambia). The database was developed using the GRID module in the ARC/INFO{trademark} geographic information system. Source data were obtained from the Food and Agriculture Organization (FAO), the U.S. National Geophysical Data Center, and a limited number of biomass-carbon density case studies. These data were used to derive the maximum potential and actual (ca. 1980) aboveground biomass values at regional and country levels. The land-use data provided were derived from a vegetation map originally produced for the FAO by the International Institute of Vegetation Mapping, Toulouse, France.

  14. Dynamics associated with total aboveground biomass, C, nutrient pools, and biomass burning of primary forest and pasture in Rondo‸nia, Brazil during SCAR-B

    NASA Astrophysics Data System (ADS)

    Guild, Liane S.; Kauffman, J. Boone; Ellingson, Lisa J.; Cummings, Dian L.; Castro, Elmar A.; Babbitt, Ron E.; Ward, Darold E.

    1998-12-01

    Burning of slashed tropical forests and pastures is among the most significant global sources of atmospheric emissions, yet the composition of the fuels and fires that creates these emissions is not well characterized. As part of the Smoke, Clouds, and Radiation-Brazil (SCAR-B) experiment, we measured total aboveground biomass (TAGB) as well as carbon, nitrogen, and sulfur pools in one cattle pasture and two slashed primary forests in Rondônia, Brazil. These pools were measured before and immediately after fires. From these data, we calculated the quantities of biomass and elements lost to the atmosphere during biomass burning. Prefire biomass in the pasture was 66 Mg ha-1; fire consumed 31% of this mass. Woody debris from the forest that occupied this site 12 years previously comprised 81% of the pasture prefire TAGB. Elemental inputs into the atmosphere (site losses) from the pasture fire were 9 Mg C ha-1, 88 kg N ha-1, and 5 kg S ha-1. Combining previous studies with this one, we calculate that the mean TAGB of Amazonian pastures is 74 Mg ha-1 with a mean combustion factor of 46%. Mean nutrient losses from pasture fires in Amazonia are 14 Mg C ha-1, 199 kg N ha-1, and 16 kg S ha-1. The TAGB of the two slashed primary forests before fire was 355 and 399 Mg ha-1 and following fire was 188 and 185 Mg ha-1 (i.e., a combustion factor of 47 and 54%), respectively. Combining this study with other studies of Amazon slashed primary forests, we calculate that the mean TAGB is 349 Mg ha-1 and the mean combustion factor is 48%. Total elemental losses arising from the primary forest slash fires in this study were notably higher than losses from the pasture site: 79 and 102 Mg C ha-1; 1019 and 1196 kg N ha-1; and 87 and 96 kg S ha-1. From this study combined with previous research in Rondônia and Pará, we calculate that mean nutrient losses from primary forest slash fires are 88 Mg C ha-1, 1181 kg N ha-1, and 107 kg S ha-1. As rates of deforestation are remaining high in

  15. Non-Parametric Responses of Aboveground Biomass and NDVI to Land Surface Parameters in Arctic-Alpine Environments

    NASA Astrophysics Data System (ADS)

    Riihimäki, H. K.; Heiskanen, J.; Luoto, M.

    2015-12-01

    Aboveground biomass (AGB) is an important carbon pool and it affects various phenomena in Arctic and alpine areas, e.g. biodiversity, surface albedo and soil conditions. The growing availability of high-resolution digital elevation models (DEM) makes it possible to utilize topographical information for modeling local ground surface conditions globally. We investigated the effect of topography on field measured AGB (n = 359) and its commonly used proxy, the Normalized Difference Vegetation Index (NDVI) calculated from SPOT 5 imagery. The study area located in an Arctic-alpine treeline environment (69 °N, 21 °E). We performed the analyses with boosted regression trees method by using elevation and four land surface parameters (LSPs), derived from 10 m DEM, as predictors. The LSPs were namely Potential Incoming Solar Radiation (PISR, MJ m-2 a-1), Topographic Position Index (TPI, r = 300 m), Slope (angle in degrees) and Topographic Wetness Index (TWI). AGB varied from 0 to 5647 g m-2, while median AGB of the data was 449 g m-2. The explained deviance of the AGB and NDVI models were 53 % and 65 %, respectively. Elevation and PISR were the most important predictors. Their interaction was also significant in both cases as the highest AGB were at low-elevation, high-radiation sites, which implicates that PISR significantly improves the modelling of temperature related growing conditions. TWI had no clear effect to AGB nor to NDVI. TPI and Slope had a minor effect on AGB, but no effect to NDVI. Areas lower than their surroundings (negative TPI) had relatively high AGB. Furthermore, steeper slopes had higher AGB compared to flat sites. This is probably caused by the presence of mountain birch (Betula pubescens ssp. czerepanovii), which favors protected and steeper topography. Local topography is an important driver of the fine scale AGB patterns. Thus, DEM derived LSPs should be taken into account when modelling current and future biomass distributions in Arctic and alpine

  16. Greening of the Arctic: Spatial and temporal (1982-2009) variation of circumpolar tundra NDVI and aboveground biomass

    NASA Astrophysics Data System (ADS)

    Walker, D. A.; Epstein, H. E.; Bhatt, U. S.; Raynolds, M. K.; Jia, G.; Comiso, J. C.; Pinzon, J.; Tucker, C. J.

    2010-12-01

    The Greening of the Arctic project examined the spatial and temporal variability of tundra productivity during field studies along two transects in North America and Russia and in circumpolar remote-sensing studies. Aboveground biomass of zonal vegetation was strongly correlated with summer air temperature along the North America transect (r2 > 0.8), but on the Yamal Peninsula, Russia, there was a nearly flat relationship with air temperature in the central part of the climate gradient. The more homogeneous nature of the Russian transect is caused by more consistent soil conditions, warmer than expected temperatures along the central part of the Russian transect, and the homogenizing effect of various landscape-scale disturbances including reindeer grazing and disturbances related to permafrost thawing. We examined the circumpolar spatial variability of the NDVI with respect to land temperatures and a suite of vegetation/terrain variables in a circumpolar geographic information system (GIS). Tests of sensitivity of NDVI to increases in summer temperature showed the largest percentage increases in NDVI will likely occur in northern partially-vegetated areas. A temporal analysis of maximum summer NDVI from 1982-2008 also indicated the ongoing changes have been strongest in the Far North. The percentage changes in the annual maximum NDVI were highest in the Baffin Bay, Beaufort Sea, Canadian Archipelago and Davis Strait areas (10-15% changes). The NDVI trends for most Arctic regions were highly positively correlated with the changes in summer land temperatures and negatively correlated with temporal changes in the near-shore early-summer sea-ice concentrations. Trends from these and several other lines of evidence point to a general increase in biomass across the Arctic during the period of satellite observations. The most rapid changes are occurring in the Far North. The impact of shrubification on these trends is probably greatest in the southern parts of the Arctic

  17. Effects of LiDAR point density and landscape context on estimates of urban forest biomass

    NASA Astrophysics Data System (ADS)

    Singh, Kunwar K.; Chen, Gang; McCarter, James B.; Meentemeyer, Ross K.

    2015-03-01

    Light Detection and Ranging (LiDAR) data is being increasingly used as an effective alternative to conventional optical remote sensing to accurately estimate aboveground forest biomass ranging from individual tree to stand levels. Recent advancements in LiDAR technology have resulted in higher point densities and improved data accuracies accompanied by challenges for procuring and processing voluminous LiDAR data for large-area assessments. Reducing point density lowers data acquisition costs and overcomes computational challenges for large-area forest assessments. However, how does lower point density impact the accuracy of biomass estimation in forests containing a great level of anthropogenic disturbance? We evaluate the effects of LiDAR point density on the biomass estimation of remnant forests in the rapidly urbanizing region of Charlotte, North Carolina, USA. We used multiple linear regression to establish a statistical relationship between field-measured biomass and predictor variables derived from LiDAR data with varying densities. We compared the estimation accuracies between a general Urban Forest type and three Forest Type models (evergreen, deciduous, and mixed) and quantified the degree to which landscape context influenced biomass estimation. The explained biomass variance of the Urban Forest model, using adjusted R2, was consistent across the reduced point densities, with the highest difference of 11.5% between the 100% and 1% point densities. The combined estimates of Forest Type biomass models outperformed the Urban Forest models at the representative point densities (100% and 40%). The Urban Forest biomass model with development density of 125 m radius produced the highest adjusted R2 (0.83 and 0.82 at 100% and 40% LiDAR point densities, respectively) and the lowest RMSE values, highlighting a distance impact of development on biomass estimation. Our evaluation suggests that reducing LiDAR point density is a viable solution to regional

  18. Potential for post-closure radionuclide redistribution due to biotic intrusion: aboveground biomass, litter production rates, and the distribution of root mass with depth at material disposal area G, Los Alamos National Laboratory

    SciTech Connect

    French, Sean B; Christensen, Candace; Jennings, Terry L; Jaros, Christopher L; Wykoff, David S; Crowell, Kelly J; Shuman, Rob

    2008-01-01

    Low-level radioactive waste (LLW) generated at the Los Alamos National Laboratories (LANL) is disposed of at LANL's Technical Area (T A) 54, Material Disposal Area (MDA) G. The ability of MDA G to safely contain radioactive waste during current and post-closure operations is evaluated as part of the facility's ongoing performance assessment (PA) and composite analysis (CA). Due to the potential for uptake and incorporation of radio nuclides into aboveground plant material, the PA and CA project that plant roots penetrating into buried waste may lead to releases of radionuclides into the accessible environment. The potential amount ofcontamination deposited on the ground surface due to plant intrusion into buried waste is a function of the quantity of litter generated by plants, as well as radionuclide concentrations within the litter. Radionuclide concentrations in plant litter is dependent on the distribution of root mass with depth and the efficiency with which radionuclides are extracted from contaminated soils by the plant's roots. In order to reduce uncertainties associated with the PA and CA for MDA G, surveys are being conducted to assess aboveground biomass, plant litter production rates, and root mass with depth for the four prominent vegetation types (grasses, forbs, shrubs and trees). The collection of aboveground biomass for grasses and forbs began in 2007. Additional sampling was conducted in October 2008 to measure root mass with depth and to collect additional aboveground biomass data for the types of grasses, forbs, shrubs, and trees that may become established at MDA G after the facility undergoes final closure, Biomass data will be used to estimate the future potential mass of contaminated plant litter fall, which could act as a latent conduit for radionuclide transport from the closed disposal area. Data collected are expected to reduce uncertainties associated with the PA and CA for MDA G and ultimately aid in the assessment and subsequent

  19. Development of a data driven process-based model for remote sensing of terrestrial ecosystem productivity, evapotranspiration, and above-ground biomass

    NASA Astrophysics Data System (ADS)

    El Masri, Bassil

    2011-12-01

    Modeling terrestrial ecosystem functions and structure has been a subject of increasing interest because of the importance of the terrestrial carbon cycle in global carbon budget and climate change. In this study, satellite data were used to estimate gross primary production (GPP), evapotranspiration (ET) for two deciduous forests: Morgan Monroe State forest (MMSF) in Indiana and Harvard forest in Massachusetts. Also, above-ground biomass (AGB) was estimated for the MMSF and the Howland forest (mixed forest) in Maine. Surface reflectance and temperature, vegetation indices, soil moisture, tree height and canopy area derived from the Moderate Resolution Imagining Spectroradiometer (MODIS), the Advanced Microwave Scanning Radiometer (AMRS-E), LIDAR, and aerial imagery respectively, were used for this purpose. These variables along with others derived from remotely sensed data were used as inputs variables to process-based models which estimated GPP and ET and to a regression model which estimated AGB. The process-based models were BIOME-BGC and the Penman-Monteith equation. Measured values for the carbon and water fluxes obtained from the Eddy covariance flux tower were compared to the modeled GPP and ET. The data driven methods produced good estimation of GPP and ET with an average root mean square error (RMSE) of 0.17 molC/m2 and 0.40 mm/day, respectively for the MMSF and the Harvard forest. In addition, allometric data for the MMSF were used to develop the regression model relating AGB with stem volume. The performance of the AGB regression model was compared to site measurements using remotely sensed data for the MMSF and the Howland forest where the model AGB RMSE ranged between 2.92--3.30 Kg C/m2. Sensitivity analysis revealed that improvement in maintenance respiration estimation and remotely sensed maximum photosynthetic activity as well as accurate estimate of canopy resistance will result in improved GPP and ET predictions. Moreover, AGB estimates were

  20. Estimation and Mapping of Coastal Mangrove Biomass Using Both Passive and Active Remote Sensing Method

    NASA Astrophysics Data System (ADS)

    Yiqiong, L.; Lu, W.; Zhou, J.; Gan, W.; Cui, X.; Lin, G., Sr.

    2015-12-01

    Mangrove forests play an important role in global carbon cycle, but carbon stocks in different mangrove forests are not easily measured at large scale. In this research, both active and passive remote sensing methods were used to estimate the aboveground biomass of dominant mangrove communities in Zhanjiang National Mangrove Nature Reserve in Guangdong, China. We set up a decision tree including spectral, texture, position and geometry indexes to achieve mangrove inter-species classification among 5 main species named Aegiceras corniculatum, Aricennia marina, Bruguiera gymnorrhiza, Kandelia candel, Sonneratia apetala by using 5.8m multispectral ZY-3 images. In addition, Lidar data were collected and used to obtain the canopy height of different mangrove species. Then, regression equations between the field measured aboveground biomass and the canopy height deduced from Lidar data were established for these mangrove species. By combining these results, we were able to establish a relatively accurate method for differentiating mangrove species and mapping their aboveground biomass distribution at the estuary scale, which could be applied to mangrove forests in other regions.

  1. Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome

    PubMed Central

    Guitet, Stéphane; Hérault, Bruno; Molto, Quentin; Brunaux, Olivier; Couteron, Pierre

    2015-01-01

    Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate “wall-to-wall” remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution. PMID

  2. Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome.

    PubMed

    Guitet, Stéphane; Hérault, Bruno; Molto, Quentin; Brunaux, Olivier; Couteron, Pierre

    2015-01-01

    Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate "wall-to-wall" remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution. PMID:26402522

  3. Sensitivity of Backscatter Intensity of ALOS/PALSAR to Above-ground Biomass and Other Biophysical Parameters of Boreal Forests in Alaska and Japan

    NASA Astrophysics Data System (ADS)

    Suzuki, R.; Hayashi, M.; Kim, Y.; Ishii, R.; Kobayashi, H.; Shoyama, K.; Adachi, M.; Takahashi, A.; Saigusa, N.; Ito, A.

    2012-12-01

    For the better understanding of the carbon cycle in the global environment, investigations on the spatio-temporal variation of the carbon stock which is stored as vegetation biomass is important. The backscatter intensity of "Phased Array type L-band Synthetic Aperture Radar (PALSAR)" onboard the satellite "Advanced Land Observing Satellite (ALOS)" provides us the information which is applicable to estimate the forest above-ground biomass (AGB). This study examines the sensitivity of the backscatter intensity of ALOS/PALSAR to the forest AGB and other biophysical parameters (tree height, tree diameter at breast height (DBH), and tree stand density) for boreal forests in two geographical regions of Alaska and Kushiro, northern Japan, and compares the sensitivities in two regions. In Alaska, a forest survey was executed in the south-north transect (about 300 km long) along a trans-Alaska pipeline which profiles the ecotone from the boreal forest to tundra in 2007. Forest AGBs and other biophysical parameters at 29 forests along the transect were measured by Bitterlich method. In Kushiro, a forest survey was carried out at 42 forests in 2011 and those parameters were similarly obtained by Bitterlich method. 20 and 2 scenes of ALOS/PALSAR FBD Level 1.5 data that cover the regions in Alaska and Kushiro, respectively, were collected and mosaicked. Backscatter intensities of ALOS/PALSAR in HH (horizontally polarized transmitted and horizontally polarized received) and HV (horizontally polarized transmitted and vertically polarized received) modes were compared with the forest AGB and other biophysical parameters. The intensity generally increased with the increase of those biophysical parameters in both HV and HH modes, but the intensity in HV mode generally had a stronger correlation to those parameters than in HH mode in both Alaska and Kushiro. The HV intensity had strong correlation to the forest AGB and DBH, while weak correlation to the tree stand density in Alaska

  4. Spectral procedures for estimating crop biomass

    SciTech Connect

    Wanjura, D.F.; Hatfield, J.L.

    1985-05-01

    Spectral reflectance was measured semi-weekly and used to estimate leaf area and plant dry weight accumulation in cotton, soybeans, and sunflower. Integration of spectral crop growth cycle curves explained up to 95 and 91%, respectively, of the variation in cotton lint yield and dry weight. A theoretical relationship for dry weight accumulation, in which only intercepted radiation or intercepted radiation and solar energy to biomass conversion efficiency were spectrally estimated, explained 99 and 96%, respectively, of the observed plant dry weight variation of the three crops. These results demonstrate the feasibility of predicting crop biomass from spectral measurements collected frequently during the growing season. 15 references.

  5. Rangeland biomass estimation demonstration. [Texas Experimenta Ranch

    NASA Technical Reports Server (NTRS)

    Newton, R. W. (Principal Investigator); Boyd, W. E.; Clark, B. V.

    1982-01-01

    Because of their sensitivity to chlorophyll density, green leaf density, and leaf water density, two hand-held radiometers which have sensor bands coinciding with thematic mapper bands 3, 4, and 5 were used to calibrate green biomass to LANDSAT spectral ratios as a step towards using portable radiometers to speed up ground data acquisition. Two field reflectance panels monitored incoming radiation concurrently with sampling. Software routines were developed and used to extract data from uncorrected tapes of MSS data provided in NASA LANDSAT universal format. A LANDSAT biomass calibration curve estimated the range biomass over a four scene area and displayed this information spatially as a product in a format of use to ranchers. The regional biomass contour map is discussed.

  6. Optimizing the number of training areas for modeling above-ground biomass with ALS and multispectral remote sensing in subtropical Nepal

    NASA Astrophysics Data System (ADS)

    Rana, Parvez; Gautam, Basanta; Tokola, Timo

    2016-07-01

    Remote sensing-based inventories of above-ground forest biomass (AGB) require a set of training plots representative of the area to be studied, the collection of which is the most expensive part of the analysis. These are time-consuming and costly because the large variety in forest conditions requires more plots to adequately capture this variability. A field campaign in general is challenging and is hampered by the complex topographic conditions, limited accessibility, steep mountainous terrains which increase labor efforts and costs. In addition it is also depend on the ratio between size of study area and number of training plots. In this study, we evaluate the number of training areas (sample size) required to estimate AGB for an area in the southern part of Nepal using airborne laser scanning (ALS), RapidEye and Landsat data. Three experiments were conducted: (i) AGB model performance, based on all the field training plots; (ii) reduction of the sample size, based on the ALS metrics and the AGB distribution; and (iii) prediction of the optimal number of training plots, based on the correlation between the remote sensing and field data. The AGB model was fitted using the sparse Bayesian method. AGB model performance was validated using an independent validation dataset. The effect of the strategies for reducing the sample size was readily apparent for the ALS-based AGB prediction, but the RapidEye and Landsat sensor data failed to capture any such effect. The results indicate that adequate coverage of the variability in tree height and density was an important condition for selecting the training plots. In addition, the ALS-based AGB prediction required the smallest number of training plots and was also quite stable with a small number of field plots.

  7. Efficient Methods of Estimating Switchgrass Biomass Supplies

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Switchgrass (Panicum virgatum L.) is being developed as a biofuel feedstock for the United States. Efficient and accurate methods to estimate switchgrass biomass feedstock supply within a production area will be required by biorefineries. Our main objective was to determine the effectiveness of in...

  8. Accurate Biomass Estimation via Bayesian Adaptive Sampling

    NASA Technical Reports Server (NTRS)

    Wheeler, Kevin R.; Knuth, Kevin H.; Castle, Joseph P.; Lvov, Nikolay

    2005-01-01

    The following concepts were introduced: a) Bayesian adaptive sampling for solving biomass estimation; b) Characterization of MISR Rahman model parameters conditioned upon MODIS landcover. c) Rigorous non-parametric Bayesian approach to analytic mixture model determination. d) Unique U.S. asset for science product validation and verification.

  9. Estimating slash pine biomass using radar backscatter

    NASA Technical Reports Server (NTRS)

    Hussin, Yousif Ali; Reich, Robin M.; Hoffer, Roger M.

    1991-01-01

    L-band HV multiple-incidence-angle aircraft synthetic aperture radar (SAR) data were analyzed in relation to average stand biomass, basal area, and tree height for 55 slash pine plantations located in northern Florida. This information was used to develop a system of equations to predict average stand biomass as a function of L-band (24.5-cm) radar backscatter. The system of equations developed in this study using three-stage least-squares and combinatorial screening accounted for 97 percent of the variability observed in average stand biomass per hectare. When applied to an independent data set, the biomass equations had an average bias of less than 1 percent with a standard error of approximately 3 percent. These results indicate that future Shuttle Imaging Radar Systems (e.g., SIR-C, which will have cross-polarized radar sensors) should be able to obtain better estimates of forest biomass than were obtained with previous satellite radar missions, which utilized only HH-polarized SAR data.

  10. Soil water content and patterns of allocation to below- and above-ground biomass in the sexes of the subdioecious plant Honckenya peploides

    PubMed Central

    Sánchez-Vilas, Julia; Bermúdez, Raimundo; Retuerto, Rubén

    2012-01-01

    Background and aims Dioecious plants often show sex-specific differences in growth and biomass allocation. These differences have been explained as a consequence of the different reproductive functions performed by the sexes. Empirical evidence strongly supports a greater reproductive investment in females. Sex differences in allocation may determine the performance of each sex in different habitats and therefore might explain the spatial segregation of the sexes described in many dimorphic plants. Here, an investigation was made of the sexual dimorphism in seasonal patterns of biomass allocation in the subdioecious perennial herb Honckenya peploides, a species that grows in embryo dunes (i.e. the youngest coastal dune formation) and displays spatial segregation of the sexes at the studied site. The water content in the soil of the male- and female-plant habitats at different times throughout the season was also examined. Methods The seasonal patterns of soil-water availability and biomass allocation were compared in two consecutive years in male and female H. peploides plants by collecting soil and plant samples in natural populations. Vertical profiles of below-ground biomass and water content were studied by sampling soil in male- and female-plant habitats at different soil depths. Key Results The sexes of H. peploides differed in their seasonal patterns of biomass allocation to reproduction. Males invested twice as much in reproduction than females early in the season, but sexual differences became reversed as the season progressed. No differences were found in above-ground biomass between the sexes, but the allocation of biomass to below-ground structures varied differently in depth for males and females, with females usually having greater below-ground biomass than males. In addition, male and female plants of H. peploides had different water-content profiles in the soil where they were growing and, when differences existed (usually in the upper layers of the

  11. Potential for radionuclide redistribution due to biotic intrusion: Aboveground biomass study at the Los Alamos National Laboratory for the closure of Material Disposal Area G

    SciTech Connect

    Beguin, K.; Pressler, R.E.; Christensen, C.; Anderson, T.; French, S.; Schuman, R.

    2008-07-01

    Low-level radioactive waste generated at the Los Alamos National Laboratories (LANL) is disposed of at Technical Area (TA) 54, Material Disposal Area (MDA) G. The ability of MDA G to safely contain radioactive waste was evaluated in the facility's performance assessment (PA) and composite analysis (CA). The PA and CA project that, due to uptake and incorporation of radionuclides into aboveground plant material, plant roots penetrating into buried waste may lead to releases of radionuclides to the accessible environment and potentially lead to the exposure to members of the public. The potential amount of contamination deposited on the ground surface, due to plant intrusion into buried waste, is a function of the quantity of litter generated by plants, as well as radionuclide concentrations within the litter. Radionuclide concentrations in plant litter is dependent on the distribution of root mass with depth and the efficiency with which radionuclides are extracted from contaminated soils by the plants roots. In order to reduce uncertainties associated with the PA and CA for MDA G, aboveground biomass surveys, plant litter production rates, and root mass with depth analyses for the four prominent vegetation types (grasses, forbs, shrubs and trees) are being conducted. Sampling occurred during the months of August and September of 2007 which measured aboveground biomass for the types of grasses and forbs that may become established at MDA G after the disposal facility undergoes final closure. Biomass data are representative of the future potential for the amount of contaminated plant litter fall, which could act as a latent conduit for radionuclide transport from the closed disposal area. Follow on work will be conducted to evaluate frequency and coverage of all growth forms, litter production rates will be measured, and root mass with depth for grasses, forbs, shrubs, and trees will be analyzed. Together, data collected are expected to reduce uncertainties

  12. Measuring bulrush culm relationships to estimate plant biomass within a southern California treatment wetland

    USGS Publications Warehouse

    Daniels, Joan S. (Thullen); Cade, Brian S.; Sartoris, James J.

    2010-01-01

    Assessment of emergent vegetation biomass can be time consuming and labor intensive. To establish a less onerous, yet accurate method, for determining emergent plant biomass than by direct measurements we collected vegetation data over a six-year period and modeled biomass using easily obtained variables: culm (stem) diameter, culm height and culm density. From 1998 through 2005, we collected emergent vegetation samples (Schoenoplectus californicus andSchoenoplectus acutus) at a constructed treatment wetland in San Jacinto, California during spring and fall. Various statistical models were run on the data to determine the strongest relationships. We found that the nonlinear relationship: CB=β0DHβ110ε, where CB was dry culm biomass (g m−2), DH was density of culms × average height of culms in a plot, and β0 and β1 were parameters to estimate, proved to be the best fit for predicting dried-live above-ground biomass of the two Schoenoplectus species. The random error distribution, ε, was either assumed to be normally distributed for mean regression estimates or assumed to be an unspecified continuous distribution for quantile regression estimates.

  13. Evaluating Post-fire Ecosystem Effects in Tussock Tundra of the Seward Peninsula: Characterizing Above-ground Biomass Accumulation, Soil Nutrient Pools, and Foliar Nitrogen.

    NASA Astrophysics Data System (ADS)

    Hollingsworth, T. N.; Mack, M. C.; Breen, A. L.

    2014-12-01

    Over the last century in the circumpolar north, changes in vegetation include shrub cover expansion and shifts in tree line. Invasion of tundra by trees and shrubs may be further facilitated by wildfire disturbance, which creates opportunities for establishment where recruitment is otherwise rare. Even moderate increases in warm-season temperatures are predicted to increase the likelihood of tundra fires. Understanding the consequences of a change in fire regime are complicated by the fact that there are relatively few large recent fires to study. However, the Seward Peninsula is a region that currently experiences more frequent and large fires than other tundra regions in Arctic Alaska. In this tundra region, there are areas of overlapping burns dating back to the 1970s. Using a chronosequence approach, we looked at post-fire biomass accumulation as well as foliar and soil C and N. Our experimental design incorporated sites that showed no evidence of recent burning, sites that burned in 1971, 1997, 2002, and 2011 as well as sites that burned multiple times over the last 30 years. We found that fire had a significant effect on total biomass and shrub basal area in tussock tundra. Our site that burned in 2011 had the lowest total biomass, about half of the biomass of our unburned site. However, our results indicated the site that burned in 1971 had over double the aboveground biomass and more soil N than the unburned site. We found that sites that repeatedly burned since 1971 were very similar in biomass to unburned tundra. This suggests that repeat fires keep a post-fire site at unburned levels of biomass. However, in these repeat fire sites, foliar C/N was ~25% greater and soil C and N was ~50% less than in unburned tundra. These results indicate that repeat fires are potentially causing nitrogen loss that not likely to be replenished into the system. As tundra fires become more frequent prediction of post-fire ecosystem effects is critical due to impacts on

  14. Estimation of aboveground net primary productivity in secondary tropical dry forests using the Carnegie–Ames–Stanford approach (CASA) model

    NASA Astrophysics Data System (ADS)

    Cao, S.; Sanchez-Azofeifa, GA; Duran, SM; Calvo-Rodriguez, S.

    2016-07-01

    Although tropical dry forests (TDFs) cover roughly 42% of all tropical ecosystems, extensive deforestation and habitat fragmentation pose important limitations for their conservation and restoration worldwide. In order to develop conservation policies for this endangered ecosystem, it is necessary to quantify their provision of ecosystems services such as carbon sequestration and primary production. In this paper we explore the potential of the Carnegie–Ames–Stanford approach (CASA) for estimating aboveground net primary productivity (ANPP) in a secondary TDF located at the Santa Rosa National Park (SRNP), Costa Rica. We calculated ANPP using the CASA model (ANPPCASA) in three successional stages (early, intermediate, and late). Each stage has a stand age of 21 years, 32 years, and 50+ years, respectively, estimated as the age since land abandonment. Our results showed that the ANPPCASA for early, intermediate, and late successional stages were 3.22 Mg C ha‑1 yr‑1, 8.90 Mg C ha‑1 yr‑1, and 7.59 Mg C ha‑1 yr‑1, respectively, which are comparable with rates of carbon uptake in other TDFs. Our results indicate that key variables that influence ANPP in our dry forest site were stand age and precipitation seasonality. Incident photosynthetically active radiation and temperature were not dominant in the ANPPCASA. The results of this study highlight the potential of the use of remote sensing techniques and the importance of incorporating successional stage in accurate regional TDF ANPP estimation.

  15. Aboveground biomass allocation of ponderosa pine along an elevational gradient: An analog for response to climate change

    SciTech Connect

    Callaway, R.M.; DeLucia, E.H.; Schlesinger, W.H. Duke Univ., Durham, NC )

    1993-06-01

    Predictions of CO[sub 2]-enhanced growth for adult trees are primarily based on leaf-level assimilation responses and improved growth rates of seedlings and saplings. Plant growth may be more dependent on biomass allocation than on rates of assimilation, but predictions have not incorporated the effects of temperature on biomass reallocation among autotrophic and heterotrophic tissues and whole-plant carbon balance. We measured biomass allocation of Pinus ponderosa on hydrothermally altered andesite in montane and desert climates, thus substrate was held constant while climate varied. Trees from montane climates supported higher leaf mass per cross-sectional sapwood area (functional conducting xylem) than trees from desert climates, suggesting that a functional response to climate had occurred. Our results also indicate that sapwood mass:leaf mass ratios of P. ponderosa may increase [approx] 50% with a 5[degrees]C change. in mean growing season temperature, approximately the difference between our montane and desert sites. Such an increase in sapwood:leaf ratio may partially offset predicted CO[sub 2]-enhancement effects and substantially reduce whole-plant carbon balance. Biomass allocation responses must be incorporated into growth-response models used to predict fluctuations in forest productivity with changes in climate and atmospheric CO[sub 2] concentration.

  16. Effect of seven years of experimental drought on the aboveground biomass storage of an eastern Amazonian rainforest

    NASA Astrophysics Data System (ADS)

    da Costa, Antonio Carlos Lola; Galbraith, David; Almeida, Samuel; Fisher, Rosie; Phillips, Oliver; Metcalfe, Daniel; Levy, Peter; Portela, Bruno; da Costa, Mauricio; Meir, Patrick

    2010-05-01

    At least one climate model predicts severe reductions of rainfall over Amazonia during this century. Long-term throughfall exclusion (TFE) experiments represent the best available means to investigate the resilience of the Amazon rainforest to such droughts. Results are presented from a 7-year TFE study at Caxiuanã National Forest, eastern Amazonia. We focus on the impacts of the drought on tree mortality, wood production and aboveground carbon storage. Tree mortality in the TFE plot over the experimental period was 2.5% yr-1, compared to 1.25% yr-1 in a nearby Control plot experiencing normal rainfall. Differences in stem mortality between plots were greatest in the largest (> 40 cm dbh) size class (4.1% yr-1 in the TFE and 1.4% yr-1 in the Control). Wood production in the TFE plot was approximately 30% lower than in the Control plot. Together, these changes resulted in a loss of 37.8 ± 2.0 Mg C ha-1 (~ 20%) in the TFE plot (2002-2008), whereas the Control plot was essentially carbon neutral(change of - 0.2 ± 1.0 Mg C ha-1). These results are remarkably consistent with those from another TFE (at Tapajós National Forest), suggesting that Amazonian forests may respond to prolonged drought in a predictable manner.

  17. The influences of CO2 fertilization and land use change on the total aboveground biomass in Amazonian tropical forest

    NASA Astrophysics Data System (ADS)

    Castanho, A. D.; Zhang, K.; Coe, M. T.; Costa, M. H.; Moorcroft, P. R.

    2012-12-01

    Field observations from undisturbed old-growth Amazonian forest plots have recently reported on the temporal variation of many of the physical and chemical characteristics such as: physiological properties of leaves, above ground live biomass, above ground productivity, mortality and turnover rates. However, although this variation has been measured, it is still not well understood what mechanisms control the observed temporal variability. The observed changes in time are believed to be a result of a combination of increasing atmospheric CO2 concentration, climate variability, recovery from natural disturbance (drought, wind blow, flood), and increase of nutrient availability. The time and spatial variability of the fertilization effect of CO2 on above ground biomass will be explored in more detail in this work. A precise understanding of the CO2 effect on the vegetation is essential for an accurate prediction of the future response of the forest to climate change. To address this issue we simultaneously explore the effects of climate variability, historical CO2 and land-use change on total biomass and productivity using two different Dynamic Global Vegetation Models (DGVM). We use the Integrated Biosphere Simulator (IBIS) and the Ecosystem Demography Model 2.1 (ED2.1). Using land use changes database from 1700 - 2008 we reconstruct the total carbon balance in the Amazonian forest in space and time and present how the models predict the forest as carbon sink or source and explore why the model and field data diverge from each other. From 1970 to 2005 the Amazonian forest has been exposed to an increase of approximately 50 ppm in the atmospheric CO2 concentration. Preliminary analyses with the IBIS and ED2.1 dynamic vegetation model shows the CO2 fertilization effect could account for an increase in above ground biomass of 0.03 and 0.04 kg-C/m2/yr on average for the Amazon basin, respectively. The annual biomass change varies temporally and spatially from about 0

  18. Contrasting impacts of continuous moderate drought and episodic severe droughts on the aboveground-biomass increment and litterfall of three coexisting Mediterranean woody species.

    PubMed

    Liu, Daijun; Ogaya, Romà; Barbeta, Adrià; Yang, Xiaohong; Peñuelas, Josep

    2015-11-01

    Climate change is predicted to increase the aridity in the Mediterranean Basin and severely affect forest productivity and composition. The responses of forests to different timescales of drought, however, are still poorly understood because extreme and persistent moderate droughts can produce nonlinear responses in plants. We conducted a rainfall-manipulation experiment in a Mediterranean forest dominated by Quercus ilex, Phillyrea latifolia, and Arbutus unedo in the Prades Mountains in southern Catalonia from 1999 to 2014. The experimental drought significantly decreased forest aboveground-biomass increment (ABI), tended to increase the litterfall, and decreased aboveground net primary production throughout the 15 years of the study. The responses to the experimental drought were highly species-specific. A. unedo suffered a significant reduction in ABI, Q. ilex experienced a decrease during the early experiment (1999-2003) and in the extreme droughts of 2005-2006 and 2011-2012, and P. latifolia was unaffected by the treatment. The drought treatment significantly increased branch litterfall, especially in the extremely dry year of 2011, and also increased overall leaf litterfall. The drought treatment reduced the fruit production of Q. ilex, which affected seedling recruitment. The ABIs of all species were highly correlated with SPEI in early spring, whereas the branch litterfalls were better correlated with summer SPEIs and the leaf and fruit litterfalls were better correlated with autumn SPEIs. These species-specific responses indicated that the dominant species (Q. ilex) could be partially replaced by the drought-resistant species (P. latifolia). However, the results of this long-term study also suggest that the effect of drought treatment has been dampened over time, probably due to a combination of demographic compensation, morphological and physiological acclimation, and epigenetic changes. However, the structure of community (e.g., species composition

  19. ROOT BIOMASS ALLOCATION IN THE WORLD'S UPLAND FORESTS

    EPA Science Inventory

    Because the world's forests play a major role in regulating nutrient and carbon cycles, there is much interest in estimating their biomass. Estimates of aboveground biomass based on well-established methods are relatively abundant; estimates of root biomass based on standard meth...

  20. Have ozone effects on carbon sequestration been over-estimated? A new biomass response function for wheat

    NASA Astrophysics Data System (ADS)

    Pleijel, H.; Danielsson, H.; Simpson, D.; Mills, G.

    2014-04-01

    Elevated levels of tropospheric ozone can significantly impair the growth of crops. The reduced removal of CO2 by plants leads to higher atmospheric concentrations of CO2, enhancing radiative forcing. Ozone effects on economic yield, e.g. the grain yield of wheat (Triticum aestivum L.) are currently used to model effects on radiative forcing. However, changes in grain yield do not necessarily reflect changes in total biomass. Based on analysis of 21 ozone exposure experiments with field-grown wheat, we investigated whether use of effects on grain yield as a~proxy for effects on biomass under- or over-estimates effects on biomass. First, we confirmed that effects on partitioning and biomass loss are both of significant importance for wheat yield loss. Then we derived ozone dose response functions for biomass loss and for harvest index (the proportion of above-ground biomass converted to grain) based on twelve experiments and recently developed ozone uptake modelling for wheat. Finally, we used a European scale chemical transport model (EMEP MSC-West) to assess the effect of ozone on biomass (-9%) and grain yield (-14%) loss over Europe. Based on yield data per grid square, we estimated above ground biomass losses due to ozone in 2000 in Europe totalling 22.2 million tonnes. Incorrectly applying the grain yield response function to model effects on biomass instead of the biomass response function of this paper would have indicated total above ground biomass losses totalling 38.1 million (i.e. overestimating effects by 15.9 million tonnes). A key conclusion from our study is that future assessments of ozone induced loss of agroecosystem carbon storage should use response functions for biomass, such as that provided in this paper, not grain yield, to avoid overestimation of the indirect radiative forcing from ozone effects on crop biomass accumulation.

  1. Structural, physiognomic and above-ground biomass variation in savanna-forest transition zones on three continents - how different are co-occurring savanna and forest formations?

    NASA Astrophysics Data System (ADS)

    Veenendaal, E. M.; Torello-Raventos, M.; Feldpausch, T. R.; Domingues, T. F.; Gerard, F.; Schrodt, F.; Saiz, G.; Quesada, C. A.; Djagbletey, G.; Ford, A.; Kemp, J.; Marimon, B. S.; Marimon-Junior, B. H.; Lenza, E.; Ratter, J. A.; Maracahipes, L.; Sasaki, D.; Sonke, B.; Zapfack, L.; Villarroel, D.; Schwarz, M.; Yoko Ishida, F.; Gilpin, M.; Nardoto, G. B.; Affum-Baffoe, K.; Arroyo, L.; Bloomfield, K.; Ceca, G.; Compaore, H.; Davies, K.; Diallo, A.; Fyllas, N. M.; Gignoux, J.; Hien, F.; Johnson, M.; Mougin, E.; Hiernaux, P.; Killeen, T.; Metcalfe, D.; Miranda, H. S.; Steininger, M.; Sykora, K.; Bird, M. I.; Grace, J.; Lewis, S.; Phillips, O. L.; Lloyd, J.

    2015-05-01

    Through interpretations of remote-sensing data and/or theoretical propositions, the idea that forest and savanna represent "alternative stable states" is gaining increasing acceptance. Filling an observational gap, we present detailed stratified floristic and structural analyses for forest and savanna stands located mostly within zones of transition (where both vegetation types occur in close proximity) in Africa, South America and Australia. Woody plant leaf area index variation was related to tree canopy cover in a similar way for both savanna and forest with substantial overlap between the two vegetation types. As total woody plant canopy cover increased, so did the relative contribution of middle and lower strata of woody vegetation. Herbaceous layer cover declined as woody cover increased. This pattern of understorey grasses and herbs progressively replaced by shrubs as the canopy closes over was found for both savanna and forests and on all continents. Thus, once subordinate woody canopy layers are taken into account, a less marked transition in woody plant cover across the savanna-forest-species discontinuum is observed compared to that inferred when trees of a basal diameter > 0.1 m are considered in isolation. This is especially the case for shrub-dominated savannas and in taller savannas approaching canopy closure. An increased contribution of forest species to the total subordinate cover is also observed as savanna stand canopy closure occurs. Despite similarities in canopy-cover characteristics, woody vegetation in Africa and Australia attained greater heights and stored a greater amount of above-ground biomass than in South America. Up to three times as much above-ground biomass is stored in forests compared to savannas under equivalent climatic conditions. Savanna-forest transition zones were also found to typically occur at higher precipitation regimes for South America than for Africa. Nevertheless, consistent across all three continents coexistence

  2. Estimating spatial variation in Alberta forest biomass from a combination of forest inventory and remote sensing data

    NASA Astrophysics Data System (ADS)

    Zhang, J.; Huang, S.; Hogg, E. H.; Lieffers, V.; Qin, Y.; He, F.

    2014-05-01

    Uncertainties in the estimation of tree biomass carbon storage across large areas pose challenges for the study of forest carbon cycling at regional and global scales. In this study, we attempted to estimate the present aboveground biomass (AGB) in Alberta, Canada, by taking advantage of a spatially explicit data set derived from a combination of forest inventory data from 1968 plots and spaceborne light detection and ranging (lidar) canopy height data. Ten climatic variables, together with elevation, were used for model development and assessment. Four approaches, including spatial interpolation, non-spatial and spatial regression models, and decision-tree-based modeling with random forests algorithm (a machine-learning technique), were compared to find the "best" estimates. We found that the random forests approach provided the best accuracy for biomass estimates. Non-spatial and spatial regression models gave estimates similar to random forests, while spatial interpolation greatly overestimated the biomass storage. Using random forests, the total AGB stock in Alberta forests was estimated to be 2.26 × 109 Mg (megagram), with an average AGB density of 56.30 ± 35.94 Mg ha-1. At the species level, three major tree species, lodgepole pine, trembling aspen and white spruce, stocked about 1.39 × 109 Mg biomass, accounting for nearly 62% of total estimated AGB. Spatial distribution of biomass varied with natural regions, land cover types, and species. Furthermore, the relative importance of predictor variables on determining biomass distribution varied with species. This study showed that the combination of ground-based inventory data, spaceborne lidar data, land cover classification, and climatic and environmental variables was an efficient way to estimate the quantity, distribution and variation of forest biomass carbon stocks across large regions.

  3. Forest Above Ground Biomass Estimation in China

    NASA Astrophysics Data System (ADS)

    Zhao, D.; Zeng, Y.; Wu, B.; Li, X.

    2013-12-01

    In order to study the carbon cycling in China deeply, a forest above ground biomass (AGB) estimation research is carried out under the support of 'Strategic Priority Research Program - Climate Change: Carbone Budget and Related Issues' of the Chinese Academy of Sciences (Carbon Project). The research aims to estimate the forest AGB in 2000, 2005 and 2010 in China, and analyzes its dynamic changes. The overall thinking of the research is using field works and airborne LiDAR data as basis to estimate the AGB in GLAS footprints, and then extrapolating discrete AGB to continuous results with optical and auxiliary data. Due to the large area of China, totally 8 sub-areas are marked out based on the different forest ecosystems and some other factors (Table 1 and Fig. 1). Here, a latest China's land cover product (the background of Fig 1), named 'ChinaCover', and also supported by the 'Carbon Project', is imported to classify the forest types. There are around 5000 sample plots (Table 1) surveyed by the 'Carbon Project'. It can provide a large number of training and validation data. At the same time, the research sets 6 other typical sample areas, which have areas of 60 to 200 km2, and airborne LiDAR flights are carried out to obtain high accuracy AGB in these areas. With the sample plots and 6 typical sample areas, the AGB in GLAS footprint is estimated. Since the sample plots and LiDAR flights were carried out in 2012, the height and area parameters extracted from GLAS footprint are corrected by tree growth model of different forest types. In a further step, extrapolation models are built together with time-series MODIS and auxiliary data. These models fully consider the time-series features and propose several long time-series indices to minimize the influence of spectral saturation. Results are validated by samples and compared to the result of some other researches. At last, the models are applied to the data of 2000, 2005 and 2010 to get the corresponding AGB maps

  4. Biomass Estimates for Five Western States.

    SciTech Connect

    Howard, James O.

