Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions

PubMed Central

Wilson, Adam M.; Jetz, Walter

2016-01-01

Cloud cover can influence numerous important ecological processes, including reproduction, growth, survival, and behavior, yet our assessment of its importance at the appropriate spatial scales has remained remarkably limited. If captured over a large extent yet at sufficiently fine spatial grain, cloud cover dynamics may provide key information for delineating a variety of habitat types and predicting species distributions. Here, we develop new near-global, fine-grain (≈1 km) monthly cloud frequencies from 15 y of twice-daily Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images that expose spatiotemporal cloud cover dynamics of previously undocumented global complexity. We demonstrate that cloud cover varies strongly in its geographic heterogeneity and that the direct, observation-based nature of cloud-derived metrics can improve predictions of habitats, ecosystem, and species distributions with reduced spatial autocorrelation compared to commonly used interpolated climate data. These findings support the fundamental role of remote sensing as an effective lens through which to understand and globally monitor the fine-grain spatial variability of key biodiversity and ecosystem properties. PMID:27031693

Improving Simulations of Fine Dust Surface Concentrations over the Western United States by Optimizing the Particle Size Distribution

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

Zhang, Li; Kok, Jasper F.; Henze, Daven

2013-06-28

To improve estimates of remote contributions of dust to fine particulate matter (PM2.5) in the western United States, new dust particle size distributions (PSDs) based upon scale-invariant fragmentation theory (Kok_PSD) with constraints from in situ measurements (IMP_PSD) are implemented in a chemical transport model (GEOS-Chem). Compared to initial simulations, this leads to reductions in the mass of emitted dust particles with radii <1.8 mm by 40%-60%. Consequently, the root-mean-square error in simulated fine dust concentrations compared to springtime surface observations in the western United States is reduced by 67%-81%. The ratio of simulated fine to coarse PM mass is alsomore » improved, which is not achievable by reductions in total dust emissions. The IMP_PSD best represents the PSD of dust transported from remote sources and reduces modeled PM2.5 concentrations up to 5 mg/m3 over the western United States, which is important when considering sources contributing to nonattainment of air quality standards. Citation: Zhang, L., J. F. Kok, D. K. Henze, Q. Li, and C. Zhao (2013), Improving simulations of fine dust surface concentrations over the western United States by optimizing the particle size distribution, Geophys. Res. Lett., 40, 3270-3275, doi:10.1002/grl.50591.« less

Using High Spatial Resolution Satellite Imagery to Map Forest Burn Severity Across Spatial Scales in a Pine Barrens Ecosystem

NASA Technical Reports Server (NTRS)

Meng, Ran; Wu, Jin; Schwager, Kathy L.; Zhao, Feng; Dennison, Philip E.; Cook, Bruce D.; Brewster, Kristen; Green, Timothy M.; Serbin, Shawn P.

2017-01-01

As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (less than or equal to 5 m) from very-high-resolution (VHR) data. We assessed a 432 ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severity was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal - pre- and post-fire event - WorldView-2 (WV-2) imagery at 2 m spatial resolution. We first evaluated our approach using 1 m by 1 m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10 m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15 m by 15 m fixed-area plots (inter-crown scale) with the post-fire 0.10 m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). This work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the less than 30 m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.

Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem

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

Meng, Ran; Wu, Jin; Schwager, Kathy L.

As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (≤ 5 m) from very-high-resolution (VHR) data. Here we assessed a 432 ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severitymore » was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal — pre- and post-fire event — WorldView-2 (WV-2) imagery at 2 m spatial resolution. We first evaluated our approach using 1 m by 1 m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10 m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15 m by 15 m fixed-area plots (inter-crown scale) with the post-fire 0.10 m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). Lastly, this work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the < 30 m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.« less

Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem

DOE PAGES

Meng, Ran; Wu, Jin; Schwager, Kathy L.; ...

2017-01-21

As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (≤ 5 m) from very-high-resolution (VHR) data. Here we assessed a 432 ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severitymore » was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal — pre- and post-fire event — WorldView-2 (WV-2) imagery at 2 m spatial resolution. We first evaluated our approach using 1 m by 1 m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10 m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15 m by 15 m fixed-area plots (inter-crown scale) with the post-fire 0.10 m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). Lastly, this work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the < 30 m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.« less

Applying narrowband remote-sensing reflectance models to wideband data.

PubMed

Lee, Zhongping

2009-06-10

Remote sensing of coastal and inland waters requires sensors to have a high spatial resolution to cover the spatial variation of biogeochemical properties in fine scales. High spatial-resolution sensors, however, are usually equipped with spectral bands that are wide in bandwidth (50 nm or wider). In this study, based on numerical simulations of hyperspectral remote-sensing reflectance of optically-deep waters, and using Landsat band specifics as an example, the impact of a wide spectral channel on remote sensing is analyzed. It is found that simple adoption of a narrowband model may result in >20% underestimation in calculated remote-sensing reflectance, and inversely may result in >20% overestimation in inverted absorption coefficients even under perfect conditions, although smaller (approximately 5%) uncertainties are found for higher absorbing waters. These results provide a cautious note, but also a justification for turbid coastal waters, on applying narrowband models to wideband data.

Determining fine-scale migratory connectivity and habitat selection for a migratory songbird by using new GPS technology

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

Fraser, Kevin C.; Shave, A.; Savage, A.

Migratory aerial insectivores are among the fastest declining avian group, but our understanding of these trends has been limited by poor knowledge of migratory connectivity and the identification of critical habitat across the vast distances they travel annually. Using new, archival GPS loggers, we tracked individual purple martins ( Progne subis) from breeding colonies across North America to determine precise (<10m) locations of migratory and overwintering roost locations in South America and to test hypotheses for fine-scale migratory connectivity and habitat use. We discovered weak migratory connectivity at the roost scale, and extensive, fine-scale mixing of birds in the Amazonmore » from distant (>2000 km) breeding sites, with some individuals sharing the same roosting trees. Despite vast tracts of contiguous forest in this region, birds occupied a much more limited habitat, with most (56%) roosts occurring on small habitat islands that were strongly associated with water. Only 17% of these roosts were in current protected areas. As a result, these data reflect a critical advance in our ability to remotely determine precise migratory connectivity and habitat selection across vast spatial scales, enhancing our understanding of population dynamics and enabling more effective conservation of species at risk.« less

Determining fine-scale migratory connectivity and habitat selection for a migratory songbird by using new GPS technology

DOE PAGES

Fraser, Kevin C.; Shave, A.; Savage, A.; ...

2016-07-27

Migratory aerial insectivores are among the fastest declining avian group, but our understanding of these trends has been limited by poor knowledge of migratory connectivity and the identification of critical habitat across the vast distances they travel annually. Using new, archival GPS loggers, we tracked individual purple martins ( Progne subis) from breeding colonies across North America to determine precise (<10m) locations of migratory and overwintering roost locations in South America and to test hypotheses for fine-scale migratory connectivity and habitat use. We discovered weak migratory connectivity at the roost scale, and extensive, fine-scale mixing of birds in the Amazonmore » from distant (>2000 km) breeding sites, with some individuals sharing the same roosting trees. Despite vast tracts of contiguous forest in this region, birds occupied a much more limited habitat, with most (56%) roosts occurring on small habitat islands that were strongly associated with water. Only 17% of these roosts were in current protected areas. As a result, these data reflect a critical advance in our ability to remotely determine precise migratory connectivity and habitat selection across vast spatial scales, enhancing our understanding of population dynamics and enabling more effective conservation of species at risk.« less

Relationships among vegetation structure, canopy composition, and avian richness patterns across an aspen-conifer forest gradient

Treesearch

Charles E. Swift; Kerri T. Vierling; Andrew T. Hudak; Lee A. Vierling

2017-01-01

Ecologists have a long-term interest in understanding the relative influence of vegetation composition and vegetation structure on avian diversity. LiDAR remote sensing is useful in studying local patterns of avian diversity because it characterizes fine-scale vegetation structure across broad extents. We used LiDAR, aerial and satellite imagery, and avian field data...

Modeling Forest Structure and Vascular Plant Diversity in Piedmont Forests

NASA Astrophysics Data System (ADS)

Hakkenberg, C.

2014-12-01

When the interacting stressors of climate change and land cover/land use change (LCLUC) overwhelm ecosystem resilience to environmental and climatic variability, forest ecosystems are at increased risk of regime shifts and hyperdynamism in process rates. To meet the growing range of novel biotic and environmental stressors on human-impacted ecosystems, the maintenance of taxonomic diversity and functional redundancy in metacommunities has been proposed as a risk spreading measure ensuring that species critical to landscape ecosystem functioning are available for recruitment as local systems respond to novel conditions. This research is the first in a multi-part study to establish a dynamic, predictive model of the spatio-temporal dynamics of vascular plant diversity in North Carolina Piedmont mixed forests using remotely sensed data inputs. While remote sensing technologies are optimally suited to monitor LCLUC over large areas, direct approaches to the remote measurement of plant diversity remain a challenge. This study tests the efficacy of predicting indices of vascular plant diversity using remotely derived measures of forest structural heterogeneity from aerial LiDAR and high spatial resolution broadband optical imagery in addition to derived topo-environmental variables. Diversity distribution modelling of this sort is predicated upon the idea that environmental filtering of dispersing species help define fine-scale (permeable) environmental envelopes within which biotic structural and compositional factors drive competitive interactions that, in addition to background stochasticity, determine fine-scale alpha diversity. Results reveal that over a range of Piedmont forest communities, increasing structural complexity is positively correlated with measures of plant diversity, though the nature of this relationship varies by environmental conditions and community type. The diversity distribution model is parameterized and cross-validated using three high quality vegetation survey datasets, including Duke Forest Korstian permanent plots, Forest Inventory Analysis (FIA), and the scale transgressive, nested module Carolina Vegetation Survey (CVS).

Downscaling SMAP Soil Moisture Using Geoinformation Data and Geostatistics

NASA Astrophysics Data System (ADS)

Xu, Y.; Wang, L.

2017-12-01

Soil moisture is important for agricultural and hydrological studies. However, ground truth soil moisture data for wide area is difficult to achieve. Microwave remote sensing such as Soil Moisture Active Passive (SMAP) can offer a solution for wide coverage. However, existing global soil moisture products only provide observations at coarse spatial resolutions, which often limit their applications in regional agricultural and hydrological studies. This paper therefore aims to generate fine scale soil moisture information and extend soil moisture spatial availability. A statistical downscaling scheme is presented that incorporates multiple fine scale geoinformation data into the downscaling of coarse scale SMAP data in the absence of ground measurement data. Geoinformation data related to soil moisture patterns including digital elevation model (DEM), land surface temperature (LST), land use and normalized difference vegetation index (NDVI) at a fine scale are used as auxiliary environmental variables for downscaling SMAP data. Generalized additive model (GAM) and regression tree are first conducted to derive statistical relationships between SMAP data and auxiliary geoinformation data at an original coarse scale, and residuals are then downscaled to a finer scale via area-to-point kriging (ATPK) by accounting for the spatial correlation information of the input residuals. The results from standard validation scores as well as the triple collocation (TC) method against soil moisture in-situ measurements show that the downscaling method can significantly improve the spatial details of SMAP soil moisture while maintain the accuracy.

Are Remotely Sensed Trends in the Built Environment and Urban Vegetation Predictive of Changes in Metropolitan Housing Markets?

NASA Astrophysics Data System (ADS)

Endsley, K. A.

2017-12-01

In the midst of a global urbanization trend, residential neighborhoods are undergoing a variety of changes, including neighborhood turnover, the re-location of employment centers, and, recently, the increasing social and economic isolation of the suburbs. In the U.S., where residential lawns account for more area than any other irrigated crop (Polsky et al. 2014, in PNAS), coeval changes in residential populations, the built environment, and vegetation have serious implications for urban sustainability. To date, detailed studies of dynamic neighborhood changes have been hampered by the lack of fine time-series data on neighborhood composition. Most notably, the U.S. Census is conducted only once every decade leading to the likely inaccurate assumption of linear change between Census years. To the extent that human activities alter the built environment and urban ecology, can remotely sensed biophysical changes serve as a good proxy for neighborhood socio-economic changes? In this study, I apply time series data on spectral reflectance, spectral indices, and land-cover abundances from 15-to-25 years of Landsat data to fine-scale data on residential property transactions in two metropolitan areas with different regional economic and environmental contexts: Detroit and Los Angeles. The real estate record provides parcel-level, monthly data on sale prices and tax foreclosures; taken together, these provide a good description of the housing market and an acceptable proxy for neighborhood stability. By comparing lagged features from the remote sensing (RS) archive at different time scales in a non-parametric statistical learning algorithm, I identify which RS features best predict changes in the housing market and compare these associations between the two metropolitan areas and across multiple spatial and temporal scales along an urban to peri-urban gradient.

Miniaturized Environmental Scanning Electron Microscope for In Situ Planetary Studies

NASA Technical Reports Server (NTRS)

Gaskin, Jessica; Abbott, Terry; Medley, Stephanie; Gregory, Don; Thaisen, Kevin; Taylor , Lawrence; Ramsey, Brian; Jerman, Gregory; Sampson, Allen; Harvey, Ralph

2010-01-01

The exploration of remote planetary surfaces calls for the advancement of low power, highly-miniaturized instrumentation. Instruments of this nature that are capable of multiple types of analyses will prove to be particularly useful as we prepare for human return to the moon, and as we continue to explore increasingly remote locations in our Solar System. To this end, our group has been developing a miniaturized Environmental-Scanning Electron Microscope (mESEM) capable of remote investigations of mineralogical samples through in-situ topographical and chemical analysis on a fine scale. The functioning of an SEM is well known: an electron beam is focused to nanometer-scale onto a given sample where resulting emissions such as backscattered and secondary electrons, X-rays, and visible light are registered. Raster scanning the primary electron beam across the sample then gives a fine-scale image of the surface topography (texture), crystalline structure and orientation, with accompanying elemental composition. The flexibility in the types of measurements the mESEM is capable of, makes it ideally suited for a variety of applications. The mESEM is appropriate for use on multiple planetary surfaces, and for a variety of mission goals (from science to non-destructive analysis to ISRU). We will identify potential applications and range of potential uses related to planetary exploration. Over the past few of years we have initiated fabrication and testing of a proof-of-concept assembly, consisting of a cold-field-emission electron gun and custom high-voltage power supply, electrostatic electron-beam focusing column, and scanning-imaging electronics plus backscatter detector. Current project status will be discussed. This effort is funded through the NASA Research Opportunities in Space and Earth Sciences - Planetary Instrument Definition and Development Program.

Fine-Scale Exposure to Allergenic Pollen in the Urban Environment: Evaluation of Land Use Regression Approach.

PubMed

Hjort, Jan; Hugg, Timo T; Antikainen, Harri; Rusanen, Jarmo; Sofiev, Mikhail; Kukkonen, Jaakko; Jaakkola, Maritta S; Jaakkola, Jouni J K

2016-05-01

Despite the recent developments in physically and chemically based analysis of atmospheric particles, no models exist for resolving the spatial variability of pollen concentration at urban scale. We developed a land use regression (LUR) approach for predicting spatial fine-scale allergenic pollen concentrations in the Helsinki metropolitan area, Finland, and evaluated the performance of the models against available empirical data. We used grass pollen data monitored at 16 sites in an urban area during the peak pollen season and geospatial environmental data. The main statistical method was generalized linear model (GLM). GLM-based LURs explained 79% of the spatial variation in the grass pollen data based on all samples, and 47% of the variation when samples from two sites with very high concentrations were excluded. In model evaluation, prediction errors ranged from 6% to 26% of the observed range of grass pollen concentrations. Our findings support the use of geospatial data-based statistical models to predict the spatial variation of allergenic grass pollen concentrations at intra-urban scales. A remote sensing-based vegetation index was the strongest predictor of pollen concentrations for exposure assessments at local scales. The LUR approach provides new opportunities to estimate the relations between environmental determinants and allergenic pollen concentration in human-modified environments at fine spatial scales. This approach could potentially be applied to estimate retrospectively pollen concentrations to be used for long-term exposure assessments. Hjort J, Hugg TT, Antikainen H, Rusanen J, Sofiev M, Kukkonen J, Jaakkola MS, Jaakkola JJ. 2016. Fine-scale exposure to allergenic pollen in the urban environment: evaluation of land use regression approach. Environ Health Perspect 124:619-626; http://dx.doi.org/10.1289/ehp.1509761.

Novel fine-scale aerial mapping approach quantifies grassland weed cover dynamics and response to management.

PubMed

Malmstrom, Carolyn M; Butterfield, H Scott; Planck, Laura; Long, Christopher W; Eviner, Valerie T

2017-01-01

Invasive weeds threaten the biodiversity and forage productivity of grasslands worldwide. However, management of these weeds is constrained by the practical difficulty of detecting small-scale infestations across large landscapes and by limits in understanding of landscape-scale invasion dynamics, including mechanisms that enable patches to expand, contract, or remain stable. While high-end hyperspectral remote sensing systems can effectively map vegetation cover, these systems are currently too costly and limited in availability for most land managers. We demonstrate application of a more accessible and cost-effective remote sensing approach, based on simple aerial imagery, for quantifying weed cover dynamics over time. In California annual grasslands, the target communities of interest include invasive weedy grasses (Aegilops triuncialis and Elymus caput-medusae) and desirable forage grass species (primarily Avena spp. and Bromus spp.). Detecting invasion of annual grasses into an annual-dominated community is particularly challenging, but we were able to consistently characterize these two communities based on their phenological differences in peak growth and senescence using maximum likelihood supervised classification of imagery acquired twice per year (in mid- and end-of season). This approach permitted us to map weed-dominated cover at a 1-m scale (correctly detecting 93% of weed patches across the landscape) and to evaluate weed cover change over time. We found that weed cover was more pervasive and persistent in management units that had no significant grazing for several years than in those that were grazed, whereas forage cover was more abundant and stable in the grazed units. This application demonstrates the power of this method for assessing fine-scale vegetation transitions across heterogeneous landscapes. It thus provides means for small-scale early detection of invasive species and for testing fundamental questions about landscape dynamics.

Novel fine-scale aerial mapping approach quantifies grassland weed cover dynamics and response to management

PubMed Central

Butterfield, H. Scott; Planck, Laura; Long, Christopher W.; Eviner, Valerie T.

2017-01-01

Invasive weeds threaten the biodiversity and forage productivity of grasslands worldwide. However, management of these weeds is constrained by the practical difficulty of detecting small-scale infestations across large landscapes and by limits in understanding of landscape-scale invasion dynamics, including mechanisms that enable patches to expand, contract, or remain stable. While high-end hyperspectral remote sensing systems can effectively map vegetation cover, these systems are currently too costly and limited in availability for most land managers. We demonstrate application of a more accessible and cost-effective remote sensing approach, based on simple aerial imagery, for quantifying weed cover dynamics over time. In California annual grasslands, the target communities of interest include invasive weedy grasses (Aegilops triuncialis and Elymus caput-medusae) and desirable forage grass species (primarily Avena spp. and Bromus spp.). Detecting invasion of annual grasses into an annual-dominated community is particularly challenging, but we were able to consistently characterize these two communities based on their phenological differences in peak growth and senescence using maximum likelihood supervised classification of imagery acquired twice per year (in mid- and end-of season). This approach permitted us to map weed-dominated cover at a 1-m scale (correctly detecting 93% of weed patches across the landscape) and to evaluate weed cover change over time. We found that weed cover was more pervasive and persistent in management units that had no significant grazing for several years than in those that were grazed, whereas forage cover was more abundant and stable in the grazed units. This application demonstrates the power of this method for assessing fine-scale vegetation transitions across heterogeneous landscapes. It thus provides means for small-scale early detection of invasive species and for testing fundamental questions about landscape dynamics. PMID:29016604

High-Resolution Remote Sensing Image Building Extraction Based on Markov Model

NASA Astrophysics Data System (ADS)

Zhao, W.; Yan, L.; Chang, Y.; Gong, L.

2018-04-01

With the increase of resolution, remote sensing images have the characteristics of increased information load, increased noise, more complex feature geometry and texture information, which makes the extraction of building information more difficult. To solve this problem, this paper designs a high resolution remote sensing image building extraction method based on Markov model. This method introduces Contourlet domain map clustering and Markov model, captures and enhances the contour and texture information of high-resolution remote sensing image features in multiple directions, and further designs the spectral feature index that can characterize "pseudo-buildings" in the building area. Through the multi-scale segmentation and extraction of image features, the fine extraction from the building area to the building is realized. Experiments show that this method can restrain the noise of high-resolution remote sensing images, reduce the interference of non-target ground texture information, and remove the shadow, vegetation and other pseudo-building information, compared with the traditional pixel-level image information extraction, better performance in building extraction precision, accuracy and completeness.

Downscaling remotely sensed imagery using area-to-point cokriging and multiple-point geostatistical simulation

NASA Astrophysics Data System (ADS)

Tang, Yunwei; Atkinson, Peter M.; Zhang, Jingxiong

2015-03-01

A cross-scale data integration method was developed and tested based on the theory of geostatistics and multiple-point geostatistics (MPG). The goal was to downscale remotely sensed images while retaining spatial structure by integrating images at different spatial resolutions. During the process of downscaling, a rich spatial correlation model in the form of a training image was incorporated to facilitate reproduction of similar local patterns in the simulated images. Area-to-point cokriging (ATPCK) was used as locally varying mean (LVM) (i.e., soft data) to deal with the change of support problem (COSP) for cross-scale integration, which MPG cannot achieve alone. Several pairs of spectral bands of remotely sensed images were tested for integration within different cross-scale case studies. The experiment shows that MPG can restore the spatial structure of the image at a fine spatial resolution given the training image and conditioning data. The super-resolution image can be predicted using the proposed method, which cannot be realised using most data integration methods. The results show that ATPCK-MPG approach can achieve greater accuracy than methods which do not account for the change of support issue.

Spatial Modeling and Uncertainty Assessment of Fine Scale Surface Processes Based on Coarse Terrain Elevation Data

NASA Astrophysics Data System (ADS)

Rasera, L. G.; Mariethoz, G.; Lane, S. N.

2017-12-01

Frequent acquisition of high-resolution digital elevation models (HR-DEMs) over large areas is expensive and difficult. Satellite-derived low-resolution digital elevation models (LR-DEMs) provide extensive coverage of Earth's surface but at coarser spatial and temporal resolutions. Although useful for large scale problems, LR-DEMs are not suitable for modeling hydrologic and geomorphic processes at scales smaller than their spatial resolution. In this work, we present a multiple-point geostatistical approach for downscaling a target LR-DEM based on available high-resolution training data and recurrent high-resolution remote sensing images. The method aims at generating several equiprobable HR-DEMs conditioned to a given target LR-DEM by borrowing small scale topographic patterns from an analogue containing data at both coarse and fine scales. An application of the methodology is demonstrated by using an ensemble of simulated HR-DEMs as input to a flow-routing algorithm. The proposed framework enables a probabilistic assessment of the spatial structures generated by natural phenomena operating at scales finer than the available terrain elevation measurements. A case study in the Swiss Alps is provided to illustrate the methodology.

Fine-Scale Mapping by Spatial Risk Distribution Modeling for Regional Malaria Endemicity and Its Implications under the Low-to-Moderate Transmission Setting in Western Cambodia

PubMed Central

Okami, Suguru; Kohtake, Naohiko

2016-01-01

The disease burden of malaria has decreased as malaria elimination efforts progress. The mapping approach that uses spatial risk distribution modeling needs some adjustment and reinvestigation in accordance with situational changes. Here we applied a mathematical modeling approach for standardized morbidity ratio (SMR) calculated by annual parasite incidence using routinely aggregated surveillance reports, environmental data such as remote sensing data, and non-environmental anthropogenic data to create fine-scale spatial risk distribution maps of western Cambodia. Furthermore, we incorporated a combination of containment status indicators into the model to demonstrate spatial heterogeneities of the relationship between containment status and risks. The explanatory model was fitted to estimate the SMR of each area (adjusted Pearson correlation coefficient R2 = 0.774; Akaike information criterion AIC = 149.423). A Bayesian modeling framework was applied to estimate the uncertainty of the model and cross-scale predictions. Fine-scale maps were created by the spatial interpolation of estimated SMRs at each village. Compared with geocoded case data, corresponding predicted values showed conformity [Spearman’s rank correlation r = 0.662 in the inverse distance weighed interpolation and 0.645 in ordinal kriging (95% confidence intervals of 0.414–0.827 and 0.368–0.813, respectively), Welch’s t-test; Not significant]. The proposed approach successfully explained regional malaria risks and fine-scale risk maps were created under low-to-moderate malaria transmission settings where reinvestigations of existing risk modeling approaches were needed. Moreover, different representations of simulated outcomes of containment status indicators for respective areas provided useful insights for tailored interventional planning, considering regional malaria endemicity. PMID:27415623

Monitoring forest dynamics with multi-scale and time series imagery.

PubMed

Huang, Chunbo; Zhou, Zhixiang; Wang, Di; Dian, Yuanyong

2016-05-01

To learn the forest dynamics and evaluate the ecosystem services of forest effectively, a timely acquisition of spatial and quantitative information of forestland is very necessary. Here, a new method was proposed for mapping forest cover changes by combining multi-scale satellite remote-sensing imagery with time series data. Using time series Normalized Difference Vegetation Index products derived from the Moderate Resolution Imaging Spectroradiometer images (MODIS-NDVI) and Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) images as data source, a hierarchy stepwise analysis from coarse scale to fine scale was developed for detecting the forest change area. At the coarse scale, MODIS-NDVI data with 1-km resolution were used to detect the changes in land cover types and a land cover change map was constructed using NDVI values at vegetation growing seasons. At the fine scale, based on the results at the coarse scale, Landsat TM/ETM+ data with 30-m resolution were used to precisely detect the forest change location and forest change trend by analyzing time series forest vegetation indices (IFZ). The method was tested using the data for Hubei Province, China. The MODIS-NDVI data from 2001 to 2012 were used to detect the land cover changes, and the overall accuracy was 94.02 % at the coarse scale. At the fine scale, the available TM/ETM+ images at vegetation growing seasons between 2001 and 2012 were used to locate and verify forest changes in the Three Gorges Reservoir Area, and the overall accuracy was 94.53 %. The accuracy of the two layer hierarchical monitoring results indicated that the multi-scale monitoring method is feasible and reliable.

Global Validation of MODIS Atmospheric Profile-Derived Near-Surface Air Temperature and Dew Point Estimates

NASA Astrophysics Data System (ADS)

Famiglietti, C.; Fisher, J.; Halverson, G. H.

2017-12-01

This study validates a method of remote sensing near-surface meteorology that vertically interpolates MODIS atmospheric profiles to surface pressure level. The extraction of air temperature and dew point observations at a two-meter reference height from 2001 to 2014 yields global moderate- to fine-resolution near-surface temperature distributions that are compared to geographically and temporally corresponding measurements from 114 ground meteorological stations distributed worldwide. This analysis is the first robust, large-scale validation of the MODIS-derived near-surface air temperature and dew point estimates, both of which serve as key inputs in models of energy, water, and carbon exchange between the land surface and the atmosphere. Results show strong linear correlations between remotely sensed and in-situ near-surface air temperature measurements (R2 = 0.89), as well as between dew point observations (R2 = 0.77). Performance is relatively uniform across climate zones. The extension of mean climate-wise percent errors to the entire remote sensing dataset allows for the determination of MODIS air temperature and dew point uncertainties on a global scale.

Outfall siting with dye-buoy remote sensing of coastal circulation

NASA Technical Reports Server (NTRS)

Munday, J. C., Jr.; Welch, C. S.; Gordon, H. H.

1978-01-01

A dye-buoy remote sensing technique has been applied to estuarine siting problems that involve fine-scale circulation. Small hard cakes of sodium fluorescein and polyvinyl alcohol, in anchored buoys and low-windage current followers, dissolve to produce dye marks resolvable in 1:60,000 scale color and color infrared imagery. Lagrangian current vectors are determined from sequential photo coverage. Careful buoy placement reveals surface currents and submergence near fronts and convergence zones. The technique has been used in siting two sewage outfalls in Hampton Roads, Virginia: In case one, the outfall region during flood tide gathered floating materials in a convergence zone, which then acted as a secondary source during ebb; for better dispersion during ebb, the proposed outfall site was moved further offshore. In case two, flow during late flood was found to divide, with one half passing over shellfish beds; the proposed outfall site was consequently moved to keep effluent in the other half.

Predictions of Tropical Forest Biomass and Biomass Growth Based on Stand Height or Canopy Area Are Improved by Landsat-Scale Phenology across Puerto Rico and the U.S. Virgin Islands

Treesearch

David Gwenzi; Eileen Helmer; Xiaolin Zhu; Michael Lefsky; Humfredo Marcano-Vega

2017-01-01

Remotely-sensed estimates of forest biomass are usually based on various measurements of canopy height, area, volume or texture, as derived from LiDAR, radar or fine spatial resolution imagery. These measurements are then calibrated to estimates of stand biomass that are primarily based on tree stem diameters. Although humid tropical...

Multi-scale functional mapping of tidal marsh vegetation for restoration monitoring

NASA Astrophysics Data System (ADS)

Tuxen Bettman, Karin

2007-12-01

Nearly half of the world's natural wetlands have been destroyed or degraded, and in recent years, there have been significant endeavors to restore wetland habitat throughout the world. Detailed mapping of restoring wetlands can offer valuable information about changes in vegetation and geomorphology, which can inform the restoration process and ultimately help to improve chances of restoration success. I studied six tidal marshes in the San Francisco Estuary, CA, US, between 2003 and 2004 in order to develop techniques for mapping tidal marshes at multiple scales by incorporating specific restoration objectives for improved longer term monitoring. I explored a "pixel-based" remote sensing image analysis method for mapping vegetation in restored and natural tidal marshes, describing the benefits and limitations of this type of approach (Chapter 2). I also performed a multi-scale analysis of vegetation pattern metrics for a recently restored tidal marsh in order to target the metrics that are consistent across scales and will be robust measures of marsh vegetation change (Chapter 3). Finally, I performed an "object-based" image analysis using the same remotely sensed imagery, which maps vegetation type and specific wetland functions at multiple scales (Chapter 4). The combined results of my work highlight important trends and management implications for monitoring wetland restoration using remote sensing, and will better enable restoration ecologists to use remote sensing for tidal marsh monitoring. Several findings important for tidal marsh restoration monitoring were made. Overall results showed that pixel-based methods are effective at quantifying landscape changes in composition and diversity in recently restored marshes, but are limited in their use for quantifying smaller, more fine-scale changes. While pattern metrics can highlight small but important changes in vegetation composition and configuration across years, scientists should exercise caution when using metrics in their studies or to validate restoration management decisions, and multi-scale analyses should be performed before metrics are used in restoration science for important management decisions. Lastly, restoration objectives, ecosystem function, and scale can each be integrated into monitoring techniques using remote sensing for improved restoration monitoring.

Improving our fundamental understanding of the role of aerosol-cloud interactions in the climate system.

PubMed

Seinfeld, John H; Bretherton, Christopher; Carslaw, Kenneth S; Coe, Hugh; DeMott, Paul J; Dunlea, Edward J; Feingold, Graham; Ghan, Steven; Guenther, Alex B; Kahn, Ralph; Kraucunas, Ian; Kreidenweis, Sonia M; Molina, Mario J; Nenes, Athanasios; Penner, Joyce E; Prather, Kimberly A; Ramanathan, V; Ramaswamy, Venkatachalam; Rasch, Philip J; Ravishankara, A R; Rosenfeld, Daniel; Stephens, Graeme; Wood, Robert

2016-05-24

The effect of an increase in atmospheric aerosol concentrations on the distribution and radiative properties of Earth's clouds is the most uncertain component of the overall global radiative forcing from preindustrial time. General circulation models (GCMs) are the tool for predicting future climate, but the treatment of aerosols, clouds, and aerosol-cloud radiative effects carries large uncertainties that directly affect GCM predictions, such as climate sensitivity. Predictions are hampered by the large range of scales of interaction between various components that need to be captured. Observation systems (remote sensing, in situ) are increasingly being used to constrain predictions, but significant challenges exist, to some extent because of the large range of scales and the fact that the various measuring systems tend to address different scales. Fine-scale models represent clouds, aerosols, and aerosol-cloud interactions with high fidelity but do not include interactions with the larger scale and are therefore limited from a climatic point of view. We suggest strategies for improving estimates of aerosol-cloud relationships in climate models, for new remote sensing and in situ measurements, and for quantifying and reducing model uncertainty.

Improving Our Fundamental Understanding of the Role of Aerosol Cloud Interactions in the Climate System

NASA Technical Reports Server (NTRS)

Seinfeld, John H.; Bretherton, Christopher; Carslaw, Kenneth S.; Coe, Hugh; DeMott, Paul J.; Dunlea, Edward J.; Feingold, Graham; Ghan, Steven; Guenther, Alex B.; Kahn, Ralph;

Improving our fundamental understanding of the role of aerosol-cloud interactions in the climate system

DOE PAGES

Seinfeld, John H.; Bretherton, Christopher; Carslaw, Kenneth S.; ...

2016-05-24

The effect of an increase in atmospheric aerosol concentrations on the distribution and radiative properties of Earth’s clouds is the most uncertain component of the overall global radiative forcing from pre-industrial time. General Circulation Models (GCMs) are the tool for predicting future climate, but the treatment of aerosols, clouds, and aerosol-cloud radiative effects carries large uncertainties that directly affect GCM predictions, such as climate sensitivity. Predictions are hampered by the large range of scales of interaction between various components that need to be captured. Observation systems (remote sensing, in situ) are increasingly being used to constrain predictions but significant challengesmore » exist, to some extent because of the large range of scales and the fact that the various measuring systems tend to address different scales. Fine-scale models represent clouds, aerosols, and aerosol-cloud interactions with high fidelity but do not include interactions with the larger scale and are therefore limited from a climatic point of view. Lastly, we suggest strategies for improving estimates of aerosol-cloud relationships in climate models, for new remote sensing and in situ measurements, and for quantifying and reducing model uncertainty.« less

Improving our fundamental understanding of the role of aerosol−cloud interactions in the climate system

PubMed Central

Seinfeld, John H.; Bretherton, Christopher; Carslaw, Kenneth S.; Coe, Hugh; DeMott, Paul J.; Dunlea, Edward J.; Feingold, Graham; Ghan, Steven; Guenther, Alex B.; Kraucunas, Ian; Molina, Mario J.; Nenes, Athanasios; Penner, Joyce E.; Prather, Kimberly A.; Ramanathan, V.; Ramaswamy, Venkatachalam; Rasch, Philip J.; Ravishankara, A. R.; Rosenfeld, Daniel; Stephens, Graeme; Wood, Robert

2016-01-01

The effect of an increase in atmospheric aerosol concentrations on the distribution and radiative properties of Earth’s clouds is the most uncertain component of the overall global radiative forcing from preindustrial time. General circulation models (GCMs) are the tool for predicting future climate, but the treatment of aerosols, clouds, and aerosol−cloud radiative effects carries large uncertainties that directly affect GCM predictions, such as climate sensitivity. Predictions are hampered by the large range of scales of interaction between various components that need to be captured. Observation systems (remote sensing, in situ) are increasingly being used to constrain predictions, but significant challenges exist, to some extent because of the large range of scales and the fact that the various measuring systems tend to address different scales. Fine-scale models represent clouds, aerosols, and aerosol−cloud interactions with high fidelity but do not include interactions with the larger scale and are therefore limited from a climatic point of view. We suggest strategies for improving estimates of aerosol−cloud relationships in climate models, for new remote sensing and in situ measurements, and for quantifying and reducing model uncertainty. PMID:27222566

Assessing species habitat using Google Street View: a case study of cliff-nesting vultures.