    1990-10-01

    The purpose of this report is to describe the woody biomass resource within US Department of Energy's Pacific Northwest and Alaska Regional Biomass Program, comprised of southeast Alaska, Idaho, Montana, Oregon, and Washington. In addition to the regional forest biomass assessment, information will be presented for logging residue, which represents current energy conversion opportunities. The information presented in the report is based on data and relationships already published. Regionally applicable biomass equations are generally not available for species occurring in the west. Because of this, a number of assumptions were made to develop whole-tree biomass tables. These assumptions are required to link algorithms from biomass studies to regional timber inventory data published by the Forest Inventory and Analysis Research Units (FIA), of the Pacific Northwest and Intermountain Research Stations, US Forest Service. These sources and assumptions will be identified later in this report. Tabular biomass data will be presented for 11 resource areas, identified in the FS inventory publications. This report does not include information for the vast area encompassing interior Alaska. Total tress biomass as defined in the report refers to the above ground weight of a tree above a 1.0 foot stump, and exclusive of foliage. A glossary is included that defines specific terms as used in the report. Inventory terminology is derived from forest inventory reports from Forest Inventory and Analysis units at the Intermountain and Pacific Northwest Research Stations. 39 refs., 15 figs., 23 tabs.

  5. Response of aboveground biomass and diversity to nitrogen addition – a five-year experiment in semi-arid grassland of Inner Mongolia, China

    PubMed Central

    He, Kejian; Qi, Yu; Huang, Yongmei; Chen, Huiying; Sheng, Zhilu; Xu, Xia; Duan, Lei

    2016-01-01

    Understanding the response of the plant community to increasing nitrogen (N) deposition is helpful for improving pasture management in semi-arid areas. We implemented a 5-year N addition experiment in a Stipa krylovii steppe of Inner Mongolia, northern China. The aboveground biomass (AGB) and species richness were measured annually. Along with the N addition levels, the species richness declined significantly, and the species composition changed noticeably. However, the total AGB did not exhibit a noticeable increase. We found that compensatory effects of the AGB occurred not only between the grasses and the forbs but also among Gramineae species. The plant responses to N addition, from the community to species level, lessened in dry years compared to wet or normal years. The N addition intensified the reduction of community productivity in dry years. Our study indicated that the compensatory effects of the AGB among the species sustained the stability of grassland productivity. However, biodiversity loss resulting from increasing N deposition might lead the semi-arid grassland ecosystem to be unsustainable, especially in dry years. PMID:27573360

  6. Response of aboveground biomass and diversity to nitrogen addition - a five-year experiment in semi-arid grassland of Inner Mongolia, China.

    PubMed

    He, Kejian; Qi, Yu; Huang, Yongmei; Chen, Huiying; Sheng, Zhilu; Xu, Xia; Duan, Lei

    2016-01-01

    Understanding the response of the plant community to increasing nitrogen (N) deposition is helpful for improving pasture management in semi-arid areas. We implemented a 5-year N addition experiment in a Stipa krylovii steppe of Inner Mongolia, northern China. The aboveground biomass (AGB) and species richness were measured annually. Along with the N addition levels, the species richness declined significantly, and the species composition changed noticeably. However, the total AGB did not exhibit a noticeable increase. We found that compensatory effects of the AGB occurred not only between the grasses and the forbs but also among Gramineae species. The plant responses to N addition, from the community to species level, lessened in dry years compared to wet or normal years. The N addition intensified the reduction of community productivity in dry years. Our study indicated that the compensatory effects of the AGB among the species sustained the stability of grassland productivity. However, biodiversity loss resulting from increasing N deposition might lead the semi-arid grassland ecosystem to be unsustainable, especially in dry years. PMID:27573360

  7. Assessment of variations in taxonomic diversity, forest structure, and aboveground biomass using remote sensing along an altitudinal gradient in tropical montane forest of Costa Rica

    NASA Astrophysics Data System (ADS)

    Robinson, C. M.; Saatchi, S. S.; Clark, D.; Fricker, G. A.; Wolf, J.; Gillespie, T. W.; Rovzar, C. M.; Andelman, S.

    2012-12-01

    This research sought to understand how alpha and beta diversity of plants vary and relate to the three-dimensional vegetation structure and aboveground biomass along environmental gradients in the tropical montane forests of Braulio Carrillo National Park in Costa Rica. There is growing evidence that ecosystem structure plays an important role in defining patterns of species diversity and along with abiotic factors (climate and edaphic) control the phenotypic and functional variations across landscapes. It is well documented that strong subdivisions at local and regional scales are found mainly on geologic or climate gradients. These general determinants of biodiversity are best demonstrated in regions with natural gradients such as tropical montane forests. Altitudinal gradients provide a landscape scale changes through variations in topography, climate, and edaphic conditions on which we tested several theoretical and biological hypotheses regarding drivers of biodiversity. The study was performed by using forest inventory and botanical data from nine 1-ha plots ranging from 100 m to 2800 m above sea level and remote sensing data from airborne lidar and radar sensors to quantify variations in forest structure. In this study we report on the effectiveness of relating patterns of tree taxonomic alpha diversity to three-dimensional structure of a tropical montane forest using lidar and radar observations of forest structure and biomass. We assessed alpha and beta diversity at the species, genus, and family levels utilizing datasets provided by the Terrestrial Ecology Assessment and Monitoring (TEAM) Network. Through the comparison to active remote sensing imagery, our results show that there is a strong relationship between forest 3D-structure, and alpha and beta diversity controlled by variations in abiotic factors along the altitudinal gradient. Using spatial analysis with the aid of remote sensing data, we find distinct patterns along the environmental gradients

  8. Evaluation of Methods to Estimate Understory Fruit Biomass

    PubMed Central

    Lashley, Marcus A.; Thompson, Jeffrey R.; Chitwood, M. Colter; DePerno, Christopher S.; Moorman, Christopher E.

    2014-01-01

    Fleshy fruit is consumed by many wildlife species and is a critical component of forest ecosystems. Because fruit production may change quickly during forest succession, frequent monitoring of fruit biomass may be needed to better understand shifts in wildlife habitat quality. Yet, designing a fruit sampling protocol that is executable on a frequent basis may be difficult, and knowledge of accuracy within monitoring protocols is lacking. We evaluated the accuracy and efficiency of 3 methods to estimate understory fruit biomass (Fruit Count, Stem Density, and Plant Coverage). The Fruit Count method requires visual counts of fruit to estimate fruit biomass. The Stem Density method uses counts of all stems of fruit producing species to estimate fruit biomass. The Plant Coverage method uses land coverage of fruit producing species to estimate fruit biomass. Using linear regression models under a censored-normal distribution, we determined the Fruit Count and Stem Density methods could accurately estimate fruit biomass; however, when comparing AIC values between models, the Fruit Count method was the superior method for estimating fruit biomass. After determining that Fruit Count was the superior method to accurately estimate fruit biomass, we conducted additional analyses to determine the sampling intensity (i.e., percentage of area) necessary to accurately estimate fruit biomass. The Fruit Count method accurately estimated fruit biomass at a 0.8% sampling intensity. In some cases, sampling 0.8% of an area may not be feasible. In these cases, we suggest sampling understory fruit production with the Fruit Count method at the greatest feasible sampling intensity, which could be valuable to assess annual fluctuations in fruit production. PMID:24819253

  9. Estimating stem volume and biomass of Pinus koraiensis using LiDAR data.

    PubMed

    Kwak, Doo-Ahn; Lee, Woo-Kyun; Cho, Hyun-Kook; Lee, Seung-Ho; Son, Yowhan; Kafatos, Menas; Kim, So-Ra

    2010-07-01

    The objective of this study was to estimate the stem volume and biomass of individual trees using the crown geometric volume (CGV), which was extracted from small-footprint light detection and ranging (LiDAR) data. Attempts were made to analyze the stem volume and biomass of Korean Pine stands (Pinus koraiensis Sieb. et Zucc.) for three classes of tree density: low (240 N/ha), medium (370 N/ha), and high (1,340 N/ha). To delineate individual trees, extended maxima transformation and watershed segmentation of image processing methods were applied, as in one of our previous studies. As the next step, the crown base height (CBH) of individual trees has to be determined; information for this was found in the LiDAR point cloud data using k-means clustering. The LiDAR-derived CGV and stem volume can be estimated on the basis of the proportional relationship between the CGV and stem volume. As a result, low tree-density plots had the best performance for LiDAR-derived CBH, CGV, and stem volume (R (2) = 0.67, 0.57, and 0.68, respectively) and accuracy was lowest for high tree-density plots (R (2) = 0.48, 0.36, and 0.44, respectively). In the case of medium tree-density plots accuracy was R (2) = 0.51, 0.52, and 0.62, respectively. The LiDAR-derived stem biomass can be predicted from the stem volume using the wood basic density of coniferous trees (0.48 g/cm(3)), and the LiDAR-derived above-ground biomass can then be estimated from the stem volume using the biomass conversion and expansion factors (BCEF, 1.29) proposed by the Korea Forest Research Institute (KFRI). PMID:20182905

  10. Aboveground storage tank regulations

    SciTech Connect

    Geyer, W. )

    1993-01-01

    There are critical differences between the potential for environmental impact of aboveground and underground oil storage. For example, while leaks from underground storage tanks (USTs) seep into soil or aquifers, the concern with aboveground storage tanks (ASTs) is that an overfill or tank rupture can cause product to escape into a navigable stream and immediately create an oil spill pollution incident. The US Environmental Protection Agency (EPA) has very distinct programs outlining regulation parameters for each type of storage, including source of authority, regulatory cutoffs and exclusions, definitions, prevention and response requirements, and penalties, etc. Engineers considering changes or recommending a change in type of storage, particularly from a UST to an AST, need to be aware of existing federal regulations. Since the federal UST program began, remediation costs have skyrocketed as a result of the need to clean up leaking tank and piping sites, backfill and surrounding soil or groundwater. Compliance with federal and state UST regulations has not been cheap, and is expected to top $23 billion, according to some estimates. Partly as a result, market demand has shifted toward use of aboveground storage tanks, a trend that is expected to continue. Industry figures show a 100% increase in factory fabricated aboveground tank activity during the last four years.

  11. The weight of nations: an estimation of adult human biomass

    PubMed Central

    2012-01-01

    Background The energy requirement of species at each trophic level in an ecological pyramid is a function of the number of organisms and their average mass. Regarding human populations, although considerable attention is given to estimating the number of people, much less is given to estimating average mass, despite evidence that average body mass is increasing. We estimate global human biomass, its distribution by region and the proportion of biomass due to overweight and obesity. Methods For each country we used data on body mass index (BMI) and height distribution to estimate average adult body mass. We calculated total biomass as the product of population size and average body mass. We estimated the percentage of the population that is overweight (BMI > 25) and obese (BMI > 30) and the biomass due to overweight and obesity. Results In 2005, global adult human biomass was approximately 287 million tonnes, of which 15 million tonnes were due to overweight (BMI > 25), a mass equivalent to that of 242 million people of average body mass (5% of global human biomass). Biomass due to obesity was 3.5 million tonnes, the mass equivalent of 56 million people of average body mass (1.2% of human biomass). North America has 6% of the world population but 34% of biomass due to obesity. Asia has 61% of the world population but 13% of biomass due to obesity. One tonne of human biomass corresponds to approximately 12 adults in North America and 17 adults in Asia. If all countries had the BMI distribution of the USA, the increase in human biomass of 58 million tonnes would be equivalent in mass to an extra 935 million people of average body mass, and have energy requirements equivalent to that of 473 million adults. Conclusions Increasing population fatness could have the same implications for world food energy demands as an extra half a billion people living on the earth. PMID:22709383

  12. Estimating vegetative biomass from LANDSAT-1 imagery for range management

    NASA Technical Reports Server (NTRS)

    Seevers, P. M.; Drew, J. V.; Carlson, M. P.

    1975-01-01

    Evaluation of LANDSAT-1, band 5 data for use in estimation of vegetative biomass for range management decisions was carried out for five selected range sites in the Sandhills region of Nebraska. Analysis of sets of optical density-vegetative biomass data indicated that comparisons of biomass estimation could be made within one frame but not between frames without correction factors. There was high correlation among sites within sets of radiance value-vegetative biomass data and also between sets, indicating comparisons of biomass could be made within and between frames. Landsat-1 data are shown to be a viable alternative to currently used methods of determining vegetative biomass production and stocking rate recommendations for Sandhills rangeland.

  13. Estimating Volume, Biomass, and Carbon in Hedmark County, Norway Using a Profiling LiDAR

    NASA Technical Reports Server (NTRS)

    Nelson, Ross; Naesset, Erik; Gobakken, T.; Gregoire, T.; Stahl, G.

    2009-01-01

    A profiling airborne LiDAR is used to estimate the forest resources of Hedmark County, Norway, a 27390 square kilometer area in southeastern Norway on the Swedish border. One hundred five profiling flight lines totaling 9166 km were flown over the entire county; east-west. The lines, spaced 3 km apart north-south, duplicate the systematic pattern of the Norwegian Forest Inventory (NFI) ground plot arrangement, enabling the profiler to transit 1290 circular, 250 square meter fixed-area NFI ground plots while collecting the systematic LiDAR sample. Seven hundred sixty-three plots of the 1290 plots were overflown within 17.8 m of plot center. Laser measurements of canopy height and crown density are extracted along fixed-length, 17.8 m segments closest to the center of the ground plot and related to basal area, timber volume and above- and belowground dry biomass. Linear, nonstratified equations that estimate ground-measured total aboveground dry biomass report an R(sup 2) = 0.63, with an regression RMSE = 35.2 t/ha. Nonstratified model results for the other biomass components, volume, and basal area are similar, with R(sup 2) values for all models ranging from 0.58 (belowground biomass, RMSE = 8.6 t/ha) to 0.63. Consistently, the most useful single profiling LiDAR variable is quadratic mean canopy height, h (sup bar)(sub qa). Two-variable models typically include h (sup bar)(sub qa) or mean canopy height, h(sup bar)(sub a), with a canopy density or a canopy height standard deviation measure. Stratification by productivity class did not improve the nonstratified models, nor did stratification by pine/spruce/hardwood. County-wide profiling LiDAR estimates are reported, by land cover type, and compared to NFI estimates.

  14. ESTIMATION OF TROPICAL FOREST STRUCTURE AND BIOMASS FROM FUSION OF RADAR AND LIDAR MEASUREMENTS (Invited)

    NASA Astrophysics Data System (ADS)

    Saatchi, S. S.; Dubayah, R.; Clark, D. B.; Chazdon, R.

    2009-12-01

    Radar and Lidar instruments are active remote sensing sensors with the potential of measuring forest vertical and horizontal structure and the aboveground biomass (AGB). In this paper, we present the analysis of radar and lidar data acquired over the La Selva Biological Station in Costa Rica. Radar polarimetry at L-band (25 cm wavelength), P-band (70 cm wavelength) and interferometry at C-band (6 cm wavelength) and VV polarization were acquired by the NASA/JPL airborne synthetic aperture radar (AIRSAR) system. Lidar images were provided by a large footprint airborne scanning Lidar known as the Laser Vegetation Imaging Sensor (LVIS). By including field measurements of structure and biomass over a variety of forest types, we examined: 1) sensitivity of radar and lidar measurements to forest structure and biomass, 2) accuracy of individual sensors for AGB estimation, and 3) synergism of radar imaging measurements with lidar imaging and sampling measurements for improving the estimation of 3-dimensional forest structure and AGB. The results showed that P-band radar combined with any interformteric measurement of forest height can capture approximately 85% of the variation of biomass in La Selva at spatial scales larger than 1 hectare. Similar analysis at L-band frequency captured only 70% of the variation. However, combination of lidar and radar measurements improved estimates of forest three-dimensional structure and biomass to above 90% for all forest types. We present a novel data fusion approach based on a Baysian estimation model with the capability of incorporating lidar samples and radar imagery. The model was used to simulate the potential of data fusion in future satellite mission scenarios as in BIOMASS (planned by ESA) at P-band and DESDynl (planned by NASA) at L-band. The estimation model was also able to quantify errors and uncertainties associated with the scale of measurements, spatial variability of forest structure, and differences in radar and lidar

  15. Estimates of U.S. Biomass Energy Consumption 1992

    EIA Publications

    1994-01-01

    This report is the seventh in a series of publications developed by the Energy Information Administration (EIA) to quantify the biomass derived primary energy used by the U.S. economy. It presents estimates of 1991 and 1992 consumption.

  16. Lidar point cloud representation of canopy structure for biomass estimation

    NASA Astrophysics Data System (ADS)

    Neuenschwander, A. L.; Krofcheck, D. J.; Litvak, M. E.

    2014-12-01

    Laser mapping systems (lidar) have become an essential remote sensing tool for determining local and regional estimates of biomass. Lidar data (possibly in conjunction with optical imagery) can be used to segment the landscape into either individual trees or clusters of trees. Canopy characteristics (i.e. max, mean height) for a segmented tree are typically derived from a rasterized canopy height model (CHM) and subsequently used in a regression model to estimate biomass. The process of rasterizing the lidar point cloud into a CHM, however, reduces the amount information about the tree structure. Here, we compute statistics for each segmented tree from the raw lidar point cloud rather than a rasterized CHM. Working directly from the lidar point cloud enables a more accurate representation of the canopy structure. Biomass estimates from the point cloud method are compared against biomass estimates derived from a CHM for a Juniper savanna in New Mexico.

  17. USING DIRICHLET TESSELLATION TO HELP ESTIMATE MICROBIAL BIOMASS CONCENTRATIONS

    EPA Science Inventory

    Dirichlet tessellation was applied to estimate microbial concentrations from microscope well slides. The use of microscopy/Dirichlet tessellation to quantify biomass was illustrated with two species of morphologically distinct cyanobacteria, and validated empirically by compariso...

  18. Estimating Aboveground Forest Carbon Stock of Major Tropical Forest Land Uses Using Airborne Lidar and Field Measurement Data in Central Sumatra

    NASA Astrophysics Data System (ADS)

    Thapa, R. B.; Watanabe, M.; Motohka, T.; Shiraishi, T.; shimada, M.

    2013-12-01

    Tropical forests are providing environmental goods and services including carbon sequestration, energy regulation, water fluxes, wildlife habitats, fuel, and building materials. Despite the policy attention, the tropical forest reserve in Southeast Asian region is releasing vast amount of carbon to the atmosphere due to deforestation. Establishing quality forest statistics and documenting aboveground forest carbon stocks (AFCS) are emerging in the region. Airborne and satellite based large area monitoring methods are developed to compliment conventional plot based field measurement methods as they are costly, time consuming, and difficult to implement for large regions. But these methods still require adequate ground measurements for calibrating accurate AFCS model. Furthermore, tropical region comprised of varieties of natural and plantation forests capping higher variability of forest structures and biomass volumes. To address this issue and the needs for ground data, we propose the systematic collection of ground data integrated with airborne light detection and ranging (LiDAR) data. Airborne LiDAR enables accurate measures of vertical forest structure, including canopy height and volume demanding less ground measurement plots. Using an appropriate forest type based LiDAR sampling framework, structural properties of forest can be quantified and treated similar to ground measurement plots, producing locally relevant information to use independently with satellite data sources including synthetic aperture radar (SAR). In this study, we examined LiDAR derived forest parameters with field measured data and developed general and specific AFCS models for tropical forests in central Sumatra. The general model is fitted for all types of natural and plantation forests while the specific model is fitted to the specific forest type. The study region consists of natural forests including peat swamp and dry moist forests, regrowth, and mangrove and plantation forests

  19. Linking Gap Model with MODIS Biophysical Products for Biomass Estimation

    NASA Astrophysics Data System (ADS)

    Wang, D.; Sun, G.; Cai, Y.; Guo, Z.; Fu, A.; Ni, W.; Liu, D.

    With the development of earth observation technology and data processing technology biophysical data from remote sensing means such as MODIS LAI and NPP are accessible now However it is still difficult for direct measurement of biomass from remote sensors One possibility for overcoming this problem is using ecological models to link the vegetation parameters currently available from remote sensing to biomass In this paper a combined work is done for estimating forest biomass A calibrated gap model ZELIG was run to simulate the forest development in a temperate forested area in NE China The output relationship between age and biomass was linked to registered MODIS LAI NPP and land cover type images of the same area From the above work forest age or biomass was estimated from existing remote sensed data Obviously there is a lot of work to be done such as optimal combination of biophysical parameters to improve the linkage between MODIS product and ecological modeling

  20. Structural, physiognomic and aboveground biomass variation in savanna-forest transition zones on three continents. How different are co-occurring savanna and forest formations?

    NASA Astrophysics Data System (ADS)

    Veenendaal, E. M.; Torello-Raventos, M.; Feldpausch, T. R.; Domingues, T. F.; Gerard, F.; Schrodt, F.; Saiz, G.; Quesada, C. A.; Djagbletey, G.; Ford, A.; Kemp, J.; Marimon, B. S.; Marimon-Junior, B. H.; Lenza, E.; Ratter, J. A.; Maracahipes, L.; Sasaki, D.; Sonké, B.; Zapfack, L.; Villarroel, D.; Schwarz, M.; Yoko Ishida, F.; Gilpin, M.; Nardoto, G. B.; Affum-Baffoe, K.; Arroyo, L.; Bloomfield, K.; Ceca, G.; Compaore, H.; Davies, K.; Diallo, A.; Fyllas, N. M.; Gignoux, J.; Hien, F.; Johnson, M.; Mougin, E.; Hiernaux, P.; Killeen, T.; Metcalfe, D.; Miranda, H. S.; Steininger, M.; Sykora, K.; Bird, M. I.; Grace, J.; Lewis, S.; Phillips, O. L.; Lloyd, J.

    2014-03-01

    Through interpretations of remote sensing data and/or theoretical propositions, the idea that forest and savanna represent "alternative stable states" is gaining increasing acceptance. Filling an observational gap, we present detailed stratified floristic and structural analyses for forest and savanna stands mostly located within zones of transition (where both vegetation types occur in close proximity) in Africa, South America and Australia. Woody plant leaf area index variation was related in a similar way to tree canopy cover for both savanna and forest with substantial overlap between the two vegetation types. As total woody plant canopy cover increased, so did the contribution of middle and lower strata of woody vegetation to this total. Herbaceous layer cover also declined as woody cover increased. This pattern of understorey grasses and herbs being progressively replaced by shrubs as canopy closure occurs was found for both savanna and forests and on all continents. Thus, once subordinate woody canopy layers are taken into account, a less marked transition in woody plant cover across the savanna-forest species discontinuum is observed compared to that implied when trees of a basal diameter > 0.1m are considered in isolation. This is especially the case for shrub-dominated savannas and in taller savannas approaching canopy closure. An increased contribution of forest species to the total subordinate cover is also observed as savanna stand canopy closure occurs. Despite similarities in canopy cover characteristics, woody vegetation in Africa and Australia attained greater heights and stored a greater concentration of above ground biomass than in South America. Up to three times as much aboveground biomass is stored in forests compared to savannas under equivalent climatic conditions. Savanna/forest transition zones were also found to typically occur at higher precipitation regimes for South America than for Africa. Nevertheless, coexistence was found to be

  1. Impacts of cattle grazing on spatio-temporal variability of soil moisture and above-ground live plant biomass in mixed grasslands

    NASA Astrophysics Data System (ADS)

    Virk, Ravinder

    Areas with relatively high spatial heterogeneity generally have more biodiversity than spatially homogeneous areas due to increased potential habitat. Management practices such as controlled grazing also affect the biodiversity in grasslands, but the nature of this impact is not well understood. Therefore this thesis studies the impacts of variation in grazing on soil moisture and biomass heterogeneity. These are not only important in terms of management of protected grasslands, but also for designing an effective grazing system from a livestock management point of view. This research is a part of the cattle grazing experiment underway in Grasslands National Park (GNP) of Canada since 2006, as part of the adaptive management process for restoring ecological integrity of the northern mixed-grass prairie region. An experimental approach using field measurements and remote sensing (Landsat) was combined with modelling (CENTURY) to examine and predict the impacts of grazing intensity on the spatial heterogeneity and patterns of above-ground live plant biomass (ALB) in experimental pastures in a mixed grassland ecosystem. The field-based research quantified the temporal patterns and spatial variability in both soil moisture (SM) and ALB, and the influence of local intra-seasonal weather variability and slope location on the spatio-temporal variability of SM and ALB at field plot scales. Significant impacts of intra-seasonal weather variability, slope position and grazing pressure on SM and ALB across a range of scales (plot and local (within pasture)) were found. Grazing intensity significantly affected the ALB even after controlling for the effect of slope position. Satellite-based analysis extended the scale of interest to full pastures and the surrounding region to assess the effects of grazing intensity on the spatio-temporal pattern of ALB in mixed grasslands. Overall, low to moderate grazing intensity showed increase in ALB heterogeneity whereas no change in ALB

  2. Productivity of aboveground coarse wood biomass and stand age related to soil hydrology of Amazonian forests in the Purus-Madeira interfluvial area

    NASA Astrophysics Data System (ADS)

    Cintra, B. B. L.; Schietti, J.; Emillio, T.; Martins, D.; Moulatlet, G.; Souza, P.; Levis, C.; Quesada, C. A.; Schöngart, J.

    2013-04-01

    The ongoing demand for information on forest productivity has increased the number of permanent monitoring plots across the Amazon. Those plots, however, do not comprise the whole diversity of forest types in the Amazon. The complex effects of soil, climate and hydrology on the productivity of seasonally waterlogged interfluvial wetland forests are still poorly understood. The presented study is the first field-based estimate for tree ages and wood biomass productivity in the vast interfluvial region between the Purus and Madeira rivers. We estimate stand age and wood biomass productivity by a combination of tree-ring data and allometric equations for biomass stocks of eight plots distributed along 600 km in the Purus-Madeira interfluvial area that is crossed by the BR-319 highway. We relate stand age and wood biomass productivity to hydrological and edaphic conditions. Mean productivity and stand age were 5.6 ± 1.1 Mg ha-1 yr-1 and 102 ± 18 yr, respectively. There is a strong relationship between tree age and diameter, as well as between mean diameter increment and mean wood density within a plot. Regarding the soil hydromorphic properties we find a positive correlation with wood biomass productivity and a negative relationship with stand age. Productivity also shows a positive correlation with the superficial phosphorus concentration. In addition, superficial phosphorus concentration increases with enhanced soil hydromorphic condition. We raise three hypotheses to explain these results: (1) the reduction of iron molecules on the saturated soils with plinthite layers close to the surface releases available phosphorous for the plants; (2) the poor structure of the saturated soils creates an environmental filter selecting tree species of faster growth rates and shorter life spans and (3) plant growth on saturated soil is favored during the dry season, since there should be low restrictions for soil water availability.

  3. Simulations of Forest Structure and Biomass across Russia for Biomass Estimation under a Changing Climate.

    NASA Astrophysics Data System (ADS)

    Shugart, H. H., Jr.; Shuman, J. K.

    2014-12-01

    An important innovation in understanding the interactions among physical of forests and measurement of forest state is the potential deployment of active (RADAR and LiDAR) satellite reconnaissance systems. We investigate the potential gain in predictive capability of structural measures determined by these instruments. Observations and model results have identified climate change as a driver of structural and compositional change in forest of Russia, which may affect climate patterns beyond the region. Using an individual-tree-based model (UVAFME) for forests at 31,000+ grid points of a 22 km×22 km grid across Russia, we inspected the relationships between above-ground biomass and structural measures including maximum tree height and Lorey's height (average height for each tree weighted by basal area). At each of the grid points 200 independent 0.1hectare plots were simulated for 100 years using two climate change scenarios following a 500-year spin-up to produce a mature forest. Other simulations project the change of a forest-landscape mosaic with equal proportions of 0, 25, 50, 75 and 100 year-old stands to mimic a heterogeneous landscape mosaic typical of reoccurring wildfires. Qualitatively, maximum height and Lorey's height seem particularly useful in detecting forest change in the vicinity of forest transitions with other ecosystems. Quantitatively, maximum height and Lorey's height account for a large component of the variability in forest biomass. Results of exponential regression between height measurements and biomass show that r2 values can exceed 0.75. Lorey's height is more capable in this regard. The relationship between these measures of height and biomass can be improved with classification of forests into types. For example, Russian forest dominated by the tall, large diameter pines (Pinus koraiensis, P. sibirica, P. sylvestris) can have exceptional biomass compared to other forests across Russia, and produced biomass and height values higher

  4. Biomass Estimation for Individual Trees using Waveform LiDAR

    NASA Astrophysics Data System (ADS)

    Wang, K.; Kumar, P.; Dutta, D.

    2015-12-01

    Vegetation biomass information is important for many ecological models that include terrestrial vegetation in their simulations. Biomass has strong influences on carbon, water, and nutrient cycles. Traditionally biomass estimation requires intensive, and often destructive, field measurements. However, with advances in technology, airborne LiDAR has become a convenient tool for acquiring such information on a large scale. In this study, we use infrared full waveform LiDAR to estimate biomass information for individual trees in the Sangamon River basin in Illinois, USA. During this process, we also develop automated geolocation calibration algorithms for raw waveform LiDAR data. In the summer of 2014, discrete and waveform LiDAR data were collected over the Sangamon River basin. Field measurements commonly used in biomass equations such as diameter at breast height and total tree height were also taken for four sites across the basin. Using discrete LiDAR data, individual trees are delineated. For each tree, a voxelization methods is applied to all waveforms associated with the tree to result in a pseudo-waveform. By relating biomass extrapolated using field measurements from a training set of trees to waveform metrics for each corresponding tree, we are able to estimate biomass on an individual tree basis. The results can be especially useful as current models increase in resolution.

  5. Estimating vegetation biomass using synthetic aperture radar

    NASA Astrophysics Data System (ADS)

    Baronti, Stefano; Luciani, S.; Paloscia, Simonetta; Schiavon, G.; Sigismondi, S.; Solimini, Domenico

    1994-12-01

    A significant experiment for evaluating the potential of Synthetic Aperture Radar (SAR) in monitoring soil and vegetation parameters is being carried out on an agricultural area located in Central Italy. The site has been imaged in 1991 by NASA/JPL AIRSAR during the MAC-91 Campaign and subsequently by ESA/ERS-1 and NASDA JERS-1 in 1992. The sensitivity to vegetation biomass of backscattering coefficient measured by ERS-1 and JERS-1 radars is discussed and compared with the best results achieved using the multifrequency polarimetric AIRSAR data.

  6. Evaluation of SPOT imagery for the estimation of grassland biomass

    NASA Astrophysics Data System (ADS)

    Dusseux, P.; Hubert-Moy, L.; Corpetti, T.; Vertès, F.

    2015-06-01

    In many regions, a decrease in grasslands and change in their management, which are associated with agricultural intensification, have been observed in the last half-century. Such changes in agricultural practices have caused negative environmental effects that include water pollution, soil degradation and biodiversity loss. Moreover, climate-driven changes in grassland productivity could have serious consequences for the profitability of agriculture. The aim of this study was to assess the ability of remotely sensed data with high spatial resolution to estimate grassland biomass in agricultural areas. A vegetation index, namely the Normalized Difference Vegetation Index (NDVI), and two biophysical variables, the Leaf Area Index (LAI) and the fraction of Vegetation Cover (fCOVER) were computed using five SPOT images acquired during the growing season. In parallel, ground-based information on grassland growth was collected to calculate biomass values. The analysis of the relationship between the variables derived from the remotely sensed data and the biomass observed in the field shows that LAI outperforms NDVI and fCOVER to estimate biomass (R2 values of 0.68 against 0.30 and 0.50, respectively). The squared Pearson correlation coefficient between observed and estimated biomass using LAI derived from SPOT images reached 0.73. Biomass maps generated from remotely sensed data were then used to estimate grass reserves at the farm scale in the perspective of operational monitoring and forecasting.

  7. Estimating forest biomass and volume using airborne laser data

    NASA Technical Reports Server (NTRS)

    Nelson, Ross; Krabill, William; Tonelli, John

    1988-01-01

    An airborne pulsed laser system was used to obtain canopy height data over a southern pine forest in Georgia in order to predict ground-measured forest biomass and timber volume. Although biomass and volume estimates obtained from the laser data were variable when compared with the corresponding ground measurements site by site, the present models are found to predict mean total tree volume within 2.6 percent of the ground value, and mean biomass within 2.0 percent. The results indicate that species stratification did not consistently improve regression relationships for four southern pine species.

  8. Crop biomass and evapotranspiration estimation using SPOT and Formosat-2 Data

    NASA Astrophysics Data System (ADS)

    Veloso, Amanda; Demarez, Valérie; Ceschia, Eric; Claverie, Martin

    2013-04-01

    The use of crop models allows simulating plant development, growth and yield under different environmental and management conditions. When combined with high spatial and temporal resolution remote sensing data, these models provide new perspectives for crop monitoring at regional scale. We propose here an approach to estimate time courses of dry aboveground biomass, yield and evapotranspiration (ETR) for summer (maize, sunflower) and winter crops (wheat) by assimilating Green Area Index (GAI) data, obtained from satellite observations, into a simple crop model. Only high spatial resolution and gap-free satellite time series can provide enough information for efficient crop monitoring applications. The potential of remote sensing data is often limited by cloud cover and/or gaps in observation. Data from different sensor systems need then to be combined. For this work, we employed a unique set of Formosat-2 and SPOT images (164 images) and in-situ measurements, acquired from 2006 to 2010 in southwest France. Among the several land surface biophysical variables accessible from satellite observations, the GAI is the one that has a key role in soil-plant-atmosphere interactions and in biomass accumulation process. Many methods have been developed to relate GAI to optical remote sensing signal. Here, seasonal dynamics of remotely sensed GAI were estimated by applying a method based on the inversion of a radiative transfer model using artificial neural networks. The modelling approach is based on the Simple Algorithm for Yield and Evapotranspiration estimate (SAFYE) model, which couples the FAO-56 model with an agro-meteorological model, based on Monteith's light-use efficiency theory. The SAFYE model is a daily time step crop model that simulates time series of GAI, dry aboveground biomass, grain yield and ETR. Crop and soil model parameters were determined using both in-situ measurements and values found in the literature. Phenological parameters were calibrated by the

  9. 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

  10. Optimal Wavelength Selection on Hyperspectral Data with Fused Lasso for Biomass Estimation of Tropical Rain Forest

    NASA Astrophysics Data System (ADS)

    Takayama, T.; Iwasaki, A.

    2016-06-01

    Above-ground biomass prediction of tropical rain forest using remote sensing data is of paramount importance to continuous large-area forest monitoring. Hyperspectral data can provide rich spectral information for the biomass prediction; however, the prediction accuracy is affected by a small-sample-size problem, which widely exists as overfitting in using high dimensional data where the number of training samples is smaller than the dimensionality of the samples due to limitation of require time, cost, and human resources for field surveys. A common approach to addressing this problem is reducing the dimensionality of dataset. Also, acquired hyperspectral data usually have low signal-to-noise ratio due to a narrow bandwidth and local or global shifts of peaks due to instrumental instability or small differences in considering practical measurement conditions. In this work, we propose a methodology based on fused lasso regression that select optimal bands for the biomass prediction model with encouraging sparsity and grouping, which solves the small-sample-size problem by the dimensionality reduction from the sparsity and the noise and peak shift problem by the grouping. The prediction model provided higher accuracy with root-mean-square error (RMSE) of 66.16 t/ha in the cross-validation than other methods; multiple linear analysis, partial least squares regression, and lasso regression. Furthermore, fusion of spectral and spatial information derived from texture index increased the prediction accuracy with RMSE of 62.62 t/ha. This analysis proves efficiency of fused lasso and image texture in biomass estimation of tropical forests.

  11. Hydrogen Production Cost Estimate Using Biomass Gasification: Independent Review

    SciTech Connect

    Ruth, M.

    2011-10-01

    This independent review is the conclusion arrived at from data collection, document reviews, interviews and deliberation from December 2010 through April 2011 and the technical potential of Hydrogen Production Cost Estimate Using Biomass Gasification. The Panel reviewed the current H2A case (Version 2.12, Case 01D) for hydrogen production via biomass gasification and identified four principal components of hydrogen levelized cost: CapEx; feedstock costs; project financing structure; efficiency/hydrogen yield. The panel reexamined the assumptions around these components and arrived at new estimates and approaches that better reflect the current technology and business environments.

  12. Long-term remote monitoring of salt marsh biomass

    NASA Technical Reports Server (NTRS)

    Gross, M. F.; Klemas, V.; Hardisky, M. A.

    1990-01-01

    Methods developed for monitoring salt-marsh biomass remotedly are considered in the framework of NASA's Biospheric Research Program. Satellite-derived estimates of the aboveground biomass is considered, and it is noted that a long-term program for long-term remote monitoring is only practical if the relationship between biomass and spectral data remains essentially constant from year to year. Emphasis is placed on ground-based sampling, satellite measurements of mean marsh live aboveground biomass, the spatial distribution of biomass within the marsh, and changes in marsh hydrography as seen from a satellite. Linking aboveground and belowground biomass is discussed, as well as the problem with obtaining cloud-free images and measuring dead biomass.

  13. Comparative study of above ground biomass estimates for conterminous US

    NASA Astrophysics Data System (ADS)

    Neeti, N.; Kennedy, R. E.

    2013-12-01

    Accurate estimates of forest biomass are important for carbon accounting at both regional and national scale. There are four above ground biomass (AGB) maps available for conterminous US, one from the National Aeronautics and Space Administration (NASA), two from the United States Forest Service (USFS) (Blackard and Wilson) and one from the Woods Hole Research Center (WHRC). Although all four maps are meant to represent similar quantities, spatial patterns of AGB vary considerably from map to map. To use any of these AGB maps for carbon accounting, it is important to understand sources of uncertainty in individual maps and agreement and disagreement among them. Therefore, we compared the four AGB maps at ecoregion and state level to gain understanding of map consistency, leveraging discrepancies among maps to gain insight into the method and data sources. We also developed statewide summaries to compare with FIA forest AGB estimates, which are typically reported at the state level. We examined both absolute differences among these aggregated maps, and relative differences among regions within each map. The result shows that NASA biomass estimates are highest and Blackard estimates are lowest compared to other maps at both ecoregion and state level. The AGB for WHRC and Wilson are very similar at both ecoregion and state level specifically in the lower biomass regions compared to higher biomass regions. This could be associated with the differences in the spatial resolution of the data sources uses to generate these maps. At state level, WHRC map is found to be most similar and NASA biomass estimates least similar to FIA plot data. We discuss these differences in light of the different methods and data sources used to generate the maps.

  14. Methyl halide emission estimates from domestic biomass burning in Africa

    NASA Astrophysics Data System (ADS)

    Mead, M. I.; Khan, M. A. H.; White, I. R.; Nickless, G.; Shallcross, D. E.

    Inventories of methyl halide emissions from domestic burning of biomass in Africa, from 1950 to the present day and projected to 2030, have been constructed. By combining emission factors from Andreae and Merlet [2001. Emission of trace gases and aerosols from biomass burning. Global Biogeochemical Cycles 15, 955-966], the biomass burning estimates from Yevich and Logan [2003. An assessment of biofuel use and burning of agricultural waste in the developing world. Global Biogeochemical Cycles 17(4), 1095, doi:10.1029/2002GB001952] and the population data from the UN population division, the emission of methyl halides from domestic biomass usage in Africa has been estimated. Data from this study suggest that methyl halide emissions from domestic biomass burning have increased by a factor of 4-5 from 1950 to 2005 and based on the expected population growth could double over the next 25 years. This estimated change has a non-negligible impact on the atmospheric budgets of methyl halides.

  15. Estimation of Boreal Forest Biomass Using Spaceborne SAR Systems

    NASA Technical Reports Server (NTRS)

    Saatchi, Sassan; Moghaddam, Mahta

    1995-01-01

    In this paper, we report on the use of a semiempirical algorithm derived from a two layer radar backscatter model for forest canopies. The model stratifies the forest canopy into crown and stem layers, separates the structural and biometric attributes of the canopy. The structural parameters are estimated by training the model with polarimetric SAR (synthetic aperture radar) data acquired over homogeneous stands with known above ground biomass. Given the structural parameters, the semi-empirical algorithm has four remaining parameters, crown biomass, stem biomass, surface soil moisture, and surface rms height that can be estimated by at least four independent SAR measurements. The algorithm has been used to generate biomass maps over the entire images acquired by JPL AIRSAR and SIR-C SAR systems. The semi-empirical algorithms are then modified to be used by single frequency radar systems such as ERS-1, JERS-1, and Radarsat. The accuracy. of biomass estimation from single channel radars is compared with the case when the channels are used together in synergism or in a polarimetric system.

  16. Dynamics of Aboveground Phytomass of the Circumpolar Arctic Tundra During the Past Three Decades

    NASA Technical Reports Server (NTRS)

    Epstein, Howard E.; Raynolds, Martha K.; Walker, Donald A.; Bhatt, Uma S.; Tucker, Compton J.; Pinzon, Jorge E.

    2012-01-01

    Numerous studies have evaluated the dynamics of Arctic tundra vegetation throughout the past few decades, using remotely sensed proxies of vegetation, such as the normalized difference vegetation index (NDVI). While extremely useful, these coarse-scale satellite-derived measurements give us minimal information with regard to how these changes are being expressed on the ground, in terms of tundra structure and function. In this analysis, we used a strong regression model between NDVI and aboveground tundra phytomass, developed from extensive field-harvested measurements of vegetation biomass, to estimate the biomass dynamics of the circumpolar Arctic tundra over the period of continuous satellite records (1982-2010). We found that the southernmost tundra subzones (C-E) dominate the increases in biomass, ranging from 20 to 26%, although there was a high degree of heterogeneity across regions, floristic provinces, and vegetation types. The estimated increase in carbon of the aboveground live vegetation of 0.40 Pg C over the past three decades is substantial, although quite small relative to anthropogenic C emissions. However, a 19.8% average increase in aboveground biomass has major implications for nearly all aspects of tundra ecosystems including hydrology, active layer depths, permafrost regimes, wildlife and human use of Arctic landscapes. While spatially extensive on-the-ground measurements of tundra biomass were conducted in the development of this analysis, validation is still impossible without more repeated, long-term monitoring of Arctic tundra biomass in the field.