PubMed

Olea, Pedro P; Mateo-Tomás, Patricia

2013-01-01

The assessment of a species' habitat is a crucial issue in ecology and conservation. While the collection of habitat data has been boosted by the availability of remote sensing technologies, certain habitat types have yet to be collected through costly, on-ground surveys, limiting study over large areas. Cliffs are ecosystems that provide habitat for a rich biodiversity, especially raptors. Because of their principally vertical structure, however, cliffs are not easy to study by remote sensing technologies, posing a challenge for many researches and managers working with cliff-related biodiversity. We explore the feasibility of Google Street View, a freely available on-line tool, to remotely identify and assess the nesting habitat of two cliff-nesting vultures (the griffon vulture and the globally endangered Egyptian vulture) in northwestern Spain. Two main usefulness of Google Street View to ecologists and conservation biologists were evaluated: i) remotely identifying a species' potential habitat and ii) extracting fine-scale habitat information. Google Street View imagery covered 49% (1,907 km) of the roads of our study area (7,000 km²). The potential visibility covered by on-ground surveys was significantly greater (mean: 97.4%) than that of Google Street View (48.1%). However, incorporating Google Street View to the vulture's habitat survey would save, on average, 36% in time and 49.5% in funds with respect to the on-ground survey only. The ability of Google Street View to identify cliffs (overall accuracy = 100%) outperformed the classification maps derived from digital elevation models (DEMs) (62-95%). Nonetheless, high-performance DEM maps may be useful to compensate Google Street View coverage limitations. Through Google Street View we could examine 66% of the vultures' nesting-cliffs existing in the study area (n = 148): 64% from griffon vultures and 65% from Egyptian vultures. It also allowed us the extraction of fine-scale features of cliffs. This World Wide Web-based methodology may be a useful, complementary tool to remotely map and assess the potential habitat of cliff-dependent biodiversity over large geographic areas, saving survey-related costs.

Assessing Species Habitat Using Google Street View: A Case Study of Cliff-Nesting Vultures

PubMed Central

Olea, Pedro P.; Mateo-Tomás, Patricia

2013-01-01

The assessment of a species’ habitat is a crucial issue in ecology and conservation. While the collection of habitat data has been boosted by the availability of remote sensing technologies, certain habitat types have yet to be collected through costly, on-ground surveys, limiting study over large areas. Cliffs are ecosystems that provide habitat for a rich biodiversity, especially raptors. Because of their principally vertical structure, however, cliffs are not easy to study by remote sensing technologies, posing a challenge for many researches and managers working with cliff-related biodiversity. We explore the feasibility of Google Street View, a freely available on-line tool, to remotely identify and assess the nesting habitat of two cliff-nesting vultures (the griffon vulture and the globally endangered Egyptian vulture) in northwestern Spain. Two main usefulness of Google Street View to ecologists and conservation biologists were evaluated: i) remotely identifying a species’ potential habitat and ii) extracting fine-scale habitat information. Google Street View imagery covered 49% (1,907 km) of the roads of our study area (7,000 km2). The potential visibility covered by on-ground surveys was significantly greater (mean: 97.4%) than that of Google Street View (48.1%). However, incorporating Google Street View to the vulture’s habitat survey would save, on average, 36% in time and 49.5% in funds with respect to the on-ground survey only. The ability of Google Street View to identify cliffs (overall accuracy = 100%) outperformed the classification maps derived from digital elevation models (DEMs) (62–95%). Nonetheless, high-performance DEM maps may be useful to compensate Google Street View coverage limitations. Through Google Street View we could examine 66% of the vultures’ nesting-cliffs existing in the study area (n = 148): 64% from griffon vultures and 65% from Egyptian vultures. It also allowed us the extraction of fine-scale features of cliffs. This World Wide Web-based methodology may be a useful, complementary tool to remotely map and assess the potential habitat of cliff-dependent biodiversity over large geographic areas, saving survey-related costs. PMID:23355880

Remotely sensed wind speed predicts soaring behaviour in a wide-ranging pelagic seabird.

PubMed

Gibb, Rory; Shoji, Akiko; Fayet, Annette L; Perrins, Chris M; Guilford, Tim; Freeman, Robin

2017-07-01

Global wind patterns affect flight strategies in many birds, including pelagic seabirds, many of which use wind-powered soaring to reduce energy costs during at-sea foraging trips and migration. Such long-distance movement patterns are underpinned by local interactions between wind conditions and flight behaviour, but these fine-scale relationships are far less well understood. Here we show that remotely sensed ocean wind speed and direction are highly significant predictors of soaring behaviour in a migratory pelagic seabird, the Manx shearwater ( Puffinus puffinus ). We used high-frequency GPS tracking data (10 Hz) and statistical behaviour state classification to identify two energetic modes in at-sea flight, corresponding to flap-like and soar-like flight. We show that soaring is significantly more likely to occur in tailwinds and crosswinds above a wind speed threshold of around 8 m s -1 , suggesting that these conditions enable birds to reduce metabolic costs by preferentially soaring over flapping. Our results suggest a behavioural mechanism by which wind conditions may shape foraging and migration ecology in pelagic seabirds, and thus indicate that shifts in wind patterns driven by climate change could impact this and other species. They also emphasize the emerging potential of high-frequency GPS biologgers to provide detailed quantitative insights into fine-scale flight behaviour in free-living animals. © 2017 The Author(s).

The Athena Mars Rover Investigation

NASA Technical Reports Server (NTRS)

Squyres, S. W.; Arvidson, R. E.; Bell, J. F., III; Carr, M.; Christensen, P.; DesMarais, D.; Economou, T.; Gorevan, S.; Haskin, L.; Herkenhoff, K.

2000-01-01

The Mars Surveyor program requires tools for martian surface exploration, including remote sensing, in-situ sensing, and sample collection. The Athena Mars rover payload is a suite of scientific instruments and sample collection tools designed to: (1) Provide color stereo imaging of martian surface environments, and remotely-sensed point discrimination of mineralogical composition; (2) Determine the elemental and mineralogical composition of martian surface materials; (3) Determine the fine-scale textural properties of these materials; and (4) Collect and store samples. The Athena payload is designed to be implemented on a long-range rover such as the one now under consideration for the 2003 Mars opportunity. The payload is at a high state of maturity, and most of the instruments have now been built for flight.

Multi-scale monitoring of landscape change after the 2011 tsunami

NASA Astrophysics Data System (ADS)

Hara, K.; Zhao, Y.; Harada, I.; Tomita, M.; Park, J.; Jung, E.; Kamagata, N.; Hirabuki, Y.

2015-04-01

The Great East Japan Earthquake (magnitude 9.0; occurred on 11th March 2011) and subsequent huge tsunami caused widespread damage along the Pacific Ocean coast of eastern Honshu, Japan. This research utilizes multi-resolution remote sensing images to clarify the impact on landscapes caused by this disaster, and also to monitor the subsequent survival and recovery process in the Sendai Bay region. The coastal landscape in the target area features a narrow strip of coastal sand barrier, historically stabilized by planted pine groves; backed by a low-lying plain that has traditionally been diked and converted to irrigated rice paddies. Farmsteads on the flat alluvial plain are surrounded by groves called "Igune", consisting primarily of conifers. MODIS data (250 m resolution) were employed to map the overall extent of inundation and damage on the regional landscape scale. The major damage caused by the tsunami, destruction of coastal pine forests and inundation or rice paddies on the plain, was identified at this level. Progressively finer scale analysis were then implemented using SPOT/HRG-2 (10 m resolution) data; GeoEye-1 fine resolution data (0.5 m) and very fine resolution aerial photographs (10 cm) and LiDAR. These results demonstrated the minute details of the damage and recovery process. Some patches of pine forest, for example, were seen to have survived, and some coastal plant communities were already recovering only a year after the disaster. Continuous monitoring using field work and remote sensing is required for balanced regional strategies that provide for economic and social recovery and as well as restoration of vegetation, biodiversity and vital ecosystem services.

Coarse-to-fine wavelet-based airport detection

NASA Astrophysics Data System (ADS)

Li, Cheng; Wang, Shuigen; Pang, Zhaofeng; Zhao, Baojun

2015-10-01

Airport detection on optical remote sensing images has attracted great interest in the applications of military optics scout and traffic control. However, most of the popular techniques for airport detection from optical remote sensing images have three weaknesses: 1) Due to the characteristics of optical images, the detection results are often affected by imaging conditions, like weather situation and imaging distortion; and 2) optical images contain comprehensive information of targets, so that it is difficult for extracting robust features (e.g., intensity and textural information) to represent airport area; 3) the high resolution results in large data volume, which makes real-time processing limited. Most of the previous works mainly focus on solving one of those problems, and thus, the previous methods cannot achieve the balance of performance and complexity. In this paper, we propose a novel coarse-to-fine airport detection framework to solve aforementioned three issues using wavelet coefficients. The framework includes two stages: 1) an efficient wavelet-based feature extraction is adopted for multi-scale textural feature representation, and support vector machine(SVM) is exploited for classifying and coarsely deciding airport candidate region; and then 2) refined line segment detection is used to obtain runway and landing field of airport. Finally, airport recognition is achieved by applying the fine runway positioning to the candidate regions. Experimental results show that the proposed approach outperforms the existing algorithms in terms of detection accuracy and processing efficiency.

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.

Future directions of meteorology related to air-quality research.

PubMed

Seaman, Nelson L

2003-06-01

Meteorology is one of the major factors contributing to air-pollution episodes. More accurate representation of meteorological fields has been possible in recent years through the use of remote sensing systems, high-speed computers and fine-mesh meteorological models. Over the next 5-20 years, better meteorological inputs for air quality studies will depend on making better use of a wealth of new remotely sensed observations in more advanced data assimilation systems. However, for fine mesh models to be successful, parameterizations used to represent physical processes must be redesigned to be more precise and better adapted for the scales at which they will be applied. Candidates for significant overhaul include schemes to represent turbulence, deep convection, shallow clouds, and land-surface processes. Improvements in the meteorological observing systems, data assimilation and modeling, coupled with advancements in air-chemistry modeling, will soon lead to operational forecasting of air quality in the US. Predictive capabilities can be expected to grow rapidly over the next decade. This will open the way for a number of valuable new services and strategies, including better warnings of unhealthy atmospheric conditions, event-dependent emissions restrictions, and now casting support for homeland security in the event of toxic releases into the atmosphere.

Estimation of Actual Crop ET of Paddy Using the Energy Balance Model SMARET and Validation with Field Water Balance Measurements and a Crop Growth Model (ORYZA)

NASA Astrophysics Data System (ADS)

Nallasamy, N. D.; Muraleedharan, B. V.; Kathirvel, K.; Narasimhan, B.

2014-12-01

Sustainable management of water resources requires reliable estimates of actual evapotranspiration (ET) at fine spatial and temporal resolution. This is significant in the case of rice based irrigation systems, one of the major consumers of surface water resources and where ET forms a major component of water consumption. However huge tradeoff in the spatial and temporal resolution of satellite images coupled with lack of adequate number of cloud free images within a growing season act as major constraints in deriving ET at fine spatial and temporal resolution using remote sensing based energy balance models. The scale at which ET is determined is decided by the spatial and temporal scale of Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), which form inputs to energy balance models. In this context, the current study employed disaggregation algorithms (NL-DisTrad and DisNDVI) to generate time series of LST and NDVI images at fine resolution. The disaggregation algorithms aimed at generating LST and NDVI at finer scale by integrating temporal information from concurrent coarse resolution data and spatial information from a single fine resolution image. The temporal frequency of the disaggregated images is further improved by employing composite images of NDVI and LST in the spatio-temporal disaggregation method. The study further employed half-hourly incoming surface insolation and outgoing long wave radiation obtained from the Indian geostationary satellite (Kalpana-1) to convert the instantaneous ET into daily ET and subsequently to the seasonal ET, thereby improving the accuracy of ET estimates. The estimates of ET were validated with field based water balance measurements carried out in Gadana, a subbasin predominated by rice paddy fields, located in Tamil Nadu, India.

Remote-sensing application for facilitating land resource assessment and monitoring for utility-scale solar energy development

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

Hamada, Yuki; Grippo, Mark A.

2015-01-01

A monitoring plan that incorporates regional datasets and integrates cost-effective data collection methods is necessary to sustain the long-term environmental monitoring of utility-scale solar energy development in expansive, environmentally sensitive desert environments. Using very high spatial resolution (VHSR; 15 cm) multispectral imagery collected in November 2012 and January 2014, an image processing routine was developed to characterize ephemeral streams, vegetation, and land surface in the southwestern United States where increased utility-scale solar development is anticipated. In addition to knowledge about desert landscapes, the methodology integrates existing spectral indices and transformation (e.g., visible atmospherically resistant index and principal components); a newlymore » developed index, erosion resistance index (ERI); and digital terrain and surface models, all of which were derived from a common VHSR image. The methodology identified fine-scale ephemeral streams with greater detail than the National Hydrography Dataset and accurately estimated vegetation distribution and fractional cover of various surface types. The ERI classified surface types that have a range of erosive potentials. The remote-sensing methodology could ultimately reduce uncertainty and monitoring costs for all stakeholders by providing a cost-effective monitoring approach that accurately characterizes the land resources at potential development sites.« less

The optical properties of absorbing aerosols with fractal soot aggregates: Implications for aerosol remote sensing

NASA Astrophysics Data System (ADS)

Cheng, Tianhai; Gu, Xingfa; Wu, Yu; Chen, Hao; Yu, Tao

2013-08-01

Applying sphere aerosol models to replace the absorbing fine-sized dominated aerosols can potentially result in significant errors in the climate models and aerosol remote sensing retrieval. In this paper, the optical properties of absorbing fine-sized dominated aerosol were modeled, which are taking into account the fresh emitted soot particles (agglomerates of primary spherules), aged soot particles (semi-externally mixed with other weakly absorbing aerosols), and coarse aerosol particles (dust particles). The optical properties of the individual fresh and aged soot aggregates are calculated using the superposition T-matrix method. In order to quantify the morphology effect of absorbing aerosol models on the aerosol remote sensing retrieval, the ensemble averaged optical properties of absorbing fine-sized dominated aerosols are calculated based on the size distribution of fine aerosols (fresh and aged soot) and coarse aerosols. The corresponding optical properties of sphere absorbing aerosol models using Lorenz-Mie solutions were presented for comparison. The comparison study demonstrates that the sphere absorbing aerosol models underestimate the absorption ability of the fine-sized dominated aerosol particles. The morphology effect of absorbing fine-sized dominated aerosols on the TOA radiances and polarized radiances is also investigated. It is found that the sphere aerosol models overestimate the TOA reflectance and polarized reflectance by approximately a factor of 3 at wavelength of 0.865 μm. In other words, the fine-sized dominated aerosol models can cause large errors in the retrieved aerosol properties if satellite reflectance measurements are analyzed using the conventional Mie theory for spherical particles.

Findings and Challenges in Fine-Resolution Large-Scale Hydrological Modeling

NASA Astrophysics Data System (ADS)

Her, Y. G.

2017-12-01

Fine-resolution large-scale (FL) modeling can provide the overall picture of the hydrological cycle and transport while taking into account unique local conditions in the simulation. It can also help develop water resources management plans consistent across spatial scales by describing the spatial consequences of decisions and hydrological events extensively. FL modeling is expected to be common in the near future as global-scale remotely sensed data are emerging, and computing resources have been advanced rapidly. There are several spatially distributed models available for hydrological analyses. Some of them rely on numerical methods such as finite difference/element methods (FDM/FEM), which require excessive computing resources (implicit scheme) to manipulate large matrices or small simulation time intervals (explicit scheme) to maintain the stability of the solution, to describe two-dimensional overland processes. Others make unrealistic assumptions such as constant overland flow velocity to reduce the computational loads of the simulation. Thus, simulation efficiency often comes at the expense of precision and reliability in FL modeling. Here, we introduce a new FL continuous hydrological model and its application to four watersheds in different landscapes and sizes from 3.5 km2 to 2,800 km2 at the spatial resolution of 30 m on an hourly basis. The model provided acceptable accuracy statistics in reproducing hydrological observations made in the watersheds. The modeling outputs including the maps of simulated travel time, runoff depth, soil water content, and groundwater recharge, were animated, visualizing the dynamics of hydrological processes occurring in the watersheds during and between storm events. Findings and challenges were discussed in the context of modeling efficiency, accuracy, and reproducibility, which we found can be improved by employing advanced computing techniques and hydrological understandings, by using remotely sensed hydrological observations such as soil moisture and radar rainfall depth and by sharing the model and its codes in public domain, respectively.

Using Lidar and Radar measurements to constrain predictions of forest ecosystem structure and function.

PubMed

Antonarakis, Alexander S; Saatchi, Sassan S; Chazdon, Robin L; Moorcroft, Paul R

2011-06-01

Insights into vegetation and aboveground biomass dynamics within terrestrial ecosystems have come almost exclusively from ground-based forest inventories that are limited in their spatial extent. Lidar and synthetic-aperture Radar are promising remote-sensing-based techniques for obtaining comprehensive measurements of forest structure at regional to global scales. In this study we investigate how Lidar-derived forest heights and Radar-derived aboveground biomass can be used to constrain the dynamics of the ED2 terrestrial biosphere model. Four-year simulations initialized with Lidar and Radar structure variables were compared against simulations initialized from forest-inventory data and output from a long-term potential-vegtation simulation. Both height and biomass initializations from Lidar and Radar measurements significantly improved the representation of forest structure within the model, eliminating the bias of too many large trees that arose in the potential-vegtation-initialized simulation. The Lidar and Radar initializations decreased the proportion of larger trees estimated by the potential vegetation by approximately 20-30%, matching the forest inventory. This resulted in improved predictions of ecosystem-scale carbon fluxes and structural dynamics compared to predictions from the potential-vegtation simulation. The Radar initialization produced biomass values that were 75% closer to the forest inventory, with Lidar initializations producing canopy height values closest to the forest inventory. Net primary production values for the Radar and Lidar initializations were around 6-8% closer to the forest inventory. Correcting the Lidar and Radar initializations for forest composition resulted in improved biomass and basal-area dynamics as well as leaf-area index. Correcting the Lidar and Radar initializations for forest composition and fine-scale structure by combining the remote-sensing measurements with ground-based inventory data further improved predictions, suggesting that further improvements of structural and carbon-flux metrics will also depend on obtaining reliable estimates of forest composition and accurate representation of the fine-scale vertical and horizontal structure of plant canopies.

Fine-scale Horizontal Structure of Arctic Mixed-Phase Clouds.

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

Rambukkange,M.; Verlinde, J.; Elorante, E.

2006-07-10

Recent in situ observations in stratiform clouds suggest that mixed phase regimes, here defined as limited cloud volumes containing both liquid and solid water, are constrained to narrow layers (order 100 m) separating all-liquid and fully glaciated volumes (Hallett and Viddaurre, 2005). The Department of Energy Atmospheric Radiation Measurement Program's (DOE-ARM, Ackerman and Stokes, 2003) North Slope of Alaska (NSA) ARM Climate Research Facility (ACRF) recently started collecting routine measurement of radar Doppler velocity power spectra from the Millimeter Cloud Radar (MMCR). Shupe et al. (2004) showed that Doppler spectra has potential to separate the contributions to the total reflectivitymore » of the liquid and solid water in the radar volume, and thus to investigate further Hallett and Viddaurre's findings. The Mixed-Phase Arctic Cloud Experiment (MPACE) was conducted along the NSA to investigate the properties of Arctic mixed phase clouds (Verlinde et al., 2006). We present surface based remote sensing data from MPACE to discuss the fine-scale structure of the mixed-phase clouds observed during this experiment.« less

A fast and fully automatic registration approach based on point features for multi-source remote-sensing images

NASA Astrophysics Data System (ADS)

Yu, Le; Zhang, Dengrong; Holden, Eun-Jung

2008-07-01

Automatic registration of multi-source remote-sensing images is a difficult task as it must deal with the varying illuminations and resolutions of the images, different perspectives and the local deformations within the images. This paper proposes a fully automatic and fast non-rigid image registration technique that addresses those issues. The proposed technique performs a pre-registration process that coarsely aligns the input image to the reference image by automatically detecting their matching points by using the scale invariant feature transform (SIFT) method and an affine transformation model. Once the coarse registration is completed, it performs a fine-scale registration process based on a piecewise linear transformation technique using feature points that are detected by the Harris corner detector. The registration process firstly finds in succession, tie point pairs between the input and the reference image by detecting Harris corners and applying a cross-matching strategy based on a wavelet pyramid for a fast search speed. Tie point pairs with large errors are pruned by an error-checking step. The input image is then rectified by using triangulated irregular networks (TINs) to deal with irregular local deformations caused by the fluctuation of the terrain. For each triangular facet of the TIN, affine transformations are estimated and applied for rectification. Experiments with Quickbird, SPOT5, SPOT4, TM remote-sensing images of the Hangzhou area in China demonstrate the efficiency and the accuracy of the proposed technique for multi-source remote-sensing image registration.

Fine motor skills in a population of children in remote Australia with high levels of prenatal alcohol exposure and Fetal Alcohol Spectrum Disorder.

PubMed

Doney, Robyn; Lucas, Barbara R; Watkins, Rochelle E; Tsang, Tracey W; Sauer, Kay; Howat, Peter; Latimer, Jane; Fitzpatrick, James P; Oscar, June; Carter, Maureen; Elliott, Elizabeth J

2017-11-21

Many children in the remote Fitzroy Valley region of Western Australia have prenatal alcohol exposure (PAE). Individuals with PAE can have neurodevelopmental impairments and be diagnosed with one of several types of Fetal Alcohol Spectrum Disorder (FASD). Fine motor skills can be impaired by PAE, but no studies have developed a comprehensive profile of fine motor skills in a population-based cohort of children with FASD. We aimed to develop a comprehensive profile of fine motor skills in a cohort of Western Australian children; determine whether these differed in children with PAE or FASD; and establish the prevalence of impairment. Children (n = 108, 7 to 9 years) were participants in a population-prevalence study of FASD in Western Australia. Fine motor skills were assessed using the Bruininks-Oseretsky Test of Motor Proficiency, which provided a Fine Motor Composite score, and evaluated Fine Manual Control (Fine Motor Precision; Fine Motor Integration) and Manual Coordination (Manual Dexterity; Upper-Limb Coordination). Descriptive statistics were reported for the overall cohort; and comparisons made between children with and without PAE and/or FASD. The prevalence of severe (≤ 2nd percentile) and moderate (≤16th percentile) impairments was determined. Overall, Fine Motor Composite scores were 'average' (M = 48.6 ± 7.4), as were Manual Coordination (M = 55.7 ± 7.9) and Fine Manual Control scores (M = 42.5 ± 6.2). Children with FASD had significantly lower Fine Motor Composite (M = 45.2 ± 7.7 p = 0.046) and Manual Coordination scores (M = 51.8 ± 7.3, p = 0.027) than children without PAE (Fine Motor Composite M = 49.8 ± 7.2; Manual Coordination M = 57.0 ± 7.7). Few children had severe impairment, but rates of moderate impairment were very high. Different types of fine motor skills should be evaluated in children with PAE or FASD. The high prevalence of fine motor impairment in our cohort, even in children without PAE, highlights the need for therapeutic intervention for many children in remote communities.

A multi-scale framework to link remotely sensed metrics with socioeconomic data

NASA Astrophysics Data System (ADS)

Watmough, Gary; Svenning, Jens-Christian; Palm, Cheryl; Sullivan, Clare; Danylo, Olha; McCallum, Ian

2017-04-01

There is increasing interest in the use of remotely sensed satellite data for estimating human poverty as it can bridge data gaps that prevent fine scale monitoring of development goals across large areas. The ways in which metrics derived from satellite imagery are linked with socioeconomic data are crucial for accurate estimation of poverty. Yet, to date, approaches in the literature linking satellite metrics with socioeconomic data are poorly characterized. Typically, approaches use a GIS approach such as circular buffer zones around a village or household or an administrative boundary such as a district or census enumeration area. These polygons are then used to extract environmental data from satellite imagery and related to the socioeconomic data in statistical analyses. The use of a single polygon to link environment and socioeconomic data is inappropriate in coupled human-natural systems as processes operate over multiple scales. Human interactions with the environment occur at multiple levels from individual (household) access to agricultural plots adjacent to homes, to communal access to common pool resources (CPR) such as forests at the village level. Here, we present a multi-scale framework that explicitly considers how people use the landscape. The framework is presented along with a case study example in Kenya. The multi-scale approach could enhance the modelling of human-environment interactions which will have important consequences for monitoring the sustainable development goals for human livelihoods and biodiversity conservation.

Robust Stability of Scaled-Four-Channel Teleoperation with Internet Time-Varying Delays

PubMed Central

Delgado, Emma; Barreiro, Antonio; Falcón, Pablo; Díaz-Cacho, Miguel

2016-01-01

We describe the application of a generic stability framework for a teleoperation system under time-varying delay conditions, as addressed in a previous work, to a scaled-four-channel (γ-4C) control scheme. Described is how varying delays are dealt with by means of dynamic encapsulation, giving rise to mu-test conditions for robust stability and offering an appealing frequency technique to deal with the stability robustness of the architecture. We discuss ideal transparency problems and we adapt classical solutions so that controllers are proper, without single or double differentiators, and thus avoid the negative effects of noise. The control scheme was fine-tuned and tested for complete stability to zero of the whole state, while seeking a practical solution to the trade-off between stability and transparency in the Internet-based teleoperation. These ideas were tested on an Internet-based application with two Omni devices at remote laboratory locations via simulations and real remote experiments that achieved robust stability, while performing well in terms of position synchronization and force transparency. PMID:27128914

Indirect quantification of fine root production in a near tropical wet mountainous region

NASA Astrophysics Data System (ADS)

Lu, X.; Zhang, J.; Huang, C.

2016-12-01

The main functions of fine root (defined as diameter <= 2 mm) are water and nutrient transports. Besides being a carbon (C) storage pool, it also provides a C flux pathway through soil and plant. Fine root takes up a small portion, normally 5%, of biomass in forest ecosystems, but 30% to 70% of total net primary production. Therefore, quantifying fine root productivity is important to study the forest C budget. Presumably, belowground growth can be indirectly estimated by the more accessible aboveground vegetation structure dynamics. To verify the relationship with fine root productivity, we take internal (floristic) and external (environmental) factors into account, including litter production, canopy density (leaf area index), leaf nutrients (N, K, Ca, Mg, P), weather and/or soil physical conditions (air temperature, humidity, precipitation, solar radiation and soil moisture). The study was conducted in near tropical broadleaf (700 m asl) and conifer (1700 m asl) forests in northeastern Taiwan, generally receiving more than 4000 mm of precipitation per year. For each site, 16 50-cm long minirhizotron tubes were installed. Fine root images were acquired every three weeks. Growth and decline, newly presence and absence of fine roots were delineated by image processing algorithms to derive fine-root productivity through time. Aforementioned internal and external attributes were simultaneously collected as well. Some of these variables were highly correlated and were detrended using principal component analysis. We found that these transformed variables (mainly associated with litter production, precipitation and solar radiation) can delineate the spatiotemporal dynamics of root production well (r2 = 0.87, p = 0.443). In conclusion, this study demonstrated the feasibility of utilized aboveground variables to indirectly assess fine root growth, which could be further developed for the regional scale mapping with aid of remote sensing.

Deriving temporally continuous soil moisture estimations at fine resolution by downscaling remotely sensed product

NASA Astrophysics Data System (ADS)

Jin, Yan; Ge, Yong; Wang, Jianghao; Heuvelink, Gerard B. M.

2018-06-01

Land surface soil moisture (SSM) has important roles in the energy balance of the land surface and in the water cycle. Downscaling of coarse-resolution SSM remote sensing products is an efficient way for producing fine-resolution data. However, the downscaling methods used most widely require full-coverage visible/infrared satellite data as ancillary information. These methods are restricted to cloud-free days, making them unsuitable for continuous monitoring. The purpose of this study is to overcome this limitation to obtain temporally continuous fine-resolution SSM estimations. The local spatial heterogeneities of SSM and multiscale ancillary variables were considered in the downscaling process both to solve the problem of the strong variability of SSM and to benefit from the fusion of ancillary information. The generation of continuous downscaled remote sensing data was achieved via two principal steps. For cloud-free days, a stepwise hybrid geostatistical downscaling approach, based on geographically weighted area-to-area regression kriging (GWATARK), was employed by combining multiscale ancillary variables with passive microwave remote sensing data. Then, the GWATARK-estimated SSM and China Soil Moisture Dataset from Microwave Data Assimilation SSM data were combined to estimate fine-resolution data for cloudy days. The developed methodology was validated by application to the 25-km resolution daily AMSR-E SSM product to produce continuous SSM estimations at 1-km resolution over the Tibetan Plateau. In comparison with ground-based observations, the downscaled estimations showed correlation (R ≥ 0.7) for both ascending and descending overpasses. The analysis indicated the high potential of the proposed approach for producing a temporally continuous SSM product at fine spatial resolution.

Visual-motor integration, visual perception, and fine motor coordination in a population of children with high levels of Fetal Alcohol Spectrum Disorder.

PubMed

Doney, Robyn; Lucas, Barbara R; Watkins, Rochelle E; Tsang, Tracey W; Sauer, Kay; Howat, Peter; Latimer, Jane; Fitzpatrick, James P; Oscar, June; Carter, Maureen; Elliott, Elizabeth J

2016-08-01

Visual-motor integration (VMI) skills are essential for successful academic performance, but to date no studies have assessed these skills in a population-based cohort of Australian Aboriginal children who, like many children in other remote, disadvantaged communities, consistently underperform academically. Furthermore, many children in remote areas of Australia have prenatal alcohol exposure (PAE) and Fetal Alcohol Spectrum Disorder (FASD), which are often associated with VMI deficits. VMI, visual perception, and fine motor coordination were assessed using The Beery-Buktenica Developmental Test of Visual-Motor Integration, including its associated subtests of Visual Perception and Fine Motor Coordination, in a cohort of predominantly Australian Aboriginal children (7.5-9.6 years, n=108) in remote Western Australia to explore whether PAE adversely affected test performance. Cohort results were reported, and comparisons made between children i) without PAE; ii) with PAE (no FASD); and iii) FASD. The prevalence of moderate (≤16th percentile) and severe (≤2nd percentile) impairment was established. Mean VMI scores were 'below average' (M=87.8±9.6), and visual perception scores were 'average' (M=97.6±12.5), with no differences between groups. Few children had severe VMI impairment (1.9%), but moderate impairment rates were high (47.2%). Children with FASD had significantly lower fine motor coordination scores and higher moderate impairment rates (M=87.9±12.5; 66.7%) than children without PAE (M=95.1±10.7; 23.3%) and PAE (no FASD) (M=96.1±10.9; 15.4%). Aboriginal children living in remote Western Australia have poor VMI skills regardless of PAE or FASD. Children with FASD additionally had fine motor coordination problems. VMI and fine motor coordination should be assessed in children with PAE, and included in FASD diagnostic assessments. Copyright © 2016 Elsevier Ltd. All rights reserved.

Analysis of Unmanned Aerial Vehicle (UAV) hyperspectral remote sensing monitoring key technology in coastal wetland

NASA Astrophysics Data System (ADS)

Ma, Yi; Zhang, Jie; Zhang, Jingyu

2016-01-01

The coastal wetland, a transitional zone between terrestrial ecosystems and marine ecosystems, is the type of great value to ecosystem services. For the recent 3 decades, area of the coastal wetland is decreasing and the ecological function is gradually degraded with the rapid development of economy, which restricts the sustainable development of economy and society in the coastal areas of China in turn. It is a major demand of the national reality to carry out the monitoring of coastal wetlands, to master the distribution and dynamic change. UAV, namely unmanned aerial vehicle, is a new platform for remote sensing. Compared with the traditional satellite and manned aerial remote sensing, it has the advantage of flexible implementation, no cloud cover, strong initiative and low cost. Image-spectrum merging is one character of high spectral remote sensing. At the same time of imaging, the spectral curve of each pixel is obtained, which is suitable for quantitative remote sensing, fine classification and target detection. Aimed at the frontier and hotspot of remote sensing monitoring technology, and faced the demand of the coastal wetland monitoring, this paper used UAV and the new remote sensor of high spectral imaging instrument to carry out the analysis of the key technologies of monitoring coastal wetlands by UAV on the basis of the current situation in overseas and domestic and the analysis of developing trend. According to the characteristic of airborne hyperspectral data on UAV, that is "three high and one many", the key technology research that should develop are promoted as follows: 1) the atmosphere correction of the UAV hyperspectral in coastal wetlands under the circumstance of complex underlying surface and variable geometry, 2) the best observation scale and scale transformation method of the UAV platform while monitoring the coastal wetland features, 3) the classification and detection method of typical features with high precision from multi scale hyperspectral images based on time sequence. The research results of this paper will help to break the traditional concept of remote sensing monitoring coastal wetlands by satellite and manned aerial vehicle, lead the trend of this monitoring technology, and put forward a new technical proposal for grasping the distribution of the coastal wetland and the changing trend and carrying out the protection and management of the coastal wetland.

Using High Resolution Remotely Sensed Data to Predict Territory Occupancy and Mircrorefugia for a Habitat Specialist, the American Pika (Ochotona princeps)

NASA Astrophysics Data System (ADS)

Beers, A.; Ray, C.

2015-12-01

Climate change is likely to affect mountainous areas unevenly due to the complex interactions between topography, vegetation, and the accumulation of snow and ice. This heterogeneity will complicate relationships between species presence and large-scale drivers such as precipitation and make predicting habitat extent and connectivity much more difficult. We studied the potential for fine-scale variation in climate and habitat use throughout the year in the American pika (Ochotona princeps), a talus specialist of mountainous western North America known for strong microhabitat affiliation. Not all areas of talus are likely to be equally hospitable, which may reduce connectivity more than predicted by large-scale occupancy drivers. We used high resolution remotely sensed data to create metrics of the terrain and land cover in the Niwot Ridge (NWT) LTER site in Colorado. We hypothesized that pikas preferentially use heterogeneous terrain, as it might foster greater snow accumulation, and used radio telemetry to test this with radio-collared pikas. Pikas use heterogeneous terrain during snow covered periods and less heterogeneous area during the summer. This suggests that not all areas of talus habitat are equally suitable as shelter from extreme conditions but that pikas need more than just shelter from winter cold. With those results we created a predictive map using the same habitat metrics to model the extent of suitable habitat across the NWT area. These strong effects of terrain on pika habitat use and territory occupancy show the great utility that high resolution remotely sensed data can have in ecological applications. With increasing effects of climate change in mountainous regions, this modeling approach is crucial for quantifying habitat connectivity at both small and large scales and to identify potential refugia for threatened or isolated species.

Multiple scattering in the remote sensing of natural surfaces

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

Li, Wen-Hao; Weeks, R.; Gillespie, A.R.

1996-07-01

Radiosity models predict the amount of light scattered many times (multiple scattering) among scene elements in addition to light interacting with a surface only once (direct reflectance). Such models are little used in remote sensing studies because they require accurate digital terrain models and, typically, large amounts of computer time. We have developed a practical radiosity model that runs relatively quickly within suitable accuracy limits, and have used it to explore problems caused by multiple-scattering in image calibration, terrain correction, and surface roughness estimation for optical images. We applied the radiosity model to real topographic surfaces sampled at two verymore » different spatial scales: 30 m (rugged mountains) and 1 cm (cobbles and gravel on an alluvial fan). The magnitude of the multiple-scattering (MS) effect varies with solar illumination geometry, surface reflectivity, sky illumination and surface roughness. At the coarse scale, for typical illumination geometries, as much as 20% of the image can be significantly affected (>5%) by MS, which can account for as much as {approximately}10% of the radiance from sunlit slopes, and much more for shadowed slopes, otherwise illuminated only by skylight. At the fine scale, radiance from as much as 30-40% of the scene can have a significant MS component, and the MS contribution is locally as high as {approximately}70%, although integrating to the meter scale reduces this limit to {approximately}10%. Because the amount of MS increases with reflectivity as well as roughness, MS effects will distort the shape of reflectance spectra as well as changing their overall amplitude. The change is proportional to surface roughness. Our results have significant implications for determining reflectivity and surface roughness in remote sensing.« less

Environmental Public Health Applications Using Remotely Sensed Data.

PubMed

Al-Hamdan, Mohammad Z; Crosson, William L; Economou, Sigrid A; Estes, Maurice G; Estes, Sue M; Hemmings, Sarah N; Kent, Shia T; Puckett, Mark; Quattrochi, Dale A; Rickman, Douglas L; Wade, Gina M; McClure, Leslie A

2014-01-01

We describe a remote sensing and GIS-based study that has three objectives: (1) characterize fine particulate matter (PM 2.5 ), insolation and land surface temperature using NASA satellite observations, EPA ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes; and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making.

Environmental Public Health Applications Using Remotely Sensed Data

PubMed Central

Al-Hamdan, Mohammad Z.; Crosson, William L.; Economou, Sigrid A.; Estes, Maurice G.; Estes, Sue M.; Hemmings, Sarah N.; Kent, Shia T.; Puckett, Mark; Quattrochi, Dale A.; Rickman, Douglas L.; Wade, Gina M.; McClure, Leslie A.

2012-01-01

We describe a remote sensing and GIS-based study that has three objectives: (1) characterize fine particulate matter (PM2.5), insolation and land surface temperature using NASA satellite observations, EPA ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes; and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making. PMID:24910505

Challenges in complementing data from ground-based sensors with satellite-derived products to measure ecological changes in relation to climate – lessons from temperate wetland-upland landscapes

USGS Publications Warehouse

Gallant, Alisa L.; Sadinski, Walter J.; Brown, Jesslyn F.; Senay, Gabriel B.; Roth, Mark F.

2018-01-01

Assessing climate-related ecological changes across spatiotemporal scales meaningful to resource managers is challenging because no one method reliably produces essential data at both fine and broad scales. We recently confronted such challenges while integrating data from ground- and satellite-based sensors for an assessment of four wetland-rich study areas in the U.S. Midwest. We examined relations between temperature and precipitation and a set of variables measured on the ground at individual wetlands and another set measured via satellite sensors within surrounding 4 km2 landscape blocks. At the block scale, we used evapotranspiration and vegetation greenness as remotely sensed proxies for water availability and to estimate seasonal photosynthetic activity. We used sensors on the ground to coincidentally measure surface-water availability and amphibian calling activity at individual wetlands within blocks. Responses of landscape blocks generally paralleled changes in conditions measured on the ground, but the latter were more dynamic, and changes in ecological conditions on the ground that were critical for biota were not always apparent in measurements of related parameters in blocks. Here, we evaluate the effectiveness of decisions and assumptions we made in applying the remotely sensed data for the assessment and the value of integrating observations across scales, sensors, and disciplines.

Challenges in Complementing Data from Ground-Based Sensors with Satellite-Derived Products to Measure Ecological Changes in Relation to Climate—Lessons from Temperate Wetland-Upland Landscapes

PubMed Central

Sadinski, Walt; Senay, Gabriel B.

2018-01-01

Assessing climate-related ecological changes across spatiotemporal scales meaningful to resource managers is challenging because no one method reliably produces essential data at both fine and broad scales. We recently confronted such challenges while integrating data from ground- and satellite-based sensors for an assessment of four wetland-rich study areas in the U.S. Midwest. We examined relations between temperature and precipitation and a set of variables measured on the ground at individual wetlands and another set measured via satellite sensors within surrounding 4 km2 landscape blocks. At the block scale, we used evapotranspiration and vegetation greenness as remotely sensed proxies for water availability and to estimate seasonal photosynthetic activity. We used sensors on the ground to coincidentally measure surface-water availability and amphibian calling activity at individual wetlands within blocks. Responses of landscape blocks generally paralleled changes in conditions measured on the ground, but the latter were more dynamic, and changes in ecological conditions on the ground that were critical for biota were not always apparent in measurements of related parameters in blocks. Here, we evaluate the effectiveness of decisions and assumptions we made in applying the remotely sensed data for the assessment and the value of integrating observations across scales, sensors, and disciplines. PMID:29547531

Using Multi-Temporal Remote Sensing Data to Analyze the Spatio-Temporal Patterns of Dry Season Rice Production in Bangladesh

NASA Astrophysics Data System (ADS)

Shew, A. M.; Ghosh, A.

2017-10-01

Remote sensing in the optical domain is widely used in agricultural monitoring; however, such initiatives pose a challenge for developing countries due to a lack of high quality in situ information. Our proposed methodology could help developing countries bridge this gap by demonstrating the potential to quantify patterns of dry season rice production in Bangladesh. To analyze approximately 90,000 km2 of cultivated land in Bangladesh at 30 m spatial resolution, we used two decades of remote sensing data from the Landsat archive and Google Earth Engine (GEE), a cloud-based geospatial data analysis platform built on Google infrastructure and capable of processing petabyte-scale remote sensing data. We reconstructed the seasonal patterns of vegetation indices (VIs) for each pixel using a harmonic time series (HTS) model, which minimizes the effects of missing observations and noise. Next, we combined the seasonality information of VIs with our knowledge of rice cultivation systems in Bangladesh to delineate rice areas in the dry season, which are predominantly hybrid and High Yielding Varieties (HYV). Based on historical Landsat imagery, the harmonic time series of vegetation indices (HTS-VIs) model estimated 4.605 million ha, 3.519 million ha, and 4.021 million ha of rice production for Bangladesh in 2005, 2010, and 2015 respectively. Fine spatial scale information on HYV rice over the last 20 years will greatly improve our understanding of double-cropped rice systems, current status of production, and potential for HYV rice adoption in Bangladesh during the dry season.

Ground based remote sensing and physiological measurements provide novel insights into canopy photosynthetic optimization in arctic shrubs

NASA Astrophysics Data System (ADS)

Magney, T. S.; Griffin, K. L.; Boelman, N.; Eitel, J.; Greaves, H.; Prager, C.; Logan, B.; Oliver, R.; Fortin, L.; Vierling, L. A.

2014-12-01

Because changes in vegetation structure and function in the Arctic are rapid and highly dynamic phenomena, efforts to understand the C balance of the tundra require repeatable, objective, and accurate remote sensing methods for estimating aboveground C pools and fluxes over large areas. A key challenge addressing the modelling of aboveground C is to utilize process-level information from fine-scale studies. Utilizing information obtained from high resolution remote sensing systems could help to better understand the C source/sink strength of the tundra, which will in part depend on changes in photosynthesis resulting from the partitioning of photosynthetic machinery within and among deciduous shrub canopies. Terrestrial LiDAR and passive hyperspectral remote sensing measurements offer an effective, repeatable, and scalable method to understand photosynthetic performance and partitioning at the canopy scale previously unexplored in arctic systems. Using a 3-D shrub canopy model derived from LiDAR, we quantified the light regime of leaves within shrub canopies to gain a better understanding of how light interception varies in response to the Arctic's complex radiation regime. This information was then coupled with pigment sampling (i.e., xanthophylls, and Chl a/b) to evaluate the optimization of foliage photosynthetic capacity within shrub canopies due to light availability. In addition, a lab experiment was performed to validate evidence of canopy level optimization via gradients of light intensity and leaf light environment. For this, hyperspectral reflectance (photochemical reflectance index (PRI)), and solar induced fluorescence (SIF)) was collected in conjunction with destructive pigment samples (xanthophylls) and chlorophyll fluorescence measurements in both sunlit and shaded canopy positions.

On the Fidelity of Semi-distributed Hydrologic Model Simulations for Large Scale Catchment Applications

NASA Astrophysics Data System (ADS)

Ajami, H.; Sharma, A.; Lakshmi, V.

2017-12-01

Application of semi-distributed hydrologic modeling frameworks is a viable alternative to fully distributed hyper-resolution hydrologic models due to computational efficiency and resolving fine-scale spatial structure of hydrologic fluxes and states. However, fidelity of semi-distributed model simulations is impacted by (1) formulation of hydrologic response units (HRUs), and (2) aggregation of catchment properties for formulating simulation elements. Here, we evaluate the performance of a recently developed Soil Moisture and Runoff simulation Toolkit (SMART) for large catchment scale simulations. In SMART, topologically connected HRUs are delineated using thresholds obtained from topographic and geomorphic analysis of a catchment, and simulation elements are equivalent cross sections (ECS) representative of a hillslope in first order sub-basins. Earlier investigations have shown that formulation of ECSs at the scale of a first order sub-basin reduces computational time significantly without compromising simulation accuracy. However, the implementation of this approach has not been fully explored for catchment scale simulations. To assess SMART performance, we set-up the model over the Little Washita watershed in Oklahoma. Model evaluations using in-situ soil moisture observations show satisfactory model performance. In addition, we evaluated the performance of a number of soil moisture disaggregation schemes recently developed to provide spatially explicit soil moisture outputs at fine scale resolution. Our results illustrate that the statistical disaggregation scheme performs significantly better than the methods based on topographic data. Future work is focused on assessing the performance of SMART using remotely sensed soil moisture observations using spatially based model evaluation metrics.

Modeling the Wake of the Marquesas Archipelago

NASA Astrophysics Data System (ADS)

Raapoto, H.; Martinez, E.; Petrenko, A.; Doglioli, A. M.; Maes, C.

2018-02-01

In this study, a high-resolution (˜2.5 km) numerical model was set up to investigate the fine-scale activity within the region of the Marquesas archipelago. This has never been performed before. The robustness of the model results is assessed by comparison with remote sensing and in situ observations. Our results highlight regions of warm waters leeward of the different islands with high eddy kinetic energy (EKE) on their sides. The analysis of energy conversion terms reveals contributions to EKE variability by wind, baroclinic, and barotropic instabilities. The use of a geometry-based eddy detection algorithm reveals the generation of cyclonic and anticyclonic eddies in the wake of the largest islands, with both an inshore and offshore effect. Maximum eddy activity occurs in austral winter following the seasonality of both wind stress and EKE intensity. Most eddies have a radius between 20 and 30 km and are generally cyclonic rather than anticyclonic. Significant vertical velocities are observed in the proximity of the islands, associated with topographically induced flow separation. Eddy trapping inshore waters are advected offshore in the wake of the islands. The overall influence of these fine-scale dynamics could explain the strong biological enhancement of the archipelago.

Modelling the Wake of the Marquesas Archipelago

NASA Astrophysics Data System (ADS)

Raapoto, H.; Martinez, E. C.; Petrenko, A. A.; Doglioli, A. M.; Maes, C.

2017-12-01

In this study, a high-resolution ( 2.5 km) numerical model was set up to investigate the fine-scale activity within the region of the Marquesas archipelago where a strong biological enhancement occurs. This has never been performed before. The robustness of the model results is assessed by comparison with remote sensing and in situ observations. Our results highlight regions of warm waters leeward of the different islands with high eddy kinetic energy (EKE) on their sides. The analysis of energy conversion terms reveals contributions to EKE variability by wind, baroclinic and barotropic instabilities. The use of a geometry-based eddy detection algorithm reveals eddy generation in the wake of the largest islands, with both an inshore and offshore effect. Maximum eddy activity occurs in austral winter following the seasonality of both wind stress and EKE intensity. Most eddies have a radius between 20 and 30 km and are generally cyclonic rather than anticyclonic. Significant vertical velocities are observed in the proximity of the islands, associated with topography induced flow separation. Eddy trapping inshore waters are advected offshore in the wake of the islands. The overall influence of these fine-scale dynamics could explain the strong biological enhancement of the archipelago.

Toward an operational framework for fine-scale urban land-cover mapping in Wallonia using submeter remote sensing and ancillary vector data

NASA Astrophysics Data System (ADS)

Beaumont, Benjamin; Grippa, Tais; Lennert, Moritz; Vanhuysse, Sabine; Stephenne, Nathalie; Wolff, Eléonore

2017-07-01

Encouraged by the EU INSPIRE directive requirements and recommendations, the Walloon authorities, similar to other EU regional or national authorities, want to develop operational land-cover (LC) and land-use (LU) mapping methods using existing geodata. Urban planners and environmental monitoring stakeholders of Wallonia have to rely on outdated, mixed, and incomplete LC and LU information. The current reference map is 10-years old. The two object-based classification methods, i.e., a rule- and a classifier-based method, for detailed regional urban LC mapping are compared. The added value of using the different existing geospatial datasets in the process is assessed. This includes the comparison between satellite and aerial optical data in terms of mapping accuracies, visual quality of the map, costs, processing, data availability, and property rights. The combination of spectral, tridimensional, and vector data provides accuracy values close to 0.90 for mapping the LC into nine categories with a minimum mapping unit of 15 m2. Such a detailed LC map offers opportunities for fine-scale environmental and spatial planning activities. Still, the regional application poses challenges regarding automation, big data handling, and processing time, which are discussed.

Ecosystem structure and function in the SPRUCE chambers at fine resolution

NASA Astrophysics Data System (ADS)

Glenn, N. F.; Graham, J.; Spaete, L.; Hanson, P. J.

2017-12-01

The Spruce and Peatland Responses Under Climatic and Environmental change (SPRUCE; operated by DOE's Oak Ridge National Laboratory) aims to assess biological and ecological responses in a peat bog to a range of increased temperatures and the presence of elevated atmospheric CO2 concentrations. We are using terrestrial laser scanning (TLS) to monitor vegetation productivity and hummock-hollow structure at cm-scale in the SPRUCE plots to complement in-situ measurements of gross and net primary production. The hummock-hollow peatland microtopography is associated with fluctuating water levels and sphagnum mosses, and ultimately controls C and methane cycling. We estimate tree growth by calculating increases in tree height and canopy voxel volume between years with the TLS data. Microtopography is also characterized over time with TLS but by using gridded cells to classify regions into hummocks or hollows. Spectroscopy to quantify water content in the sphagnum is used to further classify these microtopographic regions. As multiple years of data collection occur, we will couple our fine-scale remote sensing measurements with in-situ measurements of CO2 and CH4 flux measures to capture species-specific productivity responses to warming and increased CO2.

Assessment of Acacia koa forest health across environmental gradients in Hawai'i using fine resolution remote sensing and GIS.

PubMed

Morales, Rodolfo Martinez; Idol, Travis; Friday, James B

2011-01-01

Koa (Acacia koa) forests are found across broad environmental gradients in the Hawai'ian Islands. Previous studies have identified koa forest health problems and dieback at the plot level, but landscape level patterns remain unstudied. The availability of high-resolution satellite images from the new GeoEye1 satellite offers the opportunity to conduct landscape-level assessments of forest health. The goal of this study was to develop integrated remote sensing and geographic information systems (GIS) methodologies to characterize the health of koa forests and model the spatial distribution and variability of koa forest dieback patterns across an elevation range of 600-1,000 m asl in the island of Kaua'i, which correspond to gradients of temperature and rainfall ranging from 17-20 °C mean annual temperature and 750-1,500 mm mean annual precipitation. GeoEye1 satellite imagery of koa stands was analyzed using supervised classification techniques based on the analysis of 0.5-m pixel multispectral bands. There was clear differentiation of native koa forest from areas dominated by introduced tree species and differentiation of healthy koa stands from those exhibiting dieback symptoms. The area ratio of healthy koa to koa dieback corresponded linearly to changes in temperature across the environmental gradient, with koa dieback at higher relative abundance in warmer areas. A landscape-scale map of healthy koa forest and dieback distribution demonstrated both the general trend with elevation and the small-scale heterogeneity that exists within particular elevations. The application of these classification techniques with fine spatial resolution imagery can improve the accuracy of koa forest inventory and mapping across the islands of Hawai'i. Such findings should also improve ecological restoration, conservation and silviculture of this important native tree species.

Knowledge-guided golf course detection using a convolutional neural network fine-tuned on temporally augmented data

NASA Astrophysics Data System (ADS)

Chen, Jingbo; Wang, Chengyi; Yue, Anzhi; Chen, Jiansheng; He, Dongxu; Zhang, Xiuyan

2017-10-01

The tremendous success of deep learning models such as convolutional neural networks (CNNs) in computer vision provides a method for similar problems in the field of remote sensing. Although research on repurposing pretrained CNN to remote sensing tasks is emerging, the scarcity of labeled samples and the complexity of remote sensing imagery still pose challenges. We developed a knowledge-guided golf course detection approach using a CNN fine-tuned on temporally augmented data. The proposed approach is a combination of knowledge-driven region proposal, data-driven detection based on CNN, and knowledge-driven postprocessing. To confront data complexity, knowledge-derived cooccurrence, composition, and area-based rules are applied sequentially to propose candidate golf regions. To confront sample scarcity, we employed data augmentation in the temporal domain, which extracts samples from multitemporal images. The augmented samples were then used to fine-tune a pretrained CNN for golf detection. Finally, commission error was further suppressed by postprocessing. Experiments conducted on GF-1 imagery prove the effectiveness of the proposed approach.

Dispersal of fine sediment in nearshore coastal waters

USGS Publications Warehouse

Warrick, Jonathan A.

2013-01-01

Fine sediment (silt and clay) plays an important role in the physical, ecological, and environmental conditions of coastal systems, yet little is known about the dispersal and fate of fine sediment across coastal margin settings outside of river mouths. Here I provide simple physical scaling and detailed monitoring of a beach nourishment project near Imperial Beach, California, with a high portion of fines (40% silt and clay by weight). These results provide insights into the pathways and residence times of fine sediment transport across a wave-dominated coastal margin. Monitoring of the project used physical, optical, acoustic, and remote sensing techniques to track the fine portion of the nourishment sediment. The initial transport of fine sediment from the beach was influenced strongly by longshore currents of the surf zone that were established in response to the approach angles of the waves. The mean residence time of fine sediment in the surf zone—once it was suspended—was approximately 1 hour, and rapid decreases in surf zone fine sediment concentrations along the beach resulted from mixing and offshore transport in turbid rip heads. For example, during a day with oblique wave directions and surf zone longshore currents of approximately 25 cm/s, the offshore losses of fine sediment in rips resulted in a 95% reduction in alongshore surf zone fine sediment flux within 1 km of the nourishment site. However, because of the direct placement of nourishment sediment on the beach, fine suspended-sediment concentrations in the swash zone remained elevated for several days after nourishment, while fine sediment was winnowed from the beach. Once offshore of the surf zone, fine sediment settled downward in the water column and was observed to transport along and across the inner shelf. Vertically sheared currents influenced the directions and rates of fine sediment transport on the shelf. Sedimentation of fine sediment was greatest on the seafloor directly offshore of the nourishment site. However, a mass balance of sediment suggests that the majority of the fine sediment moved far away (over 2 km) from the nourishment site or to water depths greater than 10 m, where fine sediment represents a substantial portion of the bed material. Thus, the fate of fine sediment in nearshore waters was influenced strongly by wave conditions, surf zone and rip current transport, and the vertical density and flow conditions of coastal waters.

Spatiotemporal Modeling for Fine-Scale Maps of Regional Malaria Endemicity and Its Implications for Transitional Complexities in a Routine Surveillance Network in Western Cambodia

PubMed Central

Okami, Suguru; Kohtake, Naohiko

2017-01-01

Due to the associated and substantial efforts of many stakeholders involved in malaria containment, the disease burden of malaria has dramatically decreased in many malaria-endemic countries in recent years. Some decades after the past efforts of the global malaria eradication program, malaria elimination has again featured on the global health agenda. While risk distribution modeling and a mapping approach are effective tools to assist with the efficient allocation of limited health-care resources, these methods need some adjustment and reexamination in accordance with changes occurring in relation to malaria elimination. Limited available data, fine-scale data inaccessibility (for example, household or individual case data), and the lack of reliable data due to inefficiencies within the routine surveillance system, make it difficult to create reliable risk maps for decision-makers or health-care practitioners in the field. Furthermore, the risk of malaria may dynamically change due to various factors such as the progress of containment interventions and environmental changes. To address the complex and dynamic nature of situations in low-to-moderate malaria transmission settings, we built a spatiotemporal model of a standardized morbidity ratio (SMR) of malaria incidence, calculated through annual parasite incidence, using routinely reported surveillance data in combination with environmental indices such as remote sensing data, and the non-environmental regional containment status, to create fine-scale risk maps. A hierarchical Bayesian frame was employed to fit the transitioning malaria risk data onto the map. The model was set to estimate the SMRs of every study location at specific time intervals within its uncertainty range. Using the spatial interpolation of estimated SMRs at village level, we created fine-scale maps of two provinces in western Cambodia at specific time intervals. The maps presented different patterns of malaria risk distribution at specific time intervals. Moreover, the visualized weights estimated using the risk model, and the structure of the routine surveillance network, represent the transitional complexities emerging from ever-changing regional endemic situations. PMID:29034229

Downscaling SMAP Radiometer Soil Moisture over the CONUS using Soil-Climate Information and Ensemble Learning

NASA Astrophysics Data System (ADS)

Abbaszadeh, P.; Moradkhani, H.

2017-12-01

Soil moisture contributes significantly towards the improvement of weather and climate forecast and understanding terrestrial ecosystem processes. It is known as a key hydrologic variable in the agricultural drought monitoring, flood modeling and irrigation management. While satellite retrievals can provide an unprecedented information on soil moisture at global-scale, the products are generally at coarse spatial resolutions (25-50 km2). This often hampers their use in regional or local studies, which normally require a finer resolution of the data set. This work presents a new framework based on an ensemble learning method while using soil-climate information derived from remote-sensing and ground-based observations to downscale the level 3 daily composite version (L3_SM_P) of SMAP radiometer soil moisture over the Continental U.S. (CONUS) at 1 km spatial resolution. In the proposed method, a suite of remotely sensed and in situ data sets in addition to soil texture information and topography data among others were used. The downscaled product was validated against in situ soil moisture measurements collected from a limited number of core validation sites and several hundred sparse soil moisture networks throughout the CONUS. The obtained results indicated a great potential of the proposed methodology to derive the fine resolution soil moisture information applicable for fine resolution hydrologic modeling, data assimilation and other regional studies.

Experiments with data assimilation in comprehensive air quality models: Impacts on model predictions and observation requirements (Invited)

NASA Astrophysics Data System (ADS)

Mathur, R.

2009-12-01

Emerging regional scale atmospheric simulation models must address the increasing complexity arising from new model applications that treat multi-pollutant interactions. Sophisticated air quality modeling systems are needed to develop effective abatement strategies that focus on simultaneously controlling multiple criteria pollutants as well as use in providing short term air quality forecasts. In recent years the applications of such models is continuously being extended to address atmospheric pollution phenomenon from local to hemispheric spatial scales over time scales ranging from episodic to annual. The need to represent interactions between physical and chemical atmospheric processes occurring at these disparate spatial and temporal scales requires the use of observation data beyond traditional in-situ networks so that the model simulations can be reasonably constrained. Preliminary applications of assimilation of remote sensing and aloft observations within a comprehensive regional scale atmospheric chemistry-transport modeling system will be presented: (1) A methodology is developed to assimilate MODIS aerosol optical depths in the model to represent the impacts long-range transport associated with the summer 2004 Alaskan fires on surface-level regional fine particulate matter (PM2.5) concentrations across the Eastern U.S. The episodic impact of this pollution transport event on PM2.5 concentrations over the eastern U.S. during mid-July 2004, is quantified through the complementary use of the model with remotely-sensed, aloft, and surface measurements; (2) Simple nudging experiments with limited aloft measurements are performed to identify uncertainties in model representations of physical processes and assess the potential use of such measurements in improving the predictive capability of atmospheric chemistry-transport models. The results from these early applications will be discussed in context of uncertainties in the model and in the remote sensing data and needs for defining a future optimum observing strategy.

Quantifying the imprint of mesoscale and synoptic-scale atmospheric transport on total column carbon dioxide measurements

NASA Astrophysics Data System (ADS)

Torres, A. D.; Keppel-Aleks, G.; Doney, S. C.; Feng, S.; Lauvaux, T.; Fendrock, M. A.; Rheuben, J.

2017-12-01

Remote sensing instruments provide an unprecedented density of observations of the atmospheric CO2 column average mole fraction (denoted as XCO2), which can be used to constrain regional scale carbon fluxes. Inferring fluxes from XCO2 observations is challenging, as measurements and inversion methods are sensitive to not only the imprint local and large-scale fluxes, but also mesoscale and synoptic-scale atmospheric transport. Quantifying the fine-scale variability in XCO2 from mesoscale and synoptic-scale atmospheric transport will likely improve overall error estimates from flux inversions by improving estimates of representation errors that occur when XCO2 observations are compared to modeled XCO2 in relatively coarse transport models. Here, we utilize various statistical methods to quantify the imprint of atmospheric transport on XCO2 observations. We compare spatial variations along Orbiting Carbon Observatory (OCO-2) satellite tracks to temporal variations observed by the Total Column Carbon Observing Network (TCCON). We observe a coherent seasonal cycle of both within-day temporal and fine-scale spatial variability (of order 10 km) of XCO2 from these two datasets, suggestive of the imprint of mesoscale systems. To account for other potential sources of error in XCO2 retrieval, we compare observed temporal and spatial variations of XCO2 to high-resolution output from the Weather Research and Forecasting (WRF) model run at 9 km resolution. In both simulations and observations, the Northern hemisphere mid-latitude XCO2 showed peak variability during the growing season when atmospheric gradients are largest. These results are qualitatively consistent with our expectations of seasonal variations of the imprint of synoptic and mesoscale atmospheric transport on XCO2 observations; suggesting that these statistical methods could be sensitive to the imprint of atmospheric transport on XCO2 observations.

Towards a Near Real-Time Satellite-Based Flux Monitoring System for the MENA Region

NASA Astrophysics Data System (ADS)

Ershadi, A.; Houborg, R.; McCabe, M. F.; Anderson, M. C.; Hain, C.

2013-12-01

Satellite remote sensing has the potential to offer spatially and temporally distributed information on land surface characteristics, which may be used as inputs and constraints for estimating land surface fluxes of carbon, water and energy. Enhanced satellite-based monitoring systems for aiding local water resource assessments and agricultural management activities are particularly needed for the Middle East and North Africa (MENA) region. The MENA region is an area characterized by limited fresh water resources, an often inefficient use of these, and relatively poor in-situ monitoring as a result of sparse meteorological observations. To address these issues, an integrated modeling approach for near real-time monitoring of land surface states and fluxes at fine spatio-temporal scales over the MENA region is presented. This approach is based on synergistic application of multiple sensors and wavebands in the visible to shortwave infrared and thermal infrared (TIR) domain. The multi-scale flux mapping and monitoring system uses the Atmosphere-Land Exchange Inverse (ALEXI) model and associated flux disaggregation scheme (DisALEXI), and the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) in conjunction with model reanalysis data and multi-sensor remotely sensed data from polar orbiting (e.g. Landsat and MODerate resolution Imaging Spectroradiometer (MODIS)) and geostationary (MSG; Meteosat Second Generation) satellite platforms to facilitate time-continuous (i.e. daily) estimates of field-scale water, energy and carbon fluxes. Within this modeling system, TIR satellite data provide information about the sub-surface moisture status and plant stress, obviating the need for precipitation input and a detailed soil surface characterization (i.e. for prognostic modeling of soil transport processes). The STARFM fusion methodology blends aspects of high frequency (spatially coarse) and spatially fine resolution sensors and is applied directly to flux output fields to facilitate daily mapping of fluxes at sub-field scales. A complete processing infrastructure to automatically ingest and pre-process all required input data and to execute the integrated modeling system for near real-time agricultural monitoring purposes over targeted MENA sites is being developed, and initial results from this concerted effort will be discussed.

Registration of Laser Scanning Point Clouds: A Review.

PubMed

Cheng, Liang; Chen, Song; Liu, Xiaoqiang; Xu, Hao; Wu, Yang; Li, Manchun; Chen, Yanming

2018-05-21

The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles.

Registration of Laser Scanning Point Clouds: A Review

PubMed Central

Cheng, Liang; Chen, Song; Xu, Hao; Wu, Yang; Li, Manchun

2018-01-01

The integration of multi-platform, multi-angle, and multi-temporal LiDAR data has become important for geospatial data applications. This paper presents a comprehensive review of LiDAR data registration in the fields of photogrammetry and remote sensing. At present, a coarse-to-fine registration strategy is commonly used for LiDAR point clouds registration. The coarse registration method is first used to achieve a good initial position, based on which registration is then refined utilizing the fine registration method. According to the coarse-to-fine framework, this paper reviews current registration methods and their methodologies, and identifies important differences between them. The lack of standard data and unified evaluation systems is identified as a factor limiting objective comparison of different methods. The paper also describes the most commonly-used point cloud registration error analysis methods. Finally, avenues for future work on LiDAR data registration in terms of applications, data, and technology are discussed. In particular, there is a need to address registration of multi-angle and multi-scale data from various newly available types of LiDAR hardware, which will play an important role in diverse applications such as forest resource surveys, urban energy use, cultural heritage protection, and unmanned vehicles. PMID:29883397

Detecting Uniform Areas for Vicarious Calibration using Landsat TM Imagery: A Study using the Arabian and Saharan Deserts

NASA Technical Reports Server (NTRS)

Hilbert, Kent; Pagnutti, Mary; Ryan, Robert; Zanoni, Vicki

2002-01-01

This paper discusses a method for detecting spatially uniform sites need for radiometric characterization of remote sensing satellites. Such information is critical for scientific research applications of imagery having moderate to high resolutions (<30-m ground sampling distance (GSD)). Previously published literature indicated that areas with the African Saharan and Arabian deserts contained extremely uniform sites with respect to spatial characteristics. We developed an algorithm for detecting site uniformity and applied it to orthorectified Landsat Thematic Mapper (TM) imagery over eight uniform regions of interest. The algorithm's results were assessed using both medium-resolution (30-m GSD) Landsat 7 ETM+ and fine-resolution (<5-m GSD) IKONOS multispectral data collected over sites in Libya and Mali. Fine-resolution imagery over a Libyan site exhibited less than 1 percent nonuniformity. The research shows that Landsat TM products appear highly useful for detecting potential calibration sites for system characterization. In particular, the approach detected spatially uniform regions that frequently occur at multiple scales of observation.

Rain Check Application: Mobile tool to monitor rainfall in remote parts of Haiti

NASA Astrophysics Data System (ADS)

Huang, X.; Baird, J.; Chiu, M. T.; Morelli, R.; de Lanerolle, T. R.; Gourley, J. R.

2011-12-01

Rainfall observations performed uniformly and continuously over a period of time are valuable inputs in developing climate models and predicting events such as floods and droughts. Rain-Check is a mobile application developed in Google App Inventor Platform, for android based smart phones, to allow field researchers to monitor various rain gauges distributed though out remote regions of Haiti and send daily readings via SMS messages for further analysis and long term trending. Rainfall rate and quantity interact with many other factors to influence erosion, vegetative cover, groundwater recharge, stream water chemistry and runoff into streams impacting agriculture and livestock. Rainfall observation from various sites is especially significant in Haiti with over 80% of the country is mountainous terrain. Data sets from global models and limited number of ground stations do not capture the fine-scale rainfall patterns necessary to describe local climate. Placement and reading of rain gauges are critical to accurate measurement of rainfall.

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

PubMed

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

2013-12-01

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

Design of an Air Pollution Monitoring Campaign in Beijing for Application to Cohort Health Studies.

PubMed

Vedal, Sverre; Han, Bin; Xu, Jia; Szpiro, Adam; Bai, Zhipeng

2017-12-15

No cohort studies in China on the health effects of long-term air pollution exposure have employed exposure estimates at the fine spatial scales desirable for cohort studies with individual-level health outcome data. Here we assess an array of modern air pollution exposure estimation approaches for assigning within-city exposure estimates in Beijing for individual pollutants and pollutant sources to individual members of a cohort. Issues considered in selecting specific monitoring data or new monitoring campaigns include: needed spatial resolution, exposure measurement error and its impact on health effect estimates, spatial alignment and compatibility with the cohort, and feasibility and expense. Sources of existing data largely include administrative monitoring data, predictions from air dispersion or chemical transport models and remote sensing (specifically satellite) data. New air monitoring campaigns include additional fixed site monitoring, snapshot monitoring, passive badge or micro-sensor saturation monitoring and mobile monitoring, as well as combinations of these. Each of these has relative advantages and disadvantages. It is concluded that a campaign in Beijing that at least includes a mobile monitoring component, when coupled with currently available spatio-temporal modeling methods, should be strongly considered. Such a campaign is economical and capable of providing the desired fine-scale spatial resolution for pollutants and sources.

Design of an Air Pollution Monitoring Campaign in Beijing for Application to Cohort Health Studies

PubMed Central

Vedal, Sverre; Han, Bin; Szpiro, Adam; Bai, Zhipeng

2017-01-01

No cohort studies in China on the health effects of long-term air pollution exposure have employed exposure estimates at the fine spatial scales desirable for cohort studies with individual-level health outcome data. Here we assess an array of modern air pollution exposure estimation approaches for assigning within-city exposure estimates in Beijing for individual pollutants and pollutant sources to individual members of a cohort. Issues considered in selecting specific monitoring data or new monitoring campaigns include: needed spatial resolution, exposure measurement error and its impact on health effect estimates, spatial alignment and compatibility with the cohort, and feasibility and expense. Sources of existing data largely include administrative monitoring data, predictions from air dispersion or chemical transport models and remote sensing (specifically satellite) data. New air monitoring campaigns include additional fixed site monitoring, snapshot monitoring, passive badge or micro-sensor saturation monitoring and mobile monitoring, as well as combinations of these. Each of these has relative advantages and disadvantages. It is concluded that a campaign in Beijing that at least includes a mobile monitoring component, when coupled with currently available spatio-temporal modeling methods, should be strongly considered. Such a campaign is economical and capable of providing the desired fine-scale spatial resolution for pollutants and sources. PMID:29244738

Fine-Scale Relief in the Amazon Drives Large Scale Ecohydrological Processes

NASA Astrophysics Data System (ADS)

Nobre, A. D.; Cuartas, A.; Hodnett, M.; Saleska, S. R.

2014-12-01

Access to soil water by roots is a key ecophysiological factor for plant productivity in natural systems. Periodically during dry seasons or critically during episodic climate droughts, shortage of water supply can reduce or severely impair plant life. At the other extreme persistent soil waterlogging will limit root respiration and restrict local establishment to adapted species, usually leading to stunted and less productive communities. Soil-water availability is therefore a very important climate variable controlling plant physiology and ecosystem dynamics. Terra-firme, the non-seasonally floodable terrain that covers 82% of the landscape in Amazonia,[1] supports the most massive part of the rainforest ecosystem. The availability of soil water data for terra-firme is scant and very coarse. This lack of data has hampered observational and modeling studies aiming to develop a large-scale integrative ecohydrological picture of Amazonia and its vulnerability to climate change. We have mapped the Amazon basin with a new terrain model developed in our group (HAND, Height Above the Nearest drainage[2]), delineating soil water environments using topographical data from the SRTM digital elevation model (250 m horizontal interpolated resolution). The preliminary results show that more than 50% of Terra-firme has the water table very close to the surface (up to 2 m deep), while the remainder of the upland landscape has variable degree of dependence on non-saturated soil (vadose layer). The mapping also shows extremely heterogeneous patterns of fine-scale relief across the basin, which implies complex ecohydrological regional forcing on the forest physiology. Ecoclimate studies should therefore take into account fine-scale relief and its implications for soil-water availability to plant processes. [1] Melack, J. M., & Hess, L. L. (2011). Remote sensing of the distribution and extent of wetlands in the Amazon basin. In W. J. Junk & M. Piedade (Eds.), Amazonian floodplain forests: Ecophysiology, ecology, biodiversity and sustainable management (pp. 1-28). Ecological Studies-Springer. [2] Nobre, A. D., Cuartas, L. A., Hodnett, M., … Saleska, S. (2011). Height Above the Nearest Drainage - a hydrologically relevant new terrain model. Journal of Hydrology, 404(1-2), 13-29

Modeling habitat for Marbled Murrelets on the Siuslaw National Forest, Oregon, using lidar data

USGS Publications Warehouse

Hagar, Joan C.; Aragon, Ramiro; Haggerty, Patricia; Hollenbeck, Jeff P.