  17. A sample design for globally consistent biomass estimation using lidar data from the Geoscience Laser Altimeter System (GLAS)

    PubMed Central

    2012-01-01

    Background Lidar height data collected by the Geosciences Laser Altimeter System (GLAS) from 2002 to 2008 has the potential to form the basis of a globally consistent sample-based inventory of forest biomass. GLAS lidar return data were collected globally in spatially discrete full waveform “shots,” which have been shown to be strongly correlated with aboveground forest biomass. Relationships observed at spatially coincident field plots may be used to model biomass at all GLAS shots, and well-established methods of model-based inference may then be used to estimate biomass and variance for specific spatial domains. However, the spatial pattern of GLAS acquisition is neither random across the surface of the earth nor is it identifiable with any particular systematic design. Undefined sample properties therefore hinder the use of GLAS in global forest sampling. Results We propose a method of identifying a subset of the GLAS data which can justifiably be treated as a simple random sample in model-based biomass estimation. The relatively uniform spatial distribution and locally arbitrary positioning of the resulting sample is similar to the design used by the US national forest inventory (NFI). We demonstrated model-based estimation using a sample of GLAS data in the US state of California, where our estimate of biomass (211 Mg/hectare) was within the 1.4% standard error of the design-based estimate supplied by the US NFI. The standard error of the GLAS-based estimate was significantly higher than the NFI estimate, although the cost of the GLAS estimate (excluding costs for the satellite itself) was almost nothing, compared to at least US$ 10.5 million for the NFI estimate. Conclusions Global application of model-based estimation using GLAS, while demanding significant consolidation of training data, would improve inter-comparability of international biomass estimates by imposing consistent methods and a globally coherent sample frame. The methods presented here

  18. Corn Stover to Sustain Organic Carbon Further Constrains Biomass Supply

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Sustainable aboveground crop biomass harvest estimates for cellulosic ethanol production, to date, have been limited by the need for stover or residue to control erosion. Recently, estimates of the amount of crop biomass needed to maintain soil carbon, which is responsible for favorable soil propert...

  19. Beyond Radar Backscatter: Estimating Forest Structure and Biomass with Radar Interferometry and Lidar Remote Sensing

    NASA Astrophysics Data System (ADS)

    Lavalle, M.; Ahmed, R.

    2014-12-01

    Mapping forest structure and aboveground biomass globally is a major challenge that the remote sensing community has been facing for decades. Radar backscatter is sensitive to biomass only up to a certain amount (about 150 tons/ha at L-band and 300 tons/ha at P-band), whereas lidar remote sensing is strongly limited by poor spatial coverage. In recent years radar interferometry, including its extension to polarimetric radar interferometry (PolInSAR), has emerged as a new technique to overcome the limitations of radar backscatter. The idea of PolInSAR is to use jointly interferometric and polarimetric radar techniques to separate different scattering mechanisms and retrieve the vertical structure of forests. The advantage is to map ecosystem structure continuously over large areas and independently of cloud coverage. Experiments have shown that forest height - an important proxy for biomass - can be estimated using PolInSAR with accuracy between 15% and 20% at plot level. At AGU we will review the state-of-art of repeat-pass PolInSAR for biomass mapping, including its potential and limitations, and discuss how merging lidar data with PolInSAR data can be beneficial not only for product cross-validation but also for achieving better estimation of ecosystem properties over large areas. In particular, lidar data are expected to aid the inversion of PolInSAR models by providing (1) better identification of ground under the canopy, (2) approximate information of canopy structure in limited areas, and (3) maximum tree height useful for mapping PolInSAR temporal decorrelation. We will show our tree height and biomass maps using PolInSAR L-band JPL/UAVSAR data collected in tropical and temperate forests, and P-band ONERA/TROPISAR data acquired in French Guiana. LVIS lidar data will be used, as well as SRTM data, field measurements and inventory data to support our study. The use of two different radar frequencies and repeat-pass JPL UAVSAR data will offer also the

  20. A hyperspectral approach to estimating biomass and plant production in a heterogeneous restored temperate peatland

    NASA Astrophysics Data System (ADS)

    Byrd, K. B.; Schile, L. M.; Windham-Myers, L.; Kelly, M.; Hatala, J.; Baldocchi, D. D.

    2012-12-01

    Restoration of drained peatlands that are managed to reverse subsidence through organic accretion holds significant potential for large-scale carbon storage and sequestration. This potential has been demonstrated in an experimental wetland restoration site established by the U.S. Geological Survey in 1997 on Twitchell Island in the Sacramento-San Joaquin River Delta, where soil carbon storage is up to 1 kg C m-2 and root and rhizome production can reach over 7 kg m-2 annually. Remote sensing-based estimation of biomass and productivity over a large spatial extent helps to monitor carbon storage potential of these restored peatlands. Extensive field measurements of plant biophysical characteristics such as biomass, leaf area index, and the fraction of absorbed photosynthetically active radiation (fAPAR) [an important variable in light-use efficiency (LUE) models] have been collected for agricultural systems and forests. However the small size and local spatial variability of U.S. Pacific Coast wetlands pose new challenges for measuring these variables in the field and generating estimates through remote sensing. In particular background effects of non-photosynthetic vegetation (NPV), floating aquatic vegetation, and inundation of wetland vegetation influence the relationship between field measurements and multispectral or hyperspectral indices. Working at the USGS experimental wetland site, characterized by variable water depth and substantial NPV, or thatch, we collected field data on hardstem bulrush (Schoenoplectus acutus) and cattail (Typha spp.) coupled with reflectance data from a field spectrometer (350-2500 nm) every two to three weeks during the summers of 2011 and 2012. We calculated aboveground biomass with existing allometric relationships, and fAPAR was measured with line and point quantum sensors. We analyzed reflectance data to develop hyperspectral and multispectral indices that predict biomass and fAPAR and account for background effects of water

  1. Biomass estimation to support pasture management in Niger

    NASA Astrophysics Data System (ADS)

    Schucknecht, A.; Meroni, M.; Kayitakire, F.; Rembold, F.; Boureima, A.

    2015-04-01

    Livestock plays a central economic role in Niger, but it is highly vulnerable due to the high inter-annual variability of rain and hence pasture production. This study aims to develop an approach for mapping pasture biomass production to support activities of the Niger Ministry of Livestock for effective pasture management. Our approach utilises the observed spatiotemporal variability of biomass production to build a predictive model based on ground and remote sensing data for the period 1998-2012. Measured biomass (63 sites) at the end of the growing season was used for the model parameterisation. The seasonal cumulative Fraction of Absorbed Photosynthetically Active Radiation (CFAPAR), calculated from 10-day image composites of SPOT-VEGETATION FAPAR, was computed as a phenology-tuned proxy of biomass production. A linear regression model was tested aggregating field data at different levels (global, department, agro-ecological zone, and intersection of agro-ecological and department units) and subjected to a cross validation (cv) by leaving one full year out. An increased complexity (i.e. spatial detail) of the model increased the estimation performances indicating the potential relevance of additional and spatially heterogeneous agro-ecological characteristics for the relationship between herbaceous biomass at the end of the season and CFAPAR. The model using the department aggregation yielded the best trade-off between model complexity and predictive power (R2 = 0.55, R2cv = 0.48). The proposed approach can be used to timely produce maps of estimated biomass at the end of the growing season before ground point measurements are made available.

  2. Estimation of old field ecosystem biomass using low altitude imagery

    NASA Technical Reports Server (NTRS)

    Nor, S. M.; Safir, G.; Burton, T. M.; Hook, J. E.; Schultink, G.

    1977-01-01

    Color-infrared photography was used to evaluate the biomass of experimental plots in an old-field ecosystem that was treated with different levels of waste water from a sewage treatment facility. Cibachrome prints at a scale of approximately 1:1,600 produced from 35 mm color infrared slides were used to analyze density patterns using prepared tonal density scales and multicell grids registered to ground panels shown on the photograph. Correlation analyses between tonal density and vegetation biomass obtained from ground samples and harvests were carried out. Correlations between mean tonal density and harvest biomass data gave consistently high coefficients ranging from 0.530 to 0.896 at the 0.001 significance level. Corresponding multiple regression analysis resulted in higher correlation coefficients. The results of this study indicate that aerial infrared photography can be used to estimate standing crop biomass on waste water irrigated old field ecosystems. Combined with minimal ground truth data, this technique could enable managers of wastewater irrigation projects to precisely time harvest of such systems for maximal removal of nutrients in harvested biomass.

  3. Correlating radar backscatter with components of biomass in loblolly pine forests

    SciTech Connect

    Kasischke, E.S.; Bourgeau-Chavez, L.L.; Christensen, N.L. Jr.

    1995-05-01

    A multifrequency, multipolarization airborne SAR data set was utilized to examine the relationship between radar backscatter and the aboveground biomass in loblolly pine forests. This data set was also used to examine the potential of SAR to estimate aboveground biomass in these forests. The total aboveground biomass in the test stands used in this study ranged from <1--50 kg m{sup {minus}2}. Not only was total aboveground biomass considered, but the biomass of the tree boles, branches, and needles/leaves. Significant correlations were found in all three frequencies of radar imagery used in this study. At P- and L-bands, the greatest sensitivity to change in biomass occurred in the HH and VH polarized channels, while at C-band, the greatest sensitivity was in the VH polarized channel. The results of the correlation analyses support modeling studies which show the dominant scattering mechanisms from these pines should be double-bounce, ground-trunk scattering and canopy volume scattering. To produce equations to estimate biomass, a stepwise, multiple-linear regression approach was used, using all the radar channels as independent variables, and the log of the biomass components as the dependent variables. The authors conclude from this analysis that the image intensity signatures recorded on SAR imagery have the potential to be used as a basis for estimation of aboveground biomass in pine forests, for total stand biomass levels up to 35--40 kg m{sup {minus}2}.

  4. Closing a gap in tropical forest biomass estimation: accounting for crown mass variation in pantropical allometries

    NASA Astrophysics Data System (ADS)

    Ploton, P.; Barbier, N.; Momo, S. T.; Réjou-Méchain, M.; Boyemba Bosela, F.; Chuyong, G.; Dauby, G.; Droissart, V.; Fayolle, A.; Goodman, R. C.; Henry, M.; Kamdem, N. G.; Katembo Mukirania, J.; Kenfack, D.; Libalah, M.; Ngomanda, A.; Rossi, V.; Sonké, B.; Texier, N.; Thomas, D.; Zebaze, D.; Couteron, P.; Berger, U.; Pélissier, R.

    2015-12-01

    Accurately monitoring tropical forest carbon stocks is an outstanding challenge. Allometric models that consider tree diameter, height and wood density as predictors are currently used in most tropical forest carbon studies. In particular, a pantropical biomass model has been widely used for approximately a decade, and its most recent version will certainly constitute a reference in the coming years. However, this reference model shows a systematic bias for the largest trees. Because large trees are key drivers of forest carbon stocks and dynamics, understanding the origin and the consequences of this bias is of utmost concern. In this study, we compiled a unique tree mass dataset on 673 trees measured in five tropical countries (101 trees > 100 cm in diameter) and an original dataset of 130 forest plots (1 ha) from central Africa to quantify the error of biomass allometric models at the individual and plot levels when explicitly accounting or not accounting for crown mass variations. We first showed that the proportion of crown to total tree aboveground biomass is highly variable among trees, ranging from 3 to 88 %. This proportion was constant on average for trees < 10 Mg (mean of 34 %) but, above this threshold, increased sharply with tree mass and exceeded 50 % on average for trees ≥ 45 Mg. This increase coincided with a progressive deviation between the pantropical biomass model estimations and actual tree mass. Accounting for a crown mass proxy in a newly developed model consistently removed the bias observed for large trees (> 1 Mg) and reduced the range of plot-level error from -23-16 to 0-10 %. The disproportionally higher allocation of large trees to crown mass may thus explain the bias observed recently in the reference pantropical model. This bias leads to far-from-negligible, but often overlooked, systematic errors at the plot level and may be easily corrected by accounting for a crown mass proxy for the largest trees in a stand, thus suggesting that

  5. Contribution of aboveground plant respiration to carbon cycling in a Bornean tropical rainforet

    NASA Astrophysics Data System (ADS)

    Katayama, Ayumi; Tanaka, Kenzo; Ichie, Tomoaki; Kume, Tomonori; Matsumoto, Kazuho; Ohashi, Mizue; Kumagai, Tomo'omi

    2014-05-01

    Bornean tropical rainforests have a different characteristic from Amazonian tropical rainforests, that is, larger aboveground biomass caused by higher stand density of large trees. Larger biomass may cause different carbon cycling and allocation pattern. However, there are fewer studies on carbon allocation and each component in Bornean tropical rainforests, especially for aboveground plant respiration, compared to Amazonian forests. In this study, we measured woody tissue respiration and leaf respiration, and estimated those in ecosystem scale in a Bornean tropical rainforest. Then, we examined carbon allocation using the data of soil respiration and aboveground net primary production obtained from our previous studies. Woody tissue respiration rate was positively correlated with diameter at breast height (dbh) and stem growth rate. Using the relationships and biomass data, we estimated woody tissue respiration in ecosystem scale though methods of scaling resulted in different estimates values (4.52 - 9.33 MgC ha-1 yr-1). Woody tissue respiration based on surface area (8.88 MgC ha-1 yr-1) was larger than those in Amazon because of large aboveground biomass (563.0 Mg ha-1). Leaf respiration rate was positively correlated with height. Using the relationship and leaf area density data at each 5-m height, leaf respiration in ecosystem scale was estimated (9.46 MgC ha-1 yr-1), which was similar to those in Amazon because of comparable LAI (5.8 m2 m-2). Gross primary production estimated from biometric measurements (44.81 MgC ha-1 yr-1) was much higher than those in Amazon, and more carbon was allocated to woody tissue respiration and total belowground carbon flux. Large tree with dbh > 60cm accounted for about half of aboveground biomass and aboveground biomass increment. Soil respiration was also related to position of large trees, resulting in high soil respiration rate in this study site. Photosynthesis ability of top canopy for large trees was high and leaves for

  6. Estimation of old field ecosystem biomass using low altitude imagery

    NASA Technical Reports Server (NTRS)

    Nor, S. M.; Safir, G.; Burton, T. M.; Hook, J. E.; Schultink, G.

    1977-01-01

    Color-infrared photography was used to evaluate the biomass of experimental plots in an old-field ecosystem that was treated with different levels of waste water from a sewage treatment facility. Cibachrome prints at a scale of approximately 1:1,600 produced from 35 mm color infrared slides were used to analyze density patterns using prepared tonal density scales and multicell grids registered to ground panels shown on the photograph. Correlations between mean tonal density and harvest biomass data gave consistently high coefficients ranging from 0.530 to 0.896 at the 0.001 significance level. Corresponding multiple regression analysis resulted in higher correlation coefficients. The results indicate that aerial infrared photography can be used to estimate standing crop biomass on waste water irrigated old field ecosystems. Combined with minimal ground truth data, this technique could enable managers of waste water irrigation projects to precisely time harvest of such systems for maximal removal of nutrients in harvested biomass.

  7. Estimating externalities of biomass fuel cycles, Report 7

    SciTech Connect

    Barnthouse, L.W.; Cada, G.F.; Cheng, M.-D.; Easterly, C.E.; Kroodsma, R.L.; Lee, R.; Shriner, D.S.; Tolbert, V.R.; Turner, R.S.

    1998-01-01

    This report documents the analysis of the biomass fuel cycle, in which biomass is combusted to produce electricity. The major objectives of this study were: (1) to implement the methodological concepts which were developed in the Background Document (ORNL/RFF 1992) as a means of estimating the external costs and benefits of fuel cycles, and by so doing, to demonstrate their application to the biomass fuel cycle; (2) to develop, given the time and resources, a range of estimates of marginal (i.e., the additional or incremental) damages and benefits associated with selected impact-pathways from a new wood-fired power plant, using a representative benchmark technology, at two reference sites in the US; and (3) to assess the state of the information available to support energy decision making and the estimation of externalities, and by so doing, to assist in identifying gaps in knowledge and in setting future research agendas. The demonstration of methods, modeling procedures, and use of scientific information was the most important objective of this study. It provides an illustrative example for those who will, in the future, undertake studies of actual energy options and sites. As in most studies, a more comprehensive analysis could have been completed had budget constraints not been as severe. Particularly affected were the air and water transport modeling, estimation of ecological impacts, and economic valuation. However, the most important objective of the study was to demonstrate methods, as a detailed example for future studies. Thus, having severe budget constraints was appropriate from the standpoint that these studies could also face similar constraints. Consequently, an important result of this study is an indication of what can be done in such studies, rather than the specific numerical estimates themselves.

  8. Single Baseline Tomography SAR for Forest Above Ground Biomass Estimation

    NASA Astrophysics Data System (ADS)

    Li, Wenmei; Chen, Erxue; Li, Zengyuan; Wang, Xinshuang; Feng, Qi

    2013-01-01

    Single baseline tomography SAR is used for forest height estimation as its little restriction on the number of baselines and configurations of tracks in recent years. There existed two kinds of single baseline tomography SAR techniques, the polarimetric coherence tomography (PCT) and the sum of Kronecker product (SKP), algebraic synthesis (AS) and Capon spectral estimator approach (SKP-AS-Capon). Few researches on forest above ground biomass (AGB) estimation are there using single baseline tomography SAR. In this paper, PCT and SKP-AS-Capon approaches are proposed for forest AGB estimation. L-band data set acquired by E-SAR airborne system in 2003 for the forest test site in Traunstein, is used for this experiment. The result shows that single baseline polarimetric tomography SAR can obtain forest AGB in forest stand scale, and SKP-AS-Capon method has better detailed vertical structure information, while the Freeman 3-component combined PCT approach gets a homogenous vertical structure in forest stand.

  9. Regional contingencies in the relationship between aboveground Bbomass and litter in the world’s grasslands

    USGS Publications Warehouse

    O’Halloran, Lydia R.; Borer, Elizabeth T.; Seabloom, Eric W.; MacDougall, Andrew S.; Cleland, Elsa E.; McCulley, Rebecca L.; Hobbie, Sarah; Harpole, W. Stan; DeCrappeo, Nicole M.; Chu, Cheng-Jin; Bakker, Jonathan D.; Davies, Kendi F.; Du, Guozhen; Firn, Jennifer; Hagenah, Nicole; Hofmockel, Kirsten S.; Knops, Johannes M.H.; Li, Wei; Melbourne, Brett A.; Morgan, John W.; Orrock, John L.; Prober, Suzanne M.; Stevens, Carly J.

    2013-01-01

    Based on regional-scale studies, aboveground production and litter decomposition are thought to positively covary, because they are driven by shared biotic and climatic factors. Until now we have been unable to test whether production and decomposition are generally coupled across climatically dissimilar regions, because we lacked replicated data collected within a single vegetation type across multiple regions, obfuscating the drivers and generality of the association between production and decomposition. Furthermore, our understanding of the relationships between production and decomposition rests heavily on separate meta-analyses of each response, because no studies have simultaneously measured production and the accumulation or decomposition of litter using consistent methods at globally relevant scales. Here, we use a multi-country grassland dataset collected using a standardized protocol to show that live plant biomass (an estimate of aboveground net primary production) and litter disappearance (represented by mass loss of aboveground litter) do not strongly covary. Live biomass and litter disappearance varied at different spatial scales. There was substantial variation in live biomass among continents, sites and plots whereas among continent differences accounted for most of the variation in litter disappearance rates. Although there were strong associations among aboveground biomass, litter disappearance and climatic factors in some regions (e.g. U.S. Great Plains), these relationships were inconsistent within and among the regions represented by this study. These results highlight the importance of replication among regions and continents when characterizing the correlations between ecosystem processes and interpreting their global-scale implications for carbon flux. We must exercise caution in parameterizing litter decomposition and aboveground production in future regional and global carbon models as their relationship is complex.

  10. Estimation of alewife biomass in Lake Michigan, 1967-1978

    USGS Publications Warehouse

    Hatch, Richard W.; Haack, Paul M.; Brown, Edward H., Jr.

    1981-01-01

    The buildup of salmonid populations in Lake Michigan through annual stockings of hatchery-reared fish may become limited by the quantity of forage fish, mainly alewives Alosa pseudoharengus, available for food. As a part of a continuing examination of salmonid predator-prey relations in Lake Michigan, we traced changes in alewife biomass estimated from bottom-trawl surveys conducted in late October and early November 1967–1978. Weight of adult alewives trawled per 0.5 hectare of bottom (10-minute drag) at 16 depths along eight transects between 1973 and 1977 formed a skewed distribution: 72 of 464 drags caught no alewives; 89 drags caught less than 1 kg; and 2 drags caught more than 100 kg (maximum 159 kg). Analysis of variance in normalized catch per tow indicated highly significant differences between the main effects of years and depths, and highly significant differences in the interactions of years and transects, years and depths, and transects and depths. Five geographic and depth strata, formed by combining parts of transects wherein mean catch rate did not differ significantly, were the basis for calculating annual estimates of adult alewife biomass (with 90% confidence intervals). Estimated biomass of alewives (±90% confidence limits) in Lake Michigan proper (Green Bay and Grand Traverse Bay excluded) rose gradually from 46,000 (±9,000) t in 1967 to 114,000 (±17,000) t in 1973, declined to 45,000 (±8,000) t in 1977, and rose to 77,000 (±19,000) t in 1978.

  11. Spatially Explicit Large Area Biomass Estimation: Three Approaches Using Forest Inventory and Remotely Sensed Imagery in a GIS

    PubMed Central

    Wulder, Michael A.; White, Joanne C.; Fournier, Richard A.; Luther, Joan E.; Magnussen, Steen

    2008-01-01

    Forest inventory data often provide the required base data to enable the large area mapping of biomass over a range of scales. However, spatially explicit estimates of above-ground biomass (AGB) over large areas may be limited by the spatial extent of the forest inventory relative to the area of interest (i.e., inventories not spatially exhaustive), or by the omission of inventory attributes required for biomass estimation. These spatial and attributional gaps in the forest inventory may result in an underestimation of large area AGB. The continuous nature and synoptic coverage of remotely sensed data have led to their increased application for AGB estimation over large areas, although the use of these data remains challenging in complex forest environments. In this paper, we present an approach to generating spatially explicit estimates of large area AGB by integrating AGB estimates from multiple data sources; 1. using a lookup table of conversion factors applied to a non-spatially exhaustive forest inventory dataset (R2 = 0.64; RMSE = 16.95 t/ha), 2. applying a lookup table to unique combinations of land cover and vegetation density outputs derived from remotely sensed data (R2 = 0.52; RMSE = 19.97 t/ha), and 3. hybrid mapping by augmenting forest inventory AGB estimates with remotely sensed AGB estimates where there are spatial or attributional gaps in the forest inventory data. Over our 714,852 ha study area in central Saskatchewan, Canada, the AGB estimate generated from the forest inventory was approximately 40 Mega tonnes (Mt); however, the inventory estimate represents only 51% of the total study area. The AGB estimate generated from the remotely sensed outputs that overlap those made from the forest inventory based approach differ by only 2 %; however in total, the remotely sensed estimate is 30 % greater (58 Mt) than the estimate generated from the forest inventory when the entire study area is accounted for. Finally, using the hybrid approach, whereby

  12. The Use of Satellite Imagery to Guide Field Plot Sampling Scheme for Biomass Estimation in Ghanaian Forest

    NASA Astrophysics Data System (ADS)

    Sah, B. P.; Hämäläinen, J. M.; Sah, A. K.; Honji, K.; Foli, E. G.; Awudi, C.

    2012-07-01

    Accurate and reliable estimation of biomass in tropical forest has been a challenging task because a large proportion of forests are difficult to access or inaccessible. So, for effective implementation of REDD+ and fair benefit sharing, the proper designing of field plot sampling schemes plays a significant role in achieving robust biomass estimation. The existing forest inventory protocols using various field plot sampling schemes, including FAO's regular grid concept of sampling for land cover inventory at national level, are time and human resource intensive. Wall to wall LiDAR scanning is, however, a better approach to assess biomass with high precision and spatial resolution even though this approach suffers from high costs. Considering the above, in this study a sampling design based on a LiDAR strips sampling scheme has been devised for Ghanaian forests to support field plot sampling. Using Top-of-Atmosphere (TOA) reflectance value of satellite data, Land Use classification was carried out in accordance with IPCC definitions and the resulting classes were further stratified, incorporating existing GIS data of ecological zones in the study area. Employing this result, LiDAR sampling strips were allocated using systematic sampling techniques. The resulting LiDAR strips represented all forest categories, as well as other Land Use classes, with their distribution adequately representing the areal share of each category. In this way, out of at total area of 15,153km2 of the study area, LiDAR scanning was required for only 770 km2 (sampling intensity being 5.1%). We conclude that this systematic LiDAR sampling design is likely to adequately cover variation in above-ground biomass densities and serve as sufficient a-priori data, together with the Land Use classification produced, for designing efficient field plot sampling over the seven ecological zones.

  13. Uncertainty estimation in integrated LiDAR- and radar-derived biomass maps at key national-level map scales

    NASA Astrophysics Data System (ADS)

    Joshi, N.; Fensholt, R.; Saatchi, S. S.; Mitchard, E. T.

    2013-12-01

    The international Reducing Emissions from Deforestation and Degradation (REDD) program requires accurate and cost-effective techniques of national-level mapping of above-ground biomass (AGB) and ground-sampling strategies. This paper explores a multi-sensor (radar and low-density airborne LiDAR) integration approach for country-wide AGB estimation and mapping in Denmark, selected as a test-country due to the unique availability of country-wide remote sensing and forest inventory data. We assess the potential use of ALOS PALSAR L-band radar and ENVISAT ASAR C-band radar in prediction and mapping of AGB with accuracies similar to LiDAR-derived AGB estimates at different map scales. We start by creating a LiDAR-based ';ground truth' map, using LiDAR-derived 95th Percentile of heights >1 m weighted by the Canopy Density ratio, together with 113 AGB plots to map AGB at a 0.25 ha resolution across the country. A leave-20%-out cross-validation indicates that the AGB estimates have a mean absolute error of 41 Mg ha-1 and a negative mean bias error of 1.7 Mg ha-1. Though the LiDAR model appears to have an overall species-specific bias for conifers and broadleaf (-5.2 Mg ha-1 and +12.3 Mg ha-1 respectively), these are found to be insignificant (p>0.05) when accounting for species sampling bias and the under-prediction of plots containing high-biomass (> 350 Mg ha-1). Using the LiDAR-derived biomass map as a ';truth-map', biomass-backscatter relations will be quantified at three map scales (0.25 ha, 1 ha and 100 ha) and using three spatial sampling frameworks (full-dataset, stratified random sampling equally representing low and high biomass pixels, clustered sampling). The approach aims to derive a minimal-sampling and mapping strategy for L- and C-band radar that achieves at least 20% accuracy in AGB estimation, along with quantified sources of error from ground-AGB estimates, scaling and sampling. It is expected that mapping techniques, uncertainty quantification and

  14. Creating a Multi-Proxy Based Estimate of Regional Carbon Stocks from Ground-Based Biomass Measurements and Remotely Sensed Imagery

    NASA Astrophysics Data System (ADS)

    Kelsey, K.; Neff, J. C.

    2013-12-01

    Forest carbon storage and flux are rapidly changing in many parts of the world due to agents including wildfire, insect outbreaks, forest thinning and timber harvest. Quantifying changes in forest carbon storage requires characterizing the distribution of forest biomass across heterogeneous landscapes at finely resolved spatial and temporal scales. Here, we integrate information from multiple remote sensing platforms with US Forest Service ground-based biomass measurements to form a multi-proxy based estimate of forest carbon stocks and associated uncertainty in two vegetation types on San Juan National Forest, Colorado. We take advantage of US Forest Service Common Stand Exam plots that have been sampled for vegetation and biomass information in conjunction with forest management actions from the 1950's to the present. Due to their number and wide spatial distribution, Common Stand Exam data present an underutilized source of information for mapping vegetation and quantifying biomass on federal lands. Using information from 3188 Common Stand Exam plots within Ponderosa Pine and Spruce Fir vegetation types, we characterize aboveground carbon stocks using four methods: (1) simple extrapolation using regression tree approaches; (2) geostatistical interpolation (kriging); (3) regressions between biomass and vegetation indices derived from Landsat and MODIS imagery; (4) interfacing other biomass estimation methods with texture analysis preformed on one meter resolution areal imagery from the National Agriculture Imagery Program. Results from the different approaches generate a wide range of forest carbon estimates; however, combinations of information from ground-based measurements and remotely sensed images appear to yield the best estimate of forest carbon stocks in this region. A comparison of this relatively high resolution (0.1 to 1 km) analysis to larger scale estimates highlights the importance of regional information and high resolution data for constraining

  15. Weed management, training, and irrigation practices for organic production of trailing blackberry: III. Accumulation and removal of aboveground biomass, carbon, and nutrients

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The effects of various production practices on biomass, C, and nutrient content, accumulation, and loss were assessed over 2 years in a mature organic trailing blackberry (Rubus L. subgenus Rubus, Watson) production system. Treatments included two irrigation options (no irrigation after harvest and ...

  16. Aboveground biomass of an invasive tree Melaleuca (Melaleuca quinquenervia) before and after herbivory by adventive and introduced natural enemies: a temporal case-study in Florida

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Invasive plants may respond to injury from natural enemies by altering the quantity and distribution of biomass among woody materials, foliage, fruits, and seeds. Melaleuca, an Australian tree that has naturalized in south Florida, USA, has been reunited with two natural enemies: a weevil introduce...

  17. Uav-Based Automatic Tree Growth Measurement for Biomass Estimation

    NASA Astrophysics Data System (ADS)

    Karpina, M.; Jarząbek-Rychard, M.; Tymków, P.; Borkowski, A.

    2016-06-01

    Manual in-situ measurements of geometric tree parameters for the biomass volume estimation are time-consuming and economically non-effective. Photogrammetric techniques can be deployed in order to automate the measurement procedure. The purpose of the presented work is an automatic tree growth estimation based on Unmanned Aircraft Vehicle (UAV) imagery. The experiment was conducted in an agriculture test field with scots pine canopies. The data was collected using a Leica Aibotix X6V2 platform equipped with a Nikon D800 camera. Reference geometric parameters of selected sample plants were measured manually each week. In situ measurements were correlated with the UAV data acquisition. The correlation aimed at the investigation of optimal conditions for a flight and parameter settings for image acquisition. The collected images are processed in a state of the art tool resulting in a generation of dense 3D point clouds. The algorithm is developed in order to estimate geometric tree parameters from 3D points. Stem positions and tree tops are identified automatically in a cross section, followed by the calculation of tree heights. The automatically derived height values are compared to the reference measurements performed manually. The comparison allows for the evaluation of automatic growth estimation process. The accuracy achieved using UAV photogrammetry for tree heights estimation is about 5cm.

  18. Aboveground vs. Belowground Carbon Stocks in African Tropical Lowland Rainforest: Drivers and Implications

    PubMed Central

    Bauters, Marijn; Hufkens, Koen; Lisingo, Janvier; Baert, Geert; Verbeeck, Hans; Boeckx, Pascal

    2015-01-01

    Background African tropical rainforests are one of the most important hotspots to look for changes in the upcoming decades when it comes to C storage and release. The focus of studying C dynamics in these systems lies traditionally on living aboveground biomass. Belowground soil organic carbon stocks have received little attention and estimates of the size, controls and distribution of soil organic carbon stocks are highly uncertain. In our study on lowland rainforest in the central Congo basin, we combine both an assessment of the aboveground C stock with an assessment of the belowground C stock and analyze the latter in terms of functional pools and controlling factors. Principal Findings Our study shows that despite similar vegetation, soil and climatic conditions, soil organic carbon stocks in an area with greater tree height (= larger aboveground carbon stock) were only half compared to an area with lower tree height (= smaller aboveground carbon stock). This suggests that substantial variability in the aboveground vs. belowground C allocation strategy and/or C turnover in two similar tropical forest systems can lead to significant differences in total soil organic C content and C fractions with important consequences for the assessment of the total C stock of the system. Conclusions/Significance We suggest nutrient limitation, especially potassium, as the driver for aboveground versus belowground C allocation. However, other drivers such as C turnover, tree functional traits or demographic considerations cannot be excluded. We argue that large and unaccounted variability in C stocks is to be expected in African tropical rain-forests. Currently, these differences in aboveground and belowground C stocks are not adequately verified and implemented mechanistically into Earth System Models. This will, hence, introduce additional uncertainty to models and predictions of the response of C storage of the Congo basin forest to climate change and its contribution to

  19. POLINSAR Coherence-Based Regression Analysis of Forest Biomass Using RADARSAT-2 Datasets

    NASA Astrophysics Data System (ADS)

    Singh, J.; Kumar, S.; Kushwaha, S. P. S.

    2014-11-01

    Forests play a pivotal role in synchronizing earth's carbon cycle by absorbing carbon from the atmosphere and storing it in the form of biomass. Researchers today are trying to understand the climatic variations, especially those occurring due to destruction of forest and its corresponding biomass loss. Hence, quantification of various forest parameters such as biomass is imperative for evaluating the carbon. The objective of this research was to exploit the potential of C-band Radarsat-2 Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) technique for analysing the relationship between complex coherence and field-estimated aboveground biomass. Association between the backscatter and the aboveground biomass was also established in the process. To serve our objective, Radarsat-2 interferometric pair dated 4th March, 2013 (master image) and 28th March, 2013 (slave image) were procured for the Barkot Reserve Forest region of Dehradun, India. Field sampling was done for 30 plots (31.62 m x 31.62 m) and stem diameter and tree height were measured in each plot. The study emphasized on the application of POLINSAR coherence instead of using conventional method of relying on backscatter values for retrieving forest biomass. Coherence matrices were utilized for generating complex coherence values for different polarization channels and were regressed against field estimated aboveground biomass. Results indicated a negative linear relationship between complex coherence and aboveground biomass with the cross - polarized coherence showing the highest R2 value of 0.71. Further, the backscatter mechanism when studied with respect to aboveground biomass indicated a positive linear relationship between backscatter values and field estimated aboveground biomass with R2 value of 0.45 and 0.61 for slave and master image respectively. The results suggest that PolInSAR technique, in combination with different modelling approaches, can be adopted for estimating forest

  20. Improving artificial forest biomass estimates using afforestation age information from time series Landsat stacks.

    PubMed

    Liu, Liangyun; Peng, Dailiang; Wang, Zhihui; Hu, Yong

    2014-11-01

    China maintains the largest artificial forest area in the world. Studying the dynamic variation of forest biomass and carbon stock is important to the sustainable use of forest resources and understanding of the artificial forest carbon budget in China. In this study, we investigated the potential of Landsat time series stacks for aboveground biomass (AGB) estimation in Yulin District, a key region of the Three-North Shelter region of China. Firstly, the afforestation age was successfully retrieved from the Landsat time series stacks in the last 40 years (from 1974 to 2013) and shown to be consistent with the surveyed tree ages, with a root-mean-square error (RMSE) value of 4.32 years and a determination coefficient (R (2)) of 0.824. Then, the AGB regression models were successfully developed by integrating vegetation indices and tree age. The simple ratio vegetation index (SR) is the best candidate of the commonly used vegetation indices for estimating forest AGB, and the forest AGB model was significantly improved using the combination of SR and tree age, with R (2) values from 0.50 to 0.727. Finally, the forest AGB images were mapped at eight epochs from 1985 to 2013 using SR and afforestation age. The total forest AGB in seven counties of Yulin District increased by 20.8 G kg, from 5.8 G kg in 1986 to 26.6 G kg in 2013, a total increase of 360 %. For the persistent forest area since 1974, the forest AGB density increased from 15.72 t/ha in 1986 to 44.53 t/ha in 2013, with an annual rate of about 0.98 t/ha. For the artificial forest planted after 1974, the AGB density increased about 1.03 t/ha a year from 1974 to 2013. The results present a noticeable carbon increment for the planted artificial forest in Yulin District over the last four decades. PMID:25034235

  1. Comparison of carbon and biomass estimation methods for European forests

    NASA Astrophysics Data System (ADS)

    Neumann, Mathias; Mues, Volker; Harkonen, Sanna; Mura, Matteo; Bouriaud, Olivier; Lang, Mait; Achten, Wouter; Thivolle-Cazat, Alain; Bronisz, Karol; Merganicova, Katarina; Decuyper, Mathieu; Alberdi, Iciar; Astrup, Rasmus; Schadauer, Klemens; Hasenauer, Hubert

    2015-04-01

    National and international reporting systems as well as research, enterprises and political stakeholders require information on carbon stocks of forests. Terrestrial assessment systems like forest inventory data in combination with carbon calculation methods are often used for this purpose. To assess the effect of the calculation method used, a comparative analysis was done using the carbon calculation methods from 13 European countries and the research plots from ICP Forests (International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests). These methods are applied for five European tree species (Fagus sylvatica L., Quercus robur L., Betula pendula Roth, Picea abies (L.) Karst. and Pinus sylvestris L.) using a standardized theoretical tree dataset to avoid biases due to data collection and sample design. The carbon calculation methods use allometric biomass and volume functions, carbon and biomass expansion factors or a combination thereof. The results of the analysis show a high variation in the results for total tree carbon as well as for carbon in the single tree compartments. The same pattern is found when comparing the respective volume estimates. This is consistent for all five tree species and the variation remains when the results are grouped according to the European forest regions. Possible explanations are differences in the sample material used for the biomass models, the model variables or differences in the definition of tree compartments. The analysed carbon calculation methods have a strong effect on the results both for single trees and forest stands. To avoid misinterpretation the calculation method has to be chosen carefully along with quality checks and the calculation method needs consideration especially in comparative studies to avoid biased and misleading conclusions.

  2. Estimating forest biomass from LiDAR data: A comparison of the raster-based and point-cloud data approach

    NASA Astrophysics Data System (ADS)

    Garcia-Alonso, M.; Ferraz, A.; Saatchi, S. S.; Casas, A.; Koltunov, A.; Ustin, S.; Ramirez, C.; Balzter, H.

    2015-12-01

    Accurate knowledge of forest biomass and its dynamics is critical for better understanding the carbon cycle and improving forest management decisions to ensure forest sustainability. LiDAR technology provides accurate estimates of aboveground biomass in different ecosystems, minimizing the signal saturation problems that are common with other remote sensing technologies. LiDAR data processing can be based on two different approaches. The first is based on deriving structural metrics from returns classified as vegetation, while the second one is based on metrics derived from the canopy height model (CHM). The CHM is obtained by subtracting the digital elevation model (DEM) that was created from the ground returns, from the digital surface model (DSM), which was itself constructed using the maximum height within each grid cell. The former approach provides a better description of the vertical distribution of the vegetation, whereas the latter significantly reduces the computational burden involved in processing point cloud data at the expense of losing information. This study evaluates the performance of both approaches for biomass estimation over very different ecosystems, including a Mediterranean forest in the Sierra Nevada Mountains of California and a tropical forest in Barro Colorado Island (Panama). In addition, the effect of point density on the variables derived, and ultimately on the estimated biomass, will be assessed.

  3. Estimation of Methanogen Biomass by Quantitation of Coenzyme M

    PubMed Central

    Elias, Dwayne A.; Krumholz, Lee R.; Tanner, Ralph S.; Suflita, Joseph M.

    1999-01-01

    Determination of the role of methanogenic bacteria in an anaerobic ecosystem often requires quantitation of the organisms. Because of the extreme oxygen sensitivity of these organisms and the inherent limitations of cultural techniques, an accurate biomass value is very difficult to obtain. We standardized a simple method for estimating methanogen biomass in a variety of environmental matrices. In this procedure we used the thiol biomarker coenzyme M (CoM) (2-mercaptoethanesulfonic acid), which is known to be present in all methanogenic bacteria. A high-performance liquid chromatography-based method for detecting thiols in pore water (A. Vairavamurthy and M. Mopper, Anal. Chim. Acta 78:363–370, 1990) was modified in order to quantify CoM in pure cultures, sediments, and sewage water samples. The identity of the CoM derivative was verified by using liquid chromatography-mass spectroscopy. The assay was linear for CoM amounts ranging from 2 to 2,000 pmol, and the detection limit was 2 pmol of CoM/ml of sample. CoM was not adsorbed to sediments. The methanogens tested contained an average of 19.5 nmol of CoM/mg of protein and 0.39 ± 0.07 fmol of CoM/cell. Environmental samples contained an average of 0.41 ± 0.17 fmol/cell based on most-probable-number estimates. CoM was extracted by using 1% tri-(N)-butylphosphine in isopropanol. More than 90% of the CoM was recovered from pure cultures and environmental samples. We observed no interference from sediments in the CoM recovery process, and the method could be completed aerobically within 3 h. Freezing sediment samples resulted in 46 to 83% decreases in the amounts of detectable CoM, whereas freezing had no effect on the amounts of CoM determined in pure cultures. The method described here provides a quick and relatively simple way to estimate methanogenic biomass. PMID:10584015

  4. Aboveground and belowground competition between willow Salix caprea its understory

    NASA Astrophysics Data System (ADS)

    Mudrák, Ondřej; Hermová, Markéta; Frouz, Jan

    2016-04-01

    The effects of aboveground and belowground competition with the willow S. caprea on its understory plant community were studied in unreclaimed post-mining sites. Belowground competition was evaluated by comparing (i) frames inserted into the soil that excluded woody roots (frame treatment), (ii) frames that initially excluded woody root growth but then allowed regrowth of the roots (open-frame treatment), and (iii) undisturbed soil (no-frame treatment). These treatments were combined with S. caprea thinning to assess the effect of aboveground competition. Three years after the start of the experiment, aboveground competition from S. caprea (as modified by thinning of the S. caprea canopy) had not affected understory biomass or species number but had affected species composition. In contrast, belowground competition significantly affected both the aboveground and belowground biomass of the understory. The aboveground biomass of the understory was greater in the frame treatment (which excluded woody roots) than in the other two treatments. The belowground biomass of the understory was greater in the frame than in the open-frame treatment. Unlike aboveground competition (light availability), belowground competition did not affect understory species composition. Our results suggest that S. caprea is an important component during plant succession on post-mining sites because it considerably modifies its understory plant community. Belowground competition is a major reason for the low cover and biomass of the herbaceous understory in S. caprea stands on post-mining sites.

  5. Biomass burning emissions estimates in the boreal forests of Siberia

    NASA Astrophysics Data System (ADS)

    Kukavskaya, E. A.; Ivanova, G. A.; Soja, A. J.; Conard, S. G.

    2012-04-01

    Wildfire is the main boreal forest disturbance and can burn 10-30 million hectares annually, thus modifying the global carbon budget through direct fire emissions, postfire biogenic emissions, and by maintaining or altering ecosystems through establishing the beginning and end of successional processes. Fires in the Russian boreal forest range from low-severity surface fires to high-severity crown fires. Estimates of carbon emissions from fires in Russian boreal forests vary substantially due to differences in ecosystems types, burned area calculations, and the amount of fuel consumed. There is an urgent need to obtain more accurate and impartial fire carbon loss estimates in the boreal forests of Siberia due to their considerable contribution to the regional and global carbon balance. We examined uncertainties in estimates of carbon emissions. Area burned in the Siberian region was analyzed and compared using distinct methodologies. Differences between mapped ecosystems were also compared and contrasted to evaluate the potential for error resulting from disparate vegetation structure and fuel consumption estimates. Accurate fuel consumption estimates are obtained in the course of fire experiments with pre- and post-fire biomass measuring. Our large-scale experiments carried out in the course of the FIRE BEAR (Fire Effects in the Boreal Eurasia Region) Project provided quantitative and qualitative data on ecosystem state and carbon emissions due to fires of known behavior in major forest types of Siberia that could be used to verify large-scale carbon emissions estimates. Carbon emissions from fires vary annually and interannually and can increase several times in extreme fire years in comparison to normal fire years. Climate change and increasing drought length have increased the probability of high-severity fire occurrences. This would result in greater carbon losses and efflux to the atmosphere. This research was supported by NASA LCLUC Program, Fulbright

  6. The effect of wildfire and clear-cutting on above-ground biomass, foliar C to N ratios and fiber content throughout succession: Implications for forage quality in woodland caribou (Rangifer tarandus caribou)

    NASA Astrophysics Data System (ADS)

    Mallon, E. E.; Turetsky, M.; Thompson, I.; Noland, T. L.; Wiebe, P.