2018-03-28

Habitat models using lidar-derived variables that quantify fine-scale variation in vegetation structure can improve the accuracy of occupancy estimates for canopy-dwelling species over models that use variables derived from other remote sensing techniques. However, the ability of models developed at such a fine spatial scale to maintain accuracy at regional or larger spatial scales has not been tested. We tested the transferability of a lidar-based habitat model for the threatened Marbled Murrelet (Brachyramphus marmoratus) between two management districts within a larger regional conservation zone in coastal western Oregon. We compared the performance of the transferred model against models developed with data from the application location. The transferred model had good discrimination (AUC = 0.73) at the application location, and model performance was further improved by fitting the original model with coefficients from the application location dataset (AUC = 0.79). However, the model selection procedure indicated that neither of these transferred models were considered competitive with a model trained on local data. The new model trained on data from the application location resulted in the selection of a slightly different set of lidar metrics from the original model, but both transferred and locally trained models consistently indicated positive relationships between the probability of occupancy and lidar measures of canopy structural complexity. We conclude that while the locally trained model had superior performance for local application, the transferred model could reasonably be applied to the entire conservation zone.

Thermal Characteristics of Urban Landscapes

NASA Technical Reports Server (NTRS)

Luvall, Jeffrey C.; Quattrochi, Dale A.

1998-01-01

Although satellite data are very useful for analysis of the urban heat island effect at a coarse scale, they do not lend themselves to developing a better understanding of which surfaces across the city contribute or drive the development of the urban heat island effect. Analysis of thermal energy responses for specific or discrete surfaces typical of the urban landscape (e.g., asphalt, building rooftops, vegetation) requires measurements at a very fine spatial scale (i.e., less than 15 m) to adequately resolve these surfaces and their attendant thermal energy regimes. Additionally, very fine scale spatial resolution thermal infrared data, such as that obtained from aircraft, are very useful for demonstrating to planning officials, policy makers, and the general populace the benefits of the urban forest. These benefits include mitigating the urban heat island effect, making cities more aesthetically pleasing and more habitable environments, and aid in overall cooling of the community. High spatial resolution thermal data are required to quantify how artificial surfaces within the city contribute to an increase in urban heating and the benefit of cool surfaces (e.g., surface coatings that reflect much of the incoming solar radiation as opposed to absorbing it thereby lowering urban temperatures). The TRN (thermal response number) is a technique using aircraft remotely sensed surface temperatures to quantify the thermal response of urban surfaces. The TRN was used to quantify the thermal response of various urban surface types ranging from completely vegetated surfaces to asphalt and concrete parking lots for Huntsville, AL.

Understanding the climate-included variations in the seasonal water demands of irrigated crops in Northern India

NASA Astrophysics Data System (ADS)

Bhattarai, N.; Jain, M.

2016-12-01

Expected changes in temperature and precipitation patterns in the rice-wheat belt of Northern India have implications for balancing crop water demand and available water resources. Because the impacts of water scarcity and reduced crop production are realized at a local scale, water-saving interventions are most effective when implemented locally. However, a paucity of fine-scale studies on the relationship between variations in climate and crop water demand has limited our ability to effectively implement such interventions. In an effort to better understand the responses of irrigated crops to changing climate in Northern India at finer-scales, we propose a remote sensing based semi-empirical approach. First, we employ a multi-model surface energy balance (SEB) approach to map seasonal evapotranspiration (ET)/water use (1995-2015) at 30 to 100 m resolution from space and investigate how seasonal and inter-annual variations in temperature and precipitation are associated with regional surface-energy budgets. Second, using remote estimates of ET and other biophysical variables, such as vegetation indices, land surface temperature, and albedo, we will explain the possible relationships between climate change and seasonal water demands of crops. Our estimates of high/moderate resolution (30 to 100 m) seasonal ET maps can make clear distinctions between impacts of climate variations on crop water demand at field, plot, and regional scales in Northern India. Finally, by improving our ability to identify targeted area for water-saving interventions, this study supports agricultural resiliency of Northern India in the face of climate change.

Estimation of atmospheric columnar organic matter (OM) mass concentration from remote sensing measurements of aerosol spectral refractive indices

NASA Astrophysics Data System (ADS)

Zhang, Ying; Li, Zhengqiang; Sun, Yele; Lv, Yang; Xie, Yisong

2018-04-01

Aerosols have adverse effects on human health and air quality, changing Earth's energy balance and lead to climate change. The components of aerosol are important because of the different spectral characteristics. Based on the low hygroscopic and high scattering properties of organic matter (OM) in fine modal atmospheric aerosols, we develop an inversion algorithm using remote sensing to obtain aerosol components including black carbon (BC), organic matter (OM), ammonium nitrate-like (AN), dust-like (DU) components and aerosol water content (AW). In the algorithm, the microphysical characteristics (i.e. volume distribution and complex refractive index) of particulates are preliminarily separated to fine and coarse modes, and then aerosol components are retrieved using bimodal parameters. We execute the algorithm using remote sensing measurements of sun-sky radiometer at AERONET site (Beijing RADI) in a period from October of 2014 to January of 2015. The results show a reasonable distribution of aerosol components and a good fit for spectral feature calculations. The mean OM mass concentration in atmospheric column is account for 14.93% of the total and 56.34% of dry and fine-mode aerosol, being a fairly good correlation (R = 0.56) with the in situ observations near the surface layer.

High Resolution Insights into Snow Distribution Provided by Drone Photogrammetry

NASA Astrophysics Data System (ADS)

Redpath, T.; Sirguey, P. J.; Cullen, N. J.; Fitzsimons, S.

2017-12-01

Dynamic in time and space, New Zealand's seasonal snow is largely confined to remote alpine areas, complicating ongoing in situ measurement and characterisation. Improved understanding and modeling of the seasonal snowpack requires fine scale resolution of snow distribution and spatial variability. The potential of remotely piloted aircraft system (RPAS) photogrammetry to resolve spatial and temporal variability of snow depth and water equivalent in a New Zealand alpine catchment is assessed in the Pisa Range, Central Otago. This approach yielded orthophotomosaics and digital surface models (DSM) at 0.05 and 0.15 m spatial resolution, respectively. An autumn reference DSM allowed mapping of winter (02/08/2016) and spring (10/09/2016) snow depth at 0.15 m spatial resolution, via DSM differencing. The consistency and accuracy of the RPAS-derived surface was assessed by comparison of snow-free regions of the spring and autumn DSMs, while accuracy of RPAS retrieved snow depth was assessed with 86 in situ snow probe measurements. Results show a mean vertical residual of 0.024 m between DSMs acquired in autumn and spring. This residual approximated a Laplace distribution, reflecting the influence of large outliers on the small overall bias. Propagation of errors associated with successive DSMs saw snow depth mapped with an accuracy of ± 0.09 m (95% c.l.). Comparing RPAS and in situ snow depth measurements revealed the influence of geo-location uncertainty and interactions between vegetation and the snowpack on snow depth uncertainty and bias. Semi-variogram analysis revealed that the RPAS outperformed systematic in situ measurements in resolving fine scale spatial variability. Despite limitations accompanying RPAS photogrammetry, this study demonstrates a repeatable means of accurately mapping snow depth for an entire, yet relatively small, hydrological basin ( 0.5 km2), at high resolution. Resolving snowpack features associated with re-distribution and preferential accumulation and ablation, snow depth maps provide geostatistically robust insights into seasonal snow processes, with unprecedented detail. Such data may enhance understanding of physical processes controlling spatial and temporal distribution of seasonal snow, and their relative importance at varying spatial and temporal scales.

Recent advances in radar applications to agriculture

NASA Technical Reports Server (NTRS)

Morain, S. A.

1970-01-01

A series of remote radar sensing studies are summarized. These efforts comprise geoscience interpretations of such complex phenomena as those manifested in agricultural patterns. Considered are basic remote sensing needs in agriculture and the design and implementation of radar keys in the active microwave region as well as fine resolution radar imagery techniques for agriculture determinations and soil mapping.

Remotely sensed geology from lander-based to orbital perspectives: Results of FIDO rover May 2000 field tests

USGS Publications Warehouse

Jolliff, B.; Knoll, A.; Morris, R.V.; Moersch, J.; McSween, H.; Gilmore, M.; Arvidson, R.; Greeley, R.; Herkenhoff, K.; Squyres, S.

2002-01-01

Blind field tests of the Field Integration Design and Operations (FIDO) prototype Mars rover were carried out 7-16 May 2000. A Core Operations Team (COT), sequestered at the Jet Propulsion Laboratory without knowledge of test site location, prepared command sequences and interpreted data acquired by the rover. Instrument sensors included a stereo panoramic camera, navigational and hazard-avoidance cameras, a color microscopic imager, an infrared point spectrometer, and a rock coring drill. The COT designed command sequences, which were relayed by satellite uplink to the rover, and evaluated instrument data. Using aerial photos and Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data, and information from the rover sensors, the COT inferred the geology of the landing site during the 18 sol mission, including lithologic diversity, stratigraphic relationships, environments of deposition, and weathering characteristics. Prominent lithologic units were interpreted to be dolomite-bearing rocks, kaolinite-bearing altered felsic volcanic materials, and basalt. The color panoramic camera revealed sedimentary layering and rock textures, and geologic relationships seen in rock exposures. The infrared point spectrometer permitted identification of prominent carbonate and kaolinite spectral features and permitted correlations to outcrops that could not be reached by the rover. The color microscopic imager revealed fine-scale rock textures, soil components, and results of coring experiments. Test results show that close-up interrogation of rocks is essential to investigations of geologic environments and that observations must include scales ranging from individual boulders and outcrops (microscopic, macroscopic) to orbital remote sensing, with sufficient intermediate steps (descent images) to connect in situ and remote observations.

Mapping Migratory Bird Prevalence Using Remote Sensing Data Fusion

PubMed Central

Swatantran, Anu; Dubayah, Ralph; Goetz, Scott; Hofton, Michelle; Betts, Matthew G.; Sun, Mindy; Simard, Marc; Holmes, Richard

2012-01-01

Background Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. Methodology and Principal Findings A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy (“fusion”) models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. Conclusion and Significance Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level. PMID:22235254

Mapping migratory bird prevalence using remote sensing data fusion.

PubMed

Swatantran, Anu; Dubayah, Ralph; Goetz, Scott; Hofton, Michelle; Betts, Matthew G; Sun, Mindy; Simard, Marc; Holmes, Richard

2012-01-01

Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy ("fusion") models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level.

Detecting ecological change on coral reefs

NASA Astrophysics Data System (ADS)

Dustan, P.

2011-12-01

Remote sensing offers the potential to observe the response of coral reef ecosystems to environmental perturbations on a geographical scale not previously accessible. However, coral reef environments are optically, spatially, and temporally complex habitats which all present significant challenges for extracting meaningful information. Virtually every member of the reef community possesses some degree of photosynthetic capability. The community thus generates a matrix of fine scale features with bio-optical signatures that blend as the scale of observation increases. Furthermore, to have any validity, the remotely sensed signal must be "calibrated" to the bio-optics of the reef, a difficult and resource intensive process due to a convergence of photosynthetic light harvesting by green, red, and brown algal pigment systems. To make matters more complex, reefs are overlain by a seawater skin with its own set of hydrological optical challenges. Rather than concentrating on classification, my research has attempted to track change by following the variation in geo-referenced pixel brightness over time with a technique termed temporal texture. Environmental periodicities impart a phenology to the variation in brightness and departures from the norm are easily detected as statistical outliers. This opens the door to using current orbiting technology to efficiently examine large areas of sea for change. If hot spots are detected, higher resolution sensors and field studies can be focused as resources permit. While this technique does not identify the type of change, it is sensitive, simple to compute, easy to automate and grounded in ecological niche theory

Vasu Kilaru

EPA Pesticide Factsheets

Vasu Kilaru's expertise is in Geographic Information Systems, Spatial Analysis, and satellite remote sensing particularly with respect to trying to detect ground-level fine particles using space borne instruments.

Assessment of Acacia Koa Forest Health across Environmental Gradients in Hawai‘i Using Fine Resolution Remote Sensing and GIS

PubMed Central

Morales, Rodolfo Martinez; Idol, Travis; Friday, James B.

2011-01-01

Koa (Acacia koa) forests are found across broad environmental gradients in the Hawai‘ian Islands. Previous studies have identified koa forest health problems and dieback at the plot level, but landscape level patterns remain unstudied. The availability of high-resolution satellite images from the new GeoEye1 satellite offers the opportunity to conduct landscape-level assessments of forest health. The goal of this study was to develop integrated remote sensing and geographic information systems (GIS) methodologies to characterize the health of koa forests and model the spatial distribution and variability of koa forest dieback patterns across an elevation range of 600–1,000 m asl in the island of Kaua‘i, which correspond to gradients of temperature and rainfall ranging from 17–20 °C mean annual temperature and 750–1,500 mm mean annual precipitation. GeoEye1 satellite imagery of koa stands was analyzed using supervised classification techniques based on the analysis of 0.5-m pixel multispectral bands. There was clear differentiation of native koa forest from areas dominated by introduced tree species and differentiation of healthy koa stands from those exhibiting dieback symptoms. The area ratio of healthy koa to koa dieback corresponded linearly to changes in temperature across the environmental gradient, with koa dieback at higher relative abundance in warmer areas. A landscape-scale map of healthy koa forest and dieback distribution demonstrated both the general trend with elevation and the small-scale heterogeneity that exists within particular elevations. The application of these classification techniques with fine spatial resolution imagery can improve the accuracy of koa forest inventory and mapping across the islands of Hawai‘i. Such findings should also improve ecological restoration, conservation and silviculture of this important native tree species. PMID:22163920

Measuring Thermal Characteristics of Urban Landscapes

NASA Technical Reports Server (NTRS)

Luvall, Jeffrey C.; Quattrochi, Dale A.; Rickman, Doug L.

1999-01-01

The additional heating of the air over the city is the result of the replacement of naturally vegetated surfaces with those composed of asphalt, concrete, rooftops and other man-made materials. The temperatures of these artificial surfaces can be 20 to 40 C higher than vegetated surfaces. Materials such as asphalt store much of the sun's energy and remains hot long after sunset. This produces a dome of elevated air temperatures 5 to 8 C greater over the city, compared to the air temperatures over adjacent rural areas. This effect is called the "urban heat island". Urban landscapes are a complex mixture of vegetated and nonvegetated surfaces. It is difficult to take enough temperature measurements over a large city area to characterize the complexity of urban radiant surface temperature variability. However, the use of remotely sensed thermal data from airborne scanners are ideal for the task. In a study funded by NASA, a series of flights over Huntsville, Alabama were performed in September 1994 and over Atlanta, Georgia in May 1997. Analysis of thermal energy responses for specific or discrete surfaces typical of the urban landscape (e.g., asphalt, building rooftops, vegetation) requires measurements at a very fine spatial scale (i.e., <15 m) to adequately resolve these surfaces and their attendant thermal energy regimes. Additionally, very fine scale spatial resolution thermal infrared data, such as that obtained from aircraft, are very useful for demonstrating to planning officials, policy makers, and the general populace, what the benefits are of the urban forest in both mitigating the urban heat island effect, in making cities more aesthetically pleasing and more habitable environments, and in overall cooling of the community. In this presentation we will examine the techniques of analyzing remotely sensed data for measuring the effect of various urban surfaces on their contribution to the urban heat island effect.

Landscape characterization of peridomestic risk for Lyme disease using satellite imagery

NASA Technical Reports Server (NTRS)

Dister, S. W.; Fish, D.; Bros, S. M.; Frank, D. H.; Wood, B. L.

1997-01-01

Remotely sensed characterizations of landscape composition were evaluated for Lyme disease exposure risk on 337 residential properties in two communities of suburban Westchester County, New York. Properties were categorized as no, low, or high risk based on seasonally adjusted densities of Ixodes scapularis nymphs, determined by drag sampling during June and July 1990. Spectral indices based on Landsat Thematic Mapper data provided relative measures of vegetation structure and moisture (wetness), as well as vegetation abundance (greenness). A geographic information system (GIS) was used to spatially quantify and relate the remotely sensed landscape variables to risk category. A comparison of the two communities showed that Chappaqua, which had more high-risk properties (P < 0.001), was significantly greener and wetter than Armonk (P < 0.001). Furthermore, within Chappaqua, high-risk properties were significantly greener and wetter than lower-risk properties in this community (P < 0.01). The high-risk properties appeared to contain a greater proportion of broadleaf trees, while lower-risk properties were interpreted as having a greater proportion of nonvegetative cover and/or open lawn. The ability to distinguish these fine scale differences among communities and individual properties illustrates the efficiency of a remote sensing/GIS-based approach for identifying peridomestic risk of Lyme disease over large geographic areas.

Spectroscopic observations of the Moon at the lunar surface

NASA Astrophysics Data System (ADS)

Wu, Yunzhao; Hapke, Bruce

2018-02-01

The Moon's reflectance spectrum records many of its important properties. However, prior to Chang'E-3 (CE-3), no spectra had previously been measured on the lunar surface. Here we show the in situ reflectance spectra of the Moon acquired on the lunar surface by the Visible-Near Infrared Spectrometer (VNIS) onboard the CE-3 rover. The VNIS detected thermal radiation from the lunar regolith, though with much shorter wavelength range than typical thermal radiometer. The measured temperatures are higher than expected from theoretical model, indicating low thermal inertia of the lunar soil and the effects of grain facet on soil temperature in submillimeter scale. The in situ spectra also reveal that 1) brightness changes visible from orbit are related to the reduction in maturity due to the removal of the fine and weathered particles by the lander's rocket exhaust, not the smoothing of the surface and 2) the spectra of the uppermost soil detected by remote sensing exhibit substantial differences with that immediately beneath, which has important implications for the remote compositional analysis. The reflectance spectra measured by VNIS not only reveal the thermal, compositional, and space-weathering properties of the Moon but also provide a means for the calibration of optical instruments that view the surface remotely.

A FRAMEWORK FOR FINE-SCALE COMPUTATIONAL FLUID DYNAMICS AIR QUALITY MODELING AND ANALYSIS

EPA Science Inventory

Fine-scale Computational Fluid Dynamics (CFD) simulation of pollutant concentrations within roadway and building microenvironments is feasible using high performance computing. Unlike currently used regulatory air quality models, fine-scale CFD simulations are able to account rig...

Tracking forest canopy dynamics from an automated proximal hyperspectral monitoring system: linking remote sensing observations to leaf level photosynthetic processes

NASA Astrophysics Data System (ADS)

Woodgate, W.; van Gorsel, E.; Hughes, D.; Suarez, L.; Cabello-Leblic, A.; Held, A. A.; Norton, A.; Dempsey, R.

2017-12-01

To better understand the vegetation response to climate extremes we have developed a fully automated hyperspectral and thermal monitoring system installed on a flux tower at a mature Eucalypt forest site - Tumbarumba, Australia. The automated system bridges spatial, spectral and temporal scales between satellite and in situ observations. Here, we have been acquiring high resolution panoramic hyperspectral and thermal images of the forest canopy three times per day since mid-2014.A specific focus of the work to date has been linking light use efficiency (LUE) as measured by the flux tower to remote sensing observations from the leaf, to crown, to canopy scale. Specifically, targeted field campaigns were conducted in 2016 to establish the interrelationship between structure, function, and spectra. At the leaf level destructive sampling to quantify photosynthetic pigments was conducted to pick apart the mechanisms contributing to photosynthetic processes of non-photochemical quenching and the resultant changes in observed leaf spectra. At the crown level, Terrestrial Laser Scanning data was used to derive canopy structural information, enabling distance to crown and crown foliage density to be calculated to a fine degree of detail. This information is critical for correcting attenuation of the thermal signal from atmospheric transmission, and to distinguish the relative foliage-to-soil contribution to the thermal and hyperspectral imagery. Ancillary data streams from sap flow and dendrometer devices serve to link leaf, crown and canopy observations.Preliminary results of the leaf and crown level relationships between function and spectra will be discussed. We will demonstrate that operating in a tall canopy (40m) forest can lead to additional complexities. We have found the relationship strength between traditional remote sensing LUE proxies and photosynthetic proxies derived from pigments varies strongly with canopy height and pigment pool size. Additionally, the significance of the relationship between some leaf pigments and spectra hinged upon the inclusion of juvenile or unhealthy leaf samples, which were not representative of the canopy. This has implications for temporal scaling of remote sensing proxies from diurnal to seasonal time frames.

Husbandry Trace Gas Emissions from a Dairy Complex By Mobile in Situ and Airborne and Spaceborne Remote Sensing: A Comex Campaign Focus

NASA Astrophysics Data System (ADS)

Leifer, I.; Tratt, D. M.; Bovensmann, H.; Buckland, K. N.; Burrows, J. P.; Frash, J.; Gerilowski, K.; Iraci, L. T.; Johnson, P. D.; Kolyer, R.; Krautwurst, S.; Krings, T.; Leen, J. B.; Hu, C.; Melton, C.; Vigil, S. A.; Yates, E. L.; Zhang, M.

2014-12-01

Recent field study reviews on the greenhouse gas methane (CH4) found significant underestimation from fossil fuel industry and husbandry. The 2014 COMEX campaign seeks to develop methods to derive CH4 and carbon dioxide (CO2) from remote sensing data by combining hyperspectral imaging (HSI) and non-imaging spectroscopy (NIS) with in situ airborne and surface data. COMEX leverages synergies between high spatial resolution HSI column abundance maps and moderate spectral/spatial resolution NIS. Airborne husbandry data were collected for the Chino dairy complex (East Los Angeles Basin) by NIS-MAMAP, HSI-Mako thermal-infrared (TIR); AVIRIS NG shortwave IR (SWIR), with in situ surface mobile-AMOG Surveyor (AutoMObile greenhouse Gas)-and airborne in situ from a Twin Otter and the AlphaJet. AMOG Surveyor uses in situ Integrated Cavity Off Axis Spectroscopy (OA-ICOS) to measure CH4, CO2, H2O, H2S and NH3 at 5-10 Hz, 2D winds, and thermal anomaly in an adapted commuter car. OA-ICOS provides high precision and accuracy with excellent stability. NH3 and CH4 emissions were correlated at dairy size-scales but not sub-dairy scales in surface and Mako data, showing fine-scale structure and large variations between the numerous dairies in the complex (herd ~200,000-250,000) embedded in an urban setting. Emissions hotspots were consistent between surface and airborne surveys. In June, surface and MAMAP data showed a weak overall plume, while surface and Mako data showed a stronger plume in late (hotter) July. Multiple surface plume transects using NH3 fingerprinting showed East and then NE advection out of the LA Basin consistent with airborne data. Long-term trends were investigated in satellite data. This study shows the value of synergistically combined NH3 and CH4 remote sensing data to the task of CH4 source attribution using airborne and space-based remote sensing (IASI for NH3) and top of atmosphere sensitivity calculations for Sentinel V and Carbon Sat (CH4).

KSC-2011-4154

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- A remote controlled or autonomous excavator, called a lunabot, is on display outside of the "Lunarena" at the Kennedy Space Center Visitor Complex in Florida where university students maneuver their remote controlled lunabots, in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

Fine-scale topography in sensory systems: insights from Drosophila and vertebrates

PubMed Central

Kaneko, Takuya; Ye, Bing

2015-01-01

To encode the positions of sensory stimuli, sensory circuits form topographic maps in the central nervous system through specific point-to-point connections between pre- and post-synaptic neurons. In vertebrate visual systems, the establishment of topographic maps involves the formation of a coarse topography followed by that of fine-scale topography that distinguishes the axon terminals of neighboring neurons. It is known that intrinsic differences in the form of broad gradients of guidance molecules instruct coarse topography while neuronal activity is required for fine-scale topography. On the other hand, studies in the Drosophila visual system have shown that intrinsic differences in cell adhesion among the axon terminals of neighboring neurons instruct the fine-scale topography. Recent studies on activity-dependent topography in the Drosophila somatosensory system have revealed a role of neuronal activity in creating molecular differences among sensory neurons for establishing fine-scale topography, implicating a conserved principle. Here we review the findings in both Drosophila and vertebrates and propose an integrated model for fine-scale topography. PMID:26091779

Fine-scale topography in sensory systems: insights from Drosophila and vertebrates.

PubMed

Kaneko, Takuya; Ye, Bing

2015-09-01

To encode the positions of sensory stimuli, sensory circuits form topographic maps in the central nervous system through specific point-to-point connections between pre- and postsynaptic neurons. In vertebrate visual systems, the establishment of topographic maps involves the formation of a coarse topography followed by that of fine-scale topography that distinguishes the axon terminals of neighboring neurons. It is known that intrinsic differences in the form of broad gradients of guidance molecules instruct coarse topography while neuronal activity is required for fine-scale topography. On the other hand, studies in the Drosophila visual system have shown that intrinsic differences in cell adhesion among the axon terminals of neighboring neurons instruct the fine-scale topography. Recent studies on activity-dependent topography in the Drosophila somatosensory system have revealed a role of neuronal activity in creating molecular differences among sensory neurons for establishing fine-scale topography, implicating a conserved principle. Here we review the findings in both Drosophila and vertebrates and propose an integrated model for fine-scale topography.

Monitoring Fine-Grained Sediment in the Colorado River Ecosystem, Arizona - Control Network and Conventional Survey Techniques

USGS Publications Warehouse

Hazel, Joseph E.; Kaplinski, Matt; Parnell, Roderic A.; Kohl, Keith; Schmidt, John C.

2008-01-01

In 2002, fine-grained sediment (sand, silt, and clay) monitoring in the Colorado River downstream from Glen Canyon Dam was initiated to survey channel topography at scales previously unobtainable in this canyon setting. This report presents the methods used to establish the high-resolution global positioning system (GPS) control network required for this effort as well as the conventional surveying techniques used in the study. Using simultaneous, dual-frequency GPS vector-based methods, the network points were determined to have positioning accuracies of less than 0.03 meters (m) and ellipsoidal height accuracies of between 0.01 and 0.10 m at a 95-percent degree of confidence. We also assessed network point quality with repeated, electronic (optical) total-station observations at 39 points for a total of 362 measurements; the mean range was 0.022 m in horizontal and 0.13 in vertical at a 95-percent confidence interval. These results indicate that the control network is of sufficient spatial and vertical accuracy for collection of airborne and subaerial remote-sensing technologies and integration of these data in a geographic information system on a repeatable basis without anomalies. The monitoring methods were employed in up to 11 discrete reaches over various time intervals. The reaches varied from 1.3 to 6.4 kilometers in length. Field results from surveys in 2000, 2002, and 2004 are described, during which conventional surveying was used to collect more than 3000 points per day. Ground points were used as checkpoints and to supplement areas just below or above the water surface, where remote-sensing data are not collected or are subject to greater error. An accuracy of +or- 0.05 m was identified as the minimum precision of individual ground points. These results are important for assessing digital elevation model (DEM) quality and identifying detection limits of significant change among surfaces generated from remote-sensing technologies.

Fine-scale variation of historical fire regimes in sagebrush-steppe and juniper woodland: An example from California, USA

Treesearch

Richard F. Miller; Emily K. Heyerdahl

2008-01-01

Coarse-scale estimates of fire intervals across the mountain big sagebrush (Artemisia tridentata spp. vaseyana (Rydb.) Beetle) alliance range from decades to centuries. However, soil depth and texture can affect the abundance and continuity of fine fuels and vary at fine spatial scales, suggesting fire regimes may vary at similar scales. We explored...

Fine-scale structure in the far-infrared Milky-Way

NASA Technical Reports Server (NTRS)

Waller, William H.; Wall, William F.; Reach, William T.; Varosi, Frank; Ebert, Rick; Laughlin, Gaylin; Boulanger, Francois

1995-01-01

This final report summarizes the work performed and which falls into five broad categories: (1) generation of a new data product (mosaics of the far-infrared emission in the Milky Way); (2) acquisition of associated data products at other wavelengths; (3) spatial filtering of the far-infrared mosaics and resulting images of the FIR fine-scale structure; (4) evaluation of the spatially filtered data; (5) characterization of the FIR fine-scale structure in terms of its spatial statistics; and (6) identification of interstellar counterparts to the FIR fine-scale structure.

Image Texture Predicts Avian Density and Species Richness

PubMed Central

Wood, Eric M.; Pidgeon, Anna M.; Radeloff, Volker C.; Keuler, Nicholas S.

2013-01-01

For decades, ecologists have measured habitat attributes in the field to understand and predict patterns of animal distribution and abundance. However, the scale of inference possible from field measured data is typically limited because large-scale data collection is rarely feasible. This is problematic given that conservation and management typical require data that are fine grained yet broad in extent. Recent advances in remote sensing methodology offer alternative tools for efficiently characterizing wildlife habitat across broad areas. We explored the use of remotely sensed image texture, which is a surrogate for vegetation structure, calculated from both an air photo and from a Landsat TM satellite image, compared with field-measured vegetation structure, characterized by foliage-height diversity and horizontal vegetation structure, to predict avian density and species richness within grassland, savanna, and woodland habitats at Fort McCoy Military Installation, Wisconsin, USA. Image texture calculated from the air photo best predicted density of a grassland associated species, grasshopper sparrow (Ammodramus savannarum), within grassland habitat (R2 = 0.52, p-value <0.001), and avian species richness among habitats (R2 = 0.54, p-value <0.001). Density of field sparrow (Spizella pusilla), a savanna associated species, was not particularly well captured by either field-measured or remotely sensed vegetation structure variables, but was best predicted by air photo image texture (R2 = 0.13, p-value = 0.002). Density of ovenbird (Seiurus aurocapillus), a woodland associated species, was best predicted by pixel-level satellite data (mean NDVI, R2 = 0.54, p-value <0.001). Surprisingly and interestingly, remotely sensed vegetation structure measures (i.e., image texture) were often better predictors of avian density and species richness than field-measured vegetation structure, and thus show promise as a valuable tool for mapping habitat quality and characterizing biodiversity across broad areas. PMID:23675463

The ATLAS Event Service: A new approach to event processing

NASA Astrophysics Data System (ADS)

Calafiura, P.; De, K.; Guan, W.; Maeno, T.; Nilsson, P.; Oleynik, D.; Panitkin, S.; Tsulaia, V.; Van Gemmeren, P.; Wenaus, T.

2015-12-01

The ATLAS Event Service (ES) implements a new fine grained approach to HEP event processing, designed to be agile and efficient in exploiting transient, short-lived resources such as HPC hole-filling, spot market commercial clouds, and volunteer computing. Input and output control and data flows, bookkeeping, monitoring, and data storage are all managed at the event level in an implementation capable of supporting ATLAS-scale distributed processing throughputs (about 4M CPU-hours/day). Input data flows utilize remote data repositories with no data locality or pre-staging requirements, minimizing the use of costly storage in favor of strongly leveraging powerful networks. Object stores provide a highly scalable means of remotely storing the quasi-continuous, fine grained outputs that give ES based applications a very light data footprint on a processing resource, and ensure negligible losses should the resource suddenly vanish. We will describe the motivations for the ES system, its unique features and capabilities, its architecture and the highly scalable tools and technologies employed in its implementation, and its applications in ATLAS processing on HPCs, commercial cloud resources, volunteer computing, and grid resources. Notice: This manuscript has been authored by employees of Brookhaven Science Associates, LLC under Contract No. DE-AC02-98CH10886 with the U.S. Department of Energy. The publisher by accepting the manuscript for publication acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.

Experimental philosophy leading to a small scale digital data base of the conterminous United States for designing experiments with remotely sensed data

NASA Technical Reports Server (NTRS)

Labovitz, M. L.; Masuoka, E. J.; Broderick, P. W.; Garman, T. R.; Ludwig, R. W.; Beltran, G. N.; Heyman, P. J.; Hooker, L. K.

1983-01-01

Research using satellite remotely sensed data, even within any single scientific discipline, often lacked a unifying principle or strategy with which to plan or integrate studies conducted over an area so large that exhaustive examination is infeasible, e.g., the U.S.A. However, such a series of studies would seem to be at the heart of what makes satellite remote sensing unique, that is the ability to select for study from among remotely sensed data sets distributed widely over the U.S., over time, where the resources do not exist to examine all of them. Using this philosophical underpinning and the concept of a unifying principle, an operational procedure for developing a sampling strategy and formal testable hypotheses was constructed. The procedure is applicable across disciplines, when the investigator restates the research question in symbolic form, i.e., quantifies it. The procedure is set within the statistical framework of general linear models. The dependent variable is any arbitrary function of remotely sensed data and the independent variables are values or levels of factors which represent regional climatic conditions and/or properties of the Earth's surface. These factors are operationally defined as maps from the U.S. National Atlas (U.S.G.S., 1970). Eighty-five maps from the National Atlas, representing climatic and surface attributes, were automated by point counting at an effective resolution of one observation every 17.6 km (11 miles) yielding 22,505 observations per map. The maps were registered to one another in a two step procedure producing a coarse, then fine scale registration. After registration, the maps were iteratively checked for errors using manual and automated procedures. The error free maps were annotated with identification and legend information and then stored as card images, one map to a file. A sampling design will be accomplished through a regionalization analysis of the National Atlas data base (presently being conducted). From this analysis a map of homogeneous regions of the U.S.A. will be created and samples (LANDSAT scenes) assigned by region.

The relationship between aerosol particles chemical composition and optical properties to identify the biomass burning contribution to fine particles concentration: a case study for São Paulo city, Brazil.

PubMed

de Miranda, Regina Maura; Lopes, Fabio; do Rosário, Nilton Évora; Yamasoe, Marcia Akemi; Landulfo, Eduardo; de Fatima Andrade, Maria

2016-12-01

The air quality in the Metropolitan Area of São Paulo (MASP) is primarily determined by the local pollution source contribution, mainly the vehicular fleet, but there is a concern about the role of remote sources to the fine mode particles (PM 2.5 ) concentration and composition. One of the most important remote sources of atmospheric aerosol is the biomass burning emissions from São Paulo state's inland and from the central and north portions of Brazil. This study presents a synergy of different measurements of atmospheric aerosol chemistry and optical properties in the MASP in order to show how they can be used as a tool to identify particles from local and remote sources. For the clear identification of the local and remote source contribution, aerosol properties measurements at surface level were combined with vertical profiles information. Over 15 days in the austral winter of 2012, particulate matter (PM) was collected using a cascade impactor and a Partisol sampler in São Paulo City. Mass concentrations were determined by gravimetry, black carbon concentrations by reflectance, and trace element concentrations by X-ray fluorescence. Aerosol optical properties were studied using a multifilter rotating shadowband radiometer (MFRSR), a Lidar system and satellite data. Optical properties, concentrations, size distributions, and elemental composition of atmospheric particles were strongly related and varied according to meteorological conditions. During the sampling period, PM mean mass concentrations were 17.4 ± 10.1 and 15.3 ± 6.9 μg/m 3 for the fine and coarse fractions, respectively. The mean aerosol optical depths at 415 nm and Ångström exponent (AE) over the whole period were 0.29 ± 0.14 and 1.35 ± 0.11, respectively. Lidar ratios reached values of 75 sr. The analyses of the impacts of an event of biomass burning smoke transport to the São Paulo city revealed significant changing on local aerosol concentrations and optical parameters. The identification of the source contributions, local and remote, to the fine particles in MASP can be more precisely achieved when particle size composition and distribution, vertical profile of aerosols, and air mass trajectories are analyzed in combination.

Multi-scale 46-year remote sensing change detection of diamond mining and land cover in a conflict and post-conflict setting

USGS Publications Warehouse

Dewitt, Jessica D.; Chirico, Peter G.; Bergstresser, Sarah E.; Warner, Timothy A.

2017-01-01

The town of Tortiya was created in the rural northern region of Côte d′Ivoire in the late 1940s to house workers for a new diamond mine. Nearly three decades later, the closure of the industrial-scale diamond mine in 1975 did not diminish the importance of diamond profits to the region's economy, and resulted in the growth of artisanal and small-scale diamond mining (ASM) within the abandoned industrial-scale mining concession. In the early 2000s, the violent conflict that arose in Côte d′Ivoire highlighted the importance of ASM land use to the local economy, but also brought about international concerns that diamond profits were being used to fund the rebellion. In recent years, cashew plantations have expanded exponentially in the region, diversifying economic activity, but also creating the potential for conflict between diamond mining and agricultural land uses. As the government looks to address the future of Tortiya and this potential conflict, a detailed spatio-temporal understanding of the changes in these two land uses over time may assist in informing policymaking. Remotely sensed imagery presents an objective and detailed spatial record of land use/land cover (LULC), and change detection methods can provide quantitative insight regarding regional land cover trends. However, the vastly different scales of ASM and cashew orchards present a unique challenge to comprehensive understanding of land use change in the region. In this study, moderate-scale categories of LULC, including cashew orchards, uncultivated forest, urban space, mining/ bare, and mixed vegetation, were produced through supervised classification of Landsat multispectral imagery from 1984, 1991, 2000, 2007, and 2014. The fine-scale ASM land use was identified through manual interpretation of annually acquired high resolution satellite imagery. Corona imagery was also integrated into the study to extend the temporal duration of the remote sensing record back to the period of industrial-scale mining. These different-scale analyses were then integrated to create a record of 46 years of mining activity and land cover change in Tortiya. While similar in spatial extent, the mining/ bare class in the integrated analysis exhibits a substantially different spatial distribution than in the original classifications. This additional information regarding the locations of ASM activity in the Tortiya area is important from a policy and planning perspective. The results of this study also suggest that LULC classifications of Landsat imagery do not consistently capture areas of ASM in the Côte d′Ivoire landscape.

Remote sensing of exposure to NO2: Satellite versus ground-based measurement in a large urban area

NASA Astrophysics Data System (ADS)

Bechle, Matthew J.; Millet, Dylan B.; Marshall, Julian D.

2013-04-01

Remote sensing may be a useful tool for exploring spatial variability of air pollution exposure within an urban area. To evaluate the extent to which satellite data from the Ozone Monitoring Instrument (OMI) can resolve urban-scale gradients in ground-level nitrogen dioxide (NO2) within a large urban area, we compared estimates of surface NO2 concentrations derived from OMI measurements and US EPA ambient monitoring stations. OMI, aboard NASA's Aura satellite, provides daily afternoon (˜13:30 local time) measurements of NO2 tropospheric column abundance. We used scaling factors (surface-to-column ratios) to relate satellite column measurements to ground-level concentrations. We compared 4138 sets of paired data for 25 monitoring stations in the South Coast Air Basin of California for all of 2005. OMI measurements include more data gaps than the ground monitors (60% versus 5% of available data, respectively), owing to cloud contamination and imposed limits on pixel size. The spatial correlation between OMI columns and corrected in situ measurements is strong (r = 0.93 for annual average data), indicating that the within-urban spatial signature of surface NO2 is well resolved by the satellite sensor. Satellite-based surface estimates employing scaling factors from an urban model provide a reliable measure (annual mean bias: -13%; seasonal mean bias: <1% [spring] to -22% [fall]) of fine-scale surface NO2. We also find that OMI provides good spatial density in the study region (average area [km2] per measurement: 730 for the satellite sensor vs. 1100 for the monitors). Our findings indicate that satellite observations of NO2 from the OMI sensor provide a reliable measure of spatial variability in ground-level NO2 exposure for a large urban area.

`Dhara': An Open Framework for Critical Zone Modeling

NASA Astrophysics Data System (ADS)

Le, P. V.; Kumar, P.

2016-12-01

Processes in the Critical Zone, which sustain terrestrial life, are tightly coupled across hydrological, physical, biological, chemical, pedological, geomorphological and ecological domains over both short and long timescales. Observations and quantification of the Earth's surface across these domains using emerging high resolution measurement technologies such as light detection and ranging (lidar) and hyperspectral remote sensing are enabling us to characterize fine scale landscape attributes over large spatial areas. This presents a unique opportunity to develop novel approaches to model the Critical Zone that can capture fine scale intricate dependencies across the different processes in 3D. The development of interdisciplinary tools that transcend individual disciplines and capture new levels of complexity and emergent properties is at the core of Critical Zone science. Here we introduce an open framework for high-performance computing model (`Dhara') for modeling complex processes in the Critical Zone. The framework is designed to be modular in structure with the aim to create uniform and efficient tools to facilitate and leverage process modeling. It also provides flexibility to maintain, collaborate, and co-develop additional components by the scientific community. We show the essential framework that simulates ecohydrologic dynamics, and surface - sub-surface coupling in 3D using hybrid parallel CPU-GPU. We demonstrate that the open framework in Dhara is feasible for detailed, multi-processes, and large-scale modeling of the Critical Zone, which opens up exciting possibilities. We will also present outcomes from a Modeling Summer Institute led by Intensively Managed Critical Zone Observatory (IMLCZO) with representation from several CZOs and international representatives.

The Use of the Airborne Thermal/Visible Land Application Sensor (ATLAS) to Determine the Thermal Response Numbers for Urban Areas

NASA Technical Reports Server (NTRS)

Luvall, Jeffrey C.; Rickman, Doug; Quattroch, Dale; Estes. Maury

2007-01-01

Although satellite data are very useful for analysis of the urban heat island effect at a coarse scale, they do not lend themselves to developing a better understanding of which surfaces across the city contribute or drive the development of the urban heat island effect. Analysis of thermal energy responses for specific or discrete surfaces typical of the urban landscape (e.g., asphalt, building rooftops, vegetation) requires measurements at a very fine spatial scale (i.e., < 15m) to adequately resolve these surfaces and their attendant thermal energy regimes. Additionally, very fine scale spatial resolution thermal infrared data, such as that obtained from aircraft, are very useful for demonstrating to planning officials, policy makers, and the general populace the benefits of the urban forest. These benefits include mitigating the urban heat island effect, making cities more aesthetically pleasing and more habitable environments, and aid in overall cooling of the community. High spatial resolution thermal data are required to quantify how artificial surfaces within the city contribute to an increase in urban heating and the benefit of cool surfaces (e.g., surface coatings that reflect much of the incoming solar radiation as opposed to absorbing it thereby lowering urban temperatures). The TRN (thermal response number)(Luvall and Holbo 1989) is a technique using aircraft remotely sensed surface temperatures to quantify the thermal response of urban surfaces. The TRN was used to quantify the thermal response of various urban surface types ranging from completely vegetated surfaces to asphalt and concrete parking lots for several cities in the United States.

North American water availability under stress and duress: building understanding from simulations, observations and data products

NASA Astrophysics Data System (ADS)

Maxwell, R. M.; Condon, L. E.; Atchley, A. L.; Hector, B.

2017-12-01

Quantifying the available freshwater for human use and ecological function depends on fluxes and stores that are hard to observe. Evapotranspiration (ET) is the largest terrestrial flux of water behind precipitation but is observed with low spatial density. Likewise, groundwater is the largest freshwater store, yet is equally uncertain. The ability to upscale observations of these variables is an additional complication; point measurements are made at scales orders of magnitude smaller than remote sensing data products. Integrated hydrologic models that simulate continental extents at fine spatial resolution are now becoming an additional tool to constrain fluxes and address interconnections. For example, recent work has shown connections between water table depth and transpiration partitioning, and demonstrated the ability to reconcile point observations and large-scale inferences. Here we explore the dynamics of large hydrologic systems experiencing change and stress across continental North America using integrated model simulations, observations and data products. Simulations of aquifer depletion due to pervasive groundwater pumping diagnose both stream depletion and changes in ET. Simulations of systematic increases in temperature are used to understand the relationship between snowpack dynamics, surface and groundwater flow, ET and a changing climate. Remotely sensed products including the GRACE estimates of total storage change are downscaled using model simulations to better understand human impacts to the hydrologic cycle. These example applications motivate a path forward to better use simulations to understand water availability.

A Modeling Approach to Global Land Surface Monitoring with Low Resolution Satellite Imaging

NASA Technical Reports Server (NTRS)

Hlavka, Christine A.; Dungan, Jennifer; Livingston, Gerry P.; Gore, Warren J. (Technical Monitor)

1998-01-01

The effects of changing land use/land cover on global climate and ecosystems due to greenhouse gas emissions and changing energy and nutrient exchange rates are being addressed by federal programs such as NASA's Mission to Planet Earth (MTPE) and by international efforts such as the International Geosphere-Biosphere Program (IGBP). The quantification of these effects depends on accurate estimates of the global extent of critical land cover types such as fire scars in tropical savannas and ponds in Arctic tundra. To address the requirement for accurate areal estimates, methods for producing regional to global maps with satellite imagery are being developed. The only practical way to produce maps over large regions of the globe is with data of coarse spatial resolution, such as Advanced Very High Resolution Radiometer (AVHRR) weather satellite imagery at 1.1 km resolution or European Remote-Sensing Satellite (ERS) radar imagery at 100 m resolution. The accuracy of pixel counts as areal estimates is in doubt, especially for highly fragmented cover types such as fire scars and ponds. Efforts to improve areal estimates from coarse resolution maps have involved regression of apparent area from coarse data versus that from fine resolution in sample areas, but it has proven difficult to acquire sufficient fine scale data to develop the regression. A method for computing accurate estimates from coarse resolution maps using little or no fine data is therefore needed.

Measurement and analysis of ambient atmospheric particulate matter in urban and remote environments

NASA Astrophysics Data System (ADS)

Hagler, Gayle S. W.

Atmospheric particulate matter pollution is a challenging environmental concern in both urban and remote locations worldwide. It is intrinsically difficult to control, given numerous anthropogenic and natural sources (e.g. fossil fuel combustion, biomass burning, dust, and seaspray) and atmospheric transport up to thousands of kilometers after production. In urban regions, fine particulate matter (particles with diameters under 2.5 mum) is of special concern for its ability to penetrate the human respiratory system and threaten cardiopulmonary health. A second major impact area is climate, with particulate matter altering Earth's radiative balance through scattering and absorbing solar radiation, modifying cloud properties, and reducing surface reflectivity after deposition in snow-covered regions. While atmospheric particulate matter has been generally well-characterized in populated areas of developed countries, particulate pollution in developing nations and remote regions is relatively unexplored. This thesis characterizes atmospheric particulate matter in locations that represent the extreme ends of the spectrum in terms of air pollution-the rapidly-developing and heavily populated Pearl River Delta Region of China, the pristine and climate-sensitive Greenland Ice Sheet, and a remote site in the Colorado Rocky Mountains. In China, fine particles were studied through a year-long field campaign at seven sites surrounding the Pearl River Delta. Fine particulate matter was analyzed for chemical composition, regional variation, and meteorological impacts. On the Greenland Ice Sheet and in the Colorado Rocky Mountains, the carbonaceous fraction (organic and elemental carbon) of particulate matter was studied in the atmosphere and snow pack. Analyses included quantifying particulate chemical and optical properties, assessing atmospheric transport, and evaluating post-depositional processing of carbonaceous species in snow.

Assessing diversity of prairie plants using remote sensing

NASA Astrophysics Data System (ADS)

Gamon, J. A.; Wang, R.

2017-12-01

Biodiversity loss endangers ecosystem services and is considered as a global change that may generate unacceptable environmental consequences for the Earth system. Global biodiversity observations are needed to provide a better understanding of biodiversity - ecosystem services relationships and to provide a stronger foundation for conserving the Earth's biodiversity. While remote sensing metrics have been applied to estimate α biodiversity directly through optical diversity, a better understanding of the mechanisms behind the optical diversity-biodiversity relationship is needed. We designed a series of experiments at Cedar Creek Ecosystem Science Reserve, MN, to investigate the scale dependence of optical diversity and explore how species richness, evenness, and composition affect optical diversity. We collected hyperspectral reflectance of 16 prairie species using both a full-range field spectrometer fitted with a leaf clip, and an imaging spectrometer carried by a tram system to simulate plot-level images with different species richness, evenness, and composition. Two indicators of spectral diversity were explored: the coefficient of variation (CV) of spectral reflectance in space, and spectral classification using a Partial Least Squares Discriminant Analysis (PLS-DA). Our results showed that sampling methods (leaf clip-derived data vs. image-derived data) affected the optical diversity estimation. Both optical diversity indices were affected by species richness and evenness (P<0.001 for each case). At fine spatial scales, species composition also had a substantial influence on optical diversity. CV was sensitive to the background soil influence, but the spectral classification method was insensitive to background. These results provide a critical foundation for assessing biodiversity using imaging spectrometry and these findings can be used to guide regional studies of biodiversity estimation using high spatial and spectral resolution remote sensing.

Fire Impacts on Mixed Pine-oak Forests Assessed with High Spatial Resolution Imagery, Imaging Spectroscopy, and LiDAR

NASA Astrophysics Data System (ADS)

Meng, R.; Wu, J.; Zhao, F. R.; Kathy, S. L.; Dennison, P. E.; Cook, B.; Hanavan, R. P.; Serbin, S.

2016-12-01

As a primary disturbance agent, fire significantly influences forest ecosystems, including the modification or resetting of vegetation composition and structure, which can then significantly impact landscape-scale plant function and carbon stocks. Most ecological processes associated with fire effects (e.g. tree damage, mortality, and vegetation recovery) display fine-scale, species specific responses but can also vary spatially within the boundary of the perturbation. For example, both oak and pine species are fire-adapted, but fire can still induce changes in composition, structure, and dominance in a mixed pine-oak forest, mainly because of their varying degrees of fire adaption. Evidence of post-fire shifts in dominance between oak and pine species has been documented in mixed pine-oak forests, but these processes have been poorly investigated in a spatially explicit manner. In addition, traditional field-based means of quantifying the response of partially damaged trees across space and time is logistically challenging. Here we show how combining high resolution satellite imagery (i.e. Worldview-2,WV-2) and airborne imaging spectroscopy and LiDAR (i.e. NASA Goddard's Lidar, Hyperspectral and Thermal airborne imager, G-LiHT) can be effectively used to remotely quantify spatial and temporal patterns of vegetation recovery following a top-killing fire that occurred in 2012 within mixed pine-oak forests in the Long Island Central Pine Barrens Region, New York. We explore the following questions: 1) what are the impacts of fire on species composition, dominance, plant health, and vertical structure; 2) what are the recovery trajectories of forest biomass, structure, and spectral properties for three years following the fire; and 3) to what extent can fire impacts be captured and characterized by multi-sensor remote sensing techniques from active and passive optical remote sensing.

Application of High Resolution Air-Borne Remote Sensing Observations for Monitoring NOx Emissions

NASA Astrophysics Data System (ADS)

Souri, A.; Choi, Y.; Pan, S.; Curci, G.; Janz, S. J.; Kowalewski, M. G.; Liu, J.; Herman, J. R.; Weinheimer, A. J.

2017-12-01

Nitrogen oxides (NOx=NO+NO2) are one of the air pollutants, responsible for the formation of tropospheric ozone, acid rain and particulate nitrate. The anthropogenic NOx emissions are commonly estimated based on bottom-up inventories which are complicated by many potential sources of error. One way to improve the emission inventories is to use relevant observations to constrain them. Fortunately, Nitrogen dioxide (NO2) is one of the most successful detected species from remote sensing. Although many studies have shown the capability of using space-borne remote sensing observations for monitoring emissions, the insufficient sample number and footprint of current measurements have introduced a burden to constrain emissions at fine scales. Promisingly, there are several air-borne sensors collected for NASA's campaigns providing high spatial resolution of NO2 columns. Here, we use the well-characterized NO2 columns from the Airborne Compact Atmospheric Mapper (ACAM) onboard NASA's B200 aircraft into a 1×1 km regional model to constrain anthropogenic NOx emissions in the Houston-Galveston-Brazoria area. Firstly, in order to incorporate the data, we convert the NO2 slant column densities to vertical ones using a joint of a radiative transfer model and the 1x1 km regional model constrained by P3-B aircraft measurements. After conducting an inverse modeling method using the Kalman filter, we find the ACAM observations are resourceful at mitigating the overprediction of model in reproducing NO2 on regular days. Moreover, the ACAM provides a unique opportunity to detect an anomaly in emissions leading to strong air quality degradation that is lacking in previous works. Our study provides convincing evidence that future geostationary satellites with high spatial and temporal resolutions will give us insights into uncertainties associated with the emissions at regional scales.

Rainbow trout movement behavior and habitat occupancy are influenced by sex and Pacific salmon presence in an Alaska river system

USGS Publications Warehouse

Fraley, Kevin M.; Falke, Jeffrey A.; McPhee, Megan V.; Prakash, Anupma

2018-01-01

We used spatially continuous field-measured and remotely-sensed aquatic habitat characteristics paired with weekly ground-based telemetry tracking and snorkel surveys to describe movements and habitat occupancy of adult rainbow trout (N = 82) in a runoff-fed, salmon-influenced southcentral Alaska river system. We found that during the ice-free feeding season (June through September) rainbow trout occurrence was associated more with fine-scale (channel unit) characteristics relative to coarse-scale (stream reach) variables. The presence of Pacific salmon (which provide an important seasonal food subsidy), and habitat size were particularly useful predictors. Weekly movement distance differed between pre- and post- spawning salmon arrival, but did not vary by sex. Habitat quality, season, and the arrival of spawning salmon influenced the likelihood of rainbow trout movement, and fish moved farther to seek out higher quality habitats. Because rainbow trout respond to habitat factors at multiple scales and seek out salmon-derived subsidies, it will be important to take a multiscale approach in protecting trout and salmon populations and managing the associated fisheries.

Does soil compaction increase floods? A review

NASA Astrophysics Data System (ADS)

Alaoui, Abdallah; Rogger, Magdalena; Peth, Stephan; Blöschl, Günter

2018-02-01

Europe has experienced a series of major floods in the past years which suggests that flood magnitudes may have increased. Land degradation due to soil compaction from crop farming or grazing intensification is one of the potential drivers of this increase. A literature review suggests that most of the experimental evidence was generated at plot and hillslope scales. At larger scales, most studies are based on models. There are three ways in which soil compaction affects floods at the catchment scale: (i) through an increase in the area affected by soil compaction; (ii) by exacerbating the effects of changes in rainfall, especially for highly degraded soils; and (iii) when soil compaction coincides with soils characterized by a fine texture and a low infiltration capacity. We suggest that future research should focus on better synthesising past research on soil compaction and runoff, tailored field experiments to obtain a mechanistic understanding of the coupled mechanical and hydraulic processes, new mapping methods of soil compaction that combine mechanical and remote sensing approaches, and an effort to bridge all disciplines relevant to soil compaction effects on floods.

Towards Improved Forecasts of Atmospheric and Oceanic Circulations over the Complex Terrain of the Eastern Mediterranean

NASA Technical Reports Server (NTRS)

Chronis, Themis; Case, Jonathan L.; Papadopoulos, Anastasios; Anagnostou, Emmanouil N.; Mecikalski, John R.; Haines, Stephanie L.

2008-01-01

Forecasting atmospheric and oceanic circulations accurately over the Eastern Mediterranean has proved to be an exceptional challenge. The existence of fine-scale topographic variability (land/sea coverage) and seasonal dynamics variations can create strong spatial gradients in temperature, wind and other state variables, which numerical models may have difficulty capturing. The Hellenic Center for Marine Research (HCMR) is one of the main operational centers for wave forecasting in the eastern Mediterranean. Currently, HCMR's operational numerical weather/ocean prediction model is based on the coupled Eta/Princeton Ocean Model (POM). Since 1999, HCMR has also operated the POSEIDON floating buoys as a means of state-of-the-art, real-time observations of several oceanic and surface atmospheric variables. This study attempts a first assessment at improving both atmospheric and oceanic prediction by initializing a regional Numerical Weather Prediction (NWP) model with high-resolution sea surface temperatures (SST) from remotely sensed platforms in order to capture the small-scale characteristics.

Environmental Controls on Multi-Scale Soil Nutrient Variability in the Tropics: the Importance of Land-Cover Change

NASA Astrophysics Data System (ADS)

Holmes, K. W.; Kyriakidis, P. C.; Chadwick, O. A.; Matricardi, E.; Soares, J. V.; Roberts, D. A.

2003-12-01

The natural controls on soil variability and the spatial scales at which correlation exists among soil and environmental variables are critical information for evaluating the effects of deforestation. We detect different spatial scales of variability in soil nutrient levels over a large region (hundreds of thousands of km2) in the Amazon, analyze correlations among soil properties at these different scales, and evaluate scale-specific relationships among soil properties and the factors potentially driving soil development. Statistical relationships among physical drivers of soil formation, namely geology, precipitation, terrain attributes, classified soil types, and land cover derived from remote sensing, were included to determine which factors are related to soil biogeochemistry at each spatial scale. Surface and subsurface soil profile data from a 3000 sample database collected in Rond“nia, Brazil, were used to investigate patterns in pH, phosphorus, nitrogen, organic carbon, effective cation exchange capacity, calcium, magnesium, potassium, aluminum, sand, and clay in this environment grading from closed canopy tropical forest to savanna. We focus on pH in this presentation for simplicity, because pH is the single most important soil characteristic for determining the chemical environment of higher plants and soil microbial activity. We determined four spatial scales which characterize integrated patterns of soil chemistry: less than 3 km; 3 to 10 km; 10 to 68 km; and from 68 to 550 km (extent of study area). Although the finest observable scale was fixed by the field sampling density, the coarser scales were determined from relationships in the data through coregionalization modeling, rather than being imposed by the researcher. Processes which affect soils over short distances, such as land cover and terrain attributes, were good predictors of fine scale spatial components of nutrients; processes which affect soils over very large distances, such as precipitation and geology, were better predictors at coarse spatial scales. However, this result may be affected by the resolution of the available predictor maps. Land-cover change exerted a strong influence on soil chemistry at fine spatial scales, and had progressively less of an effect at coarser scales. It is important to note that land cover, and interactions among land cover and the other predictors, continued to be a significant predictor of soil chemistry at every spatial scale up to hundreds of thousands of kilometers.

Ultra-Fine Scale Spatially-Integrated Mapping of Habitat and Occupancy Using Structure-From-Motion.

PubMed

McDowall, Philip; Lynch, Heather J

2017-01-01

Organisms respond to and often simultaneously modify their environment. While these interactions are apparent at the landscape extent, the driving mechanisms often occur at very fine spatial scales. Structure-from-Motion (SfM), a computer vision technique, allows the simultaneous mapping of organisms and fine scale habitat, and will greatly improve our understanding of habitat suitability, ecophysiology, and the bi-directional relationship between geomorphology and habitat use. SfM can be used to create high-resolution (centimeter-scale) three-dimensional (3D) habitat models at low cost. These models can capture the abiotic conditions formed by terrain and simultaneously record the position of individual organisms within that terrain. While coloniality is common in seabird species, we have a poor understanding of the extent to which dense breeding aggregations are driven by fine-scale active aggregation or limited suitable habitat. We demonstrate the use of SfM for fine-scale habitat suitability by reconstructing the locations of nests in a gentoo penguin colony and fitting models that explicitly account for conspecific attraction. The resulting digital elevation models (DEMs) are used as covariates in an inhomogeneous hybrid point process model. We find that gentoo penguin nest site selection is a function of the topography of the landscape, but that nests are far more aggregated than would be expected based on terrain alone, suggesting a strong role of behavioral aggregation in driving coloniality in this species. This integrated mapping of organisms and fine scale habitat will greatly improve our understanding of fine-scale habitat suitability, ecophysiology, and the complex bi-directional relationship between geomorphology and habitat use.

KSC-2011-3960

NASA Image and Video Library

2011-05-24

University students prepare their team's remote controlled or autonomous excavator, called a lunabot, to maneuver in about 60 tons of ultra-fine simulated lunar soil, called BP-1. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

Photon-efficient super-resolution laser radar

NASA Astrophysics Data System (ADS)

Shin, Dongeek; Shapiro, Jeffrey H.; Goyal, Vivek K.

2017-08-01

The resolution achieved in photon-efficient active optical range imaging systems can be low due to non-idealities such as propagation through a diffuse scattering medium. We propose a constrained optimization-based frame- work to address extremes in scarcity of photons and blurring by a forward imaging kernel. We provide two algorithms for the resulting inverse problem: a greedy algorithm, inspired by sparse pursuit algorithms; and a convex optimization heuristic that incorporates image total variation regularization. We demonstrate that our framework outperforms existing deconvolution imaging techniques in terms of peak signal-to-noise ratio. Since our proposed method is able to super-resolve depth features using small numbers of photon counts, it can be useful for observing fine-scale phenomena in remote sensing through a scattering medium and through-the-skin biomedical imaging applications.

Assessing the effectiveness of scrub management at the landscape scale using rapid field assessment and remote sensing.

PubMed

Redhead, John; Cuevas-Gonzales, Maria; Smith, Geoffrey; Gerard, France; Pywell, Richard

2012-04-30

Controlling scrub encroachment is a major challenge for conservation management on chalk grasslands. However, direct comparisons of scrub removal methods have seldom been investigated, particularly at the landscape scale. Effective monitoring of grassland scrub is problematic as it requires simultaneous information on large scale patterns in scrub cover and fine-scale changes in the grassland community. This study addressed this by combining analysis of aerial imagery with rapid field surveys in order to compare the effectiveness of four scrub management strategies on Defence Training Estate Salisbury Plain, UK. Study plots were sited within areas undergoing management and in unmanaged controls. Controls showed dramatic increases in scrub cover, with encroachment of a mean 1096 m(2) per hectare over ten years. Whilst all management strategies were effective in reducing scrub encroachment, they differed in their ability to influence regeneration of scrub and grassland quality. There was a general trend, evident in both the floral community and scrub levels, of increased effectiveness with increasing management intensity. The dual methodology proved highly effective, allowing rapid collection of data over a range of variables and spatial scales unavailable to each method individually. The methodology thus demonstrates potential for a useful monitoring tool. Copyright © 2011 Elsevier Ltd. All rights reserved.

The wildland fuel cell concept: an approach to characterize fine-scale variation in fuels and fire in frequently burned longleaf pine forests

Treesearch

J. Kevin Hiers; Joseph J. O’Brien; R. J. Mitchell; John M. Grego; E. Louise Loudermilk

2009-01-01

In ecosystems with frequent surface fire regimes, fire and fuel heterogeneity has been largely overlookedowing to the lack of unburned patches and the difficulty in measuring fire behavior at fine scales (0.1–10 m). The diversevegetation in these ecosystems varies at these fine scales. This diversity could be...

Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing

DTIC Science & Technology

2016-07-15

AFRL-AFOSR-JP-TR-2016-0068 Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing Hean-Teik...SUBTITLE Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER... electromagnetics to the application in microwave remote sensing as well as extension of modelling capability with computational flexibility to study

Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing

DTIC Science & Technology

2016-07-15

AFRL-AFOSR-JP-TR-2016-0068 Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing Hean-Teik...SUBTITLE Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER...electromagnetics to the application in microwave remote sensing as well as extension of modelling capability with computational flexibility to study

A NDVI assisted remote sensing image adaptive scale segmentation method

NASA Astrophysics Data System (ADS)

Zhang, Hong; Shen, Jinxiang; Ma, Yanmei

2018-03-01

Multiscale segmentation of images can effectively form boundaries of different objects with different scales. However, for the remote sensing image which widely coverage with complicated ground objects, the number of suitable segmentation scales, and each of the scale size is still difficult to be accurately determined, which severely restricts the rapid information extraction of the remote sensing image. A great deal of experiments showed that the normalized difference vegetation index (NDVI) can effectively express the spectral characteristics of a variety of ground objects in remote sensing images. This paper presents a method using NDVI assisted adaptive segmentation of remote sensing images, which segment the local area by using NDVI similarity threshold to iteratively select segmentation scales. According to the different regions which consist of different targets, different segmentation scale boundaries could be created. The experimental results showed that the adaptive segmentation method based on NDVI can effectively create the objects boundaries for different ground objects of remote sensing images.

A multi-scale approach to monitor urban carbon-dioxide emissions in the atmosphere over Vancouver, Canada

NASA Astrophysics Data System (ADS)

Christen, A.; Crawford, B.; Ketler, R.; Lee, J. K.; McKendry, I. G.; Nesic, Z.; Caitlin, S.

2015-12-01

Measurements of long-lived greenhouse gases in the urban atmosphere are potentially useful to constrain and validate urban emission inventories, or space-borne remote-sensing products. We summarize and compare three different approaches, operating at different scales, that directly or indirectly identify, attribute and quantify emissions (and uptake) of carbon dioxide (CO2) in urban environments. All three approaches are illustrated using in-situ measurements in the atmosphere in and over Vancouver, Canada. Mobile sensing may be a promising way to quantify and map CO2 mixing ratios at fine scales across heterogenous and complex urban environments. We developed a system for monitoring CO2 mixing ratios at street level using a network of mobile CO2 sensors deployable on vehicles and bikes. A total of 5 prototype sensors were built and simultaneously used in a measurement campaign across a range of urban land use types and densities within a short time frame (3 hours). The dataset is used to aid in fine scale emission mapping in combination with simultaneous tower-based flux measurements. Overall, calculated CO2 emissions are realistic when compared against a spatially disaggregated scale emission inventory. The second approach is based on mass flux measurements of CO2 using a tower-based eddy covariance (EC) system. We present a continuous 7-year long dataset of CO2 fluxes measured by EC at the 28m tall flux tower 'Vancouver-Sunset'. We show how this dataset can be combined with turbulent source area models to quantify and partition different emission processes at the neighborhood-scale. The long-term EC measurements are within 10% of a spatially disaggregated scale emission inventory. Thirdly, at the urban scale, we present a dataset of CO2 mixing ratios measured using a tethered balloon system in the urban boundary layer above Vancouver. Using a simple box model, net city-scale CO2 emissions can be determined using measured rate of change of CO2 mixing ratios, estimated CO2 advection and entrainment fluxes. Daily city-scale emissions totals predicted by the model are within 32% of a spatially scaled municipal greenhouse gas inventory. In summary, combining information from different approaches and scales is a promising approach to establish long-term emission monitoring networks in cities.

System and method for the detection of anomalies in an image

DOEpatents

Prasad, Lakshman; Swaminarayan, Sriram

2013-09-03

Preferred aspects of the present invention can include receiving a digital image at a processor; segmenting the digital image into a hierarchy of feature layers comprising one or more fine-scale features defining a foreground object embedded in one or more coarser-scale features defining a background to the one or more fine-scale features in the segmentation hierarchy; detecting a first fine-scale foreground feature as an anomaly with respect to a first background feature within which it is embedded; and constructing an anomalous feature layer by synthesizing spatially contiguous anomalous fine-scale features. Additional preferred aspects of the present invention can include detecting non-pervasive changes between sets of images in response at least in part to one or more difference images between the sets of images.

Satellite-Scale Snow Water Equivalent Assimilation into a High-Resolution Land Surface Model

NASA Technical Reports Server (NTRS)

De Lannoy, Gabrielle J.M.; Reichle, Rolf H.; Houser, Paul R.; Arsenault, Kristi R.; Verhoest, Niko E.C.; Paulwels, Valentijn R.N.

2009-01-01

An ensemble Kalman filter (EnKF) is used in a suite of synthetic experiments to assimilate coarse-scale (25 km) snow water equivalent (SWE) observations (typical of satellite retrievals) into fine-scale (1 km) model simulations. Coarse-scale observations are assimilated directly using an observation operator for mapping between the coarse and fine scales or, alternatively, after disaggregation (re-gridding) to the fine-scale model resolution prior to data assimilation. In either case observations are assimilated either simultaneously or independently for each location. Results indicate that assimilating disaggregated fine-scale observations independently (method 1D-F1) is less efficient than assimilating a collection of neighboring disaggregated observations (method 3D-Fm). Direct assimilation of coarse-scale observations is superior to a priori disaggregation. Independent assimilation of individual coarse-scale observations (method 3D-C1) can bring the overall mean analyzed field close to the truth, but does not necessarily improve estimates of the fine-scale structure. There is a clear benefit to simultaneously assimilating multiple coarse-scale observations (method 3D-Cm) even as the entire domain is observed, indicating that underlying spatial error correlations can be exploited to improve SWE estimates. Method 3D-Cm avoids artificial transitions at the coarse observation pixel boundaries and can reduce the RMSE by 60% when compared to the open loop in this study.

Polygamy and an absence of fine-scale structure in Dendroctonus ponderosae (Hopk.) (Coleoptera: Curcilionidae) confirmed using molecular markers

PubMed Central

Janes, J K; Roe, A D; Rice, A V; Gorrell, J C; Coltman, D W; Langor, D W; Sperling, F A H

2016-01-01

An understanding of mating systems and fine-scale spatial genetic structure is required to effectively manage forest pest species such as Dendroctonus ponderosae (mountain pine beetle). Here we used genome-wide single-nucleotide polymorphisms to assess the fine-scale genetic structure and mating system of D. ponderosae collected from a single stand in Alberta, Canada. Fine-scale spatial genetic structure was absent within the stand and the majority of genetic variation was best explained at the individual level. Relatedness estimates support previous reports of pre-emergence mating. Parentage assignment tests indicate that a polygamous mating system better explains the relationships among individuals within a gallery than the previously reported female monogamous/male polygynous system. Furthermore, there is some evidence to suggest that females may exploit the galleries of other females, at least under epidemic conditions. Our results suggest that current management models are likely to be effective across large geographic areas based on the absence of fine-scale genetic structure. PMID:26286666

Spatial adaptive sampling in multiscale simulation

NASA Astrophysics Data System (ADS)

Rouet-Leduc, Bertrand; Barros, Kipton; Cieren, Emmanuel; Elango, Venmugil; Junghans, Christoph; Lookman, Turab; Mohd-Yusof, Jamaludin; Pavel, Robert S.; Rivera, Axel Y.; Roehm, Dominic; McPherson, Allen L.; Germann, Timothy C.

2014-07-01

In a common approach to multiscale simulation, an incomplete set of macroscale equations must be supplemented with constitutive data provided by fine-scale simulation. Collecting statistics from these fine-scale simulations is typically the overwhelming computational cost. We reduce this cost by interpolating the results of fine-scale simulation over the spatial domain of the macro-solver. Unlike previous adaptive sampling strategies, we do not interpolate on the potentially very high dimensional space of inputs to the fine-scale simulation. Our approach is local in space and time, avoids the need for a central database, and is designed to parallelize well on large computer clusters. To demonstrate our method, we simulate one-dimensional elastodynamic shock propagation using the Heterogeneous Multiscale Method (HMM); we find that spatial adaptive sampling requires only ≈ 50 ×N0.14 fine-scale simulations to reconstruct the stress field at all N grid points. Related multiscale approaches, such as Equation Free methods, may also benefit from spatial adaptive sampling.

Postfire soil burn severity mapping with hyperspectral image unmixing

USGS Publications Warehouse

Robichaud, P.R.; Lewis, S.A.; Laes, D.Y.M.; Hudak, A.T.; Kokaly, R.F.; Zamudio, J.A.

2007-01-01

Burn severity is mapped after wildfires to evaluate immediate and long-term fire effects on the landscape. Remotely sensed hyperspectral imagery has the potential to provide important information about fine-scale ground cover components that are indicative of burn severity after large wildland fires. Airborne hyperspectral imagery and ground data were collected after the 2002 Hayman Fire in Colorado to assess the application of high resolution imagery for burn severity mapping and to compare it to standard burn severity mapping methods. Mixture Tuned Matched Filtering (MTMF), a partial spectral unmixing algorithm, was used to identify the spectral abundance of ash, soil, and scorched and green vegetation in the burned area. The overall performance of the MTMF for predicting the ground cover components was satisfactory (r2 = 0.21 to 0.48) based on a comparison to fractional ash, soil, and vegetation cover measured on ground validation plots. The relationship between Landsat-derived differenced Normalized Burn Ratio (dNBR) values and the ground data was also evaluated (r2 = 0.20 to 0.58) and found to be comparable to the MTMF. However, the quantitative information provided by the fine-scale hyperspectral imagery makes it possible to more accurately assess the effects of the fire on the soil surface by identifying discrete ground cover characteristics. These surface effects, especially soil and ash cover and the lack of any remaining vegetative cover, directly relate to potential postfire watershed response processes. ?? 2006 Elsevier Inc. All rights reserved.