    2013-12-01

    Disturbance is known to play an important role in maintaining the productivity and biodiversity of boreal forest ecosystems. Moderate to low frequency disturbance is responsible for regeneration opportunities creating a mosaic of habitats and successional trajectories. However, large-scale deforestation and increasing wildfire frequencies exacerbate habitat loss and influence biogeochemical cycles. This has raised concern about the quality of the under-story vegetation post-disturbance and whether this may impact herbivores, especially those vulnerable to change. Forest-dwelling caribou (Rangifer tarandus caribou) are declining in several regions of Canada and are currently listed as a species at risk by COSEWIC. Predation and landscape alteration are viewed as the two main threats to woodland caribou. This has resulted in caribou utilizing low productivity peatlands as refuge and the impact of this habitat selection on their diet quality is not well understood. Therefore there are two themes in the study, 1) Forage quantity: above-ground biomass and productivity and 2) Forage quality: foliar N and C to N ratios and % fiber. The themes are addressed in three questions: 1) How does forage quantity and quality vary between upland forests and peatlands? 2) How does wildfire affect the availability and nutritional quality of forage items? 3) How does forage quality vary between sites recovering from wildfire versus timber harvest? Research sites were located in the Auden region north of Geraldton, ON. This landscape was chosen because it is known woodland caribou habitat and has thorough wildfire and silviculture data from the past 7 decades. Plant diversity, above-ground biomass, vascular green area and seasonal foliar fiber and C to N ratios were collected across a matrix of sites representing a chronosequence of time since disturbance in upland forests and peatlands. Preliminary findings revealed productivity peaked in early age stands (0-30 yrs) and biomass peaked

  7. Plant biomass in the Tanana River Basin, Alaska. Forest Service research paper

    SciTech Connect

    Mead, B.R.

    1995-01-01

    Vegetation biomass tables are presented for the Tanana River Basin. Average biomass for each species of tree, shrub, grass, forb, lichen, and moss in the 13 forest and 30 nonforest vegetation types is shown. These data combined with area estimates for each vegetation type provide a tool for estimating habitat carrying capacity for many wildlife species. Tree biomass is reported for the entire aboveground tree, thereby allowing estimates of total fiber content.

  8. Regional estimation of current and future forest biomass.

    PubMed

    Mickler, R A; Earnhardt, T S; Moore, J A

    2002-01-01

    The 90,674 wildland fires that burned 2.9 million ha at an estimated suppression cost of $1.6 billion in the United States during the 2000 fire season demonstrated that forest fuel loading has become a hazard to life, property, and ecosystem health as a result of past fire exclusion policies and practices. The fire regime at any given location in these regions is a result of complex interactions between forest biomass, topography, ignitions, and weather. Forest structure and biomass are important aspects in determining current and future fire regimes. Efforts to quantify live and dead forest biomass at the local to regional scale has been hindered by the uncertainty surrounding the measurement and modeling of forest ecosystem processes and fluxes. The interaction of elevated CO2 with climate, soil nutrients, and other forest management factors that affect forest growth and fuel loading will play a major role in determining future forest stand growth and the distribution of species across the southern United States. The use of satellite image analysis has been tested for timely and accurate measurement of spatially explicit land use change and is well suited for use in inventory and monitoring of forest carbon. The incorporation of Landsat Thematic Mapper data coupled with a physiologically based productivity model (PnET), soil water holding capacity, and historic and projected climatic data provides an opportunity to enhance field plot based forest inventory and monitoring methodologies. We use periodic forest inventory data from the USDA Forest Service's Forest Inventory and Analysis (FIA) project to obtain estimates of forest area and type to generate estimates of carbon storage for evergreen, deciduous, and mixed forest classes for use in an assessment of remotely sensed forest cover at the regional scale for the southern United States. The displays of net primary productivity (NPP) generated from the PnET model show areas of high and low forest carbon storage

  9. ESTIMATION OF SURPLUS BIOMASS OF CLUPEIDS IN SMITH MOUNTAIN LAKE, VIRGINIA

    EPA Science Inventory

    Mean annual estimates of surplus biomass of alewife Alosa pseudoharengus and gizzard shad Dorosoma cepedianum in Smith Mountain Lake, Virginia, were calculated using data on the biomass, growth, and mortality of each clupeid species. Surplus biomass, defined as production over a...

  10. High-biomass sorghum yield estimate with aerial imagery

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Abstract. To reach the goals laid out by the U.S. Government for displacing fossil fuels with biofuels, agricultural production of dedicated biomass crops is required. High-biomass sorghum is advantageous across wide regions because it requires less water per unit dry biomass and can produce very hi...

  11. Estimation of crops biomass and evapotranspiration from high spatial and temporal resolutions remote sensing data

    NASA Astrophysics Data System (ADS)

    Claverie, Martin; Demarez, Valérie; Duchemin, Benoît.; Ceschia, Eric; Hagolle, Olivier; Ducrot, Danielle; Keravec, Pascal; Beziat, Pierre; Dedieu, Pierre

    2010-05-01

    Carbon and water cycles are closely related to agricultural activities. Agriculture has been indeed identified by IPCC 2007 report as one of the options to sequester carbon in soil. Concerning the water resources, their consumptions by irrigated crops are called into question in view of demographic pressure. In the prospect of an assessment of carbon production and water consumption, the use of crop models at a regional scale is a challenging issue. The recent availability of high spatial resolution (10 m) optical sensors associated to high temporal resolution (1 day) such as FORMOSAT-2 and, in the future, Venµs and SENTINEL-2 will offer new perspectives for agricultural monitoring. In this context, the objective of this work is to show how multi-temporal satellite observations acquired at high spatial resolution are useful for a regional monitoring of following crops biophysical variables: leaf area index (LAI), aboveground biomass (AGB) and evapotranspiration (ET). This study focuses on three summer crops dominant in South-West of France: maize, sunflower and soybean. A unique images data set (82 FORMOSAT-2 images over four consecutive years, 2006-2009) was acquired for this project. The experimental data set includes LAI and AGB measurements over eight agricultural fields. Two fields were intensively monitored where ET flux were measured with a 30 minutes time step using eddy correlation methods. The modelisation approach is based on FAO-56 method coupled with a vegetation functioning model based on Monteith theory: the SAFY model [5]. The model operates at a daily time step model to provide estimates of plant characteristics (LAI, AGB), soil conditions (soil water content) and water use (ET). As a key linking variable, LAI is deduced from FORMOSAT-2 reflectances images, and then introduced into the SAFY model to provide spatial and temporal estimates of these biophysical variables. Most of the SAFY parameters are crop related and have been fixed according to

  12. Harvesting tree biomass at the stand level to assess the accuracy of field and airborne biomass estimation in savannas.

    PubMed

    Colgan, Matthew S; Asner, Gregory P; Swemmer, Tony

    2013-07-01

    Tree biomass is an integrated measure of net growth and is critical for understanding, monitoring, and modeling ecosystem functions. Despite the importance of accurately measuring tree biomass, several fundamental barriers preclude direct measurement at large spatial scales, including the facts that trees must be felled to be weighed and that even modestly sized trees are challenging to maneuver once felled. Allometric methods allow for estimation of tree mass using structural characteristics, such as trunk diameter. Savanna trees present additional challenges, including limited available allometry and a prevalence of multiple stems per individual. Here we collected airborne lidar data over a semiarid savanna adjacent to the Kruger National Park, South Africa, and then harvested and weighed woody plant biomass at the plot scale to provide a standard against which field and airborne estimation methods could be compared. For an existing airborne lidar method, we found that half of the total error was due to averaging canopy height at the plot scale. This error was eliminated by instead measuring maximum height and crown area of individual trees from lidar data using an object-based method to identify individual tree crowns and estimate their biomass. The best object-based model approached the accuracy of field allometry at both the tree and plot levels, and it more than doubled the accuracy compared to existing airborne methods (17% vs. 44% deviation from harvested biomass). Allometric error accounted for less than one-third of the total residual error in airborne biomass estimates at the plot scale when using allometry with low bias. Airborne methods also gave more accurate predictions at the plot level than did field methods based on diameter-only allometry. These results provide a novel comparison of field and airborne biomass estimates using harvested plots and advance the role of lidar remote sensing in savanna ecosystems. PMID:23967584

  13. Estimation of Canopy Foliar Biomass with Spectral Reflectance Measurements

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Canopy foliar biomass, defined as the product of leaf dry matter content and leaf area index, is an important measurement for global biogeochemical cycles. This study explores the potential for retrieving foliar biomass in green canopies using a spectral index, the Normalized Dry Matter Index (NDMI)...

  14. Biomass estimator for NIR image with a few additional spectral band images taken from light UAS

    NASA Astrophysics Data System (ADS)

    Pölönen, Ilkka; Salo, Heikki; Saari, Heikki; Kaivosoja, Jere; Pesonen, Liisa; Honkavaara, Eija

    2012-05-01

    A novel way to produce biomass estimation will offer possibilities for precision farming. Fertilizer prediction maps can be made based on accurate biomass estimation generated by a novel biomass estimator. By using this knowledge, a variable rate amount of fertilizers can be applied during the growing season. The innovation consists of light UAS, a high spatial resolution camera, and VTT's novel spectral camera. A few properly selected spectral wavelengths with NIR images and point clouds extracted by automatic image matching have been used in the estimation. The spectral wavelengths were chosen from green, red, and NIR channels.

  15. Forest biomass estimation with hemispherical photography for multiple forest types and various atmospheric conditions

    NASA Astrophysics Data System (ADS)

    Clark, Joshua Andrew

    The importance of accurately identifying inventories of domestic energy, including forest biomass, has increasingly become a priority of the US government and its citizens as the cost of fossil fuels has risen. It is useful to identify which of these resources can be processed and transported at the lowest cost for both private and public landowners. Accurate spatial inventories of forest biomass can help landowners allocate resources to maximize forest biomass utilization and provide information regarding current forest health (e.g., forest fire potential, insect susceptibility, wildlife habitat range). This research has indicated that hemispherical photography (HP) may be an accurate and low cost sensing technique for forest biomass measurements. In this dissertation: (1) It is shown that HP gap fraction measurements and both above ground biomass and crown biomass have a linear relationship. (2) It is demonstrated that careful manipulation of images improves gap fraction estimates, even under unfavorable atmospheric conditions. (3) It is shown that estimates of Leaf Area Index (LAI), based on transformations of gap fraction measurements, are the best estimator for both above ground forest biomass and crown biomass. (4) It is shown that many factors negatively influence the utility of HP for biomass estimation. (5) It is shown that biomass of forests stands with regular spacing is not modeled well using HP. As researchers continue to explore different methods for forest biomass estimation, HP is likely to remain as a viable technique, especially if LAI can be accurately estimated. However, other methods should be compared with HP, particularly for stands where LAI is poorly estimated by HP.

  16. Non-destructive lichen biomass estimation in northwestern Alaska: a comparison of methods.

    PubMed

    Rosso, Abbey; Neitlich, Peter; Smith, Robert J

    2014-01-01

    Terrestrial lichen biomass is an important indicator of forage availability for caribou in northern regions, and can indicate vegetation shifts due to climate change, air pollution or changes in vascular plant community structure. Techniques for estimating lichen biomass have traditionally required destructive harvesting that is painstaking and impractical, so we developed models to estimate biomass from relatively simple cover and height measurements. We measured cover and height of forage lichens (including single-taxon and multi-taxa "community" samples, n = 144) at 73 sites on the Seward Peninsula of northwestern Alaska, and harvested lichen biomass from the same plots. We assessed biomass-to-volume relationships using zero-intercept regressions, and compared differences among two non-destructive cover estimation methods (ocular vs. point count), among four landcover types in two ecoregions, and among single-taxon vs. multi-taxa samples. Additionally, we explored the feasibility of using lichen height (instead of volume) as a predictor of stand-level biomass. Although lichen taxa exhibited unique biomass and bulk density responses that varied significantly by growth form, we found that single-taxon sampling consistently under-estimated true biomass and was constrained by the need for taxonomic experts. We also found that the point count method provided little to no improvement over ocular methods, despite increased effort. Estimated biomass of lichen-dominated communities (mean lichen cover: 84.9±1.4%) using multi-taxa, ocular methods differed only nominally among landcover types within ecoregions (range: 822 to 1418 g m-2). Height alone was a poor predictor of lichen biomass and should always be weighted by cover abundance. We conclude that the multi-taxa (whole-community) approach, when paired with ocular estimates, is the most reasonable and practical method for estimating lichen biomass at landscape scales in northwest Alaska. PMID:25079228

  17. Non-Destructive Lichen Biomass Estimation in Northwestern Alaska: A Comparison of Methods

    PubMed Central

    Rosso, Abbey; Neitlich, Peter; Smith, Robert J.

    2014-01-01

    Terrestrial lichen biomass is an important indicator of forage availability for caribou in northern regions, and can indicate vegetation shifts due to climate change, air pollution or changes in vascular plant community structure. Techniques for estimating lichen biomass have traditionally required destructive harvesting that is painstaking and impractical, so we developed models to estimate biomass from relatively simple cover and height measurements. We measured cover and height of forage lichens (including single-taxon and multi-taxa “community” samples, n = 144) at 73 sites on the Seward Peninsula of northwestern Alaska, and harvested lichen biomass from the same plots. We assessed biomass-to-volume relationships using zero-intercept regressions, and compared differences among two non-destructive cover estimation methods (ocular vs. point count), among four landcover types in two ecoregions, and among single-taxon vs. multi-taxa samples. Additionally, we explored the feasibility of using lichen height (instead of volume) as a predictor of stand-level biomass. Although lichen taxa exhibited unique biomass and bulk density responses that varied significantly by growth form, we found that single-taxon sampling consistently under-estimated true biomass and was constrained by the need for taxonomic experts. We also found that the point count method provided little to no improvement over ocular methods, despite increased effort. Estimated biomass of lichen-dominated communities (mean lichen cover: 84.9±1.4%) using multi-taxa, ocular methods differed only nominally among landcover types within ecoregions (range: 822 to 1418 g m−2). Height alone was a poor predictor of lichen biomass and should always be weighted by cover abundance. We conclude that the multi-taxa (whole-community) approach, when paired with ocular estimates, is the most reasonable and practical method for estimating lichen biomass at landscape scales in northwest Alaska. PMID:25079228

  18. Closing a gap in tropical forest biomass estimation: taking crown mass variation into account in pantropical allometries

    NASA Astrophysics Data System (ADS)

    Ploton, Pierre; Barbier, Nicolas; Takoudjou Momo, Stéphane; Réjou-Méchain, Maxime; Boyemba Bosela, Faustin; Chuyong, Georges; Dauby, Gilles; Droissart, Vincent; Fayolle, Adeline; Calisto Goodman, Rosa; Henry, Matieu; Kamdem, Narcisse Guy; Katembo Mukirania, John; Kenfack, David; Libalah, Moses; Ngomanda, Alfred; Rossi, Vivien; Sonké, Bonaventure; Texier, Nicolas; Thomas, Duncan; Zebaze, Donatien; Couteron, Pierre; Berger, Uta; Pélissier, Raphaël

    2016-03-01

    Accurately monitoring tropical forest carbon stocks is a challenge that remains outstanding. Allometric models that consider tree diameter, height and wood density as predictors are currently used in most tropical forest carbon studies. In particular, a pantropical biomass model has been widely used for approximately a decade, and its most recent version will certainly constitute a reference model in the coming years. However, this reference model shows a systematic bias towards the largest trees. Because large trees are key drivers of forest carbon stocks and dynamics, understanding the origin and the consequences of this bias is of utmost concern. In this study, we compiled a unique tree mass data set of 673 trees destructively sampled in five tropical countries (101 trees > 100 cm in diameter) and an original data set of 130 forest plots (1 ha) from central Africa to quantify the prediction error of biomass allometric models at the individual and plot levels when explicitly taking crown mass variations into account or not doing so. We first showed that the proportion of crown to total tree aboveground biomass is highly variable among trees, ranging from 3 to 88 %. This proportion was constant on average for trees < 10 Mg (mean of 34 %) but, above this threshold, increased sharply with tree mass and exceeded 50 % on average for trees ≥ 45 Mg. This increase coincided with a progressive deviation between the pantropical biomass model estimations and actual tree mass. Taking a crown mass proxy into account in a newly developed model consistently removed the bias observed for large trees (> 1 Mg) and reduced the range of plot-level error (in %) from [-23; 16] to [0; 10]. The disproportionally higher allocation of large trees to crown mass may thus explain the bias observed recently in the reference pantropical model. This bias leads to far-from-negligible, but often overlooked, systematic errors at the plot level and may be easily corrected by taking a crown mass

  19. Developing in situ non-destructive estimates of crop biomass to address issues of scale in remote sensing

    USGS Publications Warehouse

    Marshall, Michael T.; Thenkabail, Prasad S.

    2015-01-01

    Ground-based estimates of aboveground wet (fresh) biomass (AWB) are an important input for crop growth models. In this study, we developed empirical equations of AWB for rice, maize, cotton, and alfalfa, by combining several in situ non-spectral and spectral predictors. The non-spectral predictors included: crop height (H), fraction of absorbed photosynthetically active radiation (FAPAR), leaf area index (LAI), and fraction of vegetation cover (FVC). The spectral predictors included 196 hyperspectral narrowbands (HNBs) from 350 to 2500 nm. The models for rice, maize, cotton, and alfalfa included H and HNBs in the near infrared (NIR); H, FAPAR, and HNBs in the NIR; H and HNBs in the visible and NIR; and FVC and HNBs in the visible; respectively. In each case, the non-spectral predictors were the most important, while the HNBs explained additional and statistically significant predictors, but with lower variance. The final models selected for validation yielded an R2 of 0.84, 0.59, 0.91, and 0.86 for rice, maize, cotton, and alfalfa, which when compared to models using HNBs alone from a previous study using the same spectral data, explained an additional 12%, 29%, 14%, and 6% in AWB variance. These integrated models will be used in an up-coming study to extrapolate AWB over 60 × 60 m transects to evaluate spaceborne multispectral broad bands and hyperspectral narrowbands.

  20. Precision of sugarcane biomass estimates in pot studies using fresh and dry weights

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Sugarcane (Saccharum spp.) field studies generally report fresh weight (FW) rather than dry weight (DW) due to logistical difficulties in drying large amounts of biomass. Pot studies often measure biomass of young plants with DW under the assumption that DW provides a more precise estimate of treatm...

  1. Biomass estimation of Douglas fir stands using airborne LiDAR data

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Biomass is an important parameter not only for carbon cycle modeling, but also for supporting land management operations (e.g. land use policy, forest fire management). Various remote sensing data have been utilized for biomass estimation, especially in forested areas. LiDAR (Light Detection And Ran...

  2. Estimating Root Biomass and Distribution After Fire in a Great Basin Woodland Using Cores or Pits

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Accurately estimating root biomass is critically important for understanding how below ground carbon storage is affected by different plant life forms and by fire. We compared a new soil coring technique with traditional quantitative pits for determining root biomass. We conducted the study in an ex...

  3. Assessing the uncertainty of biomass change estimates obtained using multi-temporal field, lidar sampling, and satellite imagery on the Kenai Peninsula of Alaska (Invited)

    NASA Astrophysics Data System (ADS)

    Andersen, H.

    2013-12-01

    There is increasing interest in the development of statistical sampling designs for aboveground biomass (and carbon) inventory and monitoring programs that can make efficient use of a variety of available data sources, including field plots, airborne lidar sampling, and satellite imagery. While the use of multiple sources, or levels, of remote sensing data can significantly increase the precision of biomass change estimates, especially in remote areas (such as interior Alaska) where it is extremely expensive to establish field plots, it can be challenging to accurately characterize the uncertainty (i.e. variance and bias) of the estimates obtained from these complex multi-level designs. In this study we evaluate a model-based approach to estimate changes in biomass over the western lowlands of the Kenai Peninsula of Alaska during the period 2004-2009 using a combination of field plots, lidar sampling, and satellite imagery. The model-based approach -- where all inferences are conditioned on the model relating the remote-sensing measurements to the inventory parameter of interest (e.g. biomass) - is appropriate for cases where it is cost-prohibitive, or infeasible, to establish a probability sample of field plots that are both spatially and temporally coincident with each remote sensing data set. For example, a model-based approach can be used to obtain biomass estimates over a period of time, even when field data is only available for the current time period. In this study, lidar data were collected in 2004 and 2009 over single swaths that covered 130 Forest Inventory and Analysis (FIA) plots distributed on a regular grid over the entire western Kenai. Field measurements on FIA plots were initially acquired over the period 1999-2003 and fifty-percent of these plots were remeasured in the period 2004-2009. In addition, high-accuracy coordinates (< 1 meter error) were obtained for these FIA plots using survey-grade GLONASS-enabled GPS equipment. Changes in biomass

  4. Estimating woody biomass in Sub-Saharan Africa

    SciTech Connect

    Millington, A.C.; Critchley, R.W.; Douglas, T.D.; Ryan, P.

    1994-03-01

    Woody biomass constitutes the main domestic fuel in many parts of Africa. The need for more definitive data on this resource was perceived at a Household Energy Seminar held by the World Bank Energy Sector Management Assistance Program (ESMAP) in Harare, Zimbabwe, in February 1988. The present project was conceived as a result. It is a first attempt to produce an analysis by type of land cover of the woody biomass present in Sub-Saharan. On examining the energy balance for most Sub-Saharan countries, one is struck by the dominance of woodfuel, including fuelwood and charcoal. (Copyright (c) 1994 The International Bank for Reconstruction and Development/The World Bank.)

  5. A method to estimate the biomass of Spirulina platensis cultivated on a solid medium

    PubMed Central

    Pelizer, Lúcia Helena; Moraes, Iracema de Oliveira

    2014-01-01

    This paper presents a method to estimate the biomass of Spirulina cultivated on solid medium with sugarcane bagasse as a support, in view of the difficulty in determining biomass concentrations in bioprocesses, particularly those conducted in semi-solid or solid media. The genus Spirulina of the family Oscillatoriaceae comprises the group of multicellular filamentous cyanobacteria (blue-green microalgae). Spirulina is used as fish feed in aquaculture, as a food supplement, a source of vitamins, pigments, antioxidants and fatty acids. Therefore, its growth parameters are extremely important in studies of the development and optimization of bioprocesses. For studies of biomass growth, Spirulina platensis was cultured on solid medium using sugarcane bagasse as a support. The biomass thus produced was estimated by determining the protein content of the material grown during the process, based on the ratio of dry weight to protein content obtained in the surface growth experiments. The protein content of the biomass grown in Erlenmeyer flasks on surface medium was examined daily to check the influence of culture time on the protein content of the biomass. The biomass showed an average protein content of 42.2%. This methodology enabled the concentration of biomass adhering to the sugarcane bagasse to be estimated from the indirect measurement of the protein content associated with cell growth. PMID:25477928

  6. A method to estimate the biomass of Spirulina platensis cultivated on a solid medium.

    PubMed

    Pelizer, Lúcia Helena; Moraes, Iracema de Oliveira

    2014-01-01

    This paper presents a method to estimate the biomass of Spirulina cultivated on solid medium with sugarcane bagasse as a support, in view of the difficulty in determining biomass concentrations in bioprocesses, particularly those conducted in semi-solid or solid media. The genus Spirulina of the family Oscillatoriaceae comprises the group of multicellular filamentous cyanobacteria (blue-green microalgae). Spirulina is used as fish feed in aquaculture, as a food supplement, a source of vitamins, pigments, antioxidants and fatty acids. Therefore, its growth parameters are extremely important in studies of the development and optimization of bioprocesses. For studies of biomass growth, Spirulina platensis was cultured on solid medium using sugarcane bagasse as a support. The biomass thus produced was estimated by determining the protein content of the material grown during the process, based on the ratio of dry weight to protein content obtained in the surface growth experiments. The protein content of the biomass grown in Erlenmeyer flasks on surface medium was examined daily to check the influence of culture time on the protein content of the biomass. The biomass showed an average protein content of 42.2%. This methodology enabled the concentration of biomass adhering to the sugarcane bagasse to be estimated from the indirect measurement of the protein content associated with cell growth. PMID:25477928

  7. Consequences of long-term severe industrial pollution for aboveground carbon and nitrogen pools in northern taiga forests at local and regional scales.

    PubMed

    Manninen, Sirkku; Zverev, Vitali; Bergman, Igor; Kozlov, Mikhail V

    2015-12-01

    Boreal coniferous forests act as an important sink for atmospheric carbon dioxide. The overall tree carbon (C) sink in the forests of Europe has increased during the past decades, especially due to management and elevated nitrogen (N) deposition; however, industrial atmospheric pollution, primarily sulphur dioxide and heavy metals, still negatively affect forest biomass production at different spatial scales. We report local and regional changes in forest aboveground biomass, C and N concentrations in plant tissues, and C and N pools caused by long-term atmospheric emissions from a large point source, the nickel-copper smelter in Monchegorsk, in north-western Russia. An increase in pollution load (assessed as Cu concentration in forest litter) caused C to increase in foliage but C remained unchanged in wood, while N decreased in foliage and increased in wood, demonstrating strong effects of pollution on resource translocation between green and woody tissues. The aboveground C and N pools were primarily governed by plant biomass, which strongly decreased with an increase in pollution load. In our study sites (located 1.6-39.7 km from the smelter) living aboveground plant biomass was 76 to 4888 gm(-2), and C and N pools ranged 35-2333 g C m(-2) and 0.5-35.1 g N m(-2), respectively. We estimate that the aboveground plant biomass is reduced due to chronic exposure to industrial air pollution over an area of about 107,200 km2, and the total (aboveground and belowground) loss of phytomass C stock amounts to 4.24×10(13) g C. Our results emphasize the need to account for the overall impact of industrial polluters on ecosystem C and N pools when assessing the C and N dynamics in northern boreal forests because of the marked long-term negative effects of their emissions on structure and productivity of plant communities. PMID:26254064

  8. COMBINING LIDAR ESTIMATES OF BIOMASS AND LANDSAT ESTIMATES OF STAND AGE FOR SPATIALLY EXTENSIVE VALIDATION OF MODELED FOREST PRODUCTIVITY. (R828309)

    EPA Science Inventory

    Extensive estimates of forest productivity are required to understand the
    relationships between shifting land use, changing climate and carbon storage
    and fluxes. Aboveground net primary production of wood (NPPAw) is a major component
    of total NPP and...

  9. Estimating total standing herbaceous biomass production with LANDSAT MSS digital data

    NASA Technical Reports Server (NTRS)

    Richardson, A. J.; Everitt, J. H.; Wiegand, C. L. (Principal Investigator)

    1982-01-01

    Rangeland biomass data were correlated with spectral vegetation indices, derived from LANDSAT MSS data. LANDSAT data from five range and three other land use sites in Willacv and Cameron Counties were collected on October 17 and December 10, 1975, and on July 31 and September 23, 1976. The overall linear correlation of total standing herbaceous biomass with the LANDSAT derived perpendicular vegetation index was highly significant (r = 0.90**) for these four dates. The standard error of estimate was 722 kg/ha. Biomass data were recorded for two of these range sites for 8 months (March through October) during the 1976 growing season. Standing green biomass accounted for most of the increase in herbage, starting in June and ending about September and October. These results indicate that satellite data may be useful for the estimation of total standing herbaceous biomass production that could aid range managers in assessing range condition and animal carrying capacities of large and inaccessible range holdings.

  10. Comparison of modeling approaches for carbon partitioning: Impact on estimates of global net primary production and equilibrium biomass of woody vegetation from MODIS GPP

    NASA Astrophysics Data System (ADS)

    Ise, Takeshi; Litton, Creighton M.; Giardina, Christian P.; Ito, Akihiko

    2010-12-01

    Partitioning of gross primary production (GPP) to aboveground versus belowground, to growth versus respiration, and to short versus long-lived tissues exerts a strong influence on ecosystem structure and function, with potentially large implications for the global carbon budget. A recent meta-analysis of forest ecosystems suggests that carbon partitioning to leaves, stems, and roots varies consistently with GPP and that the ratio of net primary production (NPP) to GPP is conservative across environmental gradients. To examine influences of carbon partitioning schemes employed by global ecosystem models, we used this meta-analysis-based model and a satellite-based (MODIS) terrestrial GPP data set to estimate global woody NPP and equilibrium biomass, and then compared it to two process-based ecosystem models (Biome-BGC and VISIT) using the same GPP data set. We hypothesized that different carbon partitioning schemes would result in large differences in global estimates of woody NPP and equilibrium biomass. Woody NPP estimated by Biome-BGC and VISIT was 25% and 29% higher than the meta-analysis-based model for boreal forests, with smaller differences in temperate and tropics. Global equilibrium woody biomass, calculated from model-specific NPP estimates and a single set of tissue turnover rates, was 48 and 226 Pg C higher for Biome-BGC and VISIT compared to the meta-analysis-based model, reflecting differences in carbon partitioning to structural versus metabolically active tissues. In summary, we found that different carbon partitioning schemes resulted in large variations in estimates of global woody carbon flux and storage, indicating that stand-level controls on carbon partitioning are not yet accurately represented in ecosystem models.

  11. Converting wood volume to biomass for pinyon and juniper. Forest Service research note

    SciTech Connect

    Chojnacky, D.C.; Moisen, G.G.

    1993-03-01

    A technique was developed to convert pinyon-juniper volume equation predictions to weights. The method uses specific gravity and biomass conversion equations to obtain foliage weight and total wood weight of all stems, branches, and bark. Specific gravity data are given for several Arizona pinyon-juniper species. Biomass conversion equations are constructed from pinyon-juniper data collected in Nevada. Results provide an interim means of estimating pinyon-juniper aboveground biomass from available volume inventory data.

  12. Estimations of deciduous forest biomass by analyzing vegetation microwave emission

    NASA Astrophysics Data System (ADS)

    Zhang, Zhongjun; Zhang, Lixin; Zhao, Shaojie; Wang, Huan

    2011-09-01

    Forest is important in global carbon cycle and has potential impact on global climatic change. Whether the soil moisture under forest area can be detected by microwave emission signature is unknown due to the dense forest cover. Also, the relationship between forest biomass and its microwave emissivity and transmissivity is of interest to be studied. The microwave emission contribution received by the radiometer above the forest canopy comes from both the soil surface and vegetation layer. In this study, a high-order emission model, Matrix-Doubling, was employed to simulate the emissivity of a young deciduous forest. A field experiment before and after watering the deciduous tree stand was carried in June 5, 2011 in Baoding, China to verify the model, and to measure the tree transmissivity. A tree was selected to be cut to measure the tree parameters and weighed its biomass. Assuming the forest as a non-scattering medium, the effective single-scattering albedo is obtained for 0th-order model by fitting the same emissivity from Matrix-Doubling model. For lower albedo which could be ignored, transmissivity of trees can be deduced by measured Brightness Temperatures before and after watering the underlying soil. The relationship between forest biomass and its transmissivity is presented in this paper.

  13. Potential application of multipolarization SAR for pine-plantation biomass estimation

    NASA Technical Reports Server (NTRS)

    Wu, Shih-Tseng

    1987-01-01

    This paper presents the technique and the potential utility of multipolarization Synthetic Aperture Radar (SAR) data for pine-plantation biomass estimation. Three channels of SAR data, one from the Shuttle Imaging Radar SIR-A and the other two from the aircraft SAR, were acquired over the Baldwin County, Alabama, study area. The SIR-A data were acquired with HH polarization and the aircraft SAR data with VV and VH polarizations. Linear regression techniques are used to estimate the pine-plantation biomass, tree height, and age using 21 test plots. The results indicate that the multipolarization data are highly related to the plantation biomass. The results suggest a potential application of multipolarization SAR for pine-plantation biomass estimation.

  14. Forest Fire Burned Biomass Estimation Using Satellite Images and Reference Data

    NASA Astrophysics Data System (ADS)

    Qin, Xianlin; Zu, Xiaofeng; Deng, Guang; Li, Zengyuan; Zhang, Zihui; Casanova, J. L. Sanz, Julia; Salvador, Pablo

    2014-11-01

    Vegetation biomass burning has been identified as a significant source of aerosols, carbon fluxes, and trace gases, which pollute the atmosphere and contribute to radiative forcing responsible for global climate change. To estimate the total biomass burned by forest fire using satellite images in Dragon 3, basing on the fuel load from reference data,the combustion factor getting from fieldwork at the sub-compartment level, and the results of burned scar mapping by using HJ-1A CCD and the monthly MODIS burned production datasets (MCD45A1), the biomass burned of the selected experiment area has been estimated by combining these data. The result showed that the accuracy of the biomass burned estimation mainly was affected by the accuracy of burned scar edge using the spatial resolution of satellite data.

  15. Fresh Biomass Estimation in Heterogeneous Grassland Using Hyperspectral Measurements and Multivariate Statistical Analysis

    NASA Astrophysics Data System (ADS)

    Darvishzadeh, R.; Skidmore, A. K.; Mirzaie, M.; Atzberger, C.; Schlerf, M.

    2014-12-01

    Accurate estimation of grassland biomass at their peak productivity can provide crucial information regarding the functioning and productivity of the rangelands. Hyperspectral remote sensing has proved to be valuable for estimation of vegetation biophysical parameters such as biomass using different statistical techniques. However, in statistical analysis of hyperspectral data, multicollinearity is a common problem due to large amount of correlated hyper-spectral reflectance measurements. The aim of this study was to examine the prospect of above ground biomass estimation in a heterogeneous Mediterranean rangeland employing multivariate calibration methods. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of above ground biomass for 170 sample plots. Multivariate calibrations including partial least squares regression (PLSR), principal component regression (PCR), and Least-Squared Support Vector Machine (LS-SVM) were used to estimate the above ground biomass. The prediction accuracy of the multivariate calibration methods were assessed using cross validated R2 and RMSE. The best model performance was obtained using LS_SVM and then PLSR both calibrated with first derivative reflectance dataset with R2cv = 0.88 & 0.86 and RMSEcv= 1.15 & 1.07 respectively. The weakest prediction accuracy was appeared when PCR were used (R2cv = 0.31 and RMSEcv= 2.48). The obtained results highlight the importance of multivariate calibration methods for biomass estimation when hyperspectral data are used.

  16. [Estimation of Winter Wheat Biomass Using Visible Spectral and BP Based Artificial Neural Networks].

    PubMed

    Cui, Ri-xian; Liu, Ya-dong; Fu, Jin-dong

    2015-09-01

    The objective of this study was to evaluate the feasibility of using color digital image analysis and back propagation (BP) based artificial neural networks (ANN) method to estimate above ground biomass at the canopy level of winter wheat field. Digital color images of winter wheat canopies grown under six levels of nitrogen treatments were taken with a digital camera for four times during the elongation stage and at the same time wheat plants were sampled to measure above ground biomass. Canopy cover (CC) and 10 color indices were calculated from winter wheat canopy images by using image analysis program (developed in Microsoft Visual Basic). Correlation analysis was carried out to identify the relationship between CC, 10 color indices and winter wheat above ground biomass. Stepwise multiple linear regression and BP based ANN methods were used to establish the models to estimate winter wheat above ground biomass. The results showed that CC, and two color indices had a significant cor- relation with above ground biomass. CC revealed the highest correlation with winter wheat above ground biomass. Stepwise multiple linear regression model constituting CC and color indices of NDI and b, and BP based ANN model with four variables (CC, g, b and NDI) for input was constructed to estimate winter wheat above ground biomass. The validation results indicate that the model using BP based ANN method has a better performance with higher R2 (0.903) and lower RMSE (61.706) and RRMSE (18.876) in comparation with the stepwise regression model. PMID:26669174

  17. Biomass estimation of wetland vegetation in Poyang Lake area using ENVISAT advanced synthetic aperture radar data

    NASA Astrophysics Data System (ADS)

    Liao, Jingjuan; Shen, Guozhuang; Dong, Lei

    2013-01-01

    Biomass estimation of wetlands plays a role in understanding dynamic changes of the wetland ecosystem. Poyang Lake is the largest freshwater lake in China, with an area of about 3000 km2. The lake's wetland ecosystem has a significant impact on leveraging China's environmental change. Synthetic aperture radar (SAR) data are a good choice for biomass estimation during rainy and dry seasons in this region. In this paper, we discuss the neural network algorithms (NNAs) to retrieve wetland biomass using the alternating-polarization ENVISAT advanced synthetic aperture radar (ASAR) data. Two field measurements were carried out coinciding with the satellite overpasses through the hydrological cycle in April to November. A radiative transfer model of forest canopy, the Michigan Microwave Canopy Scattering (MIMICS) model, was modified to fit to herbaceous wetland ecosystems. With both ASAR and MIMICS simulations as input data, the NNA-estimated biomass was validated with ground-measured data. This study indicates the capability of NNA combined with a modified MIMICS model to retrieve wetland biomass from SAR imagery. Finally, the overall biomass of Poyang Lake wetland vegetation has been estimated. It reached a level of 1.09×109, 1.86×108, and 9.87×108 kg in April, July, and November 2007, respectively.

  18. Estimation of potential maximum biomass of trout in Wyoming streams to assist management decisions

    USGS Publications Warehouse

    Hubert, W.A.; Marwitz, T.D.; Gerow, K.G.; Binns, N.A.; Wiley, R.W.

    1996-01-01

    Fishery managers can benefit from knowledge of the potential maximum biomass (PMB) of trout in streams when making decisions on the allocation of resources to improve fisheries. Resources are most likely to he expended on streams with high PMB and with large differences between PMB and currently measured biomass. We developed and tested a model that uses four easily measured habitat variables to estimate PMB (upper 90th percentile of predicted mean bid mass) of trout (Oncorhynchus spp., Salmo trutta, and Salvelinus fontinalis) in Wyoming streams. The habitat variables were proportion of cover, elevation, wetted width, and channel gradient. The PMB model was constructed from data on 166 stream reaches throughout Wyoming and validated on an independent data set of 50 stream reaches. Prediction of PMB in combination with estimation of current biomass and information on habitat quality can provide managers with insight into the extent to which management actions may enhance trout biomass.

  19. Belowground Biomass Sampling to Estimate Fine Root Mass across NEON Sites

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

    Production of belowground biomass is an important and relatively uncharacterized component of the net primary productivity (NPP) of ecosystems. Fine root productivity makes up a significant portion of total belowground production because fine roots turn over rapidly, and therefore contribute disproportionately to annual estimates of belowground net primary productivity (BNPP). One of the major goals of the National Ecological Observatory Network (NEON) is to quantify above- and below-ground NPP at 60 sites within 20 different eco-climactic regions. NEON's Terrestrial Observation System will carry out belowground biomass sampling throughout the life of the observatory to estimate fine root production. However, belowground biomass sampling during NEON operations will be constrained to a maximum depth of 50cm. This limited depth range leaves the question of what proportion of total fine root mass is being collected and how to optimally characterize belowground biomass given sampling depth limitations. During the construction period, NEON is characterizing fine root biomass distribution at depth down to 2m at each site, as well as physical and chemical properties in each soil horizon. Each sampling unit is a pit (2m deep and approximately 1.5m wide), dug in the site's dominant vegetation type where fine root biomass sampling will also occur during Operations. To sample fine root biomass in each pit, soil samples of a known volume are taken from three vertical profiles down the face of the pit. Samples are then wet sieved to extract fine root mass, and roots are dried at 65°C for 48 hours and then weighed. The soil pit data are used to estimate the proportion of total fine root biomass from each site as a function of depth. Non-linear curves are fitted to the data to calculate total fine root mass at depth and to provide estimates of the proportion of the total fine root mass that is sampled at each site during NEON's 30 year operational sampling. The belowground

  20. Estimating biomass, yield, evaprotranspiration and carbon fluxes for winter wheat by using high resolution remote sensing data combined with a crop model

    NASA Astrophysics Data System (ADS)

    Veloso, A.; Ceschia, E.; Demarez, V.

    2013-12-01

    The use of crop models allows simulating plant development, growth, yield, CO2 and water fluxes under different environmental and management conditions. When combined with high spatial and temporal resolution remote sensing data, these models provide new perspectives for crop monitoring at regional scale. Besides, monitoring spatial and temporal variation in water budget and amount of carbon fixed by these crops is an ultimate goal of earth climate change studies. We propose here an approach to estimate time courses of dry aboveground biomass (DAM), yield and evapotranspiration (ETR) for winter wheat by assimilating Green Area Index (GAI) data, obtained from satellite observations, into a simple crop model. This model is then coupled with a ';carbon flux module' for estimating the components of the carbon budget (gross primary production (GPP), ecosystem respiration (Reco), ...). Among the several land surface biophysical variables accessible from satellite observations, the GAI is the one that has a key role in soil-plant-atmosphere interactions and in biomass accumulation process. Many methods have been developed to relate GAI to optical remote sensing signal. Here, seasonal dynamics of remotely sensed GAI were estimated by applying a method based on the inversion of a radiative transfer model using artificial neural networks. For this work, we employed a unique set of Formosat-2 and SPOT images acquired from 2006 to 2011 in southwest France. The modelling approach is based on the Simple Algorithm for Yield and Evapotranspiration estimate (SAFYE) model, which couples the FAO-56 model with an agro-meteorological model, based on Monteith's light-use efficiency theory. The SAFYE model is a daily time step crop model that simulates time series of GAI, biomass (NPP), grain yield and ETR. The carbon flux module simulates GPP, the autotrophic respiration (Ra) that is defined as the sum of plant growth and maintenance respiration and the heterotrophic respiration (Rh

  1. Appendix C: Biomass Program inputs for FY 2008 benefits estimates

    SciTech Connect

    None, None

    2009-01-18

    Document summarizes the results of the benefits analysis of EERE’s programs, as described in the FY 2008 Budget Request. EERE estimates benefits for its overall portfolio and nine Research, Development, Demonstration, and Deployment (RD3) programs.

  2. Mangrove Canopy Height and Biomass Estimations by means of Pol-InSAR Techniques

    NASA Astrophysics Data System (ADS)

    Lee, S. K.; Fatoyinbo, T. E.; Trettin, C.; Simard, M.; Bandeira, S.