Using IKONOS Imagery to Estimate Surface Soil Property Variability in Two Alabama Physiographies

NASA Technical Reports Server (NTRS)

Sullivan, Dana; Shaw, Joey; Rickman, Doug

2005-01-01

Knowledge of surface soil properties is used to assess past erosion and predict erodibility, determine nutrient requirements, and assess surface texture for soil survey applications. This study was designed to evaluate high resolution IKONOS multispectral data as a soil- mapping tool. Imagery was acquired over conventionally tilled fields in the Coastal Plain and Tennessee Valley physiographic regions of Alabama. Acquisitions were designed to assess the impact of surface crusting, roughness and tillage on our ability to depict soil property variability. Soils consisted mostly of fine-loamy, kaolinitic, thermic Plinthic Kandiudults at the Coastal Plain site and fine, kaolinitic, thermic Rhodic Paleudults at the Tennessee Valley site. Soils were sampled in 0.20 ha grids to a depth of 15 cm and analyzed for % sand (0.05 - 2 mm), silt (0.002 -0.05 mm), clay (less than 0.002 mm), citrate dithionite extractable iron (Fe(sub d)) and soil organic carbon (SOC). Four methods of evaluating variability in soil attributes were evaluated: 1) kriging of soil attributes, 2) co-kriging with soil attributes and reflectance data, 3) multivariate regression based on the relationship between reflectance and soil properties, and 4) fuzzy c-means clustering of reflectance data. Results indicate that co-kriging with remotely sensed data improved field scale estimates of surface SOC and clay content compared to kriging and regression methods. Fuzzy c-means worked best using RS data acquired over freshly tilled fields, reducing soil property variability within soil zones compared to field scale soil property variability.

Water availability drives signatures of local adaptation in whitebark pine (Pinus albicaulis Engelm.) across fine spatial scales of the Lake Tahoe Basin, USA

Treesearch

Brandon M. Lind; Christopher J. Friedline; Jill L. Wegrzyn; Patricia E. Maloney; Detlev R. Vogler; David B. Neale; Andrew J. Eckert

2017-01-01

Patterns of local adaptation at fine spatial scales are central to understanding how evolution proceeds, and are essential to the effective management of economically and ecologically important forest tree species. Here, we employ single and multilocus analyses of genetic data (n = 116 231 SNPs) to describe signatures of fine-scale...

Using fine-scale fuel measurements to assess wildland fuels, potential fire behavior and hazard mitigation treatments in the southeastern USA

Treesearch

Roger D. Ottmar; John I. Blake; William T. Crolly

2012-01-01

The inherent spatial and temporal heterogeneity of fuel beds in forests of the southeastern United States may require fine scale fuel measurements for providing reliable fire hazard and fuel treatment effectiveness estimates. In a series of five papers, an intensive, fine scale fuel inventory from the Savanna River Site in the southeastern United States is used for...

Fine-Scale Habitat Segregation between Two Ecologically Similar Top Predators.

PubMed

Palomares, Francisco; Fernández, Néstor; Roques, Severine; Chávez, Cuauhtemoc; Silveira, Leandro; Keller, Claudia; Adrados, Begoña

2016-01-01

Similar, coexisting species often segregate along the spatial ecological axis. Here, we examine if two top predators (jaguars and pumas) present different fine-scale habitat use in areas of coexistence, and discuss if the observed pattern can be explained by the risk of interference competition between them. Interference competition theory predicts that pumas should avoid habitats or areas used by jaguars (the dominant species), and as a consequence should present more variability of niche parameters across study areas. We used non-invasive genetic sampling of faeces in 12 different areas and sensor satellite fine-scale habitat indices to answer these questions. Meta-analysis confirmed differences in fine-scale habitat use between jaguars and pumas. Furthermore, average marginality of the realized niches of pumas was more variable than those of jaguars, and tolerance (a measure of niche breadth) was on average 2.2 times higher in pumas than in jaguars, as expected under the interference competition risk hypothesis. The use of sensor satellite fine-scale habitat indices allowed the detection of subtle differences in the environmental characteristics of the habitats used by these two similar top predators, which, as a rule, until now were recorded using the same general habitat types. The detection of fine spatial segregation between these two top predators was scale-dependent.

Ultra-Fine Scale Spatially-Integrated Mapping of Habitat and Occupancy Using Structure-From-Motion

PubMed Central

McDowall, Philip; Lynch, Heather J.

2017-01-01

Organisms respond to and often simultaneously modify their environment. While these interactions are apparent at the landscape extent, the driving mechanisms often occur at very fine spatial scales. Structure-from-Motion (SfM), a computer vision technique, allows the simultaneous mapping of organisms and fine scale habitat, and will greatly improve our understanding of habitat suitability, ecophysiology, and the bi-directional relationship between geomorphology and habitat use. SfM can be used to create high-resolution (centimeter-scale) three-dimensional (3D) habitat models at low cost. These models can capture the abiotic conditions formed by terrain and simultaneously record the position of individual organisms within that terrain. While coloniality is common in seabird species, we have a poor understanding of the extent to which dense breeding aggregations are driven by fine-scale active aggregation or limited suitable habitat. We demonstrate the use of SfM for fine-scale habitat suitability by reconstructing the locations of nests in a gentoo penguin colony and fitting models that explicitly account for conspecific attraction. The resulting digital elevation models (DEMs) are used as covariates in an inhomogeneous hybrid point process model. We find that gentoo penguin nest site selection is a function of the topography of the landscape, but that nests are far more aggregated than would be expected based on terrain alone, suggesting a strong role of behavioral aggregation in driving coloniality in this species. This integrated mapping of organisms and fine scale habitat will greatly improve our understanding of fine-scale habitat suitability, ecophysiology, and the complex bi-directional relationship between geomorphology and habitat use. PMID:28076351

Disaggregation of remotely sensed soil moisture under all sky condition using machine learning approach in Northeast Asia

NASA Astrophysics Data System (ADS)

Kim, S.; Kim, H.; Choi, M.; Kim, K.

2016-12-01

Estimating spatiotemporal variation of soil moisture is crucial to hydrological applications such as flood, drought, and near real-time climate forecasting. Recent advances in space-based passive microwave measurements allow the frequent monitoring of the surface soil moisture at a global scale and downscaling approaches have been applied to improve the spatial resolution of passive microwave products available at local scale applications. However, most downscaling methods using optical and thermal dataset, are valid only in cloud-free conditions; thus renewed downscaling method under all sky condition is necessary for the establishment of spatiotemporal continuity of datasets at fine resolution. In present study Support Vector Machine (SVM) technique was utilized to downscale a satellite-based soil moisture retrievals. The 0.1 and 0.25-degree resolution of daily Land Parameter Retrieval Model (LPRM) L3 soil moisture datasets from Advanced Microwave Scanning Radiometer 2 (AMSR2) were disaggregated over Northeast Asia in 2015. Optically derived estimates of surface temperature (LST), normalized difference vegetation index (NDVI), and its cloud products were obtained from MODerate Resolution Imaging Spectroradiometer (MODIS) for the purpose of downscaling soil moisture in finer resolution under all sky condition. Furthermore, a comparison analysis between in situ and downscaled soil moisture products was also conducted for quantitatively assessing its accuracy. Results showed that downscaled soil moisture under all sky condition not only preserves the quality of AMSR2 LPRM soil moisture at 1km resolution, but also attains higher spatial data coverage. From this research we expect that time continuous monitoring of soil moisture at fine scale regardless of weather conditions would be available.

Partial Least Square Discriminant Analysis Based on Normalized Two-Stage Vegetation Indices for Mapping Damage from Rice Diseases Using PlanetScope Datasets.

PubMed

Shi, Yue; Huang, Wenjiang; Ye, Huichun; Ruan, Chao; Xing, Naichen; Geng, Yun; Dong, Yingying; Peng, Dailiang

2018-06-11

In recent decades, rice disease co-epidemics have caused tremendous damage to crop production in both China and Southeast Asia. A variety of remote sensing based approaches have been developed and applied to map diseases distribution using coarse- to moderate-resolution imagery. However, the detection and discrimination of various disease species infecting rice were seldom assessed using high spatial resolution data. The aims of this study were (1) to develop a set of normalized two-stage vegetation indices (VIs) for characterizing the progressive development of different diseases with rice; (2) to explore the performance of combined normalized two-stage VIs in partial least square discriminant analysis (PLS-DA); and (3) to map and evaluate the damage caused by rice diseases at fine spatial scales, for the first time using bi-temporal, high spatial resolution imagery from PlanetScope datasets at a 3 m spatial resolution. Our findings suggest that the primary biophysical parameters caused by different disease (e.g., changes in leaf area, pigment contents, or canopy morphology) can be captured using combined normalized two-stage VIs. PLS-DA was able to classify rice diseases at a sub-field scale, with an overall accuracy of 75.62% and a Kappa value of 0.47. The approach was successfully applied during a typical co-epidemic outbreak of rice dwarf (Rice dwarf virus, RDV), rice blast ( Magnaporthe oryzae ), and glume blight ( Phyllosticta glumarum ) in Guangxi Province, China. Furthermore, our approach highlighted the feasibility of the method in capturing heterogeneous disease patterns at fine spatial scales over the large spatial extents.

Aerosol and precipitation chemistry measurements in a remote site in Central Amazonia: the role of biogenic contribution

NASA Astrophysics Data System (ADS)

Pauliquevis, T.; Lara, L. L.; Antunes, M. L.; Artaxo, P.

2012-06-01

In this analysis a 3.5 years data set of aerosol and precipitation chemistry, obtained in a remote site in Central Amazonia (Balbina, (1°55' S, 59°29' W, 174 m a.s.l.), about 200 km north of Manaus) is discussed. Aerosols were sampled using stacked filter units (SFU), which separate fine (d < 2.5 μm) and coarse mode (2.5 μm < d < 10.0 μm) aerosol particles. Filters were analyzed for particulate mass (PM), Equivalent Black Carbon (BCE) and elemental composition by Particle Induced X-Ray Emission (PIXE). Rainwater samples were collected using a wet-only sampler and samples were analyzed for pH and ionic composition, which was determined using ionic chromatography (IC). Natural sources dominated the aerosol mass during the wet season, when it was predominantly of natural biogenic origin mostly in the coarse mode, which comprised up to 81% of PM10. Biogenic aerosol from both primary emissions and secondary organic aerosol dominates the fine mode in the wet season, with very low concentrations (average 2.2 μg m-3). Soil dust was responsible for a minor fraction of the aerosol mass (less than 17%). Sudden increases in the concentration of elements as Al, Ti and Fe were also observed, both in fine and coarse mode (mostly during the April-may months), which we attribute to episodes of Saharan dust transport. During the dry periods, a significant contribution to the fine aerosols loading was observed, due to the large-scale transport of smoke from biomass burning in other portions of the Amazon basin. This contribution is associated with the enhancement of the concentration of S, K, Zn and BCE. Chlorine, which is commonly associated to sea salt and also to biomass burning emissions, presented higher concentration not only during the dry season but also for the April-June months, due to the establishment of more favorable meteorological conditions to the transport of Atlantic air masses to Central Amazonia. The chemical composition of rainwater was similar to those ones observed in other remote sites in tropical forests. The volume-weighted mean (VWM) pH was 4.90. The most important contribution to acidity was from weak organic acids. The organic acidity was predominantly associated with the presence of acetic acid instead of formic acid, which is more often observed in pristine tropical areas. Wet deposition rates for major species did not differ significantly between dry and wet season, except for NH4+, citrate and acetate, which had smaller deposition rates during dry season. While biomass burning emissions were clearly identified in the aerosol component, it did not present a clear signature in rainwater. The biogenic component and the long-range transport of sea salt were observed both in aerosols and rainwater composition. The results shown here indicate that in Central Amazonia it is still possible to observe quite pristine atmospheric conditions, relatively free of anthropogenic influences.

Monitoring post-fire changes in species composition and stand structure in boreal forests using high-resolution, 3-D aerial drone data and Landsat

NASA Astrophysics Data System (ADS)

Alonzo, M.; Morton, D. C.; Cook, B.; Andersen, H. E.; Mack, M. C.

2017-12-01

The growing frequency and severity of boreal forest fires has important consequences for fire carbon emissions and ecosystem composition. Severe fires are typically associated with high degrees of both canopy and soil organic layer (SOL) consumption, particularly in black spruce stands. Complete canopy consumption can decrease the likelihood of spruce regeneration due to reduced viability of the aerial seedbank. Deeper burning of the SOL increases fire emissions and can expose mineral soil that promotes colonization by broadleaf species. There is mounting evidence that a disturbance-driven shift from spruce to broadleaf forests may indicate an ecological state change with feedbacks to regional and global climate. If post-fire successional dynamics can be characterized at an ecosystem scale using remote sensing data, we will be better equipped to constrain carbon and energy fluxes from SOL losses and albedo changes. In this study, we used Landsat time series, very high-resolution structure-from-motion (SFM) drone imagery, and field measurements to investigate post-fire regrowth 13 years after the 2004 Taylor Complex (TC) fires in interior Alaska. Twenty-seven TC plots span a gradient of moisture conditions and burn severity as estimated by loss of SOL. A range of variables potentially governing seedling species dominance (e.g., moisture status, distance to seed sources) have been collected systematically over the years following fire. In July 2017, we additionally collected < 2 cm resolution drone imagery over 25 of the TC plots. We processed these highly overlapped, nadir-view and oblique angle photos into extremely dense (>700 pts/m2) RGB-colored point clouds using SFM techniques. With these point clouds and high resolution orthomosaics, we estimated: 1) snag heights and biomass, 2) remnant snag fine branching, and 3) species and structure of shrubs and groundcover that have regrown since fire. We additionally assembled a dense Landsat time series arranged by day-of-year to monitor pre-fire and post-fire phenology. Our preliminary results illustrate how ultra-fine and moderate-scale remote sensing can be used to better understand the processes of ecosystem regeneration following fire.

Species classification using Unmanned Aerial Vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland

NASA Astrophysics Data System (ADS)

Lu, Bing; He, Yuhong

2017-06-01

Investigating spatio-temporal variations of species composition in grassland is an essential step in evaluating grassland health conditions, understanding the evolutionary processes of the local ecosystem, and developing grassland management strategies. Space-borne remote sensing images (e.g., MODIS, Landsat, and Quickbird) with spatial resolutions varying from less than 1 m to 500 m have been widely applied for vegetation species classification at spatial scales from community to regional levels. However, the spatial resolutions of these images are not fine enough to investigate grassland species composition, since grass species are generally small in size and highly mixed, and vegetation cover is greatly heterogeneous. Unmanned Aerial Vehicle (UAV) as an emerging remote sensing platform offers a unique ability to acquire imagery at very high spatial resolution (centimetres). Compared to satellites or airplanes, UAVs can be deployed quickly and repeatedly, and are less limited by weather conditions, facilitating advantageous temporal studies. In this study, we utilize an octocopter, on which we mounted a modified digital camera (with near-infrared (NIR), green, and blue bands), to investigate species composition in a tall grassland in Ontario, Canada. Seven flight missions were conducted during the growing season (April to December) in 2015 to detect seasonal variations, and four of them were selected in this study to investigate the spatio-temporal variations of species composition. To quantitatively compare images acquired at different times, we establish a processing flow of UAV-acquired imagery, focusing on imagery quality evaluation and radiometric correction. The corrected imagery is then applied to an object-based species classification. Maps of species distribution are subsequently used for a spatio-temporal change analysis. Results indicate that UAV-acquired imagery is an incomparable data source for studying fine-scale grassland species composition, owing to its high spatial resolution. The overall accuracy is around 85% for images acquired at different times. Species composition is spatially attributed by topographical features and soil moisture conditions. Spatio-temporal variation of species composition implies the growing process and succession of different species, which is critical for understanding the evolutionary features of grassland ecosystems. Strengths and challenges of applying UAV-acquired imagery for vegetation studies are summarized at the end.

Infrared measurements of pristine and disturbed soils 1. Spectral contrast differences between field and laboratory data

USGS Publications Warehouse

Johnson, J. R.; Lucey, P.G.; Horton, K.A.; Winter, E.M.

1998-01-01

Comparison of emissivity spectra (8-13 ??m) of pristine soils in the field with laboratory reflectance spectra of the same soils showed that laboratory spectra tend to have less spectral contrast than field spectra (see following article). We investigated this the phenomenon by measuring emission spectra of both undisturbed (in situ) and disturbed soils (prepared as if for transport to the laboratory). The disturbed soils had much less spectral contrast than the undisturbed soils in the reststrahlen region near 9 ??m. While the increased porosity of a disturbed soil can decrease spectral contrast due to multiple scattering, we hypothesize that the effect is dominantly the result of a difference in grain-size distribution of the optically active layer (i.e., fine particle coatings). This concept was proposed by Salisbury et al. (1994) to explain their observations that soils washed free of small particles adhering the larger grains exhibited greater spectral contrast than unwashed soils. Our laboratory reflectance spectra of wet- and dry-sieved soils returned from field sites also show greater spectral contrast for wet-sieved (washed) soils. We therefore propose that undisturbed soils in the field can be characterized as 'clean' soils (washed free of fine particles at the surface due to rain and wind action) and that disturbed soils represent 'dirty' soils (contaminated with fine particle coatings). The effect of packing soils in the field and laboratory also increases spectral contrast but not to the magnitude of that observed for undisturbed and wet-sieved soils. Since it is a common practice to use laboratory spectra of field samples to interpret spectra obtained remotely, we suggest that the influence of fine particle coatings on disturbed soils, if unrecognized, could influence interpretations of remote sensing data.Comparison of emissivity spectra (8-13 ??m) of pristine soils in the field with laboratory reflectance spectra of the same soils showed that laboratory spectra tend to have less spectral contrast than field spectra (see following article). We investigated this phenomenon by measuring emission spectra of both undisturbed (in situ) and disturbed soils (prepared as if for transport to the laboratory). The disturbed soils had much less spectral contrast than the undisturbed soils in the reststrahlen region near 9 ??m. While the increased porosity of a disturbed soil can decrease spectral contrast due to multiple scattering, we hypothesize that the effect is dominantly the result of a difference in grain-size distribution of the optically active layer (i.e., fine particle coatings). This concept was proposed by Salisbury et al. (1994) to explain their observations that soils washed free of small particles adhering to larger grains exhibited greater spectral contrast than unwashed soils. Our laboratory reflectance spectra of wet- and dry-sieved soils returned from field sites also show greater spectral contrast for wet-sieved (washed) soils. We therefore propose that undisturbed soils in the field can be characterized as `clean' soils (washed free of fine particles at the surface due to rain and wind action) and that disturbed soils represent `dirty' soils (contaminated with fine particle coatings). The effect of packing soils in the field and laboratory also increases spectral contrast but not to the magnitude of that observed for undisturbed and wet-sieved soils. Since it is a common practice to use laboratory spectra of field samples to interpret spectra obtained remotely, we suggest that the influence of fine particle coatings on disturbed soils, if unrecognized, could influence interpretations of remote sensing data.

Evolving technologies for Space Station Freedom computer-based workstations

NASA Technical Reports Server (NTRS)

Jensen, Dean G.; Rudisill, Marianne

1990-01-01

Viewgraphs on evolving technologies for Space Station Freedom computer-based workstations are presented. The human-computer computer software environment modules are described. The following topics are addressed: command and control workstation concept; cupola workstation concept; Japanese experiment module RMS workstation concept; remote devices controlled from workstations; orbital maneuvering vehicle free flyer; remote manipulator system; Japanese experiment module exposed facility; Japanese experiment module small fine arm; flight telerobotic servicer; human-computer interaction; and workstation/robotics related activities.

A patch-based convolutional neural network for remote sensing image classification.

PubMed

Sharma, Atharva; Liu, Xiuwen; Yang, Xiaojun; Shi, Di

2017-11-01

Availability of accurate land cover information over large areas is essential to the global environment sustainability; digital classification using medium-resolution remote sensing data would provide an effective method to generate the required land cover information. However, low accuracy of existing per-pixel based classification methods for medium-resolution data is a fundamental limiting factor. While convolutional neural networks (CNNs) with deep layers have achieved unprecedented improvements in object recognition applications that rely on fine image structures, they cannot be applied directly to medium-resolution data due to lack of such fine structures. In this paper, considering the spatial relation of a pixel to its neighborhood, we propose a new deep patch-based CNN system tailored for medium-resolution remote sensing data. The system is designed by incorporating distinctive characteristics of medium-resolution data; in particular, the system computes patch-based samples from multidimensional top of atmosphere reflectance data. With a test site from the Florida Everglades area (with a size of 771 square kilometers), the proposed new system has outperformed pixel-based neural network, pixel-based CNN and patch-based neural network by 24.36%, 24.23% and 11.52%, respectively, in overall classification accuracy. By combining the proposed deep CNN and the huge collection of medium-resolution remote sensing data, we believe that much more accurate land cover datasets can be produced over large areas. Copyright © 2017 Elsevier Ltd. All rights reserved.

IMAGING SPECTROSCOPY FOR DETERMINING RANGELAND STRESSORS TO WESTERN WATERSHEDS

EPA Science Inventory

The Environmental Protection Agency is developing rangeland ecological indicators in twelve western states using advanced remote sensing techniques. Fine spectral resolution (hyperspectral) sensors, or imaging spectrometers, can detect the subtle spectral features that make veget...

IMAGING SPECTROSCOPY FOR DETERMINING RANGELAND STRESSORS TO WESTERN WATERSHEDS

EPA Science Inventory

The Environmental Protection Agency is developing rangeland ecological indicators in eleven western states using advanced remote sensing systems. Fine spectral resolution (hyperspemal) sensors, or imaging spectrometers, can detect the subtle spectral features that makes vegetatio...

KSC-2011-4145

NASA Image and Video Library

2011-05-27

CAPE CANAVERAL, Fla. -- Inside the "Lunarena" at the Kennedy Space Center Visitor Complex in Florida, university students maneuver their remote controlled or autonomous excavators, called lunabots, in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4157

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- Inside the "Lunarena" at the Kennedy Space Center Visitor Complex in Florida, university students maneuver their remote controlled or autonomous excavators, called lunabots, in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4161

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- Inside the "Lunarena" at the Kennedy Space Center Visitor Complex in Florida, university students give their "thumbs up" after maneuvering their remote controlled or autonomous excavators, called lunabots, in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4150

NASA Image and Video Library

2011-05-27

CAPE CANAVERAL, Fla. -- University students monitor their team's remote controlled or autonomous excavator, called a lunabot, as it is maneuvered in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4018

NASA Image and Video Library

2011-05-26

CAPE CANAVERAL, Fla. -- Inside the "Lunarena" at the Kennedy Space Center Visitor in Florida, university students maneuver their remote controlled or autonomous excavators, called lunabots, in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jim Grossmann

KSC-2011-4149

NASA Image and Video Library

2011-05-27

CAPE CANAVERAL, Fla. -- University students monitor their team's remote controlled or autonomous excavator, called a lunabot, as it is maneuvered in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4162

NASA Image and Video Library

2011-05-28

CAPE CANAVERAL, Fla. -- University students monitor their team's remote controlled or autonomous excavator, called a lunabot, as it is maneuvered in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4019

NASA Image and Video Library

2011-05-26

CAPE CANAVERAL, Fla. -- University students monitor their team's remote controlled or autonomous excavator, called a lunabot, as it is maneuvered in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jim Grossmann

Comparisons of Remote Sensing Retrievals and in situ Measurements of Aerosol Fine Mode Fraction during ACE-Asia

NASA Technical Reports Server (NTRS)

Gasso, Santiago; O'Neill, Norm

2006-01-01

We present sunphotometer-retrieved and in situ fine mode fractions (FMF) measured onboard the same aircraft during the ACE-Asia experiment. Comparisons indicate that the latter can be used to identify whether the aerosol under observation is dominated by a mixture of modes or a single mode. Differences between retrieved and in situ FMF range from 5-20%. When profiles contained multiple layers of aerosols, the retrieved and measured FMF were segregated by layers. The comparison of layered and total FMF from the same profile indicates that columnar values are intermediate to those derived from layers. As a result, a remotely sensed FMF cannot be used to distinguish whether the aerosol under observation is composed of layers each with distinctive modal features or all layers with the same modal features. Thus, the use of FMF in multiple layer environments does not provide unique information on the aerosol under observation.

KSC-2011-4144

NASA Image and Video Library

2011-05-27

CAPE CANAVERAL, Fla. -- Inside the "Lunarena" at the Kennedy Space Center Visitor Complex in Florida, university students maneuver their remote controlled or autonomous excavators, called lunabots, in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

KSC-2011-4017

NASA Image and Video Library

2011-05-26

CAPE CANAVERAL, Fla. -- Inside the "Lunarena" at the Kennedy Space Center Visitor in Florida, university students maneuver their remote controlled or autonomous excavators, called lunabots, in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jim Grossmann

Global spectroscopic survey of cloud thermodynamic phase at high spatial resolution, 2005-2015

NASA Astrophysics Data System (ADS)

Thompson, David R.; Kahn, Brian H.; Green, Robert O.; Chien, Steve A.; Middleton, Elizabeth M.; Tran, Daniel Q.

2018-02-01

The distribution of ice, liquid, and mixed phase clouds is important for Earth's planetary radiation budget, impacting cloud optical properties, evolution, and solar reflectivity. Most remote orbital thermodynamic phase measurements observe kilometer scales and are insensitive to mixed phases. This under-constrains important processes with outsize radiative forcing impact, such as spatial partitioning in mixed phase clouds. To date, the fine spatial structure of cloud phase has not been measured at global scales. Imaging spectroscopy of reflected solar energy from 1.4 to 1.8 µm can address this gap: it directly measures ice and water absorption, a robust indicator of cloud top thermodynamic phase, with spatial resolution of tens to hundreds of meters. We report the first such global high spatial resolution survey based on data from 2005 to 2015 acquired by the Hyperion imaging spectrometer onboard NASA's Earth Observer 1 (EO-1) spacecraft. Seasonal and latitudinal distributions corroborate observations by the Atmospheric Infrared Sounder (AIRS). For extratropical cloud systems, just 25 % of variance observed at GCM grid scales of 100 km was related to irreducible measurement error, while 75 % was explained by spatial correlations possible at finer resolutions.

OPEN PATH OPTICAL SENSING OF PARTICULATE MATTER

EPA Science Inventory

The paper discusses the concepts behind recent developments in optical remote sensing (ORS) and the results from experiments. Airborne fugitive and fine particulate matter (PM) from various sources contribute to exceedances of state and federal PM and visibility standards. Recent...

Space robotic experiment in JEM flight demonstration

NASA Technical Reports Server (NTRS)

Nagatomo, Masanori; Tanaka, Masaki; Nakamura, Kazuyuki; Tsuda, Shinichi

1994-01-01

Japan is collaborating on the multinational space station program. The JEM, Japanese Experiment Module, has both a pressurized module and an Exposed Facility (EF). JEM Remote Manipulator System (JEMRMS) will play a dominant role in handling/servicing payloads and the maintenance of the EF, and consists of two robotic arms, a main arm and a small fine arm. JEM Flight Demonstration (JFD) is a space robotics experiment using the prototype small fine arm to demonstrate its capability, prior to the Space Station operation. The small fine arm will be installed in the Space Shuttle cargo bay and operated by a crew from a dedicated workstation in the Aft Flight Deck of the orbiter.

Evaluation of 3-D Air Quality System Remotely-Sensed Aerosol Optical Depth for the Baltimore/Washington Metropolitan Air Shed

NASA Astrophysics Data System (ADS)

Weber, S. A.; Engel-Cox, J. A.; Hoff, R. M.; Prados, A.; Zhang, H.

2008-12-01

Integrating satellite- and ground-based aerosol optical depth (AOD) observations with surface total fine particulate (PM2.5) and sulfate concentrations allows for a more comprehensive understanding of local- and urban-scale air quality. This study evaluates the utility of integrated databases being developed for NOAA and EPA through the 3D-AQS project by examining the relationship between remotely-sensed AOD and PM2.5 concentrations for each platform for the summer of 2004 and the entire year of 2005. We compare results for the Baltimore, MD/Washington, DC metropolitan air shed, incorporating AOD products from the Terra and GOES-12 satellites, AERONET sunphotometer, and ground-based lidar, and PM2.5 concentrations from five surface monitoring sites. The satellite-derived products include AOD from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectroradiometer (MISR), as well as the GOES Aerosol/Smoke Product (GASP). The vertical profile of lidar backscatter is used to retrieve the planetary boundary layer (PBL) height in an attempt to capture only that fraction of the AOD arising from near surface aerosols. Adjusting the AOD data using platform- and season-specific ratios, calculated using the parameters of the regression equations, for two case studies resulted in a more accurate representation of surface PM2.5 concentrations when compared to a constant ratio that is currently being used in the NOAA IDEA product. This work demonstrates that quantitative relationships between remotely-sensed and in-situ aerosol observations in an integrated database can be computed and applied to improve the use of remotely-sensed observations for estimating surface concentrations.

Target detection method by airborne and spaceborne images fusion based on past images

NASA Astrophysics Data System (ADS)

Chen, Shanjing; Kang, Qing; Wang, Zhenggang; Shen, ZhiQiang; Pu, Huan; Han, Hao; Gu, Zhongzheng

2017-11-01

To solve the problem that remote sensing target detection method has low utilization rate of past remote sensing data on target area, and can not recognize camouflage target accurately, a target detection method by airborne and spaceborne images fusion based on past images is proposed in this paper. The target area's past of space remote sensing image is taken as background. The airborne and spaceborne remote sensing data is fused and target feature is extracted by the means of airborne and spaceborne images registration, target change feature extraction, background noise suppression and artificial target feature extraction based on real-time aerial optical remote sensing image. Finally, the support vector machine is used to detect and recognize the target on feature fusion data. The experimental results have established that the proposed method combines the target area change feature of airborne and spaceborne remote sensing images with target detection algorithm, and obtains fine detection and recognition effect on camouflage and non-camouflage targets.

Contemporary Remotely Sensed Data Products Refine Invasive Plants Risk Mapping in Data Poor Regions.

PubMed

Truong, Tuyet T A; Hardy, Giles E St J; Andrew, Margaret E

2017-01-01

Invasive weeds are a serious problem worldwide, threatening biodiversity and damaging economies. Modeling potential distributions of invasive weeds can prioritize locations for monitoring and control efforts, increasing management efficiency. Forecasts of invasion risk at regional to continental scales are enabled by readily available downscaled climate surfaces together with an increasing number of digitized and georeferenced species occurrence records and species distribution modeling techniques. However, predictions at a finer scale and in landscapes with less topographic variation may require predictors that capture biotic processes and local abiotic conditions. Contemporary remote sensing (RS) data can enhance predictions by providing a range of spatial environmental data products at fine scale beyond climatic variables only. In this study, we used the Global Biodiversity Information Facility (GBIF) and empirical maximum entropy (MaxEnt) models to model the potential distributions of 14 invasive plant species across Southeast Asia (SEA), selected from regional and Vietnam's lists of priority weeds. Spatial environmental variables used to map invasion risk included bioclimatic layers and recent representations of global land cover, vegetation productivity (GPP), and soil properties developed from Earth observation data. Results showed that combining climate and RS data reduced predicted areas of suitable habitat compared with models using climate or RS data only, with no loss in model accuracy. However, contributions of RS variables were relatively limited, in part due to uncertainties in the land cover data. We strongly encourage greater adoption of quantitative remotely sensed estimates of ecosystem structure and function for habitat suitability modeling. Through comprehensive maps of overall predicted area and diversity of invasive species, we found that among lifeforms (herb, shrub, and vine), shrub species have higher potential invasion risk in SEA. Native invasive species, which are often overlooked in weed risk assessment, may be as serious a problem as non-native invasive species. Awareness of invasive weeds and their environmental impacts is still nascent in SEA and information is scarce. Freely available global spatial datasets, not least those provided by Earth observation programs, and the results of studies such as this one provide critical information that enables strategic management of environmental threats such as invasive species.

Contemporary Remotely Sensed Data Products Refine Invasive Plants Risk Mapping in Data Poor Regions

PubMed Central

Truong, Tuyet T. A.; Hardy, Giles E. St. J.; Andrew, Margaret E.

2017-01-01

Invasive weeds are a serious problem worldwide, threatening biodiversity and damaging economies. Modeling potential distributions of invasive weeds can prioritize locations for monitoring and control efforts, increasing management efficiency. Forecasts of invasion risk at regional to continental scales are enabled by readily available downscaled climate surfaces together with an increasing number of digitized and georeferenced species occurrence records and species distribution modeling techniques. However, predictions at a finer scale and in landscapes with less topographic variation may require predictors that capture biotic processes and local abiotic conditions. Contemporary remote sensing (RS) data can enhance predictions by providing a range of spatial environmental data products at fine scale beyond climatic variables only. In this study, we used the Global Biodiversity Information Facility (GBIF) and empirical maximum entropy (MaxEnt) models to model the potential distributions of 14 invasive plant species across Southeast Asia (SEA), selected from regional and Vietnam’s lists of priority weeds. Spatial environmental variables used to map invasion risk included bioclimatic layers and recent representations of global land cover, vegetation productivity (GPP), and soil properties developed from Earth observation data. Results showed that combining climate and RS data reduced predicted areas of suitable habitat compared with models using climate or RS data only, with no loss in model accuracy. However, contributions of RS variables were relatively limited, in part due to uncertainties in the land cover data. We strongly encourage greater adoption of quantitative remotely sensed estimates of ecosystem structure and function for habitat suitability modeling. Through comprehensive maps of overall predicted area and diversity of invasive species, we found that among lifeforms (herb, shrub, and vine), shrub species have higher potential invasion risk in SEA. Native invasive species, which are often overlooked in weed risk assessment, may be as serious a problem as non-native invasive species. Awareness of invasive weeds and their environmental impacts is still nascent in SEA and information is scarce. Freely available global spatial datasets, not least those provided by Earth observation programs, and the results of studies such as this one provide critical information that enables strategic management of environmental threats such as invasive species. PMID:28555147

Sample-based synthesis of two-scale structures with anisotropy

DOE PAGES

Liu, Xingchen; Shapiro, Vadim

2017-05-19

A vast majority of natural or synthetic materials are characterized by their anisotropic properties, such as stiffness. Such anisotropy is effected by the spatial distribution of the fine-scale structure and/or anisotropy of the constituent phases at a finer scale. In design, proper control of the anisotropy may greatly enhance the efficiency and performance of synthesized structures. In this paper, we propose a sample-based two-scale structure synthesis approach that explicitly controls anisotropic effective material properties of the structure on the coarse scale by orienting sampled material neighborhoods at the fine scale. We first characterize the non-uniform orientations distribution of the samplemore » structure by showing that the principal axes of an orthotropic material may be determined by the eigenvalue decomposition of its effective stiffness tensor. Such effective stiffness tensors can be efficiently estimated based on the two-point correlation functions of the fine-scale structures. Then we synthesize the two-scale structure by rotating fine-scale structures from the sample to follow a given target orientation field. Finally, the effectiveness of the proposed approach is demonstrated through examples in both 2D and 3D.« less

Sample-based synthesis of two-scale structures with anisotropy

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

Liu, Xingchen; Shapiro, Vadim

A vast majority of natural or synthetic materials are characterized by their anisotropic properties, such as stiffness. Such anisotropy is effected by the spatial distribution of the fine-scale structure and/or anisotropy of the constituent phases at a finer scale. In design, proper control of the anisotropy may greatly enhance the efficiency and performance of synthesized structures. In this paper, we propose a sample-based two-scale structure synthesis approach that explicitly controls anisotropic effective material properties of the structure on the coarse scale by orienting sampled material neighborhoods at the fine scale. We first characterize the non-uniform orientations distribution of the samplemore » structure by showing that the principal axes of an orthotropic material may be determined by the eigenvalue decomposition of its effective stiffness tensor. Such effective stiffness tensors can be efficiently estimated based on the two-point correlation functions of the fine-scale structures. Then we synthesize the two-scale structure by rotating fine-scale structures from the sample to follow a given target orientation field. Finally, the effectiveness of the proposed approach is demonstrated through examples in both 2D and 3D.« less

The Riverscape Analysis Project: Using Remote Sensing to Leverage Salmon Science and Management Applications Around the Pacific Rim

NASA Astrophysics Data System (ADS)

Chilcote, S.; Maumenee, N.; Lucotch, J.; Whited, D.; Bansack, T.; Kimball, J. S.; Stanford, J.