    2014-12-01

    Mangrove forests cover only about 1% of the Earth's terrestrial surface, but they are amongst the highest carbon-storing and carbon-exporting ecosystems globally. Estimating 3-D mangrove forest parameters has been challenging due to the complex physical environment of the forests. In previous works, remote sensing techniques have proven an excellent tool for the estimation of mangrove forests. Recent experiments have successfully demonstrated the global scale estimation of mangrove structure using spaceborne remote sensing data: SRTM (InSAR), ICESat/GLAS (lidar), Landsat ETM+ (passive optical). However, those systems had relatively low spatial and temporal resolutions. Polarimetric SAR Interferometry (Pol-InSAR) is a Synthetic Aperture Radar (SAR) remote sensing technique based on the coherent combination of both Polarimetric and interferometric observables. The Pol-InSAR has provided a step forward in quantitative 3D forest structure parameter estimation (e.g. forest canopy height and biomass) over a variety of forests. Recent developments of Pol-InSAR technique with TanDEM-X (TDX) data in mangroves have proven that TDX data can be used to produce global-scale mangrove canopy height and biomass maps at accuracies comparable to airborne lidar measurements. In this study we propose to generate 12m-resolution mangrove canopy height and biomass estimates for the coastline of Mozambique using Pol-InSAR techniques from single-/dual-pol TDX data and validated with commercial airborne lidar. To cover all of the mangroves in the costal area of Mozambique, which is about 3000 km, about 200 TDX data sets are selected and processed. The TDX height data are calibrated with commercial airborne lidar data acquired over 150 km2 of mangroves in the Zambezi delta of Mozambique while height and Biomass estimates are validated using in-situ forest inventory measurements and biomass. The results from the study will be the first country-wide, wall-to-wall estimate of mangrove structure

  3. Natural Forest Biomass Estimation Based on Plantation Information Using PALSAR Data

    PubMed Central

    Avtar, Ram; Suzuki, Rikie; Sawada, Haruo

    2014-01-01

    Forests play a vital role in terrestrial carbon cycling; therefore, monitoring forest biomass at local to global scales has become a challenging issue in the context of climate change. In this study, we investigated the backscattering properties of Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data in cashew and rubber plantation areas of Cambodia. The PALSAR backscattering coefficient (σ0) had different responses in the two plantation types because of differences in biophysical parameters. The PALSAR σ0 showed a higher correlation with field-based measurements and lower saturation in cashew plants compared with rubber plants. Multiple linear regression (MLR) models based on field-based biomass of cashew (C-MLR) and rubber (R-MLR) plants with PALSAR σ0 were created. These MLR models were used to estimate natural forest biomass in Cambodia. The cashew plant-based MLR model (C-MLR) produced better results than the rubber plant-based MLR model (R-MLR). The C-MLR-estimated natural forest biomass was validated using forest inventory data for natural forests in Cambodia. The validation results showed a strong correlation (R2 = 0.64) between C-MLR-estimated natural forest biomass and field-based biomass, with RMSE  = 23.2 Mg/ha in deciduous forests. In high-biomass regions, such as dense evergreen forests, this model had a weaker correlation because of the high biomass and the multiple-story tree structure of evergreen forests, which caused saturation of the PALSAR signal. PMID:24465908

  4. Quantifying the differences between Amazon forest biomass maps: uncertainty to be tackled in carbon emission estimates

    NASA Astrophysics Data System (ADS)

    Ometto, J.; Soler, L.; Assis, T.; Lapola, D.; Aguiar, A. P.; Meir, P.

    2012-12-01

    The current methods adopted to estimate the spatial variation on above- and below-ground biomass in tropical forests, in particular the Brazilian Amazon, are usually based on remote sensing and coupled with scarce and, generally poorly distributed fieldwork measurements. There are notable differences between the resulting published biomass maps and this results in high uncertainty in calculated carbon emissions from deforestation, forest degradation and other changes in the land cover. These uncertainties are particularly critical when biomass maps are coded into biomass classes referring to a specific range of values. The Brazilian Amazon is the largest continuous tropical broadleaf forest in the globe, containing a substantial amount of carbon above and below the soil surface. Analysis of land use change has shown that deforestation in the region is a patchy process, comprising different intensities and dynamics in separate and adjacent areas, such that even if when characterized by broad patterns estimates of carbon emissions can become a complicated task unless spatially accurate biomass maps are available. In this paper we analyze the differences in recently published biomass maps of the Amazon region, considering as well the official information used by the Brazilian government for its communication to the United Framework on Climate Change Convention of the United Nations. From the average biomass at deforestation areas in two different periods (1997 and 2006), maps varied from +20% to -19% in the first period and from +20% to -15% in the later, highlighting the substantial differences in the overall biomass estimate, with clear reflect on carbon emissions in the region.

  5. Use of near infrared/red radiance ratios for estimating vegetation biomass and physiological status

    NASA Technical Reports Server (NTRS)

    Tucker, C. J.

    1977-01-01

    The application of photographic infrared/red (ir/red) reflectance or radiance ratios for the estimation of vegetation biomass and physiological status were investigated by analyzing in situ spectral reflectance data from experimental grass plots. Canopy biological samples were taken for total wet biomass, total dry biomass, leaf water content, dry green biomass, dry brown biomass, and total chlorophyll content at each sampling date. Integrated red and photographic infrared radiances were regressed against the various canopy or plot variables to determine the relative significance between the red, photographic infrared, and the ir/red ratio and the canopy variables. The ir/red ratio is sensitive to the photosynthetically active or green biomass, the rate of primary production, and actually measures the interaction between the green biomass and the rate of primary production within a given species type. The ir/red ratio resulted in improved regression significance over the red or the ir/radiances taken separately. Only slight differences were found between ir/red ratio, the ir-red difference, the vegetation index, and the transformed vegetation index. The asymptotic spectral radiance properties of the ir, red, ir/red ratio, and the various transformations were evaluated.

  6. Estimation of biomass and canopy height in bermudagrass, alfalfa, and wheat using ultrasonic, laser, and spectral sensors.

    PubMed

    Pittman, Jeremy Joshua; Arnall, Daryl Brian; Interrante, Sindy M; Moffet, Corey A; Butler, Twain J

    2015-01-01

    Non-destructive biomass estimation of vegetation has been performed via remote sensing as well as physical measurements. An effective method for estimating biomass must have accuracy comparable to the accepted standard of destructive removal. Estimation or measurement of height is commonly employed to create a relationship between height and mass. This study examined several types of ground-based mobile sensing strategies for forage biomass estimation. Forage production experiments consisting of alfalfa (Medicago sativa L.), bermudagrass [Cynodon dactylon (L.) Pers.], and wheat (Triticum aestivum L.) were employed to examine sensor biomass estimation (laser, ultrasonic, and spectral) as compared to physical measurements (plate meter and meter stick) and the traditional harvest method (clipping). Predictive models were constructed via partial least squares regression and modeled estimates were compared to the physically measured biomass. Least significant difference separated mean estimates were examined to evaluate differences in the physical measurements and sensor estimates for canopy height and biomass. Differences between methods were minimal (average percent error of 11.2% for difference between predicted values versus machine and quadrat harvested biomass values (1.64 and 4.91 t·ha(-1), respectively), except at the lowest measured biomass (average percent error of 89% for harvester and quad harvested biomass < 0.79 t·ha(-1)) and greatest measured biomass (average percent error of 18% for harvester and quad harvested biomass >6.4 t·ha(-1)). These data suggest that using mobile sensor-based biomass estimation models could be an effective alternative to the traditional clipping method for rapid, accurate in-field biomass estimation. PMID:25635415

  7. Estimation of Biomass and Canopy Height in Bermudagrass, Alfalfa, and Wheat Using Ultrasonic, Laser, and Spectral Sensors

    PubMed Central

    Pittman, Jeremy Joshua; Arnall, Daryl Brian; Interrante, Sindy M.; Moffet, Corey A.; Butler, Twain J.

    2015-01-01

    Non-destructive biomass estimation of vegetation has been performed via remote sensing as well as physical measurements. An effective method for estimating biomass must have accuracy comparable to the accepted standard of destructive removal. Estimation or measurement of height is commonly employed to create a relationship between height and mass. This study examined several types of ground-based mobile sensing strategies for forage biomass estimation. Forage production experiments consisting of alfalfa (Medicago sativa L.), bermudagrass [Cynodon dactylon (L.) Pers.], and wheat (Triticum aestivum L.) were employed to examine sensor biomass estimation (laser, ultrasonic, and spectral) as compared to physical measurements (plate meter and meter stick) and the traditional harvest method (clipping). Predictive models were constructed via partial least squares regression and modeled estimates were compared to the physically measured biomass. Least significant difference separated mean estimates were examined to evaluate differences in the physical measurements and sensor estimates for canopy height and biomass. Differences between methods were minimal (average percent error of 11.2% for difference between predicted values versus machine and quadrat harvested biomass values (1.64 and 4.91 t·ha−1, respectively), except at the lowest measured biomass (average percent error of 89% for harvester and quad harvested biomass < 0.79 t·ha−1) and greatest measured biomass (average percent error of 18% for harvester and quad harvested biomass >6.4 t·ha−1). These data suggest that using mobile sensor-based biomass estimation models could be an effective alternative to the traditional clipping method for rapid, accurate in-field biomass estimation. PMID:25635415

  8. Estimation of sugarcane sucrose and biomass with remote sensing techniques

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Remote sensing techniques were used to predict sucrose levels (TRS) and gross cane yield in field-grown sugarcane. To estimate sucrose levels, leaves were collected from plant-cane and first-ratoon sugarcane plants from the variety maturity studies conducted at the USDA-ARS-SRRC, Sugarcane Research...

  9. REGIONAL ESTIMATION OF CURRENT AND FUTURE FOREST BIOMASS. (R828785)

    EPA Science Inventory

    The 90,674 wildland fires that burned 2.9 million ha at an estimated suppression cost of $1.6 billion in the United States during the 2000 fire season demonstrated that forest fuel loading has become a hazard to life, property, and ecosystem health as a result of past fire exc...

  10. Rainforest burning and the global carbon budget: Biomass, combustion efficiency, and charcoal formation in the Brazilian Amazon

    NASA Astrophysics Data System (ADS)

    Fearnside, Philip M.; Leal, Niwton; Fernandes, Fernando Moreira

    1993-01-01

    Biomass present before and after burning was measured in forest cleared for pasture in a cattle ranch (Fazenda Dimona) near Manaus, Amazonas, Brazil. Aboveground dry weight biomass loading averaged 265 t ha-1 (standard deviation (SD) = 110, n = 6 quadrats) at Fazenda Dimona, which corresponds to approximately 311 t ha-1 total dry weight biomass. A five-category visual classification at 200 points showed highly variable burn quality. Postburn aboveground biomass loading was evaluated by cutting and weighing of 100 m2 quadrats and by line intersect sampling. Quadrats had a mean dry weight of 187 t ha-1 (SD = 69, n = 10), a 29.3% reduction from the preburn mean in the same clearing. Line intersect estimates in 1.65 km of transects indicated that 265 m3 ha-1 (approximately 164 t ha-1 of aboveground dry matter) survived burning. Using carbon contents measured for different biomass components (all ˜50% carbon) and assuming a carbon content of 74.8% for charcoal (from other studies near Manaus), the destructive measurements imply a 27.6% reduction of aboveground carbon pools. Charcoal composed 2.5% of the dry weight of the remains in the postburn destructive quadrats and 2.8% of the volume in the line intersect transects. Thus approximately 2.7% of the preburn aboveground carbon stock was converted to charcoal, substantially less than is generally assumed in global carbon models. The findings confirm high values for biomass in central Amazonia. High variability indicates the need for further studies in many localities and for making maximum use of less laborious indirect methods of biomass estimation. While indirect methods are essential for regional estimates of average biomass, only direct weighing such as that reported here can yield information on combustion efficiency and charcoal formation. Both high biomass and low percentage of charcoal formation suggest the significant potential contribution of forest burning to global climate changes from CO2 and trace gases.

  11. Mapping Africa Biomass with MODIS Imagery

    NASA Astrophysics Data System (ADS)

    Laporte, N.; Baccini, A.; Houghton, R.

    2006-12-01

    Central Africa contains the second largest block of tropical forest remaining in the world, and is one of the largest carbon reservoirs on Earth. The carbon dynamics of the region differ substantially from other tropical forests because most deforestation and land use is associated with selective logging and small-scale landholders practicing traditional "slash-and-burn" agriculture. Despite estimates of 1-2 PgC/yr released to the atmosphere from tropical deforestation, the amount released from Central Africa is highly uncertain relative to the amounts released from other tropical forest areas. The uncertainty in carbon fluxes results from inadequate estimates of both rates of deforestation and standing stocks of carbon (forest biomass). Here we present new results mapping above-ground forest biomass for tropical Africa using machine learning techniques to integrate MODIS 1km spectral reflectance with forest inventory measurements to calibrate an empirical relationship. The derived forest biomass at each MODIS pixel shows the spatial distribution of forest biomass over the entire tropical forest region. The model has been tested in Uganda, Mali and part of Republic of Congo where field data were available. The regression tree model based on MODIS NBAR surface reflectance for Uganda, Mali and Republic of Congo explains 94 percent of the variance in above-ground biomass with a root mean square error (RMSE) of 27 Tons/ha. The approach shows promise for use of optical remote sensing data in mapping the spatial distribution of forest biomass across the region.

  12. Evaluation of the Environmental DNA Method for Estimating Distribution and Biomass of Submerged Aquatic Plants

    PubMed Central

    Matsuhashi, Saeko; Doi, Hideyuki; Fujiwara, Ayaka; Watanabe, Sonoko; Minamoto, Toshifumi

    2016-01-01

    The environmental DNA (eDNA) method has increasingly been recognized as a powerful tool for monitoring aquatic animal species; however, its application for monitoring aquatic plants is limited. To evaluate eDNA analysis for estimating the distribution of aquatic plants, we compared its estimated distributions with eDNA analysis, visual observation, and past distribution records for the submerged species Hydrilla verticillata. Moreover, we conducted aquarium experiments using H. verticillata and Egeria densa and analyzed the relationships between eDNA concentrations and plant biomass to investigate the potential for biomass estimation. The occurrences estimated by eDNA analysis closely corresponded to past distribution records, and eDNA detections were more frequent than visual observations, indicating that the method is potentially more sensitive. The results of the aquarium experiments showed a positive relationship between plant biomass and eDNA concentration; however, the relationship was not always significant. The eDNA concentration peaked within three days of the start of the experiment in most cases, suggesting that plants do not release constant amounts of DNA. These results showed that eDNA analysis can be used for distribution surveys, and has the potential to estimate the biomass of aquatic plants. PMID:27304876

  13. Biomass burning in Asia : annual and seasonal estimates and atmospheric emissions.

    SciTech Connect

    Streets, D. G.; Yarber, K. F.; Woo, J.-H.; Carmichael, G. R.; Decision and Information Sciences; Univ. of Iowa

    2003-10-15

    Estimates of biomass burning in Asia are developed to facilitate the modeling of Asian and global air quality. A survey of national, regional, and international publications on biomass burning is conducted to yield consensus estimates of 'typical' (i.e., non-year-specific) estimates of open burning (excluding biofuels). We conclude that 730 Tg of biomass are burned in a typical year from both anthropogenic and natural causes. Forest burning comprises 45% of the total, the burning of crop residues in the field comprises 34%, and 20% comes from the burning of grassland and savanna. China contributes 25% of the total, India 18%, Indonesia 13%, and Myanmar 8%. Regionally, forest burning in Southeast Asia dominates. National, annual totals are converted to daily and monthly estimates at 1{sup o} x 1{sup o} spatial resolution using distributions based on AVHRR fire counts for 1999--2000. Several adjustment schemes are applied to correct for the deficiencies of AVHRR data, including the use of moving averages, normalization, TOMS Aerosol Index, and masks for dust, clouds, landcover, and other fire sources. Good agreement between the national estimates of biomass burning and adjusted fire counts is obtained (R{sup 2} = 0.71--0.78). Biomass burning amounts are converted to atmospheric emissions, yielding the following estimates: 0.37 Tg of SO{sub 2}, 2.8 Tg of NO{sub x}, 1100 Tg of CO{sub 2}, 67 Tg of CO, 3.1 Tg of CH{sub 4}, 12 Tg of NMVOC, 0.45 Tg of BC, 3.3 Tg of OC, and 0.92 Tg of NH{sub 3}. Uncertainties in the emission estimates, measured as 95% confidence intervals, range from a low of {+-}65% for CO{sub 2} emissions in Japan to a high of {+-}700% for BC emissions in India.

  14. Biomass burning in Asia: Annual and seasonal estimates and atmospheric emissions

    NASA Astrophysics Data System (ADS)

    Streets, D. G.; Yarber, K. F.; Woo, J.-H.; Carmichael, G. R.

    2003-12-01

    Estimates of biomass burning in Asia are developed to facilitate the modeling of Asian and global air quality. A survey of national, regional, and international publications on biomass burning is conducted to yield consensus estimates of "typical" (i.e., non-year-specific) estimates of open burning (excluding biofuels). We conclude that 730 Tg of biomass are burned in a typical year from both anthropogenic and natural causes. Forest burning comprises 45% of the total, the burning of crop residues in the field comprises 34%, and 20% comes from the burning of grassland and savanna. China contributes 25% of the total, India 18%, Indonesia 13%, and Myanmar 8%. Regionally, forest burning in Southeast Asia dominates. National, annual totals are converted to daily and monthly estimates at 1° × 1° spatial resolution using distributions based on AVHRR fire counts for 1999-2000. Several adjustment schemes are applied to correct for the deficiencies of AVHRR data, including the use of moving averages, normalization, TOMS Aerosol Index, and masks for dust, clouds, landcover, and other fire sources. Good agreement between the national estimates of biomass burning and adjusted fire counts is obtained (R2 = 0.71-0.78). Biomass burning amounts are converted to atmospheric emissions, yielding the following estimates: 0.37 Tg of SO2, 2.8 Tg of NOx, 1100 Tg of CO2, 67 Tg of CO, 3.1 Tg of CH4, 12 Tg of NMVOC, 0.45 Tg of BC, 3.3 Tg of OC, and 0.92 Tg of NH3. Uncertainties in the emission estimates, measured as 95% confidence intervals, range from a low of ±65% for CO2 emissions in Japan to a high of ±700% for BC emissions in India.

  15. Development of visible/infrared/microwave agriculture classification and biomass estimation algorithms. [Guyton, Oklahoma and Dalhart, Texas

    NASA Technical Reports Server (NTRS)

    Rosenthal, W. D.; Mcfarland, M. J.; Theis, S. W.; Jones, C. L. (Principal Investigator)

    1982-01-01

    Agricultural crop classification models using two or more spectral regions (visible through microwave) are considered in an effort to estimate biomass at Guymon, Oklahoma Dalhart, Texas. Both grounds truth and aerial data were used. Results indicate that inclusion of C, L, and P band active microwave data, from look angles greater than 35 deg from nadir, with visible and infrared data improve crop discrimination and biomass estimates compared to results using only visible and infrared data. The microwave frequencies were sensitive to different biomass levels. The K and C band were sensitive to differences at low biomass levels, while P band was sensitive to differences at high biomass levels. Two indices, one using only active microwave data and the other using data from the middle and near infrared bands, were well correlated to total biomass. It is implied that inclusion of active microwave sensors with visible and infrared sensors on future satellites could aid in crop discrimination and biomass estimation.

  16. Equations for estimating biomass of herbaceous and woody vegetation in early-successional southern Appalachian pine-hardwood forests. Forest Service research note

    SciTech Connect

    Elliott, K.J.; Clinton, B.D.

    1993-03-31

    Allometric equations were developed to predict aboveground dry weight of herbaceous and woody species on prescribe-burned sites in the Southern Appalachians. Best-fit least-square regression models were developed using diameter, height, or both, as the independent variables and dry weight as the dependent variable. Coefficients of determination for the selected total biomass models ranged from 0.620 to 0.992 for herbaceous species and from 0.698 to 0.999 for the wood species. Equations for foliage biomass generally had lower coefficients of determination than did equations for either stem or total biomass of woody species.

  17. A SPATIAL ANALYSIS OF THE FINE ROOT BIOMASS FROM STAND DATA IN THE PACIFIC NORTHWEST

    EPA Science Inventory

    High spatial variability of fine roots in natural forest stands makes accurate estimates of stand-level fine root biomass difficult and expensive to obtain by standard coring methods. This study uses aboveground tree metrics and spatial relationships to improve core-based estima...

  18. Intercomparison of Near-Real-Time Biomass Burning Emissions Estimates Constrained by Satellite Fire Data

    EPA Science Inventory

    We compare biomass burning emissions estimates from four different techniques that use satellite based fire products to determine area burned over regional to global domains. Three of the techniques use active fire detections from polar-orbiting MODIS sensors and one uses detec...

  19. Survey estimates of fishable biomass following a mass mortality in an Australian molluscan fishery.

    PubMed

    Mayfield, S; McGarvey, R; Gorfine, H K; Peeters, H; Burch, P; Sharma, S

    2011-04-01

    Mass mortality events are relatively uncommon in commercially fished populations, but when they occur, they reduce production and degrade ecosystems. Observing and documenting mass mortalities is simpler than quantifying the impact on stocks, monitoring or predicting recovery, and re-establishing commercial fishing. Direct survey measures of abundance, distribution and harvestable biomass provide the most tenable approach to informing decisions about future harvests in cases where stock collapses have occurred because conventional methods have been disrupted and are less applicable. Abalone viral ganglioneuritis (AVG) has resulted in high levels of mortality across all length classes of blacklip abalone, Haliotis rubra Leach, off western Victoria, Australia, since May 2006. Commercial catches in this previously valuable fishery were reduced substantially. This paper describes the integration of research surveys with commercial fishermen's knowledge to estimate the biomass of abalone on AVG-impacted reefs. Experienced commercial abalone divers provided credible information on the precise locations of historical fishing grounds within which fishery-independent surveys were undertaken. Abalone density estimates remained low relative to pre-AVG levels, and total biomass estimates were similar to historical annual catch levels, indicating that the abalone populations have yet to adequately recover. Survey biomass estimates were incorporated into harvest decision tables and used with prior accumulated knowledge of the populations to determine a conservative harvest strategy for the fishery. PMID:21382050

  20. Evaluating analytical approaches for estimating pelagic fish biomass using simulated fish communities

    USGS Publications Warehouse

    Yule, Daniel L.; Adams, Jean V.; Warner, David M.; Hrabik, Thomas R.; Kocovsky, Patrick M.; Weidel, Brian C.; Rudstam, Lars G.; Sullivan, Patrick J.

    2013-01-01

    Pelagic fish assessments often combine large amounts of acoustic-based fish density data and limited midwater trawl information to estimate species-specific biomass density. We compared the accuracy of five apportionment methods for estimating pelagic fish biomass density using simulated communities with known fish numbers that mimic Lakes Superior, Michigan, and Ontario, representing a range of fish community complexities. Across all apportionment methods, the error in the estimated biomass generally declined with increasing effort, but methods that accounted for community composition changes with water column depth performed best. Correlations between trawl catch and the true species composition were highest when more fish were caught, highlighting the benefits of targeted trawling in locations of high fish density. Pelagic fish surveys should incorporate geographic and water column depth stratification in the survey design, use apportionment methods that account for species-specific depth differences, target midwater trawling effort in areas of high fish density, and include at least 15 midwater trawls. With relatively basic biological information, simulations of fish communities and sampling programs can optimize effort allocation and reduce error in biomass estimates.

  1. Estimating biomass of submersed vegetation using a simple rake sampling technique

    USGS Publications Warehouse

    Kenow, K.P.; Lyon, J.E.; Hines, R.K.; Elfessi, A.

    2007-01-01

    We evaluated the use of a simple rake sampling technique for predicting the biomass of submersed aquatic vegetation. Vegetation sampled from impounded areas of the Mississippi River using a rake sampling technique, was compared with vegetation harvested from 0.33-m2 quadrats. The resulting data were used to model the relationship between rake indices and vegetation biomass (total and for individual species). We constructed linear regression models using log-transformed biomass data for sites sampled in 1999 and 2000. Data collected in 2001 were used to validate the resulting models. The coefficient of determination (R 2) for predicting total biomass was 0.82 and ranged from 0.59 (Potamogeton pectinatus) to 0.89 (Ceratophyllum demersum) for individual species. Application of the model to estimate total submersed aquatic vegetation is illustrated using data collected independent of this study. The accuracy and precision of the models tested indicate that the rake method data may be used to predict total vegetation biomass and biomass of selected species; however, the method should be tested in other regions, in other plant communities, and on other species. ?? 2006 Springer Science+Business Media B.V.

  2. Terrestrial laser scanning for plant height measurement and biomass estimation of maize

    NASA Astrophysics Data System (ADS)

    Tilly, N.; Hoffmeister, D.; Schiedung, H.; Hütt, C.; Brands, J.; Bareth, G.

    2014-09-01

    Over the last decades, the role of remote sensing gained in importance for monitoring applications in precision agriculture. A key factor for assessing the development of crops during the growing period is the actual biomass. As non-destructive methods of directly measuring biomass do not exist, parameters like plant height are considered as estimators. In this contribution, first results of multitemporal surveys on a maize field with a terrestrial laser scanner are shown. The achieved point clouds are interpolated to generate Crop Surface Models (CSM) that represent the top canopy. These CSMs are used for visualizing the spatial distribution of plant height differences within the field and calculating plant height above ground with a high resolution of 1 cm. In addition, manual measurements of plant height were carried out corresponding to each TLS campaign to verify the results. The high coefficient of determination (R² = 0.93) between both measurement methods shows the applicability of the presented approach. The established regression model between CSM-derived plant height and destructively measured biomass shows a varying performance depending on the considered time frame during the growing period. This study shows that TLS is a suitable and promising method for measuring plant height of maize. Moreover, it shows the potential of plant height as a non-destructive estimator for biomass in the early growing period. However, challenges are the non-linear development of plant height and biomass over the whole growing period.

  3. Estimating single-tree branch biomass of Norway spruce by airborne laser scanning

    NASA Astrophysics Data System (ADS)

    Hauglin, Marius; Dibdiakova, Janka; Gobakken, Terje; Næsset, Erik

    2013-05-01

    The use of forest biomass for bioenergy purposes, directly or through refinement processes, has increased in the last decade. One example of such use is the utilization of logging residues. Branch biomass constitutes typically a considerable part of the logging residues, and should be quantified and included in future forest inventories. Airborne laser scanning (ALS) is widely used when collecting data for forest inventories, and even methods to derive information at the single-tree level has been described. Procedures for estimation of single-tree branch biomass of Norway spruce using features derived from ALS data are proposed in the present study. As field reference data the dry weight branch biomass of 50 trees were obtained through destructive sampling. Variables were further derived from the ALS echoes from each tree, including crown volume calculated from an interpolated crown surface constructed with a radial basis function. Spatial information derived from the pulse vectors were also incorporated when calculating the crown volume. Regression models with branch biomass as response variable were fit to the data, and the prediction accuracy assessed through a cross-validation procedure. Random forest regression models were compared to stepwise and simple linear least squares models. In the present study branch biomass was estimated with a higher accuracy by the best ALS-based models than by existing allometric biomass equations based on field measurements. An improved prediction accuracy was observed when incorporating information from the laser pulse vectors into the calculation of the crown volume variable, and a linear model with the crown volume as a single predictor gave the best overall results with a root mean square error of 35% in the validation.

  4. Using endmembers in AVIRIS images to estimate changes in vegetative biomass

    NASA Technical Reports Server (NTRS)

    Smith, Milton O.; Adams, John B.; Ustin, Susan L.; Roberts, Dar A.

    1992-01-01

    Field techniques for estimating vegetative biomass are labor intensive, and rarely are used to monitor changes in biomass over time. Remote-sensing offers an attractive alternative to field measurements; however, because there is no simple correspondence between encoded radiance in multispectral images and biomass, it is not possible to measure vegetative biomass directly from AVIRIS images. Ways to estimate vegetative biomass by identifying community types and then applying biomass scalars derived from field measurements are investigated. Field measurements of community-scale vegetative biomass can be made, at least for local areas, but it is not always possible to identify vegetation communities unambiguously using remote measurements and conventional image-processing techniques. Furthermore, even when communities are well characterized in a single image, it typically is difficult to assess the extent and nature of changes in a time series of images, owing to uncertainties introduced by variations in illumination geometry, atmospheric attenuation, and instrumental responses. Our objective is to develop an improved method based on spectral mixture analysis to characterize and identify vegetative communities, that can be applied to multi-temporal AVIRIS and other types of images. In previous studies, multi-temporal data sets (AVIRIS and TM) of Owens Valley, CA were analyzed and vegetation communities were defined in terms of fractions of reference (laboratory and field) endmember spectra. An advantage of converting an image to fractions of reference endmembers is that, although fractions in a given pixel may vary from image to image in a time series, the endmembers themselves typically are constant, thus providing a consistent frame of reference.

  5. A framework for creating and validating a non-linear spectrum-biomass model to estimate the secondary succession biomass in moist tropical forests

    NASA Astrophysics Data System (ADS)

    Li, Hui; Mausel, Paul; Brondizio, Eduardo; Deardorff, David

    Uncertainties remain in the use of remote sensing technologies to provide validated model-derived estimates of the biomass of the secondary succession (SS) forests in the Amazon Basin. The objectives of this study were to develop a modeling framework for creating a valid spectrum-biomass model to estimate the SS biomass, to assess the utility of the framework and the accuracy and validity of the model, and to identify the model's determinants. Data sources for this study include 1992-1993 vegetation inventory data and 1991 Landsat Thematic Mapper (TM) data on the Altamira region of Para, Brazil, and 1994-1995 vegetation inventory data and 1994 Landsat TM data on the nearby Bragantina region. The allometric approach was used to estimate the biomass of the sampled sites based on the vegetation inventory data. A framework for the spectrum-biomass regression model was developed based on the estimated biomass of the sampled sites and the Landsat data. The framework includes (1) the pooling of data from Bragantina and the use of ANCOVA to justify this approach; (2) image calibration; (3) biomass data age-adjustment, (4) selection of independent variables, (5) regression model development, and (6) model assessment and validation. The cubic regression model with TM Band5-related predictors was found to best fit the data as evidenced by an adjusted R-squared value of 0.865, mean square error (MSE) of the model, and the analysis of residuals. Residual analysis showed that the model might yield a better estimation on a lower biomass values than on higher biomass values. In addition, further analyses identified several determinants that can impact the accuracy of the spectrum-biomass model. ANCOVA confirmed that the relationship between the biomass and the spectrum is independent of the Altamira and Bragantina regions, and that it was appropriate to pool sampled data from both regions in the proposed model. The model development methodology and the model produced from this

  6. First-order estimate of the planktic foraminifer biomass in the modern ocean

    NASA Astrophysics Data System (ADS)

    Schiebel, R.; Movellan, A.

    2012-09-01

    Planktic foraminifera are heterotrophic mesozooplankton of global marine abundance. The position of planktic foraminifers in the marine food web is different compared to other protozoans and ranges above the base of heterotrophic consumers. Being secondary producers with an omnivorous diet, which ranges from algae to small metazoans, planktic foraminifers are not limited to a single food source, and are assumed to occur at a balanced abundance displaying the overall marine biological productivity at a regional scale. With a new non-destructive protocol developed from the bicinchoninic acid (BCA) method and nano-photospectrometry, we have analysed the protein-biomass, along with test size and weight, of 754 individual planktic foraminifers from 21 different species and morphotypes. From additional CHN analysis, it can be assumed that protein-biomass equals carbon-biomass. Accordingly, the average individual planktic foraminifer protein- and carbon-biomass amounts to 0.845 μg. Samples include symbiont bearing and symbiont-barren species from the sea surface down to 2500 m water depth. Conversion factors between individual biomass and assemblage-biomass are calculated for test sizes between 72 and 845 μm (minimum test diameter). Assemblage-biomass data presented here include 1128 sites and water depth intervals. The regional coverage of data includes the North Atlantic, Arabian Sea, Red Sea, and Caribbean as well as literature data from the eastern and western North Pacific, and covers a wide range of oligotrophic to eutrophic waters over six orders of magnitude of planktic-foraminifer assemblage-biomass (PFAB). A first order estimate of the average global planktic foraminifer biomass production (>125 μm) ranges from 8.2-32.7 Tg C yr-1 (i.e. 0.008-0.033 Gt C yr-1), and might be more than three times as high including neanic and juvenile individuals adding up to 25-100 Tg C yr-1. However, this is a first estimate of regional PFAB extrapolated to the global scale

  7. First-order estimate of the planktic foraminifer biomass in the modern global oceans

    NASA Astrophysics Data System (ADS)

    Schiebel, R.; Movellan, A.

    2012-04-01

    Planktic foraminifera are heterotrophic mesozooplankton of global marine abundance. The position of planktic foraminifers in the marine food web is different compared to other protozoans and ranges above the base of heterotrophic consumers. Being secondary producers with an omnivorous diet, which ranges from algae to small metazoans, planktic foraminifers are not limited to a single food source, and are assumed to occur at a balanced abundance displaying the overall marine biological productivity at a regional scale. We have calculated the assemblage carbon biomass from data on standing stocks between the sea surface and 2500 m water depth, based on 754 protein-biomass data of 21 planktic foraminifer species and morphotypes, produced with a newly developed method to analyze the protein biomass of single planktic foraminifer specimens. Samples include symbiont bearing and symbiont barren species, characteristic of surface and deep-water habitats. Conversion factors between individual protein-biomass and assemblage-biomass are calculated for test sizes between 72 and 845 μm (minimum diameter). The calculated assemblage biomass data presented here include 1057 sites and water depth intervals. Although the regional coverage of database is limited to the North Atlantic, Arabian Sea, Red Sea, and Caribbean, our data include a wide range of oligotrophic to eutrophic waters covering six orders of magnitude of assemblage biomass. A first order estimate of the global planktic foraminifer biomass from average standing stocks (>125 μm) ranges at 8.5-32.7 Tg C yr-1 (i.e. 0.008-0.033 Gt C yr-1), and might be more than three time as high including the entire fauna including neanic and juvenile individuals adding up to 25-100 Tg C yr-1. However, this is a first estimate of regional planktic-foraminifer assemblage-biomass (PFAB) extrapolated to the global scale, and future estimates based on larger data-sets might considerably deviate from the one presented here. This paper is

  8. A BIOMASS-BASED MODEL TO ESTIMATE THE PLAUSIBILITY OF EXOPLANET BIOSIGNATURE GASES

    SciTech Connect

    Seager, S.; Bains, W.; Hu, R.

    2013-10-01

    Biosignature gas detection is one of the ultimate future goals for exoplanet atmosphere studies. We have created a framework for linking biosignature gas detectability to biomass estimates, including atmospheric photochemistry and biological thermodynamics. The new framework is intended to liberate predictive atmosphere models from requiring fixed, Earth-like biosignature gas source fluxes. New biosignature gases can be considered with a check that the biomass estimate is physically plausible. We have validated the models on terrestrial production of NO, H{sub 2}S, CH{sub 4}, CH{sub 3}Cl, and DMS. We have applied the models to propose NH{sub 3} as a biosignature gas on a 'cold Haber World', a planet with a N{sub 2}-H{sub 2} atmosphere, and to demonstrate why gases such as CH{sub 3}Cl must have too large of a biomass to be a plausible biosignature gas on planets with Earth or early-Earth-like atmospheres orbiting a Sun-like star. To construct the biomass models, we developed a functional classification of biosignature gases, and found that gases (such as CH{sub 4}, H{sub 2}S, and N{sub 2}O) produced from life that extracts energy from chemical potential energy gradients will always have false positives because geochemistry has the same gases to work with as life does, and gases (such as DMS and CH{sub 3}Cl) produced for secondary metabolic reasons are far less likely to have false positives but because of their highly specialized origin are more likely to be produced in small quantities. The biomass model estimates are valid to one or two orders of magnitude; the goal is an independent approach to testing whether a biosignature gas is plausible rather than a precise quantification of atmospheric biosignature gases and their corresponding biomasses.

  9. Rainforest burning and the global carbon budget: Biomass, combustion efficiency, and charcoal formation in the Brazilian Amazon

    SciTech Connect

    Fearnside, P.M.; Leal, N. Jr.; Fernandes, F.M.

    1993-09-20

    Biomass present before and after burning was measured in forest cleared for pasture in a cattle ranch (Fazenda Dimona) near Manaus, Amazonas, Brazil. Aboveground dry weight biomass loading averaged 265 t ha{sup {minus}1} (standard deviation (SD) = 110, n = 6 quadrats) at Fazenda Dimonas. Postburn aboveground biomass loading was evaluated by cutting and weighing of 100 m{sup 2} quadrats and by line intersect sampling. Quadrats had a mean dry weight of 187 t ha{sup {minus}1} (SD = 69, n = 10), a 29.3% reduction from the preburn mean in the same clearing. Line intersect estimates in 1.65 km of transects indicated that 265 m{sup 3} ha{sup {minus}1} (approximately 164 t ha{sup {minus}1} of aboveground dry matter) survived burning. Using carbon contents measured for different biomass components (all {approximately} 50% carbon) and assuming a carbon content of 74.8% for charcoal (from other studies near Manaus), the destructive measurements imply a 27.6% reduction of aboveground carbon pools. Charcoal composed 2.5% of the dry weight of the remains in the postburn destructive quadrats and 2.8% of the volume in the line intersect transects. Thus approximately 2.7% of the preburn aboveground carbon stock was converted to charcoal, substantially less than is generally assumed in global carbon models. The findings confirm high values for biomass in central Amazonia. High variability indicates the need for further studies in many localities and for making maximum use of less laborious indirect methods of biomass estimation. While indirect methods are essential for regional estimates of average biomass, only direct weighing such as that reported here can yield information on combustion efficiency and charcoal formation. Both high biomass and low percentage of charcoal formation suggest the significant potential contribution of forest burning to global climate changes from CO{sub 2} and trace gases. 66 refs., 6 figs., 2 tabs.

  10. Use of Droplet Digital PCR for Estimation of Fish Abundance and Biomass in Environmental DNA Surveys

    PubMed Central

    Doi, Hideyuki; Uchii, Kimiko; Takahara, Teruhiko; Matsuhashi, Saeko; Yamanaka, Hiroki; Minamoto, Toshifumi

    2015-01-01

    An environmental DNA (eDNA) analysis method has been recently developed to estimate the distribution of aquatic animals by quantifying the number of target DNA copies with quantitative real-time PCR (qPCR). A new quantitative PCR technology, droplet digital PCR (ddPCR), partitions PCR reactions into thousands of droplets and detects the amplification in each droplet, thereby allowing direct quantification of target DNA. We evaluated the quantification accuracy of qPCR and ddPCR to estimate species abundance and biomass by using eDNA in mesocosm experiments involving different numbers of common carp. We found that ddPCR quantified the concentration of carp eDNA along with carp abundance and biomass more accurately than qPCR, especially at low eDNA concentrations. In addition, errors in the analysis were smaller in ddPCR than in qPCR. Thus, ddPCR is better suited to measure eDNA concentration in water, and it provides more accurate results for the abundance and biomass of the target species than qPCR. We also found that the relationship between carp abundance and eDNA concentration was stronger than that between biomass and eDNA by using both ddPCR and qPCR; this suggests that abundance can be better estimated by the analysis of eDNA for species with fewer variations in body mass. PMID:25799582

  11. Soil water content determination with cosmic-ray neutron sensor: Correcting aboveground hydrogen effects with thermal/fast neutron ratio

    NASA Astrophysics Data System (ADS)

    Tian, Zhengchao; Li, Zizhong; Liu, Gang; Li, Baoguo; Ren, Tusheng

    2016-09-01

    The cosmic-ray neutron sensor (CRNS), which estimates field scale soil water content, bridges the gap between point measurement and remote sensing. The accuracy of CRNS measurements, however, is affected by additional hydrogen pools (e.g., vegetation, snow, and rainfall interception). The objectives of this study are to: (i) evaluate the accuracy of CRNS estimates in a farmland system using depth and horizontal weighted point measurements, (ii) introduce a novel method for estimating the amounts of hydrogen from biomass and snow cover in CRNS data, and (iii) propose a simple approach for correcting the influences of aboveground hydrogen pool (expressed as aboveground water equivalent, AWE) on CRNS measurements. A field experiment was conducted in northeast China to compare soil water content results from CRNS to in-situ data with time domain reflectometry (TDR) and neutron probe (NP) in the 0-40 cm soil layers. The biomass water equivalent (BWE) and snow water equivalent (SWE) were observed to have separate linear relationships with the thermal/fast neutron ratio, and the dynamics of BWE and SWE were estimated correctly in the crop seasons and snow-covered seasons, respectively. A simple approach, which considered the AWE, AWE at calibration, and the effective measurement depth of CRNS, was introduced to correct the errors caused by BWE and SWE. After correction, the correlation coefficients between soil water contents determined by CRNS and TDR were 0.79 and 0.77 during the 2014 and 2015 crop seasons, respectively, and CRNS measurements had RMSEs of 0.028, 0.030, and 0.039 m3 m-3 in the 2014 and 2015 crop seasons and the snow-covered seasons, respectively. The experimental results also indicated that the accuracies of CRNS estimated BWE and SWE were affected by the distributions of aboveground hydrogen pools, which were related to the height of the CRNS device above ground surface.

  12. Estimating spatial variation in Alberta forest biomass from a combination of forest inventory and remote sensing data

    NASA Astrophysics Data System (ADS)

    Zhang, J.; Huang, S.; Hogg, E. H.; Lieffers, V.; Qin, Y.; He, F.

    2013-12-01

    Uncertainties in the estimation of tree biomass carbon storage across large areas pose challenges for the study of forest carbon cycling at regional and global scales. In this study, we attempted to estimate the present biomass carbon storage in Alberta, Canada, by taking advantage of a spatially explicit dataset derived from a combination of forest inventory data from 1968 plots and spaceborne light detection and ranging (LiDAR) canopy height data. Ten climatic variables together with elevation, were used for model development and assessment. Four approaches, including spatial interpolation, non-spatial and spatial regression models, and decision-tree based modelling with random forests algorithm (a machine-learning technique), were compared to find the "best" estimates. We found that the random forests approach provided the best accuracy for biomass estimates. Non-spatial and spatial regression models gave estimates similar to random forests, while spatial interpolation greatly overestimated the biomass storage. Using random forests, the total biomass stock in Alberta forests was estimated to be 3.11 × 109 Mg, with the average biomass density of 77.59 Mg ha-1. At the species level, three major tree species, lodgepole pine, trembling aspen and white spruce, stocked about 1.91 × 109 Mg biomass, accounting for 61% of total estimated biomass. Spatial distribution of biomass varied with natural regions, land cover types, and species. And the relative importance of predictor variables on determining biomass distribution varied with species. This study showed that the combination of ground-based inventory data, spaceborne LiDAR data, land cover classification, climatic and environmental variables was an efficient way to estimate the quantity, distribution and variation of forest biomass carbon stocks across large regions.

  13. Biomass estimation with high resolution satellite images: A case study of Quercus rotundifolia

    NASA Astrophysics Data System (ADS)

    Sousa, Adélia M. O.; Gonçalves, Ana Cristina; Mesquita, Paulo; Marques da Silva, José R.

    2015-03-01

    Forest biomass has had a growing importance in the world economy as a global strategic reserve, due to applications in bioenergy, bioproduct development and issues related to reducing greenhouse gas emissions. Current techniques used for forest inventory are usually time consuming and expensive. Thus, there is an urgent need to develop reliable, low cost methods that can be used for forest biomass estimation and monitoring. This study uses new techniques to process high spatial resolution satellite images (0.70 m) in order to assess and monitor forest biomass. Multi-resolution segmentation method and object oriented classification are used to obtain the area of tree canopy horizontal projection for Quercus rotundifolia. Forest inventory allows for calculation of tree and canopy horizontal projection and biomass, the latter with allometric functions. The two data sets are used to develop linear functions to assess above ground biomass, with crown horizontal projection as an independent variable. The functions for the cumulative values, both for inventory and satellite data, for a prediction error equal or smaller than the Portuguese national forest inventory (7%), correspond to stand areas of 0.5 ha, which include most of the Q.rotundifolia stands.

  14. Estimation of Biomass and Carbon Stocks in Rubber Plantation Using Thaichote Satellite Imagery

    NASA Astrophysics Data System (ADS)

    Charoenjit, Kitsanai; Zuddas, Pierpaolo; Allemand, Pascal

    2014-05-01

    This goal of study is to improve model for estimate biomass and carbon stocks of rubber plantation (clone RRIM 600) in sub-basin of mae num prasae, East Thailand with total area is 232 Km2. We mapped 2011 of the biomass and carbon stocks with the used of integrated Thaichote satellite imagery and field data. In order to tree girth prediction and tree density population, we applied the objected based image analysis (OBIA) which include image mining and modeling by linear multiple regression, then estimate biomass and carbon stocks in rubber plantation. The image mining includes spectral, vegetation, textural and mask information for modeling construction. We found an parameters of the Global Environmental Monitoring Index (GEMI) and texture of homogeneity, dissimilarity, contrast and variance were accepted relationship of tree girt prediction with R2 0.865. The total amount of biomass and carbon stocks in study area is 2,227 Kt and 991.5 KtC respectively. For summary of study area, the annual sequestered in 2011 is 121.3 tCO2 from the atmosphere and the rubber plantation at mature age stage (25 years) had highest capacity of sequestered at 33.53 tCO2 ha-1 yr-1.

  15. Estimating Biomass Burning Emissions for Carbon Cycle Science and Resource Monitoring & Management

    NASA Astrophysics Data System (ADS)

    French, N. H.; McKenzie, D.; Erickson, T. A.; McCarty, J. L.; Ottmar, R. D.; Kasischke, E. S.; Prichard, S. J.; Hoy, E.; Endsley, K.; Hamermesh, N. K.

    2012-12-01

    Biomass burning emissions, including emissions from wildland fire, agricultural and rangeland burning, and peatland fires, impact the atmosphere dramatically. Current tools to quantify emission sources are developing quickly in a response to the need by the modeling community to assess fire's role in the carbon cycle and the land management community to understand fire effects and impacts on air quality. In a project funded by NASA, our team has developed methods to spatially quantify wildland fire emissions for the contiguous United States (CONUS) and Alaska (AK) at regional scales. We have also developed a prototype web-based information system, the Wildland Fire Emissions Information System (WFEIS) to make emissions modeling tools and estimates for the CONUS and AK available to the user community. With new funding through two NASA programs, our team from MTRI, USFS, and UMd will be further developing WFEIS to provide biomass burning emissions estimates for the carbon cycle science community and for land and air quality managers, to improve the way emissions estimates are calculated for a variety of disciplines. In this poster, we review WFEIS as it currently operates and the plans to extend the current system for use by the carbon cycle science community (through the NASA Carbon Monitoring System Program) and resource management community (through the NASA Applications Program). Features to be enhanced include an improved accounting of biomass present in canopy fuels that are available for burning in a forest fire, addition of annually changing vegetation biomass/fuels used in computing fire emissions, and quantification of the errors present in the estimation methods in order to provide uncertainty of emissions estimates across CONUS and AK. Additionally, WFEIS emissions estimates will be compared with results obtained with the Global Fire Emissions Database (GFED), which operates at a global scale at a coarse spatial resolution, to help improve GFED estimates

  16. Relative contributions of sampling effort, measuring, and weighing to precision of larval sea lamprey biomass estimates

    USGS Publications Warehouse

    Slade, J.W.; Adams, J.V.; Cuddy, D.W.; Neave, F.B.; Sullivan, W.P.; Young, R.J.; Fodale, M.F.; Jones, M.L.

    2003-01-01

    We developed two weight-length models from 231 populations of larval sea lampreys (Petromyzon marinus) collected from tributaries of the Great Lakes: Lake Ontario (21), Lake Erie (6), Lake Huron (67), Lake Michigan (76), and Lake Superior (61). Both models were mixed models, which used population as a random effect and additional environmental factors as fixed effects. We resampled weights and lengths 1,000 times from data collected In each of 14 other populations not used to develop the models, obtaining a weight and length distribution from reach resampling. To test model performance, we applied the two weight-length models to the resampled length distributions and calculated the predicted mean weights. We also calculated the observed mean weight for each resampling and for each of the original 14 data sets. When the average of predicted means was compared to means from the original data in each stream, inclusion of environmental factors did not consistently improve the performance of the weight-length model. We estimated the variance associated with measures of abundance and mean weight for each of the 14 selected populations and determined that a conservative estimate of the proportional contribution to variance associated with estimating abundance accounted for 32% to 95% of the variance (mean = 66%). Variability in the biomass estimate appears more affected by variability in estimating abundance than in converting length to weight. Hence, efforts to improve the precision of biomass estimates would be aided most by reducing the variability associated with estimating abundance.

  17. Relative contributions of sampling effort, measuring, and weighing to precision of larval sea lamprey biomass estimates

    USGS Publications Warehouse

    Slade, Jeffrey W.; Adams, Jean V.; Cuddy, Douglas W.; Neave, Fraser B.; Sullivan, W. Paul; Young, Robert J.; Fodale, Michael F.; Jones, Michael L.

    2003-01-01

    We developed two weight-length models from 231 populations of larval sea lampreys (Petromyzon marinus) collected from tributaries of the Great Lakes: Lake Ontario (21), Lake Erie (6), Lake Huron (67), Lake Michigan (76), and Lake Superior (61). Both models were mixed models, which used population as a random effect and additional environmental factors as fixed effects. We resampled weights and lengths 1,000 times from data collected in each of 14 other populations not used to develop the models, obtaining a weight and length distribution from reach resampling. To test model performance, we applied the two weight-length models to the resampled length distributions and calculated the predicted mean weights. We also calculated the observed mean weight for each resampling and for each of the original 14 data sets. When the average of predicted means was compared to means from the original data in each stream, inclusion of environmental factors did not consistently improve the performance of the weight-length model. We estimated the variance associated with measures of abundance and mean weight for each of the 14 selected populations and determined that a conservative estimate of the proportional contribution to variance associated with estimating abundance accounted for 32% to 95% of the variance (mean = 66%). Variability in the biomass estimate appears more affected by variability in estimating abundance than in converting length to weight. Hence, efforts to improve the precision of biomass estimates would be aided most by reducing the variability associated with estimating abundance.

  18. A regional estimate of convective transport of CO from biomass burning

    NASA Technical Reports Server (NTRS)

    Pickering, Kenneth E.; Scala, John R.; Thompson, Anne M.; Tao, Wei-Kuo; Simpson, Joanne

    1992-01-01

    A regional-scale estimate of the fraction of biomass burning emissions that are transported to the free troposphere by deep convection is presented. The focus is on CO and the study region is a part of Brazil that underwent intensive deforestation in the 1980s. The method of calculation is stepwise, scaling up from a prototype convective event, the dynamics of which are well-characterized, to the vertical mass flux of carbon monoxide over the region. Given uncertainties in CO emissions from biomass burning and the representativeness of the prototype event, it is estimated that 10-40 percent of CO emissions from the burning region may be rapidly transported to the free troposphere over the burning region. These relatively fresh emissions will produce O3 efficiently in the free troposphere where O3 has a longer lifetime than in the boundary layer.

  19. Bias in acoustic biomass estimates of Euphausia superba due to diel vertical migration

    NASA Astrophysics Data System (ADS)

    Demer, David A.; Hewitt, Roger P.

    1995-04-01

    The diel vertical migration (DVM) of Antarctic krill ( Euphausia superba) can greatly bias the results of qualitative and quantitative hydroacoustic surveys which are conducted with a down-looking sonar and irrespective of the time of day. To demonstrate and quantify these negative biases on both the estimates of biomass distribution and abundance, a time-depth-density analysis was performed. Data were collected, as part of the United States Antarctic Marine Living Resources Program (AMLR), in the vicinities of Elephant Island, Antarctica, during the austral summers of 1992 and 1993. Five surveys were conducted in 1992; two covered a 105 by 105 n.mi. area centered on Elephant Island, two encompassed a 60 by 35 n.mi. area immediately to the north of the Island, and one covered a 1 n.mi. 2 area centered on a large krill swarm to the west of Seal Island. The 1993 data include repetitions of the two small-area and two large-area surveys. Average krill volume densities were calculated for each hour as well as for three daily periods: day, twilight and night. These data were normalized and presented as a probability of daily average density. With spectral analysis to identify the frequencies of migration, a four-term periodic function was fitted to the probability density function of average daily biomass versus local apparent time. This function was transformed to create a temporal compensation function (TCF) for upwardly adjusting acoustic biomass estimates. The TCF was then applied to the original 1992 survey data; the resulting biomass estimates are an average of 49.5% higher than those calculated disregarding biases due to diel vertical migration. The effect of DVM on the estimates of krill distribution are illustrated by a comparison of compensated and uncompensated density maps of two 1992 surveys. Through this technique, high density kril areas are revealed where uncompensated maps indicated low densities.

  20. Forest biomass variation in Southernmost Brazil: the impact of Araucaria trees.

    PubMed

    Rosenfield, Milena Fermina; Souza, Alexandre F

    2014-03-01

    A variety of environmental and biotic factors determine vegetation growth and affect plant biomass accumulation. From temperature to species composition, aboveground biomass storage in forest ecosystems is influenced by a number of variables and usually presents a high spatial variability. With this focus, the aim of the study was to evaluate the variables affecting live aboveground forest biomass (AGB) in Subtropical Moist Forests of Southern Brazil, and to analyze the spatial distribution of biomass estimates. Data from a forest inventory performed in the State of Rio Grande do Sul, Southern Brazil, was used in the present study. Thirty-eight 1-ha plots were sampled and all trees with DBH > or = 9.5cm were included for biomass estimation. Values for aboveground biomass were obtained using published allometric equations. Environmental and biotic variables (elevation, rainfall, temperature, soils, stem density and species diversity) were obtained from the literature or calculated from the dataset. For the total dataset, mean AGB was 195.2 Mg/ha. Estimates differed between Broadleaf and Mixed Coniferous-Broadleaf forests: mean AGB was lower in Broadleaf Forests (AGB(BF)=118.9 Mg/ha) when compared to Mixed Forests (AGB(MF)=250.3 Mg/ha). There was a high spatial and local variability in our dataset, even within forest types. This condition is normal in tropical forests and is usually attributed to the presence of large trees. The explanatory multiple regressions were influenced mainly by elevation and explained 50.7% of the variation in AGB. Stem density, diversity and organic matter also influenced biomass variation. The results from our study showed a positive relationship between aboveground biomass and elevation. Therefore, higher values of AGB are located at higher elevations and subjected to cooler temperatures and wetter climate. There seems to be an important contribution of the coniferous species Araucaria angustifolia in Mixed Forest plots, as it presented

  1. Enumeration and Biomass Estimation of Bacteria in Aquifer Microcosm Studies by Flow Cytometry

    PubMed Central

    DeLeo, P. C.; Baveye, P.

    1996-01-01

    Flow cytometry was used to enumerate and characterize bacteria from a sand column microcosm simulating aquifer conditions. Pure cultures of a species of Bacillus isolated from subsurface sediments or Bacillus megaterium were first evaluated to identify these organisms' characteristic histograms. Counting was then carried out with samples from the aquifer microcosms. Enumeration by flow cytometry was compared with more-traditional acridine orange direct counting. These two techniques gave statistically similar results. However, counting by flow cytometry, in this case, surveyed a sample size 700 times greater than did acridine orange direct counting (25 (mu)l versus 0.034 (mu)l) and required 1/10 the time (2 h versus 20 h). Flow cytometry was able to distinguish the same species of bacteria grown under different nutrient conditions, and it could distinguish changes in cell growth patterns, specifically single cell growth versus chained cell growth in different regions of an aquifer microcosm. A biomass estimate was calculated by calibrating the total fluorescence of a sample from a pure culture with the dry weight of a freeze-dried volume from the original pure culture. Growth conditions significantly affected histograms and biomass estimates, so the calibration was carried out with cells grown under conditions similar to those in the aquifer microcosm. Costs associated with using flow cytometry were minimal compared with the amount of time saved in counting cells and estimating biomass. PMID:16535470

  2. Matching the Best Viewing Angle in Depth Cameras for Biomass Estimation Based on Poplar Seedling Geometry

    PubMed Central

    Andújar, Dionisio; Fernández-Quintanilla, César; Dorado, José

    2015-01-01

    In energy crops for biomass production a proper plant structure is important to optimize wood yields. A precise crop characterization in early stages may contribute to the choice of proper cropping techniques. This study assesses the potential of the Microsoft Kinect for Windows v.1 sensor to determine the best viewing angle of the sensor to estimate the plant biomass based on poplar seedling geometry. Kinect Fusion algorithms were used to generate a 3D point cloud from the depth video stream. The sensor was mounted in different positions facing the tree in order to obtain depth (RGB-D) images from different angles. Individuals of two different ages, e.g., one month and one year old, were scanned. Four different viewing angles were compared: top view (0°), 45° downwards view, front view (90°) and ground upwards view (−45°). The ground-truth used to validate the sensor readings consisted of a destructive sampling in which the height, leaf area and biomass (dry weight basis) were measured in each individual plant. The depth image models agreed well with 45°, 90° and −45° measurements in one-year poplar trees. Good correlations (0.88 to 0.92) between dry biomass and the area measured with the Kinect were found. In addition, plant height was accurately estimated with a few centimeters error. The comparison between different viewing angles revealed that top views showed poorer results due to the fact the top leaves occluded the rest of the tree. However, the other views led to good results. Conversely, small poplars showed better correlations with actual parameters from the top view (0°). Therefore, although the Microsoft Kinect for Windows v.1 sensor provides good opportunities for biomass estimation, the viewing angle must be chosen taking into account the developmental stage of the crop and the desired parameters. The results of this study indicate that Kinect is a promising tool for a rapid canopy characterization, i.e., for estimating crop biomass

  3. Matching the best viewing angle in depth cameras for biomass estimation based on poplar seedling geometry.

    PubMed

    Andújar, Dionisio; Fernández-Quintanilla, César; Dorado, José

    2015-01-01

    In energy crops for biomass production a proper plant structure is important to optimize wood yields. A precise crop characterization in early stages may contribute to the choice of proper cropping techniques. This study assesses the potential of the Microsoft Kinect for Windows v.1 sensor to determine the best viewing angle of the sensor to estimate the plant biomass based on poplar seedling geometry. Kinect Fusion algorithms were used to generate a 3D point cloud from the depth video stream. The sensor was mounted in different positions facing the tree in order to obtain depth (RGB-D) images from different angles. Individuals of two different ages, e.g., one month and one year old, were scanned. Four different viewing angles were compared: top view (0°), 45° downwards view, front view (90°) and ground upwards view (-45°). The ground-truth used to validate the sensor readings consisted of a destructive sampling in which the height, leaf area and biomass (dry weight basis) were measured in each individual plant. The depth image models agreed well with 45°, 90° and -45° measurements in one-year poplar trees. Good correlations (0.88 to 0.92) between dry biomass and the area measured with the Kinect were found. In addition, plant height was accurately estimated with a few centimeters error. The comparison between different viewing angles revealed that top views showed poorer results due to the fact the top leaves occluded the rest of the tree. However, the other views led to good results. Conversely, small poplars showed better correlations with actual parameters from the top view (0°). Therefore, although the Microsoft Kinect for Windows v.1 sensor provides good opportunities for biomass estimation, the viewing angle must be chosen taking into account the developmental stage of the crop and the desired parameters. The results of this study indicate that Kinect is a promising tool for a rapid canopy characterization, i.e., for estimating crop biomass

  4. Biomass and estimated production properties of size-fractionated zooplankton in the Yellow Sea, China

    NASA Astrophysics Data System (ADS)

    Huo, Yuanzi; Sun, Song; Zhang, Fang; Wang, Minxiao; Li, Chaolun; Yang, Bo

    2012-06-01

    The size-fractionated zooplankton biomass, taxonomic composition, and production calculated by formulas basing on Ikeda-Motoda's physiological methods were studied on the basis of samples taken from six cruises in the Yellow Sea. Zooplankton was size-fractionated using sieves into ~ 2 mm, 1-2 mm, 0.5-1 mm, 0.25-0.5 mm and 0.16-0.25 mm groups. The results showed that the average zooplankton biomass was 84.03 mg DM m- 3 in May, followed in order by September, June, March, August and December with 42.34, 38.36, 32.37, 27.17 and 21.83 mg DM m- 3, respectively. The contribution of ~ 2 mm, 1-2 mm, 0.5-1 mm, 0.25-0.5 mm and 0.16-0.25 mm groups to the total biomass was in the range of 15.2-27.4%, 13.2-29.4%, 14.7-18.2%, 15.8-22.6% and 16.3-34.2%, respectively, during the investigating period. The biomass of all size groups was all highest in May, and except that the biomass of 0.16-0.25 mm group was lowest in August, the biomass of other size groups was lowest in December. The dominant zooplankton species (or taxa) in each group were similar between six cruises. The estimated zooplankton production was highest in May with 1.97 mg C m- 3 d- 1, and was lowest in December with 0.51 mg C m- 3 d- 1, and was in the range of 0.67-1.44 mg C m- 3 d- 1 in other investigating months. The estimated annual zooplankton production was 0.37 g C m- 3 y- 1. The two smallest groups aggregately comprised 59-84% of the net-zooplankton production in the Yellow Sea. The geographical distribution of size-fractionated zooplankton biomass and production was significantly affected by the complex physical features of the Yellow Sea. When the Yellow Sea Cold Bottom Water appeared from June to September, the biomass and production of zooplankton larger than 1 mm were higher inside the cold water mass area than outside it, while the zooplankton smaller than 1 mm showed contrary results. The higher biomass and production of all zooplankton groups occurred in the southern part of the study area in

  5. Have ozone effects on carbon sequestration been overestimated? A new biomass response function for wheat

    NASA Astrophysics Data System (ADS)

    Pleijel, H.; Danielsson, H.; Simpson, D.; Mills, G.

    2014-08-01

    Elevated levels of tropospheric ozone can significantly impair the growth of crops. The reduced removal of CO2 by plants leads to higher atmospheric concentrations of CO2, enhancing radiative forcing. Ozone effects on economic yield, e.g. the grain yield of wheat (Triticum aestivum L.), are currently used to model effects on radiative forcing. However, changes in grain yield do not necessarily reflect changes in total biomass. Based on an analysis of 22 ozone exposure experiments with field-grown wheat, we investigated whether the use of effects on grain yield as a proxy for effects on biomass under- or overestimates effects on biomass. First, we confirmed that effects on partitioning and biomass loss are both of significant importance for wheat yield loss. Then we derived ozone dose response functions for biomass loss and for harvest index (the proportion of above-ground biomass converted to grain) based on 12 experiments and recently developed ozone uptake modelling for wheat. Finally, we used a European-scale chemical transport model (EMEP MSC-West) to assess the effect of ozone on biomass (-9%) and grain yield (-14%) loss over Europe. Based on yield data per grid square, we estimated above-ground biomass losses due to ozone in 2000 in Europe, totalling 22.2 million tonnes. Incorrectly applying the grain yield response function to model effects on biomass instead of the biomass response function of this paper would have indicated total above-ground biomass losses totalling 38.1 million (i.e. overestimating effects by 15.9 million tonnes). A key conclusion from our study is that future assessments of ozone-induced loss of agroecosystem carbon storage should use response functions for biomass, such as that provided in this paper, not grain yield, to avoid overestimation of the indirect radiative forcing from ozone effects on crop biomass accumulation.

  6. Hyperspectral imaging based biomass and nitrogen content estimations from light-weight UAV

    NASA Astrophysics Data System (ADS)

    Pölönen, I.; Saari, H.; Kaivosoja, J.; Honkavaara, E.; Pesonen, L.

    2013-10-01

    Hyperspectral imaging based precise fertilization is challenge in the northern Europe, because of the cloud conditions. In this paper we will introduce schemes for the biomass and nitrogen content estimations from hyperspectral images. In this research we used the Fabry-Perot interferometer based hypespectral imager that enables hyperspectral imaging from lightweight UAVs. During the summers 2011 and 2012 imaging and flight campaigns were carried out on the Finnish test field. Estimation mehtod uses features from linear and non-linear unmixing and vegetation indices. The results showed that the concept of small hyperspectral imager, UAV and data analysis is ready to operational use.

  7. Comparing new and conventional methods to estimate benthic algal biomass and composition in freshwaters.

    PubMed

    Kahlert, Maria; McKie, Brendan G

    2014-11-01

    We compared conventional microscope-based methods for quantifying biomass and community composition of stream benthic algae with output obtained for these parameters from a new instrument (the BenthoTorch), which measures fluorescence of algal pigments in situ. Benthic algae were studied in 24 subarctic oligotrophic (1.7-26.9, median 7.2 μg total phosphorus L(-1)) streams in Northern Sweden. Readings for biomass of the total algal mat, quantified as chlorophyll a, did not differ significantly between the BenthoTorch (median 0.52 μg chlorophyll a cm(-2)) and the conventional method (median 0.53 μg chlorophyll a cm(-2)). However, quantification of community composition of the benthic algal mat obtained using the BenthoTorch did not match those obtained from conventional methods. The BenthoTorch indicated a dominance of diatoms, whereas microscope observations showed a fairly even distribution between diatoms, blue-green algae (mostly nitrogen-fixing) and green algae (mostly large filamentous), and also detected substantial biovolumes of red algae in some streams. These results most likely reflect differences in the exact parameters quantified by the two methods, as the BenthoTorch does not account for variability in cell size and the presence of non-chlorophyll bearing biomass in estimating the proportion of different algal groups, and does not distinguish red algal chlorophyll from that of other algal groups. Our findings suggest that the BenthoTorch has utility in quantifying biomass expressed as μg chlorophyll a cm(-2), but its output for the relative contribution of different algal groups to benthic algal biomass should be used with caution. PMID:25277172

  8. First Estimates of the Radiative Forcing of Aerosols Generated from Biomass Burning Using Satellite Data

    NASA Technical Reports Server (NTRS)

    Christopher, Sundar A.; Kliche, Donna A.; Chou, Joyce; Welch, Ronald M.

    1996-01-01

    Collocated measurements from the Advanced Very High Resolution Radiometer (AVHRR) and the Earth Radiation Budget Experiment (ERBE) scanner are used to examine the radiative forcing of atmospheric aerosols generated from biomass burning for 13 images in South America. Using the AVHRR, Local Area Coverage (LAC) data, a new technique based on a combination of spectral and textural measures is developed for detecting these aerosols. Then, the instantaneous shortwave, longwave, and net radiative forcing values are computed from the ERBE instantaneous scanner data. Results for the selected samples from 13 images show that the mean instantaneous net radiative forcing for areas with heavy aerosol loading is about -36 W/sq m and that for the optically thin aerosols are about -16 W/sq m. These results, although preliminary, provide the first estimates of radiative forcing of atmospheric aerosols from biomass burning using satellite data.

  9. First Estimates of the Radiative Forcing of Aerosols Generated from Biomass Burning using Satellite Data

    NASA Technical Reports Server (NTRS)

    Chistopher, Sundar A.; Kliche, Donna V.; Chou, Joyce; Welch, Ronald M.

    1996-01-01

    Collocated measurements from the Advanced Very High Resolution Radiometer (AVHRR) and the Earth Radiation Budget Experiment (ERBE) scanner are used to examine the radiative forcing of atmospheric aerosols generated from biomass burning for 13 images in South America. Using the AVHRR, Local Area Coverage (LAC) data, a new technique based on a combination of spectral and textural measures is developed for detecting these aerosols. Then, the instantaneous shortwave, longwave, and net radiative forcing values are computed from the ERBE instantaneous scanner data. Results for the selected samples from 13 images show that the mean instantaneous net radiative forcing for areas with heavy aerosol loading is about -36 W/sq m and that for the optically thin aerosols are about -16 W/sq m. These results, although preliminary, provide the first estimates of radiative forcing of atmospheric aerosols from biomass burning using satellite data.

  10. Impact of biomass burning on urban air quality estimated by organic tracers: Guangzhou and Beijing as cases

    NASA Astrophysics Data System (ADS)

    Wang, Qiaoqiao; Shao, Min; Liu, Ying; William, Kuster; Paul, Goldan; Li, Xiaohua; Liu, Yuan; Lu, Sihua

    The impacts of biomass burning have not been adequately studied in China. In this work, chemical compositions of volatile organic compounds and particulate organic matters were measured in August 2005 in Beijing and in October 2004 in Guangzhou city. The performance of several possible tracers for biomass burning is compared by using acetonitrile as a reference compound. The correlations between the possible tracers and acetonitrile show that the use of K + as a tracer could result in bias because of the existence of other K + sources in urban areas, while chloromethane is not reliable due to its wide use as industrial chemical. The impact of biomass burning on air quality is estimated using acetonitrile and levoglucosan as tracers. The results show that the impact of biomass burning is ubiquitous in both suburban and urban Guangzhou, and the frequencies of air pollution episodes significantly influenced by biomass burning were 100% for Xinken and 58% for downtown Guangzhou city. Fortunately, the air quality in only 2 out of 22 days was partly impacted by biomass burning in August in Beijing, the month that 2008 Olympic games will take place. The quantitative contribution of biomass burning to ambient PM2.5 concentrations in Guangzhou city was also estimated by the ratio of levoglocusan to PM2.5 in both the ambient air and biomass burning plumes. The results show that biomass burning contributes 3.0-16.8% and 4.0-19.0% of PM2.5 concentrations in Xinken and Guangzhou downtown, respectively.

  11. The Use of Aerosol Optical Depth in Estimating Trace Gas Emissions from Biomass Burning Plumes

    NASA Astrophysics Data System (ADS)

    Jones, N.; Paton-Walsh, C.; Wilson, S.; Meier, A.; Deutscher, N.; Griffith, D.; Murcray, F.

    2003-12-01

    We have observed significant correlations between aerosol optical depth (AOD) at 500 nm and column amounts of a number of biomass burning indicators (carbon monoxide, hydrogen cyanide, formaldehyde and ammonia) in bushfire smoke plumes over SE Australia during the 2001/2002 and 2002/2003 fire seasons from remote sensing measurements. The Department of Chemistry, University of Wollongong, operates a high resolution Fourier Transform Spectrometer (FTS), in the city of Wollongong, approximately 80 km south of Sydney. During the recent bushfires we collected over 1500 solar FTIR spectra directly through the smoke over Wollongong. The total column amounts of the biomass burning indicators were calculated using the profile retrieval software package SFIT2. Using the same solar beam, a small grating spectrometer equipped with a 2048 pixel CCD detector array, was used to calculate simultaneous aerosol optical depths. This dataset is therefore unique in its temporal sampling, location to active fires, and range of simultaneously measured constituents. There are several important applications of the AOD to gas column correlation. The estimation of global emissions from biomass burning currently has very large associated uncertainties. The use of visible radiances measured by satellites, and hence AOD, could significantly reduce these uncertainties by giving a direct estimate of global emissions of gases from biomass burning through application of the AOD to gas correlation. On a more local level, satellite-derived aerosol optical depth maps could be inverted to infer approximate concentration levels of smoke-related pollutants at the ground and in the lower troposphere, and thus can be used to determine the nature of any significant health impacts.

  12. An inventory-based approach for estimating the managed China's forest biomass carbon stock

    NASA Astrophysics Data System (ADS)

    Huang, M.; Yu, G.; Yue, X.; Wang, J.

    2014-12-01

    China's forests cover a large area and have the characteristics of young age thus have the potential for a major role in mitigate the rate of global climate change. On the basis of forest inventory data and spatial distribution of forest stand age and forest type, we developed an approach for estimating yearly China's forest biomass carbon stocks change. Using this approach, we estimated the changes of forest carbon stock due to management practice and forest age structure change, respectively, and predicted China's future carbon potential based on national forest expansion plan. We also discussed sustainable harvesting intensity for the expanded forest of 2020. The spatial pattern of forest biomass carbon density in 2001 showed high in southwestern and northeastern areas, and low in the other regions, meanwhile the high C sinks appeared in the southwestern and northeastern young-aged forests and low in the southwestern and northeastern old-aged forests. The total forest biomass C stock of China increased from 6.06 Pg C in 2001 to 7.88 Pg C in 2013, giving a total increase of 1.82 Pg C, in which 0.45 Pg C is caused by forest expansion. The average C sink during 2002-2013 was 151.83 Tg C, in which 75.5% is the results of forest growth and 24.5% is caused by forest expansion. With the assumption of China's forest area will expand by 40 million hectares from 2006 to 2020, the forest C stock in 2020 is predicted as 9.04 Pg C. Harvesting intensity experiments conducted on the expanded forest of 2020 shown higher harvesting level will lead to decline in forest biomass in long term. The harvesting level of 2% is an optimal harvesting intensity for sustainable development of China's forest resources.

  13. Estimating Consumption to Biomass Ratio in Non-Stationary Harvested Fish Populations.

    PubMed

    Wiff, Rodrigo; Roa-Ureta, Ruben H; Borchers, David L; Milessi, Andrés C; Barrientos, Mauricio A

    2015-01-01

    The food consumption to biomass ratio (C) is one of the most important population parameters in ecosystem modelling because its quantifies the interactions between predator and prey. Existing models for estimating C in fish populations are per-recruit cohort models or empirical models, valid only for stationary populations. Moreover, empirical models lack theoretical support. Here we develop a theory and derive a general modelling framework to estimate C in fish populations, based on length frequency data and the generalised von Bertalanffy growth function, in which models for stationary populations with a stable-age distributions are special cases. Estimates using our method are compared with estimates from per-recruit cohort models for C using simulated harvested fish populations of different lifespans. The models proposed here are also applied to three fish populations that are targets of commercial fisheries in southern Chile. Uncertainty in the estimation of C was evaluated using a resampling approach. Simulations showed that stationary and non-stationary population models produce different estimates for C and those differences depend on the lifespan, fishing mortality and recruitment variations. Estimates of C using the new model exhibited smoother inter-annual variation in comparison with a per-recruit model estimates and they were also smaller than C predicted by the empirical equations in all population assessed. PMID:26528721

  14. Estimating Consumption to Biomass Ratio in Non-Stationary Harvested Fish Populations

    PubMed Central

    Wiff, Rodrigo; Roa-Ureta, Ruben H.; Borchers, David L.; Milessi, Andrés C.; Barrientos, Mauricio A.

    2015-01-01

    The food consumption to biomass ratio (C) is one of the most important population parameters in ecosystem modelling because its quantifies the interactions between predator and prey. Existing models for estimating C in fish populations are per-recruit cohort models or empirical models, valid only for stationary populations. Moreover, empirical models lack theoretical support. Here we develop a theory and derive a general modelling framework to estimate C in fish populations, based on length frequency data and the generalised von Bertalanffy growth function, in which models for stationary populations with a stable-age distributions are special cases. Estimates using our method are compared with estimates from per-recruit cohort models for C using simulated harvested fish populations of different lifespans. The models proposed here are also applied to three fish populations that are targets of commercial fisheries in southern Chile. Uncertainty in the estimation of C was evaluated using a resampling approach. Simulations showed that stationary and non-stationary population models produce different estimates for C and those differences depend on the lifespan, fishing mortality and recruitment variations. Estimates of C using the new model exhibited smoother inter-annual variation in comparison with a per-recruit model estimates and they were also smaller than C predicted by the empirical equations in all population assessed. PMID:26528721

  15. Development of visible/infrared/microwave agriculture classification and biomass estimation algorithms, volume 2. [Oklahoma and Texas

    NASA Technical Reports Server (NTRS)

    Rosenthal, W. D.; Mcfarland, M. J.; Theis, S. W.; Jones, C. L. (Principal Investigator)

    1982-01-01

    Agricultural crop classification models using two or more spectral regions (visible through microwave) were developed and tested and biomass was estimated by including microwave with visible and infrared data. The study was conducted at Guymon, Oklahoma and Dalhart, Texas utilizing aircraft multispectral data and ground truth soil moisture and biomass information. Results indicate that inclusion of C, L, and P band active microwave data from look angles greater than 35 deg from nadir with visible and infrared data improved crop discrimination and biomass estimates compared to results using only visible and infrared data. The active microwave frequencies were sensitive to different biomass levels. In addition, two indices, one using only active microwave data and the other using data from the middle and near infrared bands, were well correlated to total biomass.

  16. [Estimating Biomass Burned Areas from Multispectral Dataset Detected by Multiple-Satellite].

    PubMed

    Yu, Chao; Chen, Liang-fu; Li, Shen-shen; Tao, Jin-hua; Su, Lin

    2015-03-01

    Biomass burning makes up an important part of both trace gases and particulate matter emissions, which can efficiently degrade air quality and reduce visibility, destabilize the global climate system at regional to global scales. Burned area is one of the primary parameters necessary to estimate emissions, and considered to be the largest source of error in the emission inventory. Satellite-based fire observations can offer a reliable source of fire occurrence data on regional and global scales, a variety of sensors have been used to detect and map fires in two general approaches: burn scar mapping and active fire detection. However, both of the two approaches have limitations. In this article, we explore the relationship between hotspot data and burned area for the Southeastern United States, where a significant amount of biomass burnings from both prescribed and wild fire took place. MODIS (Moderate resolution imaging spectrometer) data, which has high temporal-resolution, can be used to monitor ground biomass. burning in time and provided hot spot data in this study. However, pixel size of MODIS hot spot can't stand for the real ground burned area. Through analysis of the variation of vegetation band reflectance between pre- and post-burn, we extracted the burned area from Landsat-5 TM (Thematic Mapper) images by using the differential normalized burn ratio (dNBR) which is based on TM band4 (0.84 μm) and TM band 7(2.22 μm) data. We combined MODIS fire hot spot data and Landsat-5 TM burned scars data to build the burned area estimation model, results showed that the linear correlation coefficient is 0.63 and the relationships vary as a function of vegetation cover. Based on the National Land Cover Database (NLCD), we built burned area estimation model over different vegetation cover, and got effective burned area per fire pixel, values for forest, grassland, shrub, cropland and wetland are 0.69, 1.27, 0.86, 0.72 and 0.94 km2 respectively. We validated the

  17. The Uncertainty of Biomass Estimates from Modeled ICESat-2 Returns Across a Boreal Forest Gradient

    NASA Technical Reports Server (NTRS)

    Montesano, P. M.; Rosette, J.; Sun, G.; North, P.; Nelson, R. F.; Dubayah, R. O.; Ranson, K. J.; Kharuk, V.

    2014-01-01

    The Forest Light (FLIGHT) radiative transfer model was used to examine the uncertainty of vegetation structure measurements from NASA's planned ICESat-2 photon counting light detection and ranging (LiDAR) instrument across a synthetic Larix forest gradient in the taiga-tundra ecotone. The simulations demonstrate how measurements from the planned spaceborne mission, which differ from those of previous LiDAR systems, may perform across a boreal forest to non-forest structure gradient in globally important ecological region of northern Siberia. We used a modified version of FLIGHT to simulate the acquisition parameters of ICESat-2. Modeled returns were analyzed from collections of sequential footprints along LiDAR tracks (link-scales) of lengths ranging from 20 m-90 m. These link-scales traversed synthetic forest stands that were initialized with parameters drawn from field surveys in Siberian Larix forests. LiDAR returns from vegetation were compiled for 100 simulated LiDAR collections for each 10 Mg · ha(exp -1) interval in the 0-100 Mg · ha(exp -1) above-ground biomass density (AGB) forest gradient. Canopy height metrics were computed and AGB was inferred from empirical models. The root mean square error (RMSE) and RMSE uncertainty associated with the distribution of inferred AGB within each AGB interval across the gradient was examined. Simulation results of the bright daylight and low vegetation reflectivity conditions for collecting photon counting LiDAR with no topographic relief show that 1-2 photons are returned for 79%-88% of LiDAR shots. Signal photons account for approximately 67% of all LiDAR returns, while approximately 50% of shots result in 1 signal photon returned. The proportion of these signal photon returns do not differ significantly (p greater than 0.05) for AGB intervals greater than 20 Mg · ha(exp -1). The 50m link-scale approximates the finest horizontal resolution (length) at which photon counting LiDAR collection provides strong model

  18. Statistical Estimates of the Long-Term Impact of Land-Use Disturbance on Woody Biomass in the Midwest (USA)

    NASA Astrophysics Data System (ADS)

    McLachlan, J. S.; Moore, D. J.; Zhu, J.; Feng, X.; Paciorek, C. J.; Williams, J. W.; Goring, S. J.; Hartfield, K. A.

    2014-12-01

    The impact on carbon balance of land-use transformations in eastern North America since the time of Euroamerican settlement is important at the global scale. And yet, our understanding of the baseline conditions of pre-settlement vegetation is generally weak. Many estimates of terrestrial carbon pools before Euroamerican settlement are based on hypothetical potential vegetation, and even data-derived estimates of biomass do not have statistical estimates of uncertainty. We fit a spatial statistical model to forest survey (PLS) data from the time of settlement across Midwesterm states from Minnesota to Indiana. Our spatial model scales diameter data from the PLS surveys by standard allometries to produce maps at 8km resolution of biomass with associated uncertainty for all major tree taxa and plant functional types and for total woody biomass. General trends in biomass are consistent with previous estimates, but fine scale heterogeneity is more revealed in our biomass product. A full accounting of uncertainty in settlement-era biomass allows us to assess the extent to which biomass has recovered across a vegetation gradient from subboreal forests to oak savannas and prairies and across land-use histories ranging from preserved old-growth forests through areas reforesting after intensive logging and agriculture to areas currently experiencing a range of intensive human activity.

  19. Estimation of potential biomass resource and biogas production from aquatic plants in Argentina

    NASA Astrophysics Data System (ADS)

    Fitzsimons, R. E.; Laurino, C. N.; Vallejos, R. H.

    1982-08-01

    The use of aquatic plants in artificial lakes as a biomass source for biogas and fertilizer production through anaerobic fermentation is evaluated, and the magnitude of this resource and the potential production of biogas and fertilizer are estimated. The specific case considered is the artificial lake that will be created by the construction of Parana Medio Hydroelectric Project on the middle Parana River in Argentina. The growth of the main aquatic plant, water hyacinth, on the middle Parana River has been measured, and its conversion to methane by anaerobic fermentation is determined. It is estimated that gross methane production may be between 1.0-4.1 x 10 to the 9th cu cm/year. The fermentation residue can be used as a soil conditioner, and it is estimated production of the residue may represent between 54,900-221,400 tons of nitrogen/year, a value which is 2-8 times the present nitrogen fertilizer demand in Argentina.

  20. Satellite Estimates of the Direct Radiative Forcing of Biomass Burning Aerosols Over South America and Africa

    NASA Technical Reports Server (NTRS)

    Christopher, Sundar A.; Wang, Min; Kliche, Donna V.; Berendes, Todd; Welch, Ronald M.; Yang, S.K.

    1997-01-01

    Atmospheric aerosol particles, both natural and anthropogenic are important to the earth's radiative balance. Therefore it is important to provide adequate validation information on the spatial, temporal and radiative properties of aerosols. This will enable us to predict realistic global estimates of aerosol radiative effects more confidently. The current study utilizes 66 AVHRR LAC (Local Area Coverage) and coincident Earth Radiation Budget Experiment (ERBE) images to characterize the fires, smoke and radiative forcings of biomass burning aerosols over four major ecosystems of South America.

  1. Detecting tropical forest biomass dynamics from repeated airborne Lidar measurements

    NASA Astrophysics Data System (ADS)

    Meyer, V.; Saatchi, S. S.; Chave, J.; Dalling, J.; Bohlman, S.; Fricker, G. A.; Robinson, C.; Neumann, M.

    2013-02-01

    Reducing uncertainty of terrestrial carbon cycle depends strongly on the accurate estimation of changes of global forest carbon stock. However, this is a challenging problem from either ground surveys or remote sensing techniques in tropical forests. Here, we examine the feasibility of estimating changes of tropical forest biomass from two airborne Lidar measurements acquired about 10 yr apart over Barro Colorado Island (BCI), Panama from high and medium resolution airborne sensors. The estimation is calibrated with the forest inventory data over 50 ha that was surveyed every 5 yr during the study period. We estimated the aboveground forest biomass and its uncertainty for each time period at different spatial scales (0.04, 0.25, 1.0 ha) and developed a linear regression model between four Lidar height metrics and the aboveground biomass. The uncertainty associated with estimating biomass changes from both ground and Lidar data was quantified by propagating measurement and prediction errors across spatial scales. Errors associated with both the mean biomass stock and mean biomass change declined with increasing spatial scales. Biomass changes derived from Lidar and ground estimates were largely (36 out 50 plots) in the same direction at the spatial scale of 1 ha. Lidar estimation of biomass was accurate at the 1 ha scale (R2 = 0.7 and RMSEmean = 28.6 Mg ha-1). However, to predict biomass changes, errors became comparable to ground estimates only at about 10-ha or more. Our results indicate that the 50-ha BCI plot lost a~significant amount of biomass (-0.8 ± 2.2 Mg ha-1 yr-1) over the past decade (2000-2010). Over the entire island and during the same period, mean AGB change is -0.4 ± 3.7 Mg ha-1 yr-1. Old growth forests lost biomass (-0.7 ± 3.5 Mg ha-1 yr-1), whereas the secondary forests gained biomass (+0.4 ± 3.4 Mg ha-1 yr-1). Our analysis demonstrates that repeated Lidar surveys, even with two different sensors, is able to estimate biomass changes in old

  2. BOREAS RSS-15 SIR-C and Landsat TM Biomass and Landcover Maps of the NSA

    NASA Technical Reports Server (NTRS)

    Hall, Forrest G. (Editor); Nickeson, Jaime (Editor); Ranson, K. Jon

    2000-01-01

    As part of BOREAS, the RSS-15 team conducted an investigation using SIR-C, X-SAR, and Landsat TM data for estimating total above-ground dry biomass for the SSA and NSA modeling grids and component biomass for the SSA. Relationships of backscatter to total biomass and total biomass to foliage, branch, and bole biomass were used to estimate biomass density across the landscape. The procedure involved image classification with SAR and Landsat TM data and development of simple mapping techniques using combinations of SAR channels. For the SSA, the SIR-C data used were acquired on 06-Oct-1994, and the Landsat TM data used were acquired on 02-Sep-1995. The maps of the NSA were developed from SIR-C data acquired on 13-Apr-1994. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

  3. Comparison of Precision of Biomass Estimates in Regional Field Sample Surveys and Airborne LiDAR-Assisted Surveys in Hedmark County, Norway

    NASA Technical Reports Server (NTRS)

    Naesset, Erik; Gobakken, Terje; Bollandsas, Ole Martin; Gregoire, Timothy G.; Nelson, Ross; Stahl, Goeran

    2013-01-01

    Airborne scanning LiDAR (Light Detection and Ranging) has emerged as a promising tool to provide auxiliary data for sample surveys aiming at estimation of above-ground tree biomass (AGB), with potential applications in REDD forest monitoring. For larger geographical regions such as counties, states or nations, it is not feasible to collect airborne LiDAR data continuously ("wall-to-wall") over the entire area of interest. Two-stage cluster survey designs have therefore been demonstrated by which LiDAR data are collected along selected individual flight-lines treated as clusters and with ground plots sampled along these LiDAR swaths. Recently, analytical AGB estimators and associated variance estimators that quantify the sampling variability have been proposed. Empirical studies employing these estimators have shown a seemingly equal or even larger uncertainty of the AGB estimates obtained with extensive use of LiDAR data to support the estimation as compared to pure field-based estimates employing estimators appropriate under simple random sampling (SRS). However, comparison of uncertainty estimates under SRS and sophisticated two-stage designs is complicated by large differences in the designs and assumptions. In this study, probability-based principles to estimation and inference were followed. We assumed designs of a field sample and a LiDAR-assisted survey of Hedmark County (HC) (27,390 km2), Norway, considered to be more comparable than those assumed in previous studies. The field sample consisted of 659 systematically distributed National Forest Inventory (NFI) plots and the airborne scanning LiDAR data were collected along 53 parallel flight-lines flown over the NFI plots. We compared AGB estimates based on the field survey only assuming SRS against corresponding estimates assuming two-phase (double) sampling with LiDAR and employing model-assisted estimators. We also compared AGB estimates based on the field survey only assuming two-stage sampling (the NFI

  4. Estimating fresh grass/herb biomass from HYMAP data using the red edge position

    NASA Astrophysics Data System (ADS)

    Cho, Moses A.; Sobhan, Istiak M.; Skidmore, Andrew K.