2009-12-01

The Salmonid Rivers Observatory Network (SaRON) is an intensive field research project which aims to describe the relation between salmon productivion and diversity in relation to environmental drivers and physical complexity of riverine shifting habitat mosaics. The Riverscape Analysis Project (RAP) is a spatially explicit remote sensing database which quantifies and ranks different combinations of physical landscape metrics around the Pacific Rim, displaying results through a publically accessible web based decision support framework designed to empower regional management and conservation efforts for wild salmon. The objective of our research is to explicitly describe and relate different habitat types and their potential fish production at a variety of scales and throughout the range of Pacific salmon, leveraging our field research through available satellite remote sensing and geospatial analysis. We find that rivers exhibit a range of physical, chemical, and biotic conditions consistent with the shifting habitat mosaic (SHM) concept. Landscape physical variables derived from global Landsat imagery and SRTM-DEM information explain 93.2% of observed variability in over 1500 watersheds across the Pacific Rim. We expect that it is these coarse scale differences in river typologies which are responsible for the fine scale differences in habitat conditions and juvenile salmon production. Therefore, we ranked rivers using landscape scale physical variables to prioritize them for management actions based on potential productivity. For example, the Kvichak River of Bristol Bay is highly ranked, 8th, based on its physical landscape structure as well as current human impacts. Currently, the Bristol Bay fishery is extremely productive. Habitat structure can be used not only to define reference conditions and management targets for how many fish we would expect a river to produce based on its potential habitat capacity, but it also provides new analytical tools to quantitatively evaluate potential ecosystem impacts from proposed development activities. We found that proposed water extraction of 29 cubic feet per second (cfs) in a tributary of the Kvichak could potentially reduce off-channel habitat capacity by over 512 juvenile fish per hectare of habitat. In this article, we provide examples of how managers can integrate these novel data and tools into their evaluation frameworks in order to make informed, ecologically based decisions about current ecosystem conditions, desired ecological states, and potential tradeoffs in meeting salmon management goals in relation to human impacts.

Impact of extrinsic factors on fine motor performance of children attending day care.

PubMed

Corsi, Carolina; Santos, Mariana Martins Dos; Marques, Luísa de Andrade Perez; Rocha, Nelci Adriana Cicuto Ferreira

2016-12-01

To assess the impact of extrinsic factors on fine motor performance of children aged two years old. 73 children attending public and 21 private day care centers were assessed. Day care environment was evaluated using the Infant/Toddler Environment Rating Scale - Revised Edition (ITERS-R), fine motor performance was assessed through the Bayley Scales of Infant and Toddler Development - III (BSITD-III), socioeconomic data, maternal education and time of start at the day care were collected through interviews. Spearman's correlation coefficient was calculated to assess the association between the studied variables. The time at the day care was positively correlated with the children's performance in some fine motor tasks of the BSITD-III, showing that the activities developed in day care centers were important for the refinement of specific motor skills, while the overall fine motor performance by the scale was associated with maternal education and the ITERS-R scale sub-item "language and understanding". Extrinsic factors such as higher maternal education and quality of day care centers are associated with fine motor performance in children attending day care. Copyright © 2016 Sociedade de Pediatria de São Paulo. Publicado por Elsevier Editora Ltda. All rights reserved.

Modeling a historical mountain pine beetle outbreak using Landsat MSS and multiple lines of evidence

USGS Publications Warehouse

Assal, Timothy J.; Sibold, Jason; Reich, Robin M.

2014-01-01

Mountain pine beetles are significant forest disturbance agents, capable of inducing widespread mortality in coniferous forests in western North America. Various remote sensing approaches have assessed the impacts of beetle outbreaks over the last two decades. However, few studies have addressed the impacts of historical mountain pine beetle outbreaks, including the 1970s event that impacted Glacier National Park. The lack of spatially explicit data on this disturbance represents both a major data gap and a critical research challenge in that wildfire has removed some of the evidence from the landscape. We utilized multiple lines of evidence to model forest canopy mortality as a proxy for outbreak severity. We incorporate historical aerial and landscape photos, aerial detection survey data, a nine-year collection of satellite imagery and abiotic data. This study presents a remote sensing based framework to (1) relate measurements of canopy mortality from fine-scale aerial photography to coarse-scale multispectral imagery and (2) classify the severity of mountain pine beetle affected areas using a temporal sequence of Landsat data and other landscape variables. We sampled canopy mortality in 261 plots from aerial photos and found that insect effects on mortality were evident in changes to the Normalized Difference Vegetation Index (NDVI) over time. We tested multiple spectral indices and found that a combination of NDVI and the green band resulted in the strongest model. We report a two-step process where we utilize a generalized least squares model to account for the large-scale variability in the data and a binary regression tree to describe the small-scale variability. The final model had a root mean square error estimate of 9.8% canopy mortality, a mean absolute error of 7.6% and an R2 of 0.82. The results demonstrate that a model of percent canopy mortality as a continuous variable can be developed to identify a gradient of mountain pine beetle severity on the landscape.

The utility of satellite observations for constraining fine-scale and transient methane sources

NASA Astrophysics Data System (ADS)

Turner, A. J.; Jacob, D.; Benmergui, J. S.; Brandman, J.; White, L.; Randles, C. A.

2017-12-01

Resolving differences between top-down and bottom-up emissions of methane from the oil and gas industry is difficult due, in part, to their fine-scale and often transient nature. There is considerable interest in using atmospheric observations to detect these sources. Satellite-based instruments are an attractive tool for this purpose and, more generally, for quantifying methane emissions on fine scales. A number of instruments are planned for launch in the coming years from both low earth and geostationary orbit, but the extent to which they can provide fine-scale information on sources has yet to be explored. Here we present an observation system simulation experiment (OSSE) exploring the tradeoffs between pixel resolution, measurement frequency, and instrument precision on the fine-scale information content of a space-borne instrument measuring methane. We use the WRF-STILT Lagrangian transport model to generate more than 200,000 column footprints at 1.3×1.3 km2 spatial resolution and hourly temporal resolution over the Barnett Shale in Texas. We sub-sample these footprints to match the observing characteristics of the planned TROPOMI and GeoCARB instruments as well as different hypothetical observing configurations. The information content of the various observing systems is evaluated using the Fisher information matrix and its singular values. We draw conclusions on the capabilities of the planned satellite instruments and how these capabilities could be improved for fine-scale source detection.

Coexistence between wildlife and humans at fine spatial scales.

PubMed

Carter, Neil H; Shrestha, Binoj K; Karki, Jhamak B; Pradhan, Narendra Man Babu; Liu, Jianguo

2012-09-18

Many wildlife species face imminent extinction because of human impacts, and therefore, a prevailing belief is that some wildlife species, particularly large carnivores and ungulates, cannot coexist with people at fine spatial scales (i.e., cannot regularly use the exact same point locations). This belief provides rationale for various conservation programs, such as resettling human communities outside protected areas. However, quantitative information on the capacity and mechanisms for wildlife to coexist with humans at fine spatial scales is scarce. Such information is vital, because the world is becoming increasingly crowded. Here, we provide empirical information about the capacity and mechanisms for tigers (a globally endangered species) to coexist with humans at fine spatial scales inside and outside Nepal's Chitwan National Park, a flagship protected area for imperiled wildlife. Information obtained from field cameras in 2010 and 2011 indicated that human presence (i.e., people on foot and vehicles) was ubiquitous and abundant throughout the study site; however, tiger density was also high. Surprisingly, even at a fine spatial scale (i.e., camera locations), tigers spatially overlapped with people on foot and vehicles in both years. However, in both years, tigers offset their temporal activity patterns to be much less active during the day when human activity peaked. In addition to temporal displacement, tiger-human coexistence was likely enhanced by abundant tiger prey and low levels of tiger poaching. Incorporating fine-scale spatial and temporal activity patterns into conservation plans can help address a major global challenge-meeting human needs while sustaining wildlife.

A FRAMEWORK FOR FINE-SCALE COMPUTATIONAL FLUID DYNAMICS AIR QUALITY MODELING AND ANALYSIS

EPA Science Inventory

This paper discusses a framework for fine-scale CFD modeling that may be developed to complement the present Community Multi-scale Air Quality (CMAQ) modeling system which itself is a computational fluid dynamics model. A goal of this presentation is to stimulate discussions on w...

Connotations of pixel-based scale effect in remote sensing and the modified fractal-based analysis method

NASA Astrophysics Data System (ADS)

Feng, Guixiang; Ming, Dongping; Wang, Min; Yang, Jianyu

2017-06-01

Scale problems are a major source of concern in the field of remote sensing. Since the remote sensing is a complex technology system, there is a lack of enough cognition on the connotation of scale and scale effect in remote sensing. Thus, this paper first introduces the connotations of pixel-based scale and summarizes the general understanding of pixel-based scale effect. Pixel-based scale effect analysis is essentially important for choosing the appropriate remote sensing data and the proper processing parameters. Fractal dimension is a useful measurement to analysis pixel-based scale. However in traditional fractal dimension calculation, the impact of spatial resolution is not considered, which leads that the scale effect change with spatial resolution can't be clearly reflected. Therefore, this paper proposes to use spatial resolution as the modified scale parameter of two fractal methods to further analyze the pixel-based scale effect. To verify the results of two modified methods (MFBM (Modified Windowed Fractal Brownian Motion Based on the Surface Area) and MDBM (Modified Windowed Double Blanket Method)); the existing scale effect analysis method (information entropy method) is used to evaluate. And six sub-regions of building areas and farmland areas were cut out from QuickBird images to be used as the experimental data. The results of the experiment show that both the fractal dimension and information entropy present the same trend with the decrease of spatial resolution, and some inflection points appear at the same feature scales. Further analysis shows that these feature scales (corresponding to the inflection points) are related to the actual sizes of the geo-object, which results in fewer mixed pixels in the image, and these inflection points are significantly indicative of the observed features. Therefore, the experiment results indicate that the modified fractal methods are effective to reflect the pixel-based scale effect existing in remote sensing data and it is helpful to analyze the observation scale from different aspects. This research will ultimately benefit for remote sensing data selection and application.

A diffuse radar scattering model from Martian surface rocks

NASA Technical Reports Server (NTRS)

Calvin, W. M.; Jakosky, B. M.; Christensen, P. R.

1987-01-01

Remote sensing of Mars has been done with a variety of instrumentation at various wavelengths. Many of these data sets can be reconciled with a surface model of bonded fines (or duricrust) which varies widely across the surface and a surface rock distribution which varies less so. A surface rock distribution map from -60 to +60 deg latitude has been generated by Christensen. Our objective is to model the diffuse component of radar reflection based on this surface distribution of rocks. The diffuse, rather than specular, scattering is modeled because the diffuse component arises due to scattering from rocks with sizes on the order of the wavelength of the radar beam. Scattering for radio waves of 12.5 cm is then indicative of the meter scale and smaller structure of the surface. The specular term is indicative of large scale surface undulations and should not be causally related to other surface physical properties. A simplified model of diffuse scattering is described along with two rock distribution models. The results of applying the models to a planet of uniform fractional rock coverage with values ranging from 5 to 20% are discussed.

High resolution mapping of riffle-pool dynamics based on ADCP and close-range remote sensing data

NASA Astrophysics Data System (ADS)

Salmela, Jouni; Kasvi, Elina; Alho, Petteri

2017-04-01

Present development of mobile laser scanning (MLS) and close-range photogrammetry with unmanned aerial vehicle (UAV) enable us to create seamless digital elevation models (DEMs) of the riverine environment. Remote-controlled flow measurement platforms have also improved spatio-temporal resolution of the flow field data. In this study, acoustic Doppler current profiler (ADCP) attached to remote-controlled mini-boat, UAV-based bathymetry and MLS techniques were utilized to create the high-resolution DEMs of the river channel. These high-resolution measurements can be used in many fluvial applications such as computational fluid dynamics, channel change detection, habitat mapping or hydro-electric power plant planning. In this study we aim: 1) to analyze morphological changes of river channel especially riffle and pool formations based on fine-scale DEMs and ADCP measurements, 2) to analyze flow fields and their effect on morphological changes. The interest was mainly focused on reach-scale riffle-pool dynamics within two-year period of 2013 and 2014. The study was performed in sub-arctic meandering Pulmankijoki River located in Northern Finland. The river itself has shallow and clear water and sandy bed sediment. Discharge remains typically below 10 m3s-1 most of the year but during snow melt period in spring the discharge may exceed 70 m3s-1. We compared DEMs and ADCP measurements to understand both magnitude and spatio-temporal change of the river bed. Models were accurate enough to study bed form changes and locations and persistence of riffles and pools. We analyzed their locations with relation to flow during the peak and low discharge. Our demonstrated method has improved significantly spatio-temporal resolution of riverine DEMs compared to other cross-sectional and photogrammetry based models. Together with flow field measurements we gained better understanding of riverbed-water interaction

Cyberinfrastructure for remote environmental observatories: a model homogeneous sensor network in the Great Basin, USA

NASA Astrophysics Data System (ADS)

Strachan, Scotty; Slater, David; Fritzinger, Eric; Lyles, Bradley; Kent, Graham; Smith, Kenneth; Dascalu, Sergiu; Harris, Frederick

2017-04-01

Sensor-based data collection has changed the potential scale and resolution of in-situ environmental studies by orders of magnitude, increasing expertise and management requirements accordingly. Cost-effective management of these observing systems is possible by leveraging cyberinfrastructure resources. Presented is a case study environmental observation network in the Great Basin region, USA, the Nevada Climate-ecohydrological Assessment Network (NevCAN). NevCAN stretches hundreds of kilometers across several mountain ranges and monitors climate and ecohydrological conditions from low desert (900 m ASL) to high subalpine treeline (3360 m ASL) down to 1-minute timescales. The network has been operating continuously since 2010, collecting billions of sensor data points and millions of camera images that record hourly conditions at each site, despite requiring relatively low annual maintenance expenditure. These data have provided unique insight into fine-scale processes across mountain gradients, which is crucial scientific information for a water-scarce region. The key to maintaining data continuity for these remotely-located study sites has been use of uniform data transport and management systems, coupled with high-reliability power system designs. Enabling non-proprietary digital communication paths to all study sites and sensors allows the research team to acquire data in near-real-time, troubleshoot problems, and diversify sensor hardware. A wide-area network design based on common Internet Protocols (IP) has been extended into each study site, providing production bandwidth of between 2 Mbps and 60 Mbps, depending on local conditions. The network architecture and site-level support systems (such as power generation) have been implemented with the core objectives of capacity, redundancy, and modularity. NevCAN demonstrates that by following simple but uniform "best practices", the next generation of regionally-specific environmental observatories can evolve to provide dramatically improved levels of scientific and hazard monitoring that span complex topographies and remote geography.

SPARTAN: a global network to evaluate and enhance satellite-based estimates of ground-level particulate matter for global health applications

NASA Astrophysics Data System (ADS)

Snider, G.; Weagle, C. L.; Martin, R. V.; van Donkelaar, A.; Conrad, K.; Cunningham, D.; Gordon, C.; Zwicker, M.; Akoshile, C.; Artaxo, P.; Anh, N. X.; Brook, J.; Dong, J.; Garland, R. M.; Greenwald, R.; Griffith, D.; He, K.; Holben, B. N.; Kahn, R.; Koren, I.; Lagrosas, N.; Lestari, P.; Ma, Z.; Vanderlei Martins, J.; Quel, E. J.; Rudich, Y.; Salam, A.; Tripathi, S. N.; Yu, C.; Zhang, Q.; Zhang, Y.; Brauer, M.; Cohen, A.; Gibson, M. D.; Liu, Y.

2015-01-01

Ground-based observations have insufficient spatial coverage to assess long-term human exposure to fine particulate matter (PM2.5) at the global scale. Satellite remote sensing offers a promising approach to provide information on both short- and long-term exposure to PM2.5 at local-to-global scales, but there are limitations and outstanding questions about the accuracy and precision with which ground-level aerosol mass concentrations can be inferred from satellite remote sensing alone. A key source of uncertainty is the global distribution of the relationship between annual average PM2.5 and discontinuous satellite observations of columnar aerosol optical depth (AOD). We have initiated a global network of ground-level monitoring stations designed to evaluate and enhance satellite remote sensing estimates for application in health-effects research and risk assessment. This Surface PARTiculate mAtter Network (SPARTAN) includes a global federation of ground-level monitors of hourly PM2.5 situated primarily in highly populated regions and collocated with existing ground-based sun photometers that measure AOD. The instruments, a three-wavelength nephelometer and impaction filter sampler for both PM2.5 and PM10, are highly autonomous. Hourly PM2.5 concentrations are inferred from the combination of weighed filters and nephelometer data. Data from existing networks were used to develop and evaluate network sampling characteristics. SPARTAN filters are analyzed for mass, black carbon, water-soluble ions, and metals. These measurements provide, in a variety of regions around the world, the key data required to evaluate and enhance satellite-based PM2.5 estimates used for assessing the health effects of aerosols. Mean PM2.5 concentrations across sites vary by more than 1 order of magnitude. Our initial measurements indicate that the ratio of AOD to ground-level PM2.5 is driven temporally and spatially by the vertical profile in aerosol scattering. Spatially this ratio is also strongly influenced by the mass scattering efficiency.

Scaling up functional traits for ecosystem services with remote sensing: concepts and methods.

PubMed

Abelleira Martínez, Oscar J; Fremier, Alexander K; Günter, Sven; Ramos Bendaña, Zayra; Vierling, Lee; Galbraith, Sara M; Bosque-Pérez, Nilsa A; Ordoñez, Jenny C

2016-07-01

Ecosystem service-based management requires an accurate understanding of how human modification influences ecosystem processes and these relationships are most accurate when based on functional traits. Although trait variation is typically sampled at local scales, remote sensing methods can facilitate scaling up trait variation to regional scales needed for ecosystem service management. We review concepts and methods for scaling up plant and animal functional traits from local to regional spatial scales with the goal of assessing impacts of human modification on ecosystem processes and services. We focus our objectives on considerations and approaches for (1) conducting local plot-level sampling of trait variation and (2) scaling up trait variation to regional spatial scales using remotely sensed data. We show that sampling methods for scaling up traits need to account for the modification of trait variation due to land cover change and species introductions. Sampling intraspecific variation, stratification by land cover type or landscape context, or inference of traits from published sources may be necessary depending on the traits of interest. Passive and active remote sensing are useful for mapping plant phenological, chemical, and structural traits. Combining these methods can significantly improve their capacity for mapping plant trait variation. These methods can also be used to map landscape and vegetation structure in order to infer animal trait variation. Due to high context dependency, relationships between trait variation and remotely sensed data are not directly transferable across regions. We end our review with a brief synthesis of issues to consider and outlook for the development of these approaches. Research that relates typical functional trait metrics, such as the community-weighted mean, with remote sensing data and that relates variation in traits that cannot be remotely sensed to other proxies is needed. Our review narrows the gap between functional trait and remote sensing methods for ecosystem service management.

Breed locally, disperse globally: Fine-scale genetic structure despite landscape-scale panmixia in a fire-specialist

Treesearch

Jennifer C. Pierson; Fred W. Allendorf; Pierre Drapeau; Michael K. Schwartz

2013-01-01

An exciting advance in the understanding of metapopulation dynamics has been the investigation of how populations respond to ephemeral patches that go 'extinct' during the lifetime of an individual. Previous research has shown that this scenario leads to genetic homogenization across large spatial scales. However, little is known about fine-scale genetic...

Fine-Scale Population Estimation by 3D Reconstruction of Urban Residential Buildings

PubMed Central

Wang, Shixin; Tian, Ye; Zhou, Yi; Liu, Wenliang; Lin, Chenxi

2016-01-01

Fine-scale population estimation is essential in emergency response and epidemiological applications as well as urban planning and management. However, representing populations in heterogeneous urban regions with a finer resolution is a challenge. This study aims to obtain fine-scale population distribution based on 3D reconstruction of urban residential buildings with morphological operations using optical high-resolution (HR) images from the Chinese No. 3 Resources Satellite (ZY-3). Specifically, the research area was first divided into three categories when dasymetric mapping was taken into consideration. The results demonstrate that the morphological building index (MBI) yielded better results than built-up presence index (PanTex) in building detection, and the morphological shadow index (MSI) outperformed color invariant indices (CIIT) in shadow extraction and height retrieval. Building extraction and height retrieval were then combined to reconstruct 3D models and to estimate population. Final results show that this approach is effective in fine-scale population estimation, with a mean relative error of 16.46% and an overall Relative Total Absolute Error (RATE) of 0.158. This study gives significant insights into fine-scale population estimation in complicated urban landscapes, when detailed 3D information of buildings is unavailable. PMID:27775670

Quantification and site-specification of the support practice factor when mapping soil erosion risk associated with olive plantations in the Mediterranean island of Crete.

PubMed

Karydas, Christos G; Sekuloska, Tijana; Silleos, Georgios N

2009-02-01

Due to inappropriate agricultural management practices, soil erosion is becoming one of the most dangerous forms of soil degradation in many olive farming areas in the Mediterranean region, leading to significant decrease of soil fertility and yield. In order to prevent further soil degradation, proper measures are necessary to be locally implemented. In this perspective, an increase in the spatial accuracy of remote sensing datasets and advanced image analysis are significant tools necessary and efficient for mapping soil erosion risk on a fine scale. In this study, the Revised Universal Soil Loss Equation (RUSLE) was implemented in the spatial domain using GIS, while a very high resolution satellite image, namely a QuickBird image, was used for deriving cover management (C) and support practice (P) factors, in order to map the risk of soil erosion in Kolymvari, a typical olive farming area in the island of Crete, Greece. The results comprised a risk map of soil erosion when P factor was taken uniform (conventional approach) and a risk map when P factor was quantified site-specifically using object-oriented image analysis. The results showed that the QuickBird image was necessary in order to achieve site-specificity of the P factor and therefore to support fine scale mapping of soil erosion risk in an olive cultivation area, such as the one of Kolymvari in Crete. Increasing the accuracy of the QB image classification will further improve the resulted soil erosion mapping.

Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis

USGS Publications Warehouse

Flint, Lorraine E.; Flint, Alan L.

2012-01-01

The methodology, which includes a sequence of rigorous analyses and calculations, is intended to reduce the addition of uncertainty to the climate data as a result of the downscaling while providing the fine-scale climate information necessary for ecological analyses. It results in new but consistent data sets for the US at 4 km, the southwest US at 270 m, and California at 90 m and illustrates the utility of fine-scale downscaling to analyses of ecological processes influenced by topographic complexity.

MFD - Documentation of small fine arm in stowed position

NASA Image and Video Library

1997-08-12

S85-E-5044 (12 August 1997) --- View of the payload bay of the Earth-orbiting Space Shuttle Discovery looking toward the shuttle's vertical stabilizer with clouds in the background. Easily recognized is the Manipulator Flight Demonstration (MFD), which is sponsored by Japan's National Space Development Agency (NASDA). MFD will evaluate the use of the Small Fine Arm (SFA) that is planned to be part of the future Japanese Experiment Module's Remote Manipulator System (RMS) on the International Space Station (ISS). The photograph was taken with the Electronic Still Camera (ESC).

Lack of sex-biased dispersal promotes fine-scale genetic structure in alpine ungulates

Treesearch

Gretchen H. Roffler; Sandra L. Talbot; Gordon Luikart; George K. Sage; Kristy L. Pilgrim; Layne G. Adams; Michael K. Schwartz

2014-01-01

Identifying patterns of fine-scale genetic structure in natural populations can advance understanding of critical ecological processes such as dispersal and gene flow across heterogeneous landscapes. Alpine ungulates generally exhibit high levels of genetic structure due to female philopatry and patchy configuration of mountain habitats. We assessed the spatial scale...

Fine-scale habitat characteristics related to occupancy of the Yosemite Toad, Anaxyrus canorus

Treesearch

Christina T. Liang; Robert L. Grasso; Julie J. Nelson-Paul; Kim E. Vincent; Amy J. Lind

2017-01-01

Fine-scale habitat information can provide insight into species occupancy and persistence that is not apparent at the landscape-scale. Such information is particularly important for rare species that are experiencing population declines, such as the threatened Yosemite Toad (Anaxyrus canorus). Our study examined differences in physical...

RESOLVING FINE SCALE IN AIR TOXICS MODELING AND THE IMPORTANCE OF ITS SUB-GRID VARIABILITY FOR EXPOSURE ESTIMATES

EPA Science Inventory

This presentation explains the importance of the fine-scale features for air toxics exposure modeling. The paper presents a new approach to combine local-scale and regional model results for the National Air Toxic Assessment. The technique has been evaluated with a chemical tra...

Aerosol Measurements in the Atmospheric Surface Layer at L'Aquila, Italy: Focus on Biogenic Primary Particles

NASA Astrophysics Data System (ADS)

Pitari, Giovanni; Coppari, Eleonora; De Luca, Natalia; Di Carlo, Piero; Pace, Loretta

2014-09-01

Two year measurements of aerosol concentration and size distribution (0.25 μm < d < 30 μm) in the atmospheric surface layer, collected in L'Aquila (Italy) with an optical particle counter, are reported and analysed for the different modes of the particle size distribution. A different seasonal behaviour is shown for fine mode aerosols (largely produced by anthropogenic combustion), coarse mode and large-sized aerosols, whose abundance is regulated not only by anthropogenic local production, but also by remote natural sources (via large scale atmospheric transport) and by local sources of primary biogenic aerosols. The observed total abundance of large particles with diameter larger than 10 μm is compared with a statistical counting of primary biogenic particles, made with an independent technique. Results of these two observational approaches are analysed and compared to each other, with the help of a box model driven by observed meteorological parameters and validated with measurements of fine and coarse mode aerosols and of an atmospheric primary pollutant of anthropogenic origin (NOx). Except in winter months, primary biogenic particles in the L'Aquila measurement site are shown to dominate the atmospheric boundary layer population of large aerosol particles with diameter larger than 10 μm (about 80 % of the total during summer months), with a pronounced seasonal cycle, contrary to fine mode aerosols of anthropogenic origin. In order to explain these findings, the main mechanisms controlling the abundance and variability of particulate matter tracers in the atmospheric surface layer are analysed with the numerical box-model.

Evaluation of Rock Powdering Methods to Obtain Fine-grained Samples for CHEMIN, a Combined XRD/XRF Instrument

NASA Technical Reports Server (NTRS)

Chipera, S. J.; Vaniman, D. T.; Bish, D. L.; Sarrazin, P.; Feldman, S.; Blake, D. F.; Bearman, G.; Bar-Cohen, Y.

2004-01-01

A miniature XRD/XRF (X-ray diffraction / X-ray fluorescence) instrument, CHEMIN, is currently being developed for definitive mineralogic analysis of soils and rocks on Mars. One of the technical issues that must be addressed to enable remote XRD analysis is how best to obtain a representative sample powder for analysis. For powder XRD analyses, it is beneficial to have a fine-grained sample to reduce preferred orientation effects and to provide a statistically significant number of crystallites to the X-ray beam. Although a two-dimensional detector as used in the CHEMIN instrument will produce good results even with poorly prepared powder, the quality of the data will improve and the time required for data collection will be reduced if the sample is fine-grained and randomly oriented. A variety of methods have been proposed for XRD sample preparation. Chipera et al. presented grain size distributions and XRD results from powders generated with an Ultrasonic/Sonic Driller/Corer (USDC) currently being developed at JPL. The USDC was shown to be an effective instrument for sampling rock to produce powder suitable for XRD. In this paper, we compare powder prepared using the USDC with powder obtained with a miniaturized rock crusher developed at JPL and with powder obtained with a rotary tungsten carbide bit to powders obtained from a laboratory bench-scale Retsch mill (provides benchmark mineralogical data). These comparisons will allow assessment of the suitability of these methods for analysis by an XRD/XRF instrument such as CHEMIN.

Remote monitoring of left ventricular assist device parameters after HeartAssist-5 implantation.

PubMed

Pektok, Erman; Demirozu, Zumrut Tuba; Arat, Nurcan; Yildiz, Omer; Oklu, Emine; Eker, Deniz; Ece, Ferah; Ciftci, Cavlan; Yazicioglu, Nuran; Bayindir, Osman; Kucukaksu, Deniz Suha

2013-09-01

Although several left ventricular assist devices (LVADs) have been used widely, remote monitoring of LVAD parameters has been available only recently. We present our remote monitoring experience with an axial-flow LVAD (HeartAssist-5, MicroMed Cardiovascular, Inc., Houston, TX, USA). Five consecutive patients who were implanted a HeartAssist-5 LVAD because of end-stage heart failure due to ischemic (n=4) or idiopathic (n=1) cardiomyopathy, and discharged from hospital between December 2011 and January 2013 were analyzed. The data (pump speed, pump flow, power consumption) obtained from clinical visits and remote monitoring were studied. During a median follow-up of 253 (range: 80-394) days, fine tuning of LVADs was performed at clinical visits. All patients are doing well and are in New York Heart Association Class-I/II. A total of 39 alarms were received from three patients. One patient was hospitalized for suspected thrombosis and was subjected to physical examinations as well as laboratory and echocardiographic evaluations; however, no evidence of thrombus washout or pump thrombus was found. The patient was treated conservatively. Remaining alarms were due to insufficient water intake and were resolved by increased water consumption at night and summer times, and fine tuning of pump speed. No alarms were received from the remaining two patients. We believe that remote monitoring is a useful technology for early detection and treatment of serious problems occurring out of hospital thereby improving patient care. Future developments may ease troubleshooting, provide more data from the patient and the pump, and eventually increase physician and patient satisfaction. Despite all potential clinical benefits, remote monitoring should be taken as a supplement to rather than a substitute for routine clinical visits for patient follow-up. © 2013 Wiley Periodicals, Inc. and International Center for Artificial Organs and Transplantation.

Development of fine-resolution analyses and expanded large-scale forcing properties. Part I: Methodology and evaluation

DOE PAGES

Li, Zhijin; Vogelmann, Andrew M.; Feng, Sha; ...

2015-01-20

We produce fine-resolution, three-dimensional fields of meteorological and other variables for the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Southern Great Plains site. The Community Gridpoint Statistical Interpolation system is implemented in a multiscale data assimilation (MS-DA) framework that is used within the Weather Research and Forecasting model at a cloud-resolving resolution of 2 km. The MS-DA algorithm uses existing reanalysis products and constrains fine-scale atmospheric properties by assimilating high-resolution observations. A set of experiments show that the data assimilation analysis realistically reproduces the intensity, structure, and time evolution of clouds and precipitation associated with a mesoscale convective system.more » Evaluations also show that the large-scale forcing derived from the fine-resolution analysis has an overall accuracy comparable to the existing ARM operational product. For enhanced applications, the fine-resolution fields are used to characterize the contribution of subgrid variability to the large-scale forcing and to derive hydrometeor forcing, which are presented in companion papers.« less

[Modeling continuous scaling of NDVI based on fractal theory].

PubMed

Luan, Hai-Jun; Tian, Qing-Jiu; Yu, Tao; Hu, Xin-Li; Huang, Yan; Du, Ling-Tong; Zhao, Li-Min; Wei, Xi; Han, Jie; Zhang, Zhou-Wei; Li, Shao-Peng

2013-07-01

Scale effect was one of the very important scientific problems of remote sensing. The scale effect of quantitative remote sensing can be used to study retrievals' relationship between different-resolution images, and its research became an effective way to confront the challenges, such as validation of quantitative remote sensing products et al. Traditional up-scaling methods cannot describe scale changing features of retrievals on entire series of scales; meanwhile, they are faced with serious parameters correction issues because of imaging parameters' variation of different sensors, such as geometrical correction, spectral correction, etc. Utilizing single sensor image, fractal methodology was utilized to solve these problems. Taking NDVI (computed by land surface radiance) as example and based on Enhanced Thematic Mapper Plus (ETM+) image, a scheme was proposed to model continuous scaling of retrievals. Then the experimental results indicated that: (a) For NDVI, scale effect existed, and it could be described by fractal model of continuous scaling; (2) The fractal method was suitable for validation of NDVI. All of these proved that fractal was an effective methodology of studying scaling of quantitative remote sensing.

Variation in soil carbon dioxide efflux at two spatial scales in a topographically complex boreal forest

USGS Publications Warehouse

Kelsey, Katharine C.; Wickland, Kimberly P.; Striegl, Robert G.; Neff, Jason C.

2012-01-01

Carbon dynamics of high-latitude regions are an important and highly uncertain component of global carbon budgets, and efforts to constrain estimates of soil-atmosphere carbon exchange in these regions are contingent on accurate representations of spatial and temporal variability in carbon fluxes. This study explores spatial and temporal variability in soilatmosphere carbon dynamics at both fine and coarse spatial scales in a high-elevation, permafrost-dominated boreal black spruce forest. We evaluate the importance of landscape-level investigations of soil-atmosphere carbon dynamics by characterizing seasonal trends in soil-atmosphere carbon exchange, describing soil temperature-moisture-respiration relations, and quantifying temporal and spatial variability at two spatial scales: the plot scale (0–5 m) and the landscape scale (500–1000 m). Plot-scale spatial variability (average variation on a given measurement day) in soil CO2 efflux ranged from a coefficient of variation (CV) of 0.25 to 0.69, and plot-scale temporal variability (average variation of plots across measurement days) in efflux ranged from a CV of 0.19 to 0.36. Landscape-scale spatial and temporal variability in efflux was represented by a CV of 0.40 and 0.31, respectively, indicating that plot-scale spatial variability in soil respiration is as great as landscape-scale spatial variability at this site. While soil respiration was related to soil temperature at both the plot- and landscape scale, landscape-level descriptions of soil moisture were necessary to define soil respiration-moisture relations. Soil moisture variability was also integral to explaining temporal variability in soil respiration. Our results have important implications for research efforts in high-latitude regions where remote study sites make landscape-scale field campaigns challenging.

Genome-Wide Fine-Scale Recombination Rate Variation in Drosophila melanogaster

PubMed Central

Song, Yun S.

2012-01-01

Estimating fine-scale recombination maps of Drosophila from population genomic data is a challenging problem, in particular because of the high background recombination rate. In this paper, a new computational method is developed to address this challenge. Through an extensive simulation study, it is demonstrated that the method allows more accurate inference, and exhibits greater robustness to the effects of natural selection and noise, compared to a well-used previous method developed for studying fine-scale recombination rate variation in the human genome. As an application, a genome-wide analysis of genetic variation data is performed for two Drosophila melanogaster populations, one from North America (Raleigh, USA) and the other from Africa (Gikongoro, Rwanda). It is shown that fine-scale recombination rate variation is widespread throughout the D. melanogaster genome, across all chromosomes and in both populations. At the fine-scale, a conservative, systematic search for evidence of recombination hotspots suggests the existence of a handful of putative hotspots each with at least a tenfold increase in intensity over the background rate. A wavelet analysis is carried out to compare the estimated recombination maps in the two populations and to quantify the extent to which recombination rates are conserved. In general, similarity is observed at very broad scales, but substantial differences are seen at fine scales. The average recombination rate of the X chromosome appears to be higher than that of the autosomes in both populations, and this pattern is much more pronounced in the African population than the North American population. The correlation between various genomic features—including recombination rates, diversity, divergence, GC content, gene content, and sequence quality—is examined using the wavelet analysis, and it is shown that the most notable difference between D. melanogaster and humans is in the correlation between recombination and diversity. PMID:23284288

[A review of atmospheric aerosol research by using polarization remote sensing].

PubMed

Guo, Hong; Gu, Xing-Fa; Xie, Dong-Hai; Yu, Tao; Meng, Qing-Yan

2014-07-01

In the present paper, aerosol research by using polarization remote sensing in last two decades (1993-2013) was reviewed, including aerosol researches based on POLDER/PARASOL, APS(Aerosol Polarimetry Sensor), Polarized Airborne camera and Ground-based measurements. We emphasize the following three aspects: (1) The retrieval algorithms developed for land and marine aerosol by using POLDER/PARASOL; The validation and application of POLDER/PARASOL AOD, and cross-comparison with AOD of other satellites, such as MODIS AOD. (2) The retrieval algorithms developed for land and marine aerosol by using MICROPOL and RSP/APS. We also introduce the new progress in aerosol research based on The Directional Polarimetric Camera (DPC), which was produced by Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences (CAS). (3) The aerosol retrieval algorithms by using measurements from ground-based instruments, such as CE318-2 and CE318-DP. The retrieval results from spaceborne sensors, airborne camera and ground-based measurements include total AOD, fine-mode AOD, coarse-mode AOD, size distribution, particle shape, complex refractive indices, single scattering albedo, scattering phase function, polarization phase function and AOD above cloud. Finally, based on the research, the authors present the problems and prospects of atmospheric aerosol research by using polarization remote sensing, and provide a valuable reference for the future studies of atmospheric aerosol.

Fire Radiative Power (FRP)-based Emission Factors of PM2.5, CO and NOX for Remote Sensing of Biomass Burning Emissions

NASA Astrophysics Data System (ADS)

Karandana Gamalathge, T. D.; Chen, L. W. A.

2015-12-01

Large-scale biomass burning such as forest fires represents an important and yet uncertain source of air pollutants and greenhouse gases on a global scale. Due to the highly accidental nature of forest fires, satellite remote sensing could be a promising method to develop regional and global fire emission inventories on a real-time basis. Reliable fire radiative power (FRP)-based fuel consumption and emission factors are critical in this approach. In an attempt to obtain the information, laboratory combustion experiments were conducted to simultaneously monitor FRP, fuel consumption, and emissions of fine particulate matter (PM2.5), carbon monoxide (CO), and reactive nitrogen oxides (NO and NO2). FRP were quantified using temperature-resolved values from a thermal imager instead of conventionally used average temperature, as the former provides more realistic estimates. For dry Ponderosa pine branches, a common fuel in the Sierra Nevada, a strong correlation (r2 ~ 0.8) between FRP and the mass reduction rate (MRR) was found. This led to a radiative energy yield (REY) of 8.5 ± 1.2 MJ/kg, assuming blackbody radiation and a flame emissivity of 0.5. Mass-based emission factors were determined with the carbon balance approach. Considering the ratio of mass-based emission factors and the REY, FRP-based emission factors: PM2.5: 11 g/MJ, CO: 8.0 g/MJ, NO: 0.33 g/MJ, and NO2: 0.07 g/MJ were quantified. The application of this approach to other fuel types and uncertainties in the measurements will be discussed.

Long-term GPS tracking of ocean sunfish Mola mola offers a new direction in fish monitoring.

PubMed

Sims, David W; Queiroz, Nuno; Humphries, Nicolas E; Lima, Fernando P; Hays, Graeme C

2009-10-09

Satellite tracking of large pelagic fish provides insights on free-ranging behaviour, distributions and population structuring. Up to now, such fish have been tracked remotely using two principal methods: direct positioning of transmitters by Argos polar-orbiting satellites, and satellite relay of tag-derived light-level data for post hoc track reconstruction. Error fields associated with positions determined by these methods range from hundreds of metres to hundreds of kilometres. However, low spatial accuracy of tracks masks important details, such as foraging patterns. Here we use a fast-acquisition global positioning system (Fastloc GPS) tag with remote data retrieval to track long-term movements, in near real time and position accuracy of <70 m, of the world's largest bony fish, the ocean sunfish Mola mola. Search-like movements occurred over at least three distinct spatial scales. At fine scales, sunfish spent longer in highly localised areas with faster, straighter excursions between them. These 'stopovers' during long-distance movement appear consistent with finding and exploiting food patches. This demonstrates the feasibility of GPS tagging to provide tracks of unparalleled accuracy for monitoring movements of large pelagic fish, and with nearly four times as many locations obtained by the GPS tag than by a conventional Argos transmitter. The results signal the potential of GPS-tagged pelagic fish that surface regularly to be detectors of resource 'hotspots' in the blue ocean and provides a new capability for understanding large pelagic fish behaviour and habitat use that is relevant to ocean management and species conservation.

Long-Term GPS Tracking of Ocean Sunfish Mola mola Offers a New Direction in Fish Monitoring

PubMed Central

Sims, David W.; Queiroz, Nuno; Humphries, Nicolas E.; Lima, Fernando P.; Hays, Graeme C.

2009-01-01

Satellite tracking of large pelagic fish provides insights on free-ranging behaviour, distributions and population structuring. Up to now, such fish have been tracked remotely using two principal methods: direct positioning of transmitters by Argos polar-orbiting satellites, and satellite relay of tag-derived light-level data for post hoc track reconstruction. Error fields associated with positions determined by these methods range from hundreds of metres to hundreds of kilometres. However, low spatial accuracy of tracks masks important details, such as foraging patterns. Here we use a fast-acquisition global positioning system (Fastloc GPS) tag with remote data retrieval to track long-term movements, in near real time and position accuracy of <70 m, of the world's largest bony fish, the ocean sunfish Mola mola. Search-like movements occurred over at least three distinct spatial scales. At fine scales, sunfish spent longer in highly localised areas with faster, straighter excursions between them. These ‘stopovers’ during long-distance movement appear consistent with finding and exploiting food patches. This demonstrates the feasibility of GPS tagging to provide tracks of unparalleled accuracy for monitoring movements of large pelagic fish, and with nearly four times as many locations obtained by the GPS tag than by a conventional Argos transmitter. The results signal the potential of GPS-tagged pelagic fish that surface regularly to be detectors of resource ‘hotspots’ in the blue ocean and provides a new capability for understanding large pelagic fish behaviour and habitat use that is relevant to ocean management and species conservation. PMID:19816576

Coexistence between wildlife and humans at fine spatial scales

PubMed Central

Carter, Neil H.; Shrestha, Binoj K.; Karki, Jhamak B.; Pradhan, Narendra Man Babu; Liu, Jianguo

2012-01-01

Many wildlife species face imminent extinction because of human impacts, and therefore, a prevailing belief is that some wildlife species, particularly large carnivores and ungulates, cannot coexist with people at fine spatial scales (i.e., cannot regularly use the exact same point locations). This belief provides rationale for various conservation programs, such as resettling human communities outside protected areas. However, quantitative information on the capacity and mechanisms for wildlife to coexist with humans at fine spatial scales is scarce. Such information is vital, because the world is becoming increasingly crowded. Here, we provide empirical information about the capacity and mechanisms for tigers (a globally endangered species) to coexist with humans at fine spatial scales inside and outside Nepal’s Chitwan National Park, a flagship protected area for imperiled wildlife. Information obtained from field cameras in 2010 and 2011 indicated that human presence (i.e., people on foot and vehicles) was ubiquitous and abundant throughout the study site; however, tiger density was also high. Surprisingly, even at a fine spatial scale (i.e., camera locations), tigers spatially overlapped with people on foot and vehicles in both years. However, in both years, tigers offset their temporal activity patterns to be much less active during the day when human activity peaked. In addition to temporal displacement, tiger–human coexistence was likely enhanced by abundant tiger prey and low levels of tiger poaching. Incorporating fine-scale spatial and temporal activity patterns into conservation plans can help address a major global challenge—meeting human needs while sustaining wildlife. PMID:22949642

KSC-2011-4148

NASA Image and Video Library

2011-05-27

CAPE CANAVERAL, Fla. -- While an event judge looks on, university students monitor their team's remote controlled or autonomous excavator, called a lunabot, as it is maneuvered in a "sand box" of ultra-fine simulated lunar soil during NASA's second annual Lunabotics Mining Competition at the Kennedy Space Center Visitor Complex in Florida. Thirty-six teams of undergraduate and graduate students from the United States, Bangladesh, Canada, Colombia and India will participate in NASA's Lunabotics Mining Competition May 26 - 28 at the agency's Kennedy Space Center in Florida. The competition is designed to engage and retain students in science, technology, engineering and mathematics (STEM). Teams will maneuver their remote controlled or autonomous excavators, called lunabots, in about 60 tons of ultra-fine simulated lunar soil, called BP-1. The competition is an Exploration Systems Mission Directorate project managed by Kennedy's Education Division. The event also provides a competitive environment that could result in innovative ideas and solutions for NASA's future excavation of the moon. Photo credit: NASA/Jack Pfaller

Discrimination of active and inactive sand from remote sensing - Kelso dunes, Mojave Desert, California

NASA Technical Reports Server (NTRS)

Paisley, Elizabeth C. I.; Lancaster, Nicholas; Gaddis, Lisa R.; Greeley, Ronald

1991-01-01

Landsat TM images, field data, and laboratoray reflectance spectra were examined for the Kelso dunes, Mojave Desert, California to assess the use of visible and near-infrared (VNIR) remote sensing data to discriminate aeolian sand populations on the basis of spectral brightness. Results show that areas of inactive sand have a larger percentage of dark, fine-grained materials compared to those composed of active sand, which contain less dark fines and a higher percentage of quartz sand-size grains. Both areas are spectrally distinct in the VNIR, suggesting that VNIR spectral data can be used to discriminate active and inactive sand populations in the Mojave Desert. Analysis of laboratory spectra was complicated by the presence of magnetite in the active sands, which decreases their laboratory reflectance values to those of inactive sands. For this application, comparison of TM and laboratory spectra suggests that less than 35 percent vegetation cover does not influence the TM spectra.

NPP-VIIRS DNB-based reallocating subpopulations to mercury in Urumqi city cluster, central Asia

NASA Astrophysics Data System (ADS)

Zhou, X.; Feng, X. B.; Dai, W.; Li, P.; Ju, C. Y.; Bao, Z. D.; Han, Y. L.

2017-02-01

Accurate and update assignment of population-related environmental matters onto fine grid cells in oasis cities of arid areas remains challenging. We present the approach based on Suomi National Polar-orbiting Partnership (S-NPP) -Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) to reallocate population onto a regular finer surface. The number of potential population to the mercury were reallocated onto 0.1x0.1 km reference grid in Urumqi city cluster of China’s Xinjiang, central Asia. The result of Monte Carlo modelling indicated that the range of 0.5 to 2.4 million people was reliable. The study highlights that the NPP-VIIRS DNB-based multi-layered, dasymetric, spatial method enhances our abilities to remotely estimate the distribution and size of target population at the street-level scale and has the potential to transform control strategies for epidemiology, public policy and other socioeconomic fields.

Precipitation in a boiling soup: is microphysics driving the statistical properties of intense turbulent convection?

NASA Astrophysics Data System (ADS)

Parodi, A.; von Hardenberg, J.; Provenzale, A.

2012-04-01

Intense precipitation events are often associated with strong convective phenomena in the atmosphere. A deeper understanding of how microphysics affects the spatial and temporal variability of convective processes is relevant for many hydro-meteorological applications, such as the estimation of rainfall using remote sensing techniques and the ability to predict severe precipitation processes. In this paper, high-resolution simulations (0.1-1 km) of an atmosphere in radiative-convective equilibrium are performed using the Weather Research and Forecasting (WRF) model by prescribing different microphysical parameterizations. The dependence of fine-scale spatio-temporal properties of convective structures on microphysical details are investigated and the simulation results are compared with the known properties of radar maps of precipitation fields. We analyze and discuss similarities and differences and, based also on previous results on the dependence of precipitation statistics on the raindrop terminal velocity, try to draw some general inferences.

Multi-source remotely sensed data fusion for improving land cover classification

NASA Astrophysics Data System (ADS)

Chen, Bin; Huang, Bo; Xu, Bing

2017-02-01

Although many advances have been made in past decades, land cover classification of fine-resolution remotely sensed (RS) data integrating multiple temporal, angular, and spectral features remains limited, and the contribution of different RS features to land cover classification accuracy remains uncertain. We proposed to improve land cover classification accuracy by integrating multi-source RS features through data fusion. We further investigated the effect of different RS features on classification performance. The results of fusing Landsat-8 Operational Land Imager (OLI) data with Moderate Resolution Imaging Spectroradiometer (MODIS), China Environment 1A series (HJ-1A), and Advanced Spaceborne Thermal Emission and Reflection (ASTER) digital elevation model (DEM) data, showed that the fused data integrating temporal, spectral, angular, and topographic features achieved better land cover classification accuracy than the original RS data. Compared with the topographic feature, the temporal and angular features extracted from the fused data played more important roles in classification performance, especially those temporal features containing abundant vegetation growth information, which markedly increased the overall classification accuracy. In addition, the multispectral and hyperspectral fusion successfully discriminated detailed forest types. Our study provides a straightforward strategy for hierarchical land cover classification by making full use of available RS data. All of these methods and findings could be useful for land cover classification at both regional and global scales.

Restricted cross-scale habitat selection by American beavers.

PubMed

Francis, Robert A; Taylor, Jimmy D; Dibble, Eric; Strickland, Bronson; Petro, Vanessa M; Easterwood, Christine; Wang, Guiming

2017-12-01

Animal habitat selection, among other ecological phenomena, is spatially scale dependent. Habitat selection by American beavers Castor canadensis (hereafter, beaver) has been studied at singular spatial scales, but to date no research addresses multi-scale selection. Our objectives were to determine if beaver habitat selection was specialized to semiaquatic habitats and if variables explaining habitat selection are consistent between landscape and fine spatial scales. We built maximum entropy (MaxEnt) models to relate landscape-scale presence-only data to landscape variables, and used generalized linear mixed models to evaluate fine spatial scale habitat selection using global positioning system (GPS) relocation data. Explanatory variables between the landscape and fine spatial scale were compared for consistency. Our findings suggested that beaver habitat selection at coarse (study area) and fine (within home range) scales was congruent, and was influenced by increasing amounts of woody wetland edge density and shrub edge density, and decreasing amounts of open water edge density. Habitat suitability at the landscape scale also increased with decreasing amounts of grass frequency. As territorial, central-place foragers, beavers likely trade-off open water edge density (i.e., smaller non-forested wetlands or lodges closer to banks) for defense and shorter distances to forage and obtain construction material. Woody plants along edges and expanses of open water for predator avoidance may limit beaver fitness and subsequently determine beaver habitat selection.

Restricted cross-scale habitat selection by American beavers

PubMed Central

Taylor, Jimmy D; Dibble, Eric; Strickland, Bronson; Petro, Vanessa M; Easterwood, Christine; Wang, Guiming

2017-01-01

Abstract Animal habitat selection, among other ecological phenomena, is spatially scale dependent. Habitat selection by American beavers Castor canadensis (hereafter, beaver) has been studied at singular spatial scales, but to date no research addresses multi-scale selection. Our objectives were to determine if beaver habitat selection was specialized to semiaquatic habitats and if variables explaining habitat selection are consistent between landscape and fine spatial scales. We built maximum entropy (MaxEnt) models to relate landscape-scale presence-only data to landscape variables, and used generalized linear mixed models to evaluate fine spatial scale habitat selection using global positioning system (GPS) relocation data. Explanatory variables between the landscape and fine spatial scale were compared for consistency. Our findings suggested that beaver habitat selection at coarse (study area) and fine (within home range) scales was congruent, and was influenced by increasing amounts of woody wetland edge density and shrub edge density, and decreasing amounts of open water edge density. Habitat suitability at the landscape scale also increased with decreasing amounts of grass frequency. As territorial, central-place foragers, beavers likely trade-off open water edge density (i.e., smaller non-forested wetlands or lodges closer to banks) for defense and shorter distances to forage and obtain construction material. Woody plants along edges and expanses of open water for predator avoidance may limit beaver fitness and subsequently determine beaver habitat selection. PMID:29492032

Fine Scale Baleen Whale Behavior Observed Via Tagging Over Daily Time Scales

DTIC Science & Technology

2015-09-30

1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Fine Scale Baleen Whale Behavior Observed Via Tagging...followed over time scales of days from an oceanographic vessel so that environmental sampling can be conducted in proximity to the tagged whale ...characterize the relationship between diel variability in the foraging behavior of baleen whales (North Atlantic right whales and sei whales ) and the

Doubly stochastic Poisson process models for precipitation at fine time-scales

NASA Astrophysics Data System (ADS)

Ramesh, Nadarajah I.; Onof, Christian; Xie, Dichao

2012-09-01

This paper considers a class of stochastic point process models, based on doubly stochastic Poisson processes, in the modelling of rainfall. We examine the application of this class of models, a neglected alternative to the widely-known Poisson cluster models, in the analysis of fine time-scale rainfall intensity. These models are mainly used to analyse tipping-bucket raingauge data from a single site but an extension to multiple sites is illustrated which reveals the potential of this class of models to study the temporal and spatial variability of precipitation at fine time-scales.

Fine-Scale Spatial Variability of Pedestrian-Level Particulate Matters in Compact Urban Commercial Districts in Hong Kong

PubMed Central

Ng, Edward

2017-01-01

Particulate matters (PM) at the pedestrian level significantly raises the health impacts in the compact urban environment of Hong Kong. A detailed investigation of the fine-scale spatial variation of pedestrian-level PM is necessary to assess the health risk to pedestrians in the outdoor environment. However, the collection of PM data is difficult in the compact urban environment of Hong Kong due to the limited amount of roadside monitoring stations and the complicated urban context. In this study, we measured the fine-scale spatial variability of the PM in three of the most representative commercial districts of Hong Kong using a backpack outdoor environmental measuring unit. Based on the measurement data, 13 types of geospatial interpolation methods were examined for the spatial mapping of PM2.5 and PM10 with a group of building geometrical covariates. Geostatistical modelling was adopted as the basis of spatial interpolation of the PM. The results show that the original cokriging with the exponential kernel function provides the best performance in the PM mapping. Using the fine-scale building geometrical features as covariates slightly improves the interpolation performance. The study results also imply that the fine-scale, localized pollution emission sources heavily influence pedestrian exposure to PM. PMID:28869527

Post-LHC7 fine-tuning in the minimal supergravity/CMSSM model with a 125 GeV Higgs boson

NASA Astrophysics Data System (ADS)

Baer, Howard; Barger, Vernon; Huang, Peisi; Mickelson, Dan; Mustafayev, Azar; Tata, Xerxes

2013-02-01

The recent discovery of a 125 GeV Higgs-like resonance at LHC, coupled with the lack of evidence for weak scale supersymmetry (SUSY), has severely constrained SUSY models such as minimal supergravity (mSUGRA)/CMSSM. As LHC probes deeper into SUSY model parameter space, the little hierarchy problem—how to reconcile the Z and Higgs boson mass scale with the scale of SUSY breaking—will become increasingly exacerbated unless a sparticle signal is found. We evaluate two different measures of fine-tuning in the mSUGRA/CMSSM model. The more stringent of these, ΔHS, includes effects that arise from the high-scale origin of the mSUGRA parameters while the second measure, ΔEW, is determined only by weak scale parameters: hence, it is universal to any model with the same particle spectrum and couplings. Our results incorporate the latest constraints from LHC7 sparticle searches, LHCb limits from Bs→μ+μ- and also require a light Higgs scalar with mh˜123-127GeV. We present fine-tuning contours in the m0 vs m1/2 plane for several sets of A0 and tan⁡β values. We also present results for ΔHS and ΔEW from a scan over the entire viable model parameter space. We find a ΔHS≳103, or at best 0.1%, fine-tuning. For the less stringent electroweak fine-tuning, we find ΔEW≳102, or at best 1%, fine-tuning. Two benchmark points are presented that have the lowest values of ΔHS and ΔEW. Our results provide a quantitative measure for ascertaining whether or not the remaining mSUGRA/CMSSM model parameter space is excessively fine-tuned and so could provide impetus for considering alternative SUSY models.

[Combustion temperature measurement of pyrotechnic composition using remote sensing Fourier transform infrared spectrometry].

PubMed

Zhou, Xin-li; Li, Yan; Liu, Zu-liang; Zhu, Chang-jiang; Wang, Jun-de; Lu, Chun-xu

2002-10-01

In this paper, combustion characterization of pyrotechnic composition is investigated using a remote sensing Fourier transform infrared spectrometry. The emission spectra have been recorded between 4,700 and 740 cm-1 with a spectral resolution of 4 cm-1. The combustion temperature can be determined remotely from spectral line intensity distribution of the fine structure of the emission fundamental band of gaseous products such as HF. The relationship between combustion temperature and combustion time has been given. Results show that there is a violent mutative temperature field with bigger temperature gradient near combustion surface. It reveals that the method of temperature measurement using remote sensing FTIR for flame temperature of unstable, violent and short time combustion on real time is a rapid, accurate and sensitive technique without interference the flame temperature field. Potential prospects of temperature measurement, gas product concentration measurement and combustion mechanism are also revealed.

Hydrologic downscaling of soil moisture using global data without site-specific calibration

USDA-ARS?s Scientific Manuscript database

Numerous applications require fine-resolution (10-30 m) soil moisture patterns, but most satellite remote sensing and land-surface models provide coarse-resolution (9-60 km) soil moisture estimates. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales soil moistu...

ROADSIDE AMMONIA MEASUREMENTS USING OPTICAL REMOTE SENSING INSTRUMENTS

EPA Science Inventory

Fine particles less than 2.5 microns in diameter have been identified as a causal agent of excess mortality and other undesirable health impacts. A large part of these airborne particles, generally more than one-half, are formed in the atmosphere by reactions of ammonia with acid...

EVA 2 activity on Flight Day 5 to service the Hubble Space Telescope

NASA Image and Video Library

1997-02-15

S82-E-5407 (15 Feb. 1997) --- Astronauts Gregory J. Harbaugh (left) and Joseph R. Tanner on Remote Manipulator System (RMS) during accessing Fine Guidance Sensor (FGS) in the F site. This view was taken with an Electronic Still Camera (ESC).

EVA 2 activity on Flight Day 5 to service the Hubble Space Telescope

NASA Image and Video Library

1997-02-15

S82-E-5404 (15 Feb. 1997) --- Astronaut Gregory J. Harbaugh on the Remote Manipulator System (RMS) with the Fine Guidance Sensor (FGS), during the repair of the Hubble Space Telescope (HST). This view was taken with an Electronic Still Camera (ESC).

What You See Depends on Your Point of View: Comparison of Greenness Indices Across Spatial and Temporal Scales and What That Means for Mule Deer Migration and Fitness

NASA Astrophysics Data System (ADS)

Miller, B. W.; Chong, G.; Steltzer, H.; Aikens, E.; Morisette, J. T.; Talbert, C.; Talbert, M.; Shory, R.; Krienert, J. M.; Gurganus, D.

2015-12-01

Climate change models for the north­ern Rocky Mountains predict warming and changes in water availability that may alter vegetation. Changes to vegetation may include timing of plant life-history events, or phenology, such as green-up, flower­ing, and senescence. These changes could make forage available earlier in the growing season, but shifts in phenol­ogy may also result in earlier senescence (die-off or dormancy) and reduced overall production. Greenness indices such as the normalized difference vegetation index (NDVI) are regularly used to quantify greenness over large areas using remotely sensed reflectance data. The timing and scale of current satellite data, however, may be insufficient to capture fine-scale differences in phenology that are important indicators of habitat quality. The Wyoming Range Mule Deer herd is one of the largest in the west but it declined precipitously in the early 1990s and has not recovered. Accurate measurement of greenness over space and time would allow managers to better understand the role of plant phenology and productivity in mule deer population dynamics, for example. To connect spatial and temporal patterns of plant productivity with habitat quality, we compare greenness patterns (MODIS data) with migratory mule deer movement (GPS collars). Sagebrush systems provide winter habitat for mule deer. To understand sagebrush phenology as an indicator of productivity, we constructed NDVI time series and compared dates of phenological stages and magnitudes of greenness from three perspectives: at-surface/species-specific (mantis sensors: downward looking, <1m above vegetation); near surface/site-specific (PhenoCam: oblique, 2m); and satellite/landscape-scale (varied platforms). Greenness indices from these sensors contribute unique insights to understanding vegetation phenology, snow cover and reflectance. Understanding phenology and productivity at multiple scales can help guide resource management decisions related to habitat quality, and evaluate what remotely sensed phenology measurements mean on the ground. Monitoring changes in phenology and productivity over the long-term can provide insight into ecosystem responses to climate change.

Increasing precision of turbidity-based suspended sediment concentration and load estimates.

PubMed

Jastram, John D; Zipper, Carl E; Zelazny, Lucian W; Hyer, Kenneth E

2010-01-01

Turbidity is an effective tool for estimating and monitoring suspended sediments in aquatic systems. Turbidity can be measured in situ remotely and at fine temporal scales as a surrogate for suspended sediment concentration (SSC), providing opportunity for a more complete record of SSC than is possible with physical sampling approaches. However, there is variability in turbidity-based SSC estimates and in sediment loadings calculated from those estimates. This study investigated the potential to improve turbidity-based SSC, and by extension the resulting sediment loading estimates, by incorporating hydrologic variables that can be monitored remotely and continuously (typically 15-min intervals) into the SSC estimation procedure. On the Roanoke River in southwestern Virginia, hydrologic stage, turbidity, and other water-quality parameters were monitored with in situ instrumentation; suspended sediments were sampled manually during elevated turbidity events; samples were analyzed for SSC and physical properties including particle-size distribution and organic C content; and rainfall was quantified by geologic source area. The study identified physical properties of the suspended-sediment samples that contribute to SSC estimation variance and hydrologic variables that explained variability of those physical properties. Results indicated that the inclusion of any of the measured physical properties in turbidity-based SSC estimation models reduces unexplained variance. Further, the use of hydrologic variables to represent these physical properties, along with turbidity, resulted in a model, relying solely on data collected remotely and continuously, that estimated SSC with less variance than a conventional turbidity-based univariate model, allowing a more precise estimate of sediment loading, Modeling results are consistent with known mechanisms governing sediment transport in hydrologic systems.

The Gateway from Near into Remote Oceania: New Insights from Genome-Wide Data

PubMed Central

Pugach, Irina; Duggan, Ana T; Merriwether, D Andrew; Friedlaender, Françoise R; Friedlaender, Jonathan S; Stoneking, Mark

2018-01-01

Abstract A widely accepted two-wave scenario of human settlement of Oceania involves the first out-of-Africa migration circa 50,000 years ago (ya), and the more recent Austronesian expansion, which reached the Bismarck Archipelago by 3,450 ya. Whereas earlier genetic studies provided evidence for extensive sex-biased admixture between the incoming and the indigenous populations, some archaeological, linguistic, and genetic evidence indicates a more complicated picture of settlement. To study regional variation in Oceania in more detail, we have compiled a genome-wide data set of 823 individuals from 72 populations (including 50 populations from Oceania) and over 620,000 autosomal single nucleotide polymorphisms (SNPs). We show that the initial dispersal of people from the Bismarck Archipelago into Remote Oceania occurred in a “leapfrog” fashion, completely by-passing the main chain of the Solomon Islands, and that the colonization of the Solomon Islands proceeded in a bidirectional manner. Our results also support a divergence between western and eastern Solomons, in agreement with the sharp linguistic divide known as the Tryon–Hackman line. We also report substantial post-Austronesian gene flow across the Solomons. In particular, Santa Cruz (in Remote Oceania) exhibits extraordinarily high levels of Papuan ancestry that cannot be explained by a simple bottleneck/founder event scenario. Finally, we use simulations to show that discrepancies between different methods for dating admixture likely reflect different sensitivities of the methods to multiple admixture events from the same (or similar) sources. Overall, this study points to the importance of fine-scale sampling to understand the complexities of human population history. PMID:29301001

Measurements of Ultra-fine and Fine Aerosol Particles over Siberia: Large-scale Airborne Campaigns

NASA Astrophysics Data System (ADS)

Arshinov, Mikhail; Paris, Jean-Daniel; Stohl, Andreas; Belan, Boris; Ciais, Philippe; Nédélec, Philippe

2010-05-01

In this paper we discuss the results of in-situ measurements of ultra-fine and fine aerosol particles carried out in the troposphere from 500 to 7000 m in the framework of several International and Russian State Projects. Number concentrations of ultra-fine and fine aerosol particles measured during intensive airborne campaigns are presented. Measurements carried over a great part of Siberia were focused on particles with diameters from 3 to 21 nm to study new particle formation in the free/upper troposphere over middle and high latitudes of Asia, which is the most unexplored region of the Northern Hemisphere. Joint International airborne surveys were performed along the following routes: Novosibirsk-Salekhard-Khatanga-Chokurdakh-Pevek-Yakutsk-Mirny-Novosibirsk (YAK-AEROSIB/PLARCAT2008 Project) and Novosibirsk-Mirny-Yakutsk-Lensk-Bratsk-Novosibirsk (YAK-AEROSIB Project). The flights over Lake Baikal was conducted under Russian State contract. Concentrations of ultra-fine and fine particles were measured with automated diffusion battery (ADB, designed by ICKC SB RAS, Novosibirsk, Russia) modified for airborne applications. The airborne ADB coupled with CPC has an additional aspiration unit to compensate ambient pressure and changing flow rate. It enabled to classify nanoparticles in three size ranges: 3-6 nm, 6-21 nm, and 21-200 nm. To identify new particle formation events we used similar specific criteria as Young et al. (2007): (1) N3-6nm >10 cm-3, (2) R1=N3-6/N621 >1 and R2=N321/N21200 >0.5. So when one of the ratios R1 or R2 tends to decrease to the above limits the new particle formation is weakened. It is very important to notice that space scale where new particle formation was observed is rather large. All the events revealed in the FT occurred under clean air conditions (low CO mixing ratios). Measurements carried out in the atmospheric boundary layer over Baikal Lake did not reveal any event of new particle formation. Concentrations of ultra-fine particles were even lower than ones observed in the polar FT. Summarising the data obtained during two intensive measurement campaigns carried out over the vast territory of Siberia we can draw the conclusion that remote Siberian troposphere is a relatively efficient source of recently formed particles. Measurements carried out in the FT (3-7 km) showed that about 44% of them satisfied criteria of new particle formation. At the same time, more favourable conditions are observed between 5 and 7 km (48%). The present work was funded by ANR grant BLAN06-1_137670, CNRS, CEA, the French Ministry of Research, the French Ministry of Foreign Affairs (YAK-AEROSIB project) and by RFBR (grants 07-05-00645, 08-05-10033 and 08-05-92499) and by the Norwegian Research Council as part of POLARCAT-Norway. Flights over Baikal Lake were financed by Russian Government (State Contract No 02.515.11.5087). Young, L.H., Benson, D.R., Montanaro, W.M., Lee, S.H., Pan, L.L., Rogers, D.C., Jensen, J., Stith, J.L., Davis, C.A., Campos, T.L., Bowman, K.P., Cooper,W.A., Lait, L.R., 2007. Enhanced new particle formation observed in the northern midlatitude tropopause region. Journal of Geophysical Research 112. doi:10.1029/2006JD008109

Assessment of fine-scale parameterizations of turbulent dissipation rates in the Southern Ocean

NASA Astrophysics Data System (ADS)

Takahashi, A.; Hibiya, T.

2016-12-01

To sustain the global overturning circulation, more mixing is required in the ocean than has been observed. The most likely candidates for this missing mixing are breaking of wind-induced near-inertial waves and bottom-generated internal lee waves in the sparsely observed Southern Ocean. Nevertheless, there is a paucity of direct microstructure measurements in the Southern Ocean where energy dissipation rates have been estimated mostly using fine-scale parameterizations. In this study, we assess the validity of the existing fine-scale parameterizations in the Antarctic Circumpolar Current (ACC) region using the data obtained from simultaneous full-depth measurements of micro-scale turbulence and fine-scale shear/strain carried out south of Australia during January 17 to February 2, 2016. Although the fine-scale shear/strain ratio (Rω) is close to the Garrett-Munk (GM) value at the station north of Subtropical Front, the values of Rω at the stations south of Subantarctic Front well exceed the GM value, suggesting that the local internal wave spectra are significantly biased to lower frequencies. We find that not all of the observed energy dissipation rates at these locations are well predicted using Gregg-Henyey-Polzin (GHP; Gregg et al., 2003) and Ijichi-Hibiya (IH; Ijichi and Hibiya, 2015) parameterizations, both of which take into account the spectral distortion in terms of Rω; energy dissipation rates at some locations are obviously overestimated by GHP and IH, although only the strain-based Wijesekera (Wijesekera et al., 1993) parameterization yields fairly good predictions. One possible explanation for this result is that a significant portion of the observed shear variance at these locations might be attributed to kinetic-energy-dominant small-scale eddies associated with the ACC, so that fine-scale strain rather than Rω becomes a more appropriate parameter to characterize the actual internal wave field.

Depth and Medium-Scale Spatial Processes Influence Fish Assemblage Structure of Unconsolidated Habitats in a Subtropical Marine Park

PubMed Central

Schultz, Arthur L.; Malcolm, Hamish A.; Bucher, Daniel J.; Linklater, Michelle; Smith, Stephen D. A.

2014-01-01

Where biological datasets are spatially limited, abiotic surrogates have been advocated to inform objective planning for Marine Protected Areas. However, this approach assumes close correlation between abiotic and biotic patterns. The Solitary Islands Marine Park, northern NSW, Australia, currently uses a habitat classification system (HCS) to assist with planning, but this is based only on data for reefs. We used Baited Remote Underwater Videos (BRUVs) to survey fish assemblages of unconsolidated substrata at different depths, distances from shore, and across an along-shore spatial scale of 10 s of km (2 transects) to examine how well the HCS works for this dominant habitat. We used multivariate regression modelling to examine the importance of these, and other environmental factors (backscatter intensity, fine-scale bathymetric variation and rugosity), in structuring fish assemblages. There were significant differences in fish assemblages across depths, distance from shore, and over the medium spatial scale of the study: together, these factors generated the optimum model in multivariate regression. However, marginal tests suggested that backscatter intensity, which itself is a surrogate for sediment type and hardness, might also influence fish assemblages and needs further investigation. Species richness was significantly different across all factors: however, total MaxN only differed significantly between locations. This study demonstrates that the pre-existing abiotic HCS only partially represents the range of fish assemblages of unconsolidated habitats in the region. PMID:24824998

A Universe without Weak Interactions

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

Harnik, Roni; Kribs, Graham D.; Perez, Gilad

2006-04-07

A universe without weak interactions is constructed that undergoes big-bang nucleosynthesis, matter domination, structure formation, and star formation. The stars in this universe are able to burn for billions of years, synthesize elements up to iron, and undergo supernova explosions, dispersing heavy elements into the interstellar medium. These definitive claims are supported by a detailed analysis where this hypothetical ''Weakless Universe'' is matched to our Universe by simultaneously adjusting Standard Model and cosmological parameters. For instance, chemistry and nuclear physics are essentially unchanged. The apparent habitability of the Weakless Universe suggests that the anthropic principle does not determine the scalemore » of electroweak breaking, or even require that it be smaller than the Planck scale, so long as technically natural parameters may be suitably adjusted. Whether the multi-parameter adjustment is realized or probable is dependent on the ultraviolet completion, such as the string landscape. Considering a similar analysis for the cosmological constant, however, we argue that no adjustments of other parameters are able to allow the cosmological constant