    2006-08-01

    Remote sensing of grass/herb quantity is essential for rangeland management of livestock and wildlife. Spectral indices such as NDVI, determined from red and near infrared bands are affected by variable soil and atmospheric conditions and saturate in dense vegetation. Alternatively, the wavelength of maximum slope in the red-NIR transition, termed the red edge position (REP) has potential to mitigate these effects. But the utility of the REP using air- and space-borne imagery is determined by the availability of narrow bands in the region of the red edge and the simplicity of the extraction method. Very recently, we proposed a simple technique for extracting the REP called the linear extrapolation method [Cho and Skidmore, Remote Sens. Environ., 101(2006)118.]. The purpose of this study was to evaluate the potential of the linear extrapolation method for estimating fresh grass/herb biomass and compare its performance with the four-point linear interpolation and three-point Lagrangian interpolation methods. The REPs were derived from atmospherically corrected HYMAP images collected over Majella National Park, Italy in July 2004. The predictive capabilities of various REP linear regression models were evaluated using leave-one-out cross validation and test set validation methods. For both validation methods, the linear extrapolation REP models produced higher correlations with grass/herb biomass and lower prediction errors compared with the linear interpolation and Lagrangian REP models. This study demonstrates the potential of REPs extracted by the linear extrapolation method using HYMAP data for estimating fresh grass/herb biomass.

  5. Estimating Vegetative Fuel Loadings and Fuel Moisture Using Satellite Data for Modeling Biomass Burning Emissions

    NASA Astrophysics Data System (ADS)

    Zhang, X.; Kondragunta, S.; Kogan, F.; Tarpley, J. D.; Guo, W.; Wiedinmyer, C.; Schmidt, C.

    2005-12-01

    Biomass burning is the second largest source of aerosols, which affects air quality and the Earth's radiation budget. Because the emissions of aerosols is strongly influenced by factors such as biomass density, combustion efficiency, and burned area, current burning emission estimates are rather imprecise and vary markedly with different methodologies. The aim of this study is to model biomass burning emissions using satellite-derived vegetative fuel loadings, fuel moisture, and burned areas in the USA. For this purpose, we first developed an approach for mapping vegetative fuel loadings using Moderate-Resolution Imaging Spectroradiometer (MODIS) data at a spatial resolution of 1 km. MODIS data used in this study are land cover types, vegetation continuous fields, and a time series of leaf-area index (LAI). The LAI data were used to produce live leaf fuel loadings varying with vegetation types and vegetation fractions. For forest regions, the maximum leaf fuel loading within a year was applied to calculate branch fuel loadings and total tree fuel loadings using tree allometric models. Since fuel combustion efficiency and emission factors are functions of fuel moisture, we then determined weekly fuel moisture categories from AVHRR-based vegetation condition index (VCI). The VCI was calculated by normalizing the NDVI (normalized difference vegetation index) to the difference of the extreme NDVI fluctuations (maximum and minimum) from 1982-2004. This dataset is reliable since it is calibrated using post-launch algorithms and temporally smoothed. Further, we derived sub-pixel fire size from GOES WF-ABBA fire product. This fire product is available at 30 minutes interval. We used all these inputs to estimate aerosols (PM2.5, particulate mass for particles with diameter < 2.5 μ-m) for each individual fire in 2002 across the USA. We will present the algorithm details and the analysis of the derived emissions.

  6. Density and Biomass Estimates by Removal for an Amazonian Crocodilian, Paleosuchus palpebrosus

    PubMed Central

    2016-01-01

    Direct counts of crocodilians are rarely feasible and it is difficult to meet the assumptions of mark-recapture methods for most species in most habitats. Catch-out experiments are also usually not logistically or morally justifiable because it would be necessary to destroy the habitat in order to be confident that most individuals had been captured. We took advantage of the draining and filling of a large area of flooded forest during the building of the Santo Antônio dam on the Madeira River to obtain accurate estimates of the density and biomass of Paleosuchus palpebrosus. The density, 28.4 non-hatchling individuals per km2, is one of the highest reported for any crocodilian, except for species that are temporarily concentrated in small areas during dry-season drought. The biomass estimate of 63.15 kg*km-2 is higher than that for most or even all mammalian carnivores in tropical forest. P. palpebrosus may be one of the World´s most abundant crocodilians. PMID:27224473

  7. Density and Biomass Estimates by Removal for an Amazonian Crocodilian, Paleosuchus palpebrosus.

    PubMed

    Campos, Zilca; Magnusson, William E

    2016-01-01

    Direct counts of crocodilians are rarely feasible and it is difficult to meet the assumptions of mark-recapture methods for most species in most habitats. Catch-out experiments are also usually not logistically or morally justifiable because it would be necessary to destroy the habitat in order to be confident that most individuals had been captured. We took advantage of the draining and filling of a large area of flooded forest during the building of the Santo Antônio dam on the Madeira River to obtain accurate estimates of the density and biomass of Paleosuchus palpebrosus. The density, 28.4 non-hatchling individuals per km2, is one of the highest reported for any crocodilian, except for species that are temporarily concentrated in small areas during dry-season drought. The biomass estimate of 63.15 kg*km-2 is higher than that for most or even all mammalian carnivores in tropical forest. P. palpebrosus may be one of the World´s most abundant crocodilians. PMID:27224473

  8. Modeling the spatial distribution of above-ground carbon in Mexican coniferous forests using remote sensing and a geostatistical approach

    NASA Astrophysics Data System (ADS)

    Galeana-Pizaña, J. Mauricio; López-Caloca, Alejandra; López-Quiroz, Penélope; Silván-Cárdenas, José Luis; Couturier, Stéphane

    2014-08-01

    Forest conservation is considered an option for mitigating the effect of greenhouse gases on global climate, hence monitoring forest carbon pools at global and local levels is important. The present study explores the capability of remote-sensing variables (vegetation indices and textures derived from SPOT-5; backscattering coefficient and interferometric coherence of ALOS PALSAR images) for modeling the spatial distribution of above-ground biomass in the Environmental Conservation Zone of Mexico City. Correlation and spatial autocorrelation coefficients were used to select significant explanatory variables in fir and pine forests. The correlation for interferometric coherence in HV polarization was negative, with correlations coefficients r = -0.83 for the fir and r = -0.75 for the pine forests. Regression-kriging showed the least root mean square error among the spatial interpolation methods used, with 37.75 tC/ha for fir forests and 29.15 tC/ha for pine forests. The results showed that a hybrid geospatial method, based on interferometric coherence data and a regression-kriging interpolator, has good potential for estimating above-ground biomass carbon.

  9. Canopy Vertical Spatial Scales which Constrain Biomass in a Tropical Forest at the Plot Level: Unifying Lidar and InSAR for Biomass Estimation

    NASA Astrophysics Data System (ADS)

    Treuhaft, R. N.; Goncalves, F. G.; Drake, J. B.; Chapman, B. D.; Dos Santos, J. R.; Dutra, L. V.; Graca, P. M.; Purcell, G. H.

    2009-12-01

    Structural remote sensing of forest biomass, using lidar and/or interferometric synthetic aperture radar (InSAR), often involves regressing field measured biomass against remotely sensed characteristics of the vertical density profile. Because spaceborne lidar or InSAR sensors will estimate structural characteristics averaged at the plot level (0.04-1 hectare), and because tropical forests contain 40% of the Earth’s forested biomass, this study focuses on the scales of vertical characteristics which best correlate with tropical forest biomass. This work suggests that the structural characteristics used in both lidar and InSAR biomass estimation, such as mean height or total height or height of median energy, are based on the behavior of Fourier vertical frequency components of vegetation density near zero frequency; that is, they are very low-spatial frequency characteristics of the vertical vegetation distribution. In this work, we ask which other vertical Fourier frequencies in lidar- or InSAR-produced structure metrics can best correlate with field biomass. Using lidar (LVIS) data from La Selva Biological Station, Costa Rica, taken in 2005, lidar canopy observations are Fourier transformed in the vertical direction to decompose into vertical frequency components. Each baseline of an InSAR observation, the complex coherence, is this Fourier transform of the canopy, if the ground contribution can be neglected. Using the qualitative similarity in vertical profiles seen by lidar, InSAR (at C-band, from AirSAR in 2004), and field measurements in the La Selva data, we produce the equivalent many (1000’s of) InSAR baselines from the lidar data and, using the lidar-simulated InSAR, determine the optimal spatial frequencies—baselines at DESDynI orbital altitudes for InSAR—which would estimate biomass in this wet tropical forest most accurately for either technique. For biomass ranging from 39-490 Mg/ha, regressing field biomass against some function of height

  10. Improved estimates of forest vegetation structure and biomass with a LiDAR-optimized sampling design

    NASA Astrophysics Data System (ADS)

    Hawbaker, Todd J.; Keuler, Nicholas S.; Lesak, Adrian A.; Gobakken, Terje; Contrucci, Kirk; Radeloff, Volker C.

    2009-06-01

    LiDAR data are increasingly available from both airborne and spaceborne missions to map elevation and vegetation structure. Additionally, global coverage may soon become available with NASA's planned DESDynI sensor. However, substantial challenges remain to using the growing body of LiDAR data. First, the large volumes of data generated by LiDAR sensors require efficient processing methods. Second, efficient sampling methods are needed to collect the field data used to relate LiDAR data with vegetation structure. In this paper, we used low-density LiDAR data, summarized within pixels of a regular grid, to estimate forest structure and biomass across a 53,600 ha study area in northeastern Wisconsin. Additionally, we compared the predictive ability of models constructed from a random sample to a sample stratified using mean and standard deviation of LiDAR heights. Our models explained between 65 to 88% of the variability in DBH, basal area, tree height, and biomass. Prediction errors from models constructed using a random sample were up to 68% larger than those from the models built with a stratified sample. The stratified sample included a greater range of variability than the random sample. Thus, applying the random sample model to the entire population violated a tenet of regression analysis; namely, that models should not be used to extrapolate beyond the range of data from which they were constructed. Our results highlight that LiDAR data integrated with field data sampling designs can provide broad-scale assessments of vegetation structure and biomass, i.e., information crucial for carbon and biodiversity science.

  11. Estimation of black carbon content for biomass burning aerosols from multi-channel Raman lidar data

    NASA Astrophysics Data System (ADS)

    Talianu, Camelia; Marmureanu, Luminita; Nicolae, Doina

    2015-04-01

    Biomass burning due to natural processes (forest fires) or anthropical activities (agriculture, thermal power stations, domestic heating) is an important source of aerosols with a high content of carbon components (black carbon and organic carbon). Multi-channel Raman lidars provide information on the spectral dependence of the backscatter and extinction coefficients, embedding information on the black carbon content. Aerosols with a high content of black carbon have large extinction coefficients and small backscatter coefficients (strong absorption), while aerosols with high content of organic carbon have large backscatter coefficients (weak absorption). This paper presents a method based on radiative calculations to estimate the black carbon content of biomass burning aerosols from 3b+2a+1d lidar signals. Data is collected at Magurele, Romania, at the cross-road of air masses coming from Ukraine, Russia and Greece, where burning events are frequent during both cold and hot seasons. Aerosols are transported in the free troposphere, generally in the 2-4 km altitude range, and reaches the lidar location after 2-3 days. Optical data are collected between 2011-2012 by a multi-channel Raman lidar and follows the quality assurance program of EARLINET. Radiative calculations are made with libRadTran, an open source radiative model developed by ESA. Validation of the retrievals is made by comparison to a co-located C-ToF Aerosol Mass Spectrometer. Keywords: Lidar, aerosols, biomass burning, radiative model, black carbon Acknowledgment: This work has been supported by grants of the Romanian National Authority for Scientific Research, Programme for Research- Space Technology and Advanced Research - STAR, project no. 39/2012 - SIAFIM, and by Romanian Partnerships in priority areas PNII implemented with MEN-UEFISCDI support, project no. 309/2014 - MOBBE

  12. Prediction intervals: Placing real bounds on regression-based allometric estimates of biomass.

    PubMed

    Ward, Peter J

    2015-07-01

    Biomass allometry studies routinely assume that regression models can be applied across species and sites, and that goodness of fit of a regression model to its derivation dataset indicates both the relevance of the model to a new dataset and the likely error. Assuming that a model is relevant for a new sample, a prediction interval is a useful error measure for stand mass. Prediction coverage tests whether the model and hence the interval are appropriate in the new sample. Data for three similar shrubby species from four similar sites were combined in various ways to test the impact of varying levels of biodiverse heterogeneity on the performance of the four models most commonly used in published biomass studies. No one model performed consistently well predicting new data, and validation checks were not good indicators of prediction coverage. The highly variable results suggest that the common models might contain insufficient variables. Euclidean distance was used to quantify the relative similarity of samples as a possible means of estimating prediction coverage; it proved unsuccessful with these data. PMID:25974741

  13. Initial estimates of mercury emissions to the atmosphere from global biomass burning.

    PubMed

    Friedli, H R; Arellano, A F; Cinnirella, S; Pirrone, N

    2009-05-15

    The average global annual mercury emission estimate from biomass burning (BMB) for 1997-2006 is 675 +/- 240 Mg/year. This is equivalentto 8% of all currently known anthropogenic and natural mercury emissions. By season, the largest global emissions occur in August and September, the lowest during northern winters. The interannual variability is large and region-specific, and responds to drought conditions. During this particular time period, the largest mercury emissions are from tropical and boreal Asia, followed by Africa and South America. They do not coincide with the largest carbon biomass burning emissions, which originate from Africa. Frequently burning grasslands in Africa and Australia, and agricultural waste burning globally, contribute relatively little to the mercury budget The released mercury from BMB is eventually deposited locally and globally and contributes to the formation of toxic bioaccumulating methyl mercury. Furthermore, increasing temperature in boreal regions, where the largest soil mercury pools reside, is expected to exacerbate mercury emission because of more frequent larger, and more intense fires. PMID:19544847

  14. Plant-mediated links between detritivores and aboveground herbivores

    PubMed Central

    Wurst, Susanne

    2013-01-01

    Most studies on plant-mediated above–belowground interactions focus on soil biota with direct trophic links to plant roots such as root herbivores, pathogens, and symbionts. Detritivorous soil fauna, though ubiquitous and present in high abundances and biomasses in soil, are under-represented in those studies. Understanding of their impact on plants is mainly restricted to growth and nutrient uptake parameters. Detritivores have been shown to affect secondary metabolites and defense gene expression in aboveground parts of plants, with potential impacts on aboveground plant–herbivore interactions. The proposed mechanisms range from nutrient mobilization effects and impacts on soil microorganisms to defense induction by passive or active ingestion of roots. Since their negative effects (disruption or direct feeding of roots) may be counterbalanced by their overall beneficial effects (nutrient mobilization), detritivores may not harm, but rather enable plants to respond to aboveground herbivore attacks in a more efficient way. Both more mechanistic and holistic approaches are needed to better understand the involvement of detritivores in plant-mediated above–belowground interactions and their potential for sustainable agriculture. PMID:24069027

  15. Is the simple auger coring method reliable for below-ground standing biomass estimation in Eucalyptus forest plantations?

    PubMed Central

    Levillain, Joseph; Thongo M'Bou, Armel; Deleporte, Philippe; Saint-André, Laurent; Jourdan, Christophe

    2011-01-01

    Background and Aims Despite their importance for plant production, estimations of below-ground biomass and its distribution in the soil are still difficult and time consuming, and no single reliable methodology is available for different root types. To identify the best method for root biomass estimations, four different methods, with labour requirements, were tested at the same location. Methods The four methods, applied in a 6-year-old Eucalyptus plantation in Congo, were based on different soil sampling volumes: auger (8 cm in diameter), monolith (25 × 25 cm quadrate), half Voronoi trench (1·5 m3) and a full Voronoi trench (3 m3), chosen as the reference method. Key Results With the reference method (0–1m deep), fine-root biomass (FRB, diameter <2 mm) was estimated at 1·8 t ha−1, medium-root biomass (MRB diameter 2–10 mm) at 2·0 t ha−1, coarse-root biomass (CRB, diameter >10 mm) at 5·6 t ha−1 and stump biomass at 6·8 t ha−1. Total below-ground biomass was estimated at 16·2 t ha−1 (root : shoot ratio equal to 0·23) for this 800 tree ha−1 eucalypt plantation density. The density of FRB was very high (0·56 t ha−1) in the top soil horizon (0–3 cm layer) and decreased greatly (0·3 t ha−1) with depth (50–100 cm). Without labour requirement considerations, no significant differences were found between the four methods for FRB and MRB; however, CRB was better estimated by the half and full Voronoi trenches. When labour requirements were considered, the most effective method was auger coring for FRB, whereas the half and full Voronoi trenches were the most appropriate methods for MRB and CRB, respectively. Conclusions As CRB combined with stumps amounted to 78 % of total below-ground biomass, a full Voronoi trench is strongly recommended when estimating total standing root biomass. Conversely, for FRB estimation, auger coring is recommended with a design pattern accounting for the spatial variability of fine-root distribution. PMID

  16. Subtropical Forest Biomass Estimation Using Airborne LiDAR and Hyperspectral Data

    NASA Astrophysics Data System (ADS)

    Pang, Yong; Li, Zengyuan

    2016-06-01

    Forests have complex vertical structure and spatial mosaic pattern. Subtropical forest ecosystem consists of vast vegetation species and these species are always in a dynamic succession stages. It is very challenging to characterize the complexity of subtropical forest ecosystem. In this paper, CAF's (The Chinese Academy of Forestry) LiCHy (LiDAR, CCD and Hyperspectral) Airborne Observation System was used to collect waveform Lidar and hyperspectral data in Puer forest region, Yunnan province in the Southwest of China. The study site contains typical subtropical species of coniferous forest, evergreen broadleaf forest, and some other mixed forests. The hypersectral images were orthorectified and corrected into surface reflectance with support of Lidar DTM product. The fusion of Lidar and hyperspectral can classify dominate forest types. The lidar metrics improved the classification accuracy. Then forest biomass estimation was carried out for each dominate forest types using waveform Lidar data, which get improved than single Lidar data source.

  17. Remote sensing of biomass and annual net aerial primary productivity of a salt marsh

    NASA Technical Reports Server (NTRS)

    Hardisky, M. A.; Klemas, V.; Daiber, F. C.; Roman, C. T.

    1984-01-01

    Net aerial primary productivity is the rate of storage of organic matter in above-ground plant issues exceeding the respiratory use by the plants during the period of measurement. It is pointed out that this plant tissue represents the fixed carbon available for transfer to and consumption by the heterotrophic organisms in a salt marsh or the estuary. One method of estimating annual net aerial primary productivity (NAPP) required multiple harvesting of the marsh vegetation. A rapid nondestructive remote sensing technique for estimating biomass and NAPP would, therefore, be a significant asset. The present investigation was designed to employ simple regression models, equating spectral radiance indices with Spartina alterniflora biomass to nondestructively estimate salt marsh biomass. The results of the study showed that the considered approach can be successfully used to estimate salt marsh biomass.

  18. PALSAR 50 m mosaic data based national level biomass estimation in Cambodia for implementation of REDD+ mechanism.

    PubMed

    Avtar, Ram; Suzuki, Rikie; Takeuchi, Wataru; Sawada, Haruo

    2013-01-01

    Tropical countries like Cambodia require information about forest biomass for successful implementation of climate change mitigation mechanism related to Reducing Emissions from Deforestation and forest Degradation (REDD+). This study investigated the potential of Phased Array-type L-band Synthetic Aperture Radar Fine Beam Dual (PALSAR FBD) 50 m mosaic data to estimate Above Ground Biomass (AGB) in Cambodia. AGB was estimated using a bottom-up approach based on field measured biomass and backscattering (σ(o)) properties of PALSAR data. The relationship between the PALSAR σ(o) HV and HH/HV with field measured biomass was strong with R(2) = 0.67 and 0.56, respectively. PALSAR estimated AGB show good results in deciduous forests because of less saturation as compared to dense evergreen forests. The validation results showed a high coefficient of determination R(2) = 0.61 with RMSE  = 21 Mg/ha using values up to 200 Mg/ha biomass. There were some uncertainties because of the uncertainty in the field based measurement and saturation of PALSAR data. AGB map of Cambodian forests could be useful for the implementation of forest management practices for REDD+ assessment and policies implementation at the national level. PMID:24116012

  19. PALSAR 50 m Mosaic Data Based National Level Biomass Estimation in Cambodia for Implementation of REDD+ Mechanism

    PubMed Central

    Avtar, Ram; Suzuki, Rikie; Takeuchi, Wataru; Sawada, Haruo

    2013-01-01

    Tropical countries like Cambodia require information about forest biomass for successful implementation of climate change mitigation mechanism related to Reducing Emissions from Deforestation and forest Degradation (REDD+). This study investigated the potential of Phased Array-type L-band Synthetic Aperture Radar Fine Beam Dual (PALSAR FBD) 50 m mosaic data to estimate Above Ground Biomass (AGB) in Cambodia. AGB was estimated using a bottom-up approach based on field measured biomass and backscattering (σo) properties of PALSAR data. The relationship between the PALSAR σo HV and HH/HV with field measured biomass was strong with R2 = 0.67 and 0.56, respectively. PALSAR estimated AGB show good results in deciduous forests because of less saturation as compared to dense evergreen forests. The validation results showed a high coefficient of determination R2 = 0.61 with RMSE  = 21 Mg/ha using values up to 200 Mg/ha biomass. There were some uncertainties because of the uncertainty in the field based measurement and saturation of PALSAR data. AGB map of Cambodian forests could be useful for the implementation of forest management practices for REDD+ assessment and policies implementation at the national level. PMID:24116012

  20. Ocean Lidar Measurements of Beam Attenuation and a Roadmap to Accurate Phytoplankton Biomass Estimates

    NASA Astrophysics Data System (ADS)

    Hu, Yongxiang; Behrenfeld, Mike; Hostetler, Chris; Pelon, Jacques; Trepte, Charles; Hair, John; Slade, Wayne; Cetinic, Ivona; Vaughan, Mark; Lu, Xiaomei; Zhai, Pengwang; Weimer, Carl; Winker, David; Verhappen, Carolus C.; Butler, Carolyn; Liu, Zhaoyan; Hunt, Bill; Omar, Ali; Rodier, Sharon; Lifermann, Anne; Josset, Damien; Hou, Weilin; MacDonnell, David; Rhew, Ray

    2016-06-01

    Beam attenuation coefficient, c, provides an important optical index of plankton standing stocks, such as phytoplankton biomass and total particulate carbon concentration. Unfortunately, c has proven difficult to quantify through remote sensing. Here, we introduce an innovative approach for estimating c using lidar depolarization measurements and diffuse attenuation coefficients from ocean color products or lidar measurements of Brillouin scattering. The new approach is based on a theoretical formula established from Monte Carlo simulations that links the depolarization ratio of sea water to the ratio of diffuse attenuation Kd and beam attenuation C (i.e., a multiple scattering factor). On July 17, 2014, the CALIPSO satellite was tilted 30° off-nadir for one nighttime orbit in order to minimize ocean surface backscatter and demonstrate the lidar ocean subsurface measurement concept from space. Depolarization ratios of ocean subsurface backscatter are measured accurately. Beam attenuation coefficients computed from the depolarization ratio measurements compare well with empirical estimates from ocean color measurements. We further verify the beam attenuation coefficient retrievals using aircraft-based high spectral resolution lidar (HSRL) data that are collocated with in-water optical measurements.

  1. Bringing Together Users and Developers of Forest Biomass Maps

    NASA Technical Reports Server (NTRS)

    Brown, Molly E.; Macauley, Molly

    2011-01-01

    Forests store carbon and thus represent important sinks for atmospheric carbon dioxide. Reducing uncertainty in current estimates of the amount of carbon in standing forests will improve precision of estimates of anthropogenic contributions to carbon dioxide in the atmosphere due to deforestation. Although satellite remote sensing has long been an important tool for mapping land cover, until recently aboveground forest biomass estimates have relied mostly on systematic ground sampling of forests. In alignment with fiscal year 2010 congressional direction, NASA has initiated work toward a carbon monitoring system (CMS) that includes both maps of forest biomass and total carbon flux estimates. A goal of the project is to ensure that the products are useful to a wide community of scientists, managers, and policy makers, as well as to carbon cycle scientists. Understanding the needs and requirements of these data users is helpful not just to the NASA CMS program but also to the entire community working on carbon-related activities. To that end, this meeting brought together a small group of natural resource managers and policy makers who use information on forests in their work with NASA scientists who are working to create aboveground forest biomass maps. These maps, derived from combining remote sensing and ground plots, aim to be more accurate than current inventory approaches when applied at local and regional scales.

  2. Landscape-Scale Controls on Aboveground Forest Carbon Stocks on the Osa Peninsula, Costa Rica.

    PubMed

    Taylor, Philip; Asner, Gregory; Dahlin, Kyla; Anderson, Christopher; Knapp, David; Martin, Roberta; Mascaro, Joseph; Chazdon, Robin; Cole, Rebecca; Wanek, Wolfgang; Hofhansl, Florian; Malavassi, Edgar; Vilchez-Alvarado, Braulio; Townsend, Alan

    2015-01-01

    Tropical forests store large amounts of carbon in tree biomass, although the environmental controls on forest carbon stocks remain poorly resolved. Emerging airborne remote sensing techniques offer a powerful approach to understand how aboveground carbon density (ACD) varies across tropical landscapes. In this study, we evaluate the accuracy of the Carnegie Airborne Observatory (CAO) Light Detection and Ranging (LiDAR) system to detect top-of-canopy tree height (TCH) and ACD across the Osa Peninsula, Costa Rica. LiDAR and field-estimated TCH and ACD were highly correlated across a wide range of forest ages and types. Top-of-canopy height (TCH) reached 67 m, and ACD surpassed 225 Mg C ha-1, indicating both that airborne CAO LiDAR-based estimates of ACD are accurate in tall, high-biomass forests and that the Osa Peninsula harbors some of the most carbon-rich forests in the Neotropics. We also examined the relative influence of lithologic, topoedaphic and climatic factors on regional patterns in ACD, which are known to influence ACD by regulating forest productivity and turnover. Analyses revealed a spatially nested set of factors controlling ACD patterns, with geologic variation explaining up to 16% of the mapped ACD variation at the regional scale, while local variation in topographic slope explained an additional 18%. Lithologic and topoedaphic factors also explained more ACD variation at 30-m than at 100-m spatial resolution, suggesting that environmental filtering depends on the spatial scale of terrain variation. Our result indicate that patterns in ACD are partially controlled by spatial variation in geologic history and geomorphic processes underpinning topographic diversity across landscapes. ACD also exhibited spatial autocorrelation, which may reflect biological processes that influence ACD, such as the assembly of species or phenotypes across the landscape, but additional research is needed to resolve how abiotic and biotic factors contribute to ACD

  3. Landscape-Scale Controls on Aboveground Forest Carbon Stocks on the Osa Peninsula, Costa Rica

    PubMed Central

    Taylor, Philip; Asner, Gregory; Dahlin, Kyla; Anderson, Christopher; Knapp, David; Martin, Roberta; Mascaro, Joseph; Chazdon, Robin; Cole, Rebecca; Wanek, Wolfgang; Hofhansl, Florian; Malavassi, Edgar; Vilchez-Alvarado, Braulio; Townsend, Alan

    2015-01-01

    Tropical forests store large amounts of carbon in tree biomass, although the environmental controls on forest carbon stocks remain poorly resolved. Emerging airborne remote sensing techniques offer a powerful approach to understand how aboveground carbon density (ACD) varies across tropical landscapes. In this study, we evaluate the accuracy of the Carnegie Airborne Observatory (CAO) Light Detection and Ranging (LiDAR) system to detect top-of-canopy tree height (TCH) and ACD across the Osa Peninsula, Costa Rica. LiDAR and field-estimated TCH and ACD were highly correlated across a wide range of forest ages and types. Top-of-canopy height (TCH) reached 67 m, and ACD surpassed 225 Mg C ha-1, indicating both that airborne CAO LiDAR-based estimates of ACD are accurate in tall, high-biomass forests and that the Osa Peninsula harbors some of the most carbon-rich forests in the Neotropics. We also examined the relative influence of lithologic, topoedaphic and climatic factors on regional patterns in ACD, which are known to influence ACD by regulating forest productivity and turnover. Analyses revealed a spatially nested set of factors controlling ACD patterns, with geologic variation explaining up to 16% of the mapped ACD variation at the regional scale, while local variation in topographic slope explained an additional 18%. Lithologic and topoedaphic factors also explained more ACD variation at 30-m than at 100-m spatial resolution, suggesting that environmental filtering depends on the spatial scale of terrain variation. Our result indicate that patterns in ACD are partially controlled by spatial variation in geologic history and geomorphic processes underpinning topographic diversity across landscapes. ACD also exhibited spatial autocorrelation, which may reflect biological processes that influence ACD, such as the assembly of species or phenotypes across the landscape, but additional research is needed to resolve how abiotic and biotic factors contribute to ACD

  4. Estimates of forest biomass carbon storage inLiaoning Province of Northeast China: a review and assessment.

    PubMed

    Yu, Dapao; Wang, Xiaoyu; Yin, You; Zhan, Jinyu; Lewis, Bernard J; Tian, Jie; Bao, Ye; Zhou, Wangming; Zhou, Li; Dai, Limin

    2014-01-01

    Accurate estimates of forest carbon storage and changes in storage capacity are critical for scientific assessment of the effects of forest management on the role of forests as carbon sinks. Up to now, several studies reported forest biomass carbon (FBC) in Liaoning Province based on data from China's Continuous Forest Inventory, however, their accuracy were still not known. This study compared estimates of FBC in Liaoning Province derived from different methods. We found substantial variation in estimates of FBC storage for young and middle-age forests. For provincial forests with high proportions in these age classes, the continuous biomass expansion factor method (CBM) by forest type with age class is more accurate and therefore more appropriate for estimating forest biomass. Based on the above approach designed for this study, forests in Liaoning Province were found to be a carbon sink, with carbon stocks increasing from 63.0 TgC in 1980 to 120.9 TgC in 2010, reflecting an annual increase of 1.9 TgC. The average carbon density of forest biomass in the province has increased from 26.2 Mg ha(-1) in 1980 to 31.0 Mg ha(-1) in 2010. While the largest FBC occurred in middle-age forests, the average carbon density decreased in this age class during these three decades. The increase in forest carbon density resulted primarily from the increased area and carbon storage of mature forests. The relatively long age interval in each age class for slow-growing forest types increased the uncertainty of FBC estimates by CBM-forest type with age class, and further studies should devote more attention to the time span of age classes in establishing biomass expansion factors for use in CBM calculations. PMID:24586881

  5. Using simple environmental variables to estimate below-ground productivity in grasslands

    USGS Publications Warehouse

    Gill, R.A.; Kelly, R.H.; Parton, W.J.; Day, K.A.; Jackson, R.B.; Morgan, J.A.; Scurlock, J.M.O.; Tieszen, L.L.; Castle, J.V.; Ojima, D.S.; Zhang, X.S.

    2002-01-01

    In many temperate and annual grasslands, above-ground net primary productivity (NPP) can be estimated by measuring peak above-ground biomass. Estimates of below-ground net primary productivity and, consequently, total net primary productivity, are more difficult. We addressed one of the three main objectives of the Global Primary Productivity Data Initiative for grassland systems to develop simple models or algorithms to estimate missing components of total system NPP. Any estimate of below-ground NPP (BNPP) requires an accounting of total root biomass, the percentage of living biomass and annual turnover of live roots. We derived a relationship using above-ground peak biomass and mean annual temperature as predictors of below-ground biomass (r2 = 0.54; P = 0.01). The percentage of live material was 0.6, based on published values. We used three different functions to describe root turnover: constant, a direct function of above-ground biomass, or as a positive exponential relationship with mean annual temperature. We tested the various models against a large database of global grassland NPP and the constant turnover and direct function models were approximately equally descriptive (r2 = 0.31 and 0.37), while the exponential function had a stronger correlation with the measured values (r2 = 0.40) and had a better fit than the other two models at the productive end of the BNPP gradient. When applied to extensive data we assembled from two grassland sites with reliable estimates of total NPP, the direct function was most effective, especially at lower productivity sites. We provide some caveats for its use in systems that lie at the extremes of the grassland gradient and stress that there are large uncertainties associated with measured and modelled estimates of BNPP.

  6. Thermal efficiency and particulate pollution estimation of four biomass fuels grown on wasteland

    SciTech Connect

    Kandpal, J.B.; Madan, M.

    1996-10-01

    The thermal performance and concentration of suspended particulate matter were studied for 1-hour combustion of four biomass fuels, namely Acacia nilotica, Leucaena leucocepholea, Jatropha curcus, and Morus alba grown in wasteland. Among the four biomass fuels, the highest thermal efficiency was achieved with Acacia nilotica. The suspended particulate matter concentration for 1-hour combustion of four biomass fuels ranged between 850 and 2,360 {micro}g/m{sup 3}.

  7. Biomass production, nutrient cycling, and carbon fixation by Salicornia brachiata Roxb.: A promising halophyte for coastal saline soil rehabilitation.

    PubMed

    Rathore, Aditya P; Chaudhary, Doongar R; Jha, Bhavanath

    2016-08-01

    In order to increase our understanding of the interaction of soil-halophyte (Salicornia brachiata) relations and phytoremediation, we investigated the aboveground biomass, carbon fixation, and nutrient composition (N, P, K, Na, Ca, and Mg) of S. brachiata using six sampling sites with varying characteristics over one growing season in intertidal marshes. Simultaneously, soil characteristics and nutrient concentrations were also estimated. There was a significant variation in soil characteristics and nutrient contents spatially (except pH) as well as temporally. Nutrient contents in aboveground biomass of S. brachiata were also significantly differed spatially (except C and Cl) as well as temporally. Aboveground biomass of S. brachiata ranged from 2.51 to 6.07 t/ha at maturity and it was positively correlated with soil electrical conductivity and available Na, whereas negatively with soil pH. The K/Na ratio in plant was below one, showing tolerance to salinity. The aboveground C fixation values ranged from 0.77 to 1.93 C t/ha at all six sampling sites. This study provides new understandings into nutrient cycling-C fixation potential of highly salt-tolerant halophyte S. brachiata growing on intertidal soils of India. S. brachiata have a potential for amelioration of the salinity due to higher Na bioaccumulation factor. PMID:26852782

  8. Aboveground carbon loss in natural and managed tropical forests from 2000 to 2012

    NASA Astrophysics Data System (ADS)

    Tyukavina, A.; Baccini, A.; Hansen, M. C.; Potapov, P. V.; Stehman, S. V.; Houghton, R. A.; Krylov, A. M.; Turubanova, S.; Goetz, S. J.

    2015-07-01

    Tropical forests provide global climate regulation ecosystem services and their clearing is a significant source of anthropogenic greenhouse gas (GHG) emissions and resultant radiative forcing of climate change. However, consensus on pan-tropical forest carbon dynamics is lacking. We present a new estimate that employs recommended good practices to quantify gross tropical forest aboveground carbon (AGC) loss from 2000 to 2012 through the integration of Landsat-derived tree canopy cover, height, intactness and forest cover loss and GLAS-lidar derived forest biomass. An unbiased estimate of forest loss area is produced using a stratified random sample with strata derived from a wall-to-wall 30 m forest cover loss map. Our sample-based results separate the gross loss of forest AGC into losses from natural forests (0.59 PgC yr-1) and losses from managed forests (0.43 PgC yr-1) including plantations, agroforestry systems and subsistence agriculture. Latin America accounts for 43% of gross AGC loss and 54% of natural forest AGC loss, with Brazil experiencing the highest AGC loss for both categories at national scales. We estimate gross tropical forest AGC loss and natural forest loss to account for 11% and 6% of global year 2012 CO2 emissions, respectively. Given recent trends, natural forests will likely constitute an increasingly smaller proportion of tropical forest GHG emissions and of global emissions as fossil fuel consumption increases, with implications for the valuation of co-benefits in tropical forest conservation.

  9. Impacts of fire management on aboveground tree carbon stocks in Yosemite and Sequoia & Kings Canyon national parks.

    USGS Publications Warehouse

    Matchett, John R.; Lutz, Jamea A.; Tarnay, Leland W.; Smith, Douglas G.; Becker, Kendall M.L.; Brooks, Matthew L.

    2015-01-01

    Forest biomass on Sierra Nevada landscapes constitutes one of the largest carbon stocks in California, and its stability is tightly linked to the factors driving fire regimes. Research suggests that fire suppression, logging, climate change, and present management practices in Sierra Nevada forests have altered historic patterns of landscape carbon storage, and over a century of fire suppression and the resulting accumulation in surface fuels have been implicated in contributing to recent increases in high severity, stand-replacing fires. For over 30 years, fire management at Yosemite (YOSE) and Sequoia & Kings Canyon (SEKI) national parks has led the nation in restoring fire to park landscapes; however, the impacts on the stability and magnitude of carbon stocks have not been thoroughly examined. The purpose of this study is to quantify relationships between recent fire patterns and aboveground tree carbon stocks in YOSE and SEKI. Our approach focuses on evaluating fire effects on 1) amounts of aboveground tree carbon on the landscape, and 2) rates of carbon accumulation by individual trees. In 2010, we compiled a database of existing plot data for our analyses. In 2011, our field crews acquired vegetation data and collected tree growth cores from 105 plots. In 2012, we completed an interpretive component and began data analyses. In 2013, processing of tree cores began. In 2014, final processing of tree cores, data analyses, and manuscript preparation was conducted. The work for this project was facilitated through an interagency agreement between the National Park Service and the U.S. Geological Survey, and through a Cooperative Ecosystems Studies Unit (CESU) agreement with the University of Washington. In order to accurately quantify landscape-level carbon stocks, our analyses accounted for major sources of measurement errors, propagating those errors as we scaled plot-based carbon density estimates up to landscape-level totals. Using Monte Carlo simulation

  10. A Bayesian approach to estimate the biomass of anchovies off the coast of Perú.

    PubMed

    Quiroz, Zaida C; Prates, Marcos O; Rue, Håvard

    2015-03-01

    The Northern Humboldt Current System (NHCS) is the world's most productive ecosystem in terms of fish. In particular, the Peruvian anchovy (Engraulis ringens) is the major prey of the main top predators, like seabirds, fish, humans, and other mammals. In this context, it is important to understand the dynamics of the anchovy distribution to preserve it as well as to exploit its economic capacities. Using the data collected by the "Instituto del Mar del Perú" (IMARPE) during a scientific survey in 2005, we present a statistical analysis that has as main goals: (i) to adapt to the characteristics of the sampled data, such as spatial dependence, high proportions of zeros and big size of samples; (ii) to provide important insights on the dynamics of the anchovy population; and (iii) to propose a model for estimation and prediction of anchovy biomass in the NHCS offshore from Perú. These data were analyzed in a Bayesian framework using the integrated nested Laplace approximation (INLA) method. Further, to select the best model and to study the predictive power of each model, we performed model comparisons and predictive checks, respectively. Finally, we carried out a Bayesian spatial influence diagnostic for the preferred model. PMID:25257036

  11. Above ground biomass estimation from lidar and hyperspectral airbone data in West African moist forests.

    NASA Astrophysics Data System (ADS)

    Vaglio Laurin, Gaia; Chen, Qi; Lindsell, Jeremy; Coomes, David; Cazzolla-Gatti, Roberto; Grieco, Elisa; Valentini, Riccardo

    2013-04-01

    The development of sound methods for the estimation of forest parameters such as Above Ground Biomass (AGB) and the need of data for different world regions and ecosystems, are widely recognized issues due to their relevance for both carbon cycle modeling and conservation and policy initiatives, such as the UN REDD+ program (Gibbs et al., 2007). The moist forests of the Upper Guinean Belt are poorly studied ecosystems (Vaglio Laurin et al. 2013) but their role is important due to the drier condition expected along the West African coasts according to future climate change scenarios (Gonzales, 2001). Remote sensing has proven to be an effective tool for AGB retrieval when coupled with field data. Lidar, with its ability to penetrate the canopy provides 3D information and best results. Nevertheless very limited research has been conducted in Africa tropical forests with lidar and none to our knowledge in West Africa. Hyperspectral sensors also offer promising data, being able to evidence very fine radiometric differences in vegetation reflectance. Their usefulness in estimating forest parameters is still under evaluation with contrasting findings (Andersen et al. 2008, Latifi et al. 2012), and additional studies are especially relevant in view of forthcoming satellite hyperspectral missions. In the framework of the EU ERC Africa GHG grant #247349, an airborne campaign collecting lidar and hyperspectral data has been conducted in March 2012 over forests reserves in Sierra Leone and Ghana, characterized by different logging histories and rainfall patterns, and including Gola Rainforest National Park, Ankasa National Park, Bia and Boin Forest Reserves. An Optech Gemini sensor collected the lidar dataset, while an AISA Eagle sensor collected hyperspectral data over 244 VIS-NIR bands. The lidar dataset, with a point density >10 ppm was processed using the TIFFS software (Toolbox for LiDAR Data Filtering and Forest Studies)(Chen 2007). The hyperspectral dataset, geo

  12. Biogas production from Pongamia biomass wastes and a model to estimate biodegradability from their composition.

    PubMed

    Gunaseelan, Victor Nallathambi

    2014-02-01

    In this study, I investigated the chemical characteristics, biochemical methane potential, conversion kinetics and biodegradability of untreated and NaOH-treated Pongamia plant parts, and pod husk and press cake from the biodiesel industry to evaluate their suitability as an alternative feedstock for biogas production. The untreated Pongamia seeds exhibited the maximum CH4 yield of 473 ml g (-1) volatile solid (VS) added. Yellow, withered leaves gave a yield as low as 122 ml CH4 g (-1) VS added. There were significant variations in the CH4 production rate constants, which ranged from 0.02 to 0.15 d (-1), and biodegradability, which ranged from 0.25 to 0.98. NaOH treatment of leaf and pod husk, which were highly rich in fibers, increased the yields by 15-22% and CH4 production rate constants by 20-75%. Utilization of Pongamia wastes in biogas digesters not only influences the economics of biodiesel production but also yields CH4 fuel and protects the environment. The experimental data from this study were used to develop a multiple regression model, which could estimate biodegradability based on biochemical characteristics. The model predicted the biodegradability of previously published biomass wastes (r(2) = 0.88) from their biochemical composition. The theoretical CH4 yields estimated as 350 ml g(-1) chemical oxygen demand destroyed are much higher than the experimental yields as 100% biodegradability is assumed for each substrate. Upon correcting the theoretical CH4 yields with biodegradability data obtained from chemical analyses of substrates, their ultimate CH4 yields could be predicted rapidly. PMID:24519227

  13. Spatially explicit estimates of stock size, structure and biomass of North Atlantic albacore Tuna (Thunnus alalunga)

    NASA Astrophysics Data System (ADS)

    Lehodey, P.; Senina, I.; Dragon, A.-C.; Arrizabalaga, H.

    2014-04-01

    The development of the ecosystem approach and models for the management of ocean marine resources requires easy access to standard validated datasets of historical catch data for the main exploited species. They are used to measure the impact of biomass removal by fisheries and to evaluate the models skills, while the use of standard dataset facilitates models inter-comparison. Unlike standard stock assessment models, new state-of-the-art ecosystem models require geo-referenced fishing data with highest possible spatial resolution. This study presents an application to the north Atlantic albacore tuna stock with a careful definition and validation of a spatially explicit fishing dataset prepared from publically available sources (ICCAT) for its use in a spatial ecosystem and population dynamics model (SEAPODYM) to provide the first spatially explicit estimate of albacore density in the North Atlantic by life stage. Density distributions are provided (http://doi.pangaea.de/10.1594/PANGAEA.831499) together with the fishing data used for these estimates http://doi.pangaea.de/10.1594/PANGAEA.830797, http://doi.pangaea.de/10.15 1594/PANGAEA.828168, http://doi.pangaea.de/10.1594/PANGAEA.828170, and http://doi.pangaea.de/10.1594/PANGAEA.828171 (see section Source Data References).

  14. Comparative biomass structure and estimated carbon flow in food webs in the deep Gulf of Mexico

    NASA Astrophysics Data System (ADS)

    Rowe, Gilbert T.; Wei, Chihlin; Nunnally, Clifton; Haedrich, Richard; Montagna, Paul; Baguley, Jeffrey G.; Bernhard, Joan M.; Wicksten, Mary; Ammons, Archie; Briones, Elva Escobar; Soliman, Yousra; Deming, Jody W.

    2008-12-01

    A budget of the standing stocks and cycling of organic carbon associated with the sea floor has been generated for seven sites across a 3-km depth gradient in the NE Gulf of Mexico, based on a series of reports by co-authors on specific biotic groups or processes. The standing stocks measured at each site were bacteria, Foraminifera, metazoan meiofauna, macrofauna, invertebrate megafauna, and demersal fishes. Sediment community oxygen consumption (SCOC) by the sediment-dwelling organisms was measured at each site using a remotely deployed benthic lander, profiles of oxygen concentration in the sediment pore water of recovered cores and ship-board core incubations. The long-term incorporation and burial of organic carbon into the sediments has been estimated using profiles of a combination of stable and radiocarbon isotopes. The total stock estimates, carbon burial, and the SCOC allowed estimates of living and detrital carbon residence time within the sediments, illustrating that the total biota turns over on time scales of months on the upper continental slope but this is extended to years on the abyssal plain at 3.6 km depth. The detrital carbon turnover is many times longer, however, over the same depths. A composite carbon budget illustrates that total carbon biomass and associated fluxes declined precipitously with increasing depth. Imbalances in the carbon budgets suggest that organic detritus is exported from the upper continental slope to greater depths offshore. The respiration of each individual "size" or functional group within the community has been estimated from allometric models, supplemented by direct measurements in the laboratory. The respiration and standing stocks were incorporated into budgets of carbon flow through and between the different size groups in hypothetical food webs. The decline in stocks and respiration with depth were more abrupt in the larger forms (fishes and megafauna), resulting in an increase in the relative predominance of

  15. [Models for biomass estimation of four shrub species planted in urban area of Xi'an city, Northwest China].

    PubMed

    Yao, Zheng-Yang; Liu, Jian-Jun

    2014-01-01

    Four common greening shrub species (i. e. Ligustrum quihoui, Buxus bodinieri, Berberis xinganensis and Buxus megistophylla) in Xi'an City were selected to develop the highest correlation and best-fit estimation models for the organ (branch, leaf and root) and total biomass against different independent variables. The results indicated that the organ and total biomass optimal models of the four shrubs were power functional model (CAR model) except for the leaf biomass model of B. megistophylla which was logarithmic functional model (VAR model). The independent variables included basal diameter, crown diameter, crown diameter multiplied by height, canopy area and canopy volume. B. megistophylla significantly differed from the other three shrub species in the independent variable selection, which were basal diameter and crown-related factors, respectively. PMID:24765849

  16. Water- and radiation-use efficiency models for estimating biomass production

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Simple biomass production models are used to assess crop productivity under a wide range of environmental conditions, typically as a component of comprehensive crop growth models. Two simple biomass production models are widely used, one based on radiation-use efficiency, e (B=e St fi), and the oth...

  17. Gradient anaysis of biomass in Costa Rica and a first estimate of total emissions of greenhouse gases from biomass burning

    SciTech Connect

    Helmer, E.H.; Brown, S.

    1997-12-31

    One important component of sustainable development for a nation is the degree to which it can balance greenhouse gas (GHG) exchange with the atmosphere. Scientists at NHEERL-WED recently estimated the release of such GHGs from the conversion of a range of forest types in Costa Rica between 1940-1983. They also evaluated the influence of environmental gradients that affect the rates and patterns of deforestation and the carbon pools of the forest cleared on GHG emissions.

  18. Twig and foliar biomass estimation equations for major plant species in the Tanana River basin of interior Alaska. Forest Service research paper

    SciTech Connect

    Yarie, J.; Mead, B.R.

    1988-09-01

    Equations are presented for estimating the twig, foliage, and combined biomass for 58 plant species in interior Alaska. The equations can be used for estimating biomass from percentage of the foliar cover of 10-centimeter layers in a vertical profile from 0 to 6 meters. Few differences were found in regressions of the same species between layers except when the ratio of foliar-to-twig biomass changed drastically between layers, for example, Rosa acicularis Lindl. Eighteen species were tested for regression differences between years. Thirteen showed no significant differences, five were different. Of these five, three were feather mosses for which live and dead biomass are easily confused when measured.

  19. Biomass estimation during macro-scale solid-state fermentation of basidiomycetes using established and novel approaches.

    PubMed

    Steudler, Susanne; Bley, Thomas

    2015-07-01

    Solid-state fermentation (SSF) has been utilised in food production for millennia and is well suited for the cultivation of basidiomycetes, due to the robustness of the process and the possibility of using lignocellulose as the substrate. Basidiomycetes produce diverse enzymes and various primary and secondary metabolites, many of which have biotechnological potential. The quantification of the fungal biomass present is essential for the characterisation of growth kinetics in processes such as SSF. In SSF, fungi grow into the substrate and use it as a nutrient source. Therefore, direct biomass determination is not possible and indirect methods have to be employed. In the presented study, we compared 11 methods for quantifying fungal biomass during SSF of the basidiomycete Trametes hirsuta in a newly developed laboratory reactor (working volume 10 L). The methods were based on measuring the levels of six cell-specific components (ergosterol, glucosamine, nucleic acids, number of fungal nuclei, protein and genomic DNA) and estimations of biological activity (respiration, activities of lignolytic and cellulolytic enzymes, and the glucose and protein contents of the liquid). The methods were evaluated with regards to reproducibility and plausibility of the results, time and resource requirements, possible influential factors, and matrix effects. The most reliable biomass estimates were obtained from measurements of ergosterol content, number of nuclei, and respiration. Thus, these three methods were deemed most suitable for process control and modelling. PMID:25656698

  20. Hydroacoustic estimation of zooplankton biomass at two shoal complexes in the Apostle Islands Region of Lake Superior

    USGS Publications Warehouse

    Holbrook, B.V.; Hrabik, T.R.; Branstrator, D.K.; Yule, D.L.; Stockwell, J.D.

    2006-01-01

    Hydroacoustics can be used to assess zooplankton populations, however, backscatter must be scaled to be biologically meaningful. In this study, we used a general model to correlate site-specific hydroacoustic backscatter with zooplankton dry weight biomass estimated from net tows. The relationship between zooplankton dry weight and backscatter was significant (p < 0.001) and explained 76% of the variability in the dry weight data. We applied this regression to hydroacoustic data collected monthly in 2003 and 2004 at two shoals in the Apostle Island Region of Lake Superior. After applying the regression model to convert hydroacoustic backscatter to zooplankton dry weight biomass, we used geostatistics to analyze the mean and variance, and ordinary kriging to create spatial zooplankton distribution maps. The mean zooplankton dry weight biomass estimates from plankton net tows and hydroacoustics were not significantly different (p = 0.19) but the hydroacoustic data had a significantly lower coefficient of variation (p < 0.001). The maps of zooplankton distribution illustrated spatial trends in zooplankton dry weight biomass that were not discernable from the overall means.

  1. Genetic variation in biomass traits among 20 diverse rice varieties.

    PubMed

    Jahn, Courtney E; Mckay, John K; Mauleon, Ramil; Stephens, Janice; McNally, Kenneth L; Bush, Daniel R; Leung, Hei; Leach, Jan E

    2011-01-01

    Biofuels provide a promising route of producing energy while reducing reliance on petroleum. Developing sustainable liquid fuel production from cellulosic feedstock is a major challenge and will require significant breeding efforts to maximize plant biomass production. Our approach to elucidating genes and genetic pathways that can be targeted for improving biomass production is to exploit the combination of genomic tools and genetic diversity in rice (Oryza sativa). In this study, we analyzed a diverse set of 20 recently resequenced rice varieties for variation in biomass traits at several different developmental stages. The traits included plant size and architecture, aboveground biomass, and underlying physiological processes. We found significant genetic variation among the 20 lines in all morphological and physiological traits. Although heritability estimates were significant for all traits, heritabilities were higher in traits relating to plant size and architecture than for physiological traits. Trait variation was largely explained by variety and breeding history (advanced versus landrace) but not by varietal groupings (indica, japonica, and aus). In the context of cellulosic biofuels development, cell wall composition varied significantly among varieties. Surprisingly, photosynthetic rates among the varieties were inversely correlated with biomass accumulation. Examining these data in an evolutionary context reveals that rice varieties have achieved high biomass production via independent developmental and physiological pathways, suggesting that there are multiple targets for biomass improvement. Future efforts to identify loci and networks underlying this functional variation will facilitate the improvement of biomass traits in other grasses being developed as energy crops. PMID:21062890

  2. Achieving Accuracy Requirements for Forest Biomass Mapping: A Data Fusion Method for Estimating Forest Biomass and LiDAR Sampling Error with Spaceborne Data

    NASA Technical Reports Server (NTRS)

    Montesano, P. M.; Cook, B. D.; Sun, G.; Simard, M.; Zhang, Z.; Nelson, R. F.; Ranson, K. J.; Lutchke, S.; Blair, J. B.

    2012-01-01

    The synergistic use of active and passive remote sensing (i.e., data fusion) demonstrates the ability of spaceborne light detection and ranging (LiDAR), synthetic aperture radar (SAR) and multispectral imagery for achieving the accuracy requirements of a global forest biomass mapping mission. This data fusion approach also provides a means to extend 3D information from discrete spaceborne LiDAR measurements of forest structure across scales much larger than that of the LiDAR footprint. For estimating biomass, these measurements mix a number of errors including those associated with LiDAR footprint sampling over regional - global extents. A general framework for mapping above ground live forest biomass (AGB) with a data fusion approach is presented and verified using data from NASA field campaigns near Howland, ME, USA, to assess AGB and LiDAR sampling errors across a regionally representative landscape. We combined SAR and Landsat-derived optical (passive optical) image data to identify forest patches, and used image and simulated spaceborne LiDAR data to compute AGB and estimate LiDAR sampling error for forest patches and 100m, 250m, 500m, and 1km grid cells. Forest patches were delineated with Landsat-derived data and airborne SAR imagery, and simulated spaceborne LiDAR (SSL) data were derived from orbit and cloud cover simulations and airborne data from NASA's Laser Vegetation Imaging Sensor (L VIS). At both the patch and grid scales, we evaluated differences in AGB estimation and sampling error from the combined use of LiDAR with both SAR and passive optical and with either SAR or passive optical alone. This data fusion approach demonstrates that incorporating forest patches into the AGB mapping framework can provide sub-grid forest information for coarser grid-level AGB reporting, and that combining simulated spaceborne LiDAR with SAR and passive optical data are most useful for estimating AGB when measurements from LiDAR are limited because they minimized

  3. Estimating the loss of C, N and microbial biomass from Biological Soil Crusts under simulated rainfall

    NASA Astrophysics Data System (ADS)

    Gommeaux, M.; Malam Issa, O.; Bouchet, T.; Valentin, C.; Rajot, J.-L.; Bertrand, I.; Alavoine, G.; Desprats, J.-F.; Cerdan, O.; Fatondji, D.

    2012-04-01

    Most areas where biological soil crusts (BSC) develop undergo a climate with heavy but sparse rainfall events. The hydrological response of the BSC, namely the amount of runoff, is highly variable. Rainfall simulation experiments were conducted in Sadoré, south-western Niger. The aim was to estimate the influence of the BSC coverage on the quantity and quality of water, particles and solutes exported during simulated rainfall events. Ten 1 m2 plots were selected based on their various degree of BSC cover (4-89%) and type of underlying physical crust (structural or erosion crusts). The plots are located on similar sandy soil with moderate slope (3-6%). The experiments consisted of two rainfall events, spaced at 22-hours interval: 60 mm/h for 20 min, and 120 mm/h for 10 min. During each experiments particles dectached and runoff water were collected and filtered in the laboratory. C and N content were determined both in water and sediments samples.. These analyses were completed by measurements of phospholipid fatty acids and chlorophyll a contents in sediments and BSC samples collected before and after the rainfall. Mineral N and microbial biomass carbon of BSC samples were also analysed. The results confirmed that BSC reduce the loss of particles and exert a protective effect on soils with regard to particle detachment by raindrop. However there is no general relationship between the BSC coverage and the loss of C and N due to runoff. Contrarily, the C and N content in the sediments is negatively correlated to their mass. The type of physical crust on which the BSC develop also has to be taken into account. These results will contribute to the region-wide modeling of the role of BSC in biogeochemical cycles.

  4. Harvesting Duke FACE: improving estimates of productivity and biomass under elevated CO2

    NASA Astrophysics Data System (ADS)

    McCarthy, H. R.; Oren, R.; Kim, D.; Tor-ngern, P.; Johnsen, K. H.; Maier, C. A.

    2013-12-01

    Free air CO2 enrichment experiments (FACE) have greatly advanced our knowledge on the impacts of increasing atmospheric CO2 concentrations in developing and mature ecosystems. These experiments have provided years of data on changes in physiology and ecosystem functions, such as photosynthesis, water use, net primary productivity (NPP), ecosystem carbon storage, and nutrient cycling. As these experiments come to a close, there has also been the opportunity to add critically lacking biometric data, which can be obtained only through destructive measurements. After 15 years of CO2 elevation at the Duke Forest FACE, a 28 year old pine plantation with a hardwood understory, a vast array of biometric data was obtained through harvesting of >1150 trees in both elevated and ambient CO2 plots. Harvested trees included pines and hardwoods, understory and overstory trees. The harvest provided direct assessments of leaf, stem and branch biomass, as well as the vertical distribution of these masses. In combination with leaf and wood level properties (e.g. specific leaf area, wood density), it was possible to explore potential CO2 effects on allometric relationships between plant parts, and stem and canopy shape and distribution. Although stimulatory effects of elevated CO2 on NPP are well established in this forest (averaging 27%), harvest results thus far indicate few changes in basic allometric relationships, such as height-diameter relationships, proportion of mass contained in different plant parts (stems vs. leaves vs. branches), distribution of leaves within the canopy and stem shape. The coupling of site-specific biometric relationships with long-term data on tree growth and mortality will reduce current sources of uncertainty in estimates of NPP and carbon storage under future increased CO2 conditions. Recent efforts in data-model synthesis have demonstrated the critical need for such data as constraints and initial values in ecosystem and earth system models; these

  5. Above-ground woody carbon sequestration measured from tree rings is coherent with net ecosystem productivity at five eddy-covariance sites.

    PubMed

    Babst, Flurin; Bouriaud, Olivier; Papale, Dario; Gielen, Bert; Janssens, Ivan A; Nikinmaa, Eero; Ibrom, Andreas; Wu, Jian; Bernhofer, Christian; Köstner, Barbara; Grünwald, Thomas; Seufert, Günther; Ciais, Philippe; Frank, David

    2014-03-01

    • Attempts to combine biometric and eddy-covariance (EC) quantifications of carbon allocation to different storage pools in forests have been inconsistent and variably successful in the past. • We assessed above-ground biomass changes at five long-term EC forest stations based on tree-ring width and wood density measurements, together with multiple allometric models. Measurements were validated with site-specific biomass estimates and compared with the sum of monthly CO₂ fluxes between 1997 and 2009. • Biometric measurements and seasonal net ecosystem productivity (NEP) proved largely compatible and suggested that carbon sequestered between January and July is mainly used for volume increase, whereas that taken up between August and September supports a combination of cell wall thickening and storage. The inter-annual variability in above-ground woody carbon uptake was significantly linked with wood production at the sites, ranging between 110 and 370 g C m(-2) yr(-1) , thereby accounting for 10-25% of gross primary productivity (GPP), 15-32% of terrestrial ecosystem respiration (TER) and 25-80% of NEP. • The observed seasonal partitioning of carbon used to support different wood formation processes refines our knowledge on the dynamics and magnitude of carbon allocation in forests across the major European climatic zones. It may thus contribute, for example, to improved vegetation model parameterization and provides an enhanced framework to link tree-ring parameters with EC measurements. PMID:24206564

  6. Using LANDSAT digital data for estimating green biomass. [Throckmorton, Texas test site and Great Plans Corridor, US

    NASA Technical Reports Server (NTRS)

    Deering, D. W.; Haas, R. H. (Principal Investigator)

    1980-01-01

    The author has identified the following significant results. Relationships between the quantity of mixed prairie rangeland vegetation and LANDSAT MSS response were studied during four growing seasons at test sites throughout the United States Great Plans region. A LANDSAT derived parameter, the normalized difference was developed from theoretical considerations fro statistical estimation of the amount and seasonal condition of rangeland vegetation. This parameter was tested for application to local assessment of green forage biomass and regional monitoring of range feed conditions and drought. Results show that for grasslands in the Great Plains with near continuous vegetative cover and free of heavy brush and forbs, the LANDSAT digital data can provide a useful estimate of the quantity of green forage biomass (within 250 kg/ha), and at least five levels of pasture and range feed conditions can be adequately mapped for extended regions.

  7. A Rao-Blackwellized particle filter for joint parameter estimation and biomass tracking in a stochastic predator-prey system.

    PubMed

    Martín-Fernández, Laura; Gilioli, Gianni; Lanzarone, Ettore; Miguez, Joaquin; Pasquali, Sara; Ruggeri, Fabrizio; Ruiz, Diego P

    2014-06-01

    Functional response estimation and population tracking in predator-prey systems are critical problems in ecology. In this paper we consider a stochastic predator-prey system with a Lotka-Volterra functional response and propose a particle filtering method for: (a) estimating the behavioral parameter representing the rate of effective search per predator in the functional response and (b) forecasting the population biomass using field data. In particular, the proposed technique combines a sequential Monte Carlo sampling scheme for tracking the time-varying biomass with the analytical integration of the unknown behavioral parameter. In order to assess the performance of the method, we show results for both synthetic and observed data collected in an acarine predator-prey system, namely the pest mite Tetranychus urticae and the predatory mite Phytoseiulus persimilis. PMID:24506552

  8. Biomass production efficiency controlled by management in temperate and boreal ecosystems

    NASA Astrophysics Data System (ADS)

    Campioli, M.; Vicca, S.; Luyssaert, S.; Bilcke, J.; Ceschia, E.; Chapin, F. S., III; Ciais, P.; Fernández-Martínez, M.; Malhi, Y.; Obersteiner, M.; Olefeldt, D.; Papale, D.; Piao, S. L.; Peñuelas, J.; Sullivan, P. F.; Wang, X.; Zenone, T.; Janssens, I. A.

    2015-11-01

    Plants acquire carbon through photosynthesis to sustain biomass production, autotrophic respiration and production of non-structural compounds for multiple purposes. The fraction of photosynthetic production used for biomass production, the biomass production efficiency, is a key determinant of the conversion of solar energy to biomass. In forest ecosystems, biomass production efficiency was suggested to be related to site fertility. Here we present a database of biomass production efficiency from 131 sites compiled from individual studies using harvest, biometric, eddy covariance, or process-based model estimates of production. The database is global, but dominated by data from Europe and North America. We show that instead of site fertility, ecosystem management is the key factor that controls biomass production efficiency in terrestrial ecosystems. In addition, in natural forests, grasslands, tundra, boreal peatlands and marshes, biomass production efficiency is independent of vegetation, environmental and climatic drivers. This similarity of biomass production efficiency across natural ecosystem types suggests that the ratio of biomass production to gross primary productivity is constant across natural ecosystems. We suggest that plant adaptation results in similar growth efficiency in high- and low-fertility natural systems, but that nutrient influxes under managed conditions favour a shift to carbon investment from the belowground flux of non-structural compounds to aboveground biomass.

  9. Aboveground and belowground responses to nutrient additions and herbivore exclusion in Arctic tundra ecosystems in northern Alaska

    NASA Astrophysics Data System (ADS)

    Moore, J. C.; Gough, L.; Simpson, R.; Johnson, D. R.

    2011-12-01

    The Arctic has experienced significant increased regional warming over the past 30 years. Warming generally increases tundra soil nutrient availability by creating a more favorable environment for plant growth, decomposition and nutrient mineralization. Aboveground there has been a "greening" of the Arctic with increased net primary productivity (NPP), and an increase in woody vegetation. Concurrent with the changes aboveground has been an increase in root growth at lower depths and a loss of soil organic C (40 -100 g C m-2 yr-1). Given that arctic soils contain 14% of the global soil C pool, understanding the mechanisms behind shifts of this magnitude that are changing arctic soils from a net sink to a net source of atmospheric C is critical. We took an integrated multi-trophic level approach to examine how altering soil nutrients and mammalian herbivore activity affects vegetation, soil fauna, and microbial communities as well as soil physical characteristics in moist acidic (MAT) and dry heath (DH) tundra. Our work was conducted at the Arctic LTER site in northern Alaska. We sampled the nutrient (controls and annual N+P additions) and herbivore (controls and exclosures) manipulations established in 1996 after 10 years of treatment. Models that incorporated the biomass estimates from the field were used to characterize the trophic structure of the belowground food web and to estimate carbon flux among soil organisms and C-mineralization rates. Both MAT and DH exhibited significant increases in NPP and root growth and changes in vegetation structure with transitions from a mixed community to deciduous shrubs in MAT and from lichens to grasses and shrubs in DH, with nutrient additions and herbivore exclosures. Belowground responses to the treatments were dependent on ecosystem type, but exposed alterations in trophic structure that included changes in microbial biomass, the establishment of microbivorous enchytreaids, increases in root-feeding nematodes, and

  10. Estimating hillslope-scale soil strength for regional landslide forecasting

    NASA Astrophysics Data System (ADS)

    Hales, Tristram; Miniat, Chelcy; Hwang, Taehee; Band, Lawrence

    2015-04-01

    Estimating the distribution of soil strength across hillslopes is essential for the forecasting of future landslide hazards. This challenge is particularly difficult as the distribution of soil and plant root properties that control soil strength are difficult to estimate at a hillslope-scale. The distribution of root strength is of particular importance in soil-mantled mountain ranges, where colluvial soils provide little additional cohesion to the soil. We present a novel, new model of hillslope-scale root cohesion that combines empirical relationships of root biomass based on extensive field experimentation with remote sensing of aboveground biomass using airborne LiDAR. Our field experiments show that root biomass, its diameter distribution with depth, and root elastic properties vary systematically along hillslope scale soil moisture gradients. Higher elevation trees on drier noses have proportionally greater belowground biomass than lower elevation plants and those in hollows. Root strengths vary as a function of local soil moisture contents and the average wood density of the species of interest. Allocation ratios of above to belowground biomass also vary systematically with age. Our empirical data show systematic changes in the allocation of aboveground and belowground biomass along age and soil moisture gradients. We combine these topographic functional relationships with measurements of aboveground biomass based on LiDAR-derived canopy heights to map the spatial distribution of root cohesion across the Southern Appalachians. To validate this new model of root reinforcement we coupled the new root cohesion with the shallow landslide model SINMAP and compared our predicted ranges of instability with a landslide inventory collected in the area. The new root cohesion model significantly improves our prediction of root cohesion, reducing the number of false positive results. We show that this new, physiologically based model of root cohesion can be upscaled to

  11. Aboveground storage tanks: Understanding the rules

    SciTech Connect

    Kitchen, T.; McCallion, J.

    1995-10-01

    Facility owners and operators using aboveground tanks for storing or processing hazardous wastes or oils must follow Environmental Protection Agency (EPA) or Occupational Safety and Health Administration (OSHA) regulations, or they risk heavy fines and penalties. Every facility storing more than 1320 gallons of hazardous waste or oil aboveground or more than 660 gallons in a single tank are required to have a spill prevention control and countermeasures plan. Given in the article is a table of aboveground tank standards under various agencies or acts. The subject and location of these regulations from the EPA, OSHA, Resource Conservation and Recovery Act (RCRA), Underwriter`s Laboratory (UL), the American Petroleum Institute (API), and the American Society of Mechanical Engineers (ASME) cover various aspects of tank construction and safety. Understanding and complying with the codes and regulations can be arduous, but the rewards in safety and environmental stewardship and the potential savings in fines make the effort worthwhile.

  12. Biomass burning emissions of reactive gases estimated from satellite data analysis and ecosystem modeling for the Brazilian Amazon region

    NASA Astrophysics Data System (ADS)

    Potter, Christopher; Brooks-Genovese, Vanessa; Klooster, Steven; Torregrosa, Alicia

    2002-10-01

    To produce a new daily record of trace gas emissions from biomass burning events for the Brazilian Legal Amazon, we have combined satellite advanced very high resolution radiometer (AVHRR) data on fire counts together for the first time with vegetation greenness imagery as inputs to an ecosystem biomass model at 8 km spatial resolution. This analysis goes beyond previous estimates for reactive gas emissions from Amazon fires, owing to a more detailed geographic distribution estimate of vegetation biomass, coupled with daily fire activity for the region (original 1 km resolution), and inclusion of fire effects in extensive areas of the Legal Amazon (defined as the Brazilian states of Acre, Amapá, Amazonas, Maranhao, Mato Grosso, Pará, Rondônia, Roraima, and Tocantins) covered by open woodland, secondary forests, savanna, and pasture vegetation. Results from our emissions model indicate that annual emissions from Amazon deforestation and biomass burning in the early 1990s total to 102 Tg yr-1 carbon monoxide (CO) and 3.5 Tg yr-1 nitrogen oxides (NOx). Peak daily burning emissions, which occurred in early September 1992, were estimated at slightly more than 3 Tg d-1for CO and 0.1 Tg d-1for NOx flux to the atmosphere. Other burning source fluxes of gases with relatively high emission factors are reported, including methane (CH4), nonmethane hydrocarbons (NMHC), and sulfur dioxide (SO2), in addition to total particulate matter (TPM). We estimate the Brazilian Amazon region to be a source of between one fifth and one third for each of these global emission fluxes to the atmosphere. The regional distribution of burning emissions appears to be highest in the Brazilian states of Maranhao and Tocantins, mainly from burning outside of moist forest areas, and in Pará and Mato Grosso, where we identify important contributions from primary forest cutting and burning. These new daily emission estimates of reactive gases from biomass burning fluxes are designed to be used as

  13. Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data

    NASA Astrophysics Data System (ADS)

    Ramoelo, Abel; Cho, M. A.; Mathieu, R.; Madonsela, S.; van de Kerchove, R.; Kaszta, Z.; Wolff, E.

    2015-12-01

    Land use and climate change could have huge impacts on food security and the health of various ecosystems. Leaf nitrogen (N) and above-ground biomass are some of the key factors limiting agricultural production and ecosystem functioning. Leaf N and biomass can be used as indicators of rangeland quality and quantity. Conventional methods for assessing these vegetation parameters at landscape scale level are time consuming and tedious. Remote sensing provides a bird-eye view of the landscape, which creates an opportunity to assess these vegetation parameters over wider rangeland areas. Estimation of leaf N has been successful during peak productivity or high biomass and limited studies estimated leaf N in dry season. The estimation of above-ground biomass has been hindered by the signal saturation problems using conventional vegetation indices. The objective of this study is to monitor leaf N and above-ground biomass as an indicator of rangeland quality and quantity using WorldView-2 satellite images and random forest technique in the north-eastern part of South Africa. Series of field work to collect samples for leaf N and biomass were undertaken in March 2013, April or May 2012 (end of wet season) and July 2012 (dry season). Several conventional and red edge based vegetation indices were computed. Overall results indicate that random forest and vegetation indices explained over 89% of leaf N concentrations for grass and trees, and less than 89% for all the years of assessment. The red edge based vegetation indices were among the important variables for predicting leaf N. For the biomass, random forest model explained over 84% of biomass variation in all years, and visible bands including red edge based vegetation indices were found to be important. The study demonstrated that leaf N could be monitored using high spatial resolution with the red edge band capability, and is important for rangeland assessment and monitoring.

  14. Beak measurements of octopus ( Octopus variabilis) in Jiaozhou Bay and their use in size and biomass estimation

    NASA Astrophysics Data System (ADS)

    Xue, Ying; Ren, Yiping; Meng, Wenrong; Li, Long; Mao, Xia; Han, Dongyan; Ma, Qiuyun

    2013-09-01

    Cephalopods play key roles in global marine ecosystems as both predators and preys. Regressive estimation of original size and weight of cephalopod from beak measurements is a powerful tool of interrogating the feeding ecology of predators at higher trophic levels. In this study, regressive relationships among beak measurements and body length and weight were determined for an octopus species ( Octopus variabilis), an important endemic cephalopod species in the northwest Pacific Ocean. A total of 193 individuals (63 males and 130 females) were collected at a monthly interval from Jiaozhou Bay, China. Regressive relationships among 6 beak measurements (upper hood length, UHL; upper crest length, UCL; lower hood length, LHL; lower crest length, LCL; and upper and lower beak weights) and mantle length (ML), total length (TL) and body weight (W) were determined. Results showed that the relationships between beak size and TL and beak size and ML were linearly regressive, while those between beak size and W fitted a power function model. LHL and UCL were the most useful measurements for estimating the size and biomass of O. variabilis. The relationships among beak measurements and body length (either ML or TL) were not significantly different between two sexes; while those among several beak measurements (UHL, LHL and LBW) and body weight (W) were sexually different. Since male individuals of this species have a slightly greater body weight distribution than female individuals, the body weight was not an appropriate measurement for estimating size and biomass, especially when the sex of individuals in the stomachs of predators was unknown. These relationships provided essential information for future use in size and biomass estimation of O. variabilis, as well as the estimation of predator/prey size ratios in the diet of top predators.

  15. Estimating fresh biomass of maize plants from their RGB images in greenhouse phenotyping

    NASA Astrophysics Data System (ADS)

    Ge, Yufeng; Pandey, Piyush; Bai, Geng

    2016-05-01

    High throughput phenotyping (HTP) is an emerging frontier field across many basic and applied plant science disciplines. RGB imaging is most widely used in HTP to extract image-based phenotypes such as pixel volume or projected area. These image-based phenotypes are further used to derive plant physical parameters including plant fresh biomass, plant dry biomass, water use efficiency etc. In this paper, we investigated the robustness of regression models to predict fresh biomass of maize plants from image-based phenotypes. Data used in this study were from three different experiments. Data were grouped into five datasets, two for model development and three for independent model validation. Three image-derived phenotypes were investigated: BioVolume, Projected.Area.1, and Projected.Area.2. Models were assessed with R2, Bias, and RMSEP (Root Mean Squared Error of Prediction). The results showed that almost all models were validated with high R2 values, indicating that these digital phenotypes can be useful to rank plant biomass on a relative basis. However, in many occasions when accurate prediction of plant biomass is needed, it is important for researchers to know that models that relate image-based phenotypes to plant biomass should be carefully constructed. Our results show that the range of plant size and the genotypic diversity of the calibration sets in relation to the validation sets have large impact on the model accuracy. Large maize plants cause systematic bias as they grow toward the top-view camera. Excluding top-view images from modeling can there benefit modeling for the experiments involving large maize plants.

  16. Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression

    NASA Astrophysics Data System (ADS)

    Cho, Moses Azong; Skidmore, Andrew; Corsi, Fabio; van Wieren, Sipke E.; Sobhan, Istiak

    2007-12-01

    The main objective was to determine whether partial least squares (PLS) regression improves grass/herb biomass estimation when compared with hyperspectral indices, that is normalised difference vegetation index (NDVI) and red-edge position (REP). To achieve this objective, fresh green grass/herb biomass and airborne images (HyMap) were collected in the Majella National Park, Italy in the summer of 2005. The predictive performances of hyperspectral indices and PLS regression models were then determined and compared using calibration ( n = 30) and test ( n = 12) data sets. The regression model derived from NDVI computed from bands at 740 and 771 nm produced a lower standard error of prediction (SEP = 264 g m -2) on the test data compared with the standard NDVI involving bands at 665 and 801 nm (SEP = 331 g m -2), but comparable results with REPs determined by various methods (SEP = 261 to 295 g m -2). PLS regression models based on original, derivative and continuum-removed spectra produced lower prediction errors (SEP = 149 to 256 g m -2) compared with NDVI and REP models. The lowest prediction error (SEP = 149 g m -2, 19% of mean) was obtained with PLS regression involving continuum-removed bands. In conclusion, PLS regression based on airborne hyperspectral imagery provides a better alternative to univariate regression involving hyperspectral indices for grass/herb biomass estimation in the Majella National Park.

  17. Estimating the variable cost for high-volume and long-haul transportation of densified biomass and biofuel

    SciTech Connect

    Jacob J. Jacobson; Erin Searcy; Md. S. Roni; Sandra D. Eksioglu

    2014-06-01

    This article analyzes rail transportation costs of products that have similar physical properties as densified biomass and biofuel. The results of this cost analysis are useful to understand the relationship and quantify the impact of a number of factors on rail transportation costs of denisfied biomass and biofuel. These results will be beneficial and help evaluate the economic feasibility of high-volume and long-haul transportation of biomass and biofuel. High-volume and long-haul rail transportation of biomass is a viable transportation option for biofuel plants, and for coal plants which consider biomass co-firing. Using rail optimizes costs, and optimizes greenhouse gas (GHG) emissions due to transportation. Increasing bioenergy production would consequently result in lower GHG emissions due to displacing fossil fuels. To estimate rail transportation costs we use the carload waybill data, provided by Department of Transportation’s Surface Transportation Board for products such as grain and liquid type commodities for 2009 and 2011. We used regression analysis to quantify the relationship between variable transportation unit cost ($/ton) and car type, shipment size, rail movement type, commodity type, etc. The results indicate that: (a) transportation costs for liquid is $2.26/ton–$5.45/ton higher than grain type commodity; (b) transportation costs in 2011 were $1.68/ton–$5.59/ton higher than 2009; (c) transportation costs for single car shipments are $3.6/ton–$6.68/ton higher than transportation costs for multiple car shipments of grains; (d) transportation costs for multiple car shipments are $8.9/ton and $17.15/ton higher than transportation costs for unit train shipments of grains.

  18. Spatially explicit estimates of stock size, structure and biomass of North Atlantic albacore tuna (Thunnus alalunga)

    NASA Astrophysics Data System (ADS)

    Lehodey, P.; Senina, I.; Dragon, A.-C.; Arrizabalaga, H.

    2014-09-01

    The development of the ecosystem approach and models for the management of ocean marine resources requires easy access to standard validated data sets of historical catch data for the main exploited species. They are used to measure the impact of biomass removal by fisheries and to evaluate the models outputs, while the use of a standard data set facilitates models inter-comparison. Unlike standard stock assessment models, new state-of-the-art ecosystem models require geo-referenced fishing data with the highest possible spatial resolution. This study presents an application to the North Atlantic albacore tuna stock with a careful definition and validation of a spatially explicit fishing data set prepared from publicly available sources (ICCAT) for its use in a spatial ecosystem and population dynamics model (SEAPODYM) to provide the first spatially explicit estimate of albacore density in the North Atlantic by life stage. Density distributions together with the fishing data used for the estimates are provided at http://doi.pangaea.de/ (see section Source Data References) (doi:10.1594/PANGAEA.828115; doi:10.1594/PANGAEA.828226; doi:10.1594/PANGAEA.828227; doi:10.1594/PANGAEA.828228; doi:10.1594/PANGAEA.828229; doi:10.1594/PANGAEA.828230; doi:10.1594/PANGAEA.828231; doi:10.1594/PANGAEA.828232; doi:10.1594/PANGAEA.828232; doi:10.1594/PANGAEA.828233; Sub-Pixel Reflectance Unmixing in Estimating Vegetation Water Content and Dry Biomass of Corn and Soybeans Cropland using Normalized Difference Water Index (NDWI) from Satellites

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Estimating vegetation cover, water content and dry biomass from space plays a significant role in a variety of scientific fields including drought monitoring, climate modeling, and agricultural prediction. However, getting accurate and consistent measurements of vegetation is complicated very often ...

  19. Emission estimates of organic and elemental carbon from household biomass fuel used over the Indo-Gangetic Plain (IGP), India

    NASA Astrophysics Data System (ADS)

    Saud, T.; Gautam, R.; Mandal, T. K.; Gadi, Ranu; Singh, D. P.; Sharma, S. K.; Dahiya, Manisha; Saxena, M.

    2012-12-01

    Biomass burning emits large amount of aerosols and trace gases into the atmosphere, which have significant impact on atmospheric chemistry and climate. In the present study, we have selected seven Indian states (Delhi, Punjab, Haryana, Uttar Pradesh, Uttarakhand, Bihar and West Bengal) over the IGP, India. Samples of biomass fuel (Fuel Wood, Crop Residue and Dung Cake) from rural household have been collected (Saud et al., 2011a). The burning process has been simulated using a dilution sampler following the methodology developed by Venkatraman et al. (2005). In the present study, emission factor represents the total period of burning including pyrolysis, flaming and smoldering. We have determined the emission factors of organic carbon (OC) and elemental carbon (EC) from different types of biomass fuels collected over the study area. Average emission factors of OC from dung cake, fuel wood and crop residue over IGP, India are estimated as 3.87 ± 1.09 g kg-1, 0.95 ± 0.27 g kg-1, 1.46 ± 0.73 g kg-1, respectively. Similarly, average emission factors of EC from dung cake, fuel wood and crop residue over IGP, India are found to be 0.49 ± 0.25 g kg-1, 0.35 ± 0.07 g kg-1 and 0.37 ± 0.14 g kg-1, respectively. Dung cake and crop residue are normally not used in Uttarakhand. Annual budget of OC and EC from biomass fuels used as energy in rural households of IGP, India is estimated as 361.96 ± 170.18 Gg and 56.44 ± 29.06 Gg respectively. This study shows the regional emission inventory from Indian scenario with spatial variability.

  1. Biomass resilience of Neotropical secondary forests

    NASA Astrophysics Data System (ADS)

    Poorter, Lourens; Bongers, Frans; Aide, T. Mitchell; Almeyda Zambrano, Angélica M.; Balvanera, Patricia; Becknell, Justin M.; Boukili, Vanessa; Brancalion, Pedro H. S.; Broadbent, Eben N.; Chazdon, Robin L.; Craven, Dylan; de Almeida-Cortez, Jarcilene S.; Cabral, George A. L.; de Jong, Ben H. J.; Denslow, Julie S.; Dent, Daisy H.; Dewalt, Saara J.; Dupuy, Juan M.; Durán, Sandra M.; Espírito-Santo, Mario M.; Fandino, María C.; César, Ricardo G.; Hall, Jefferson S.; Hernandez-Stefanoni, José Luis; Jakovac, Catarina C.; Junqueira, André B.; Kennard, Deborah; Letcher, Susan G.; Licona, Juan-Carlos; Lohbeck, Madelon; Marín-Spiotta, Erika; Martínez-Ramos, Miguel; Massoca, Paulo; Meave, Jorge A.; Mesquita, Rita; Mora, Francisco; Muñoz, Rodrigo; Muscarella, Robert; Nunes, Yule R. F.; Ochoa-Gaona, Susana; de Oliveira, Alexandre A.; Orihuela-Belmonte, Edith; Peña-Claros, Marielos; Pérez-García, Eduardo A.; Piotto, Daniel; Powers, Jennifer S.; Rodríguez-Velázquez, Jorge; Romero-Pérez, I. Eunice; Ruíz, Jorge; Saldarriaga, Juan G.; Sanchez-Azofeifa, Arturo; Schwartz, Naomi B.; Steininger, Marc K.; Swenson, Nathan G.; Toledo, Marisol; Uriarte, Maria; van Breugel, Michiel; van der Wal, Hans; Veloso, Maria D. M.; Vester, Hans F. M.; Vicentini, Alberto; Vieira, Ima C. G.; Bentos, Tony Vizcarra; Williamson, G. Bruce; Rozendaal, Danaë M. A.

    2016-02-01

    Land-use change occurs nowhere more rapidly than in the tropics, where the imbalance between deforestation and forest regrowth has large consequences for the global carbon cycle. However, considerable uncertainty remains about the rate of biomass recovery in secondary forests, and how these rates are influenced by climate, landscape, and prior land use. Here we analyse aboveground biomass recovery during secondary succession in 45 forest sites and about 1,500 forest plots covering the major environmental gradients in the Neotropics. The studied secondary forests are highly productive and resilient. Aboveground biomass recovery after 20 years was on average 122 megagrams per hectare (Mg ha-1), corresponding to a net carbon uptake of 3.05 Mg C ha-1 yr-1, 11 times the uptake rate of old-growth forests. Aboveground biomass stocks took a median time of 66 years to recover to 90% of old-growth values. Aboveground biomass recovery after 20 years varied 11.3-fold (from 20 to 225 Mg ha-1) across sites, and this recovery increased with water availability (higher local rainfall and lower climatic water deficit). We present a biomass recovery map of Latin America, which illustrates geographical and climatic variation in carbon sequestration potential during forest regrowth. The map will support policies to minimize forest loss in areas where biomass resilience is naturally low (such as seasonally dry forest regions) and promote forest regeneration and restoration in humid tropical lowland areas with high biomass resilience.

  2. Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data

    NASA Astrophysics Data System (ADS)

    Dong, Taifeng; Liu, Jiangui; Qian, Budong; Zhao, Ting; Jing, Qi; Geng, Xiaoyuan; Wang, Jinfei; Huffman, Ted; Shang, Jiali

    2016-07-01

    A sufficient number of satellite acquisitions in a growing season are essential for deriving agronomic indicators, such as green leaf area index (GLAI), to be assimilated into crop models for crop productivity estimation. However, for most high resolution orbital optical satellites, it is often difficult to obtain images frequently due to their long revisit cycles and unfavorable weather conditions. Data fusion algorithms, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), have been developed to generate synthetic data with high spatial and temporal resolution to address this issue. In this study, we evaluated the approach of assimilating GLAI into the Simple Algorithm for Yield Estimation model (SAFY) for winter wheat biomass estimation. GLAI was estimated using the two-band Enhanced Vegetation Index (EVI2) derived from data acquired by the Operational Land Imager (OLI) onboard the Landsat-8 and a fusion dataset generated by blending the Moderate-Resolution Imaging Spectroradiometer (MODIS) data and the OLI data using the STARFM and ESTARFM models. The fusion dataset had the temporal resolution of the MODIS data and the spatial resolution of the OLI data. Key parameters of the SAFY model were optimised through assimilation of the estimated GLAI into the crop model using the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm. A good agreement was achieved between the estimated and field measured biomass by assimilating the GLAI derived from the OLI data (GLAIL) alone (R2 = 0.77 and RMSE = 231 g m-2). Assimilation of GLAI derived from the fusion dataset (GLAIF) resulted in a R2 of 0.71 and RMSE of 193 g m-2 while assimilating the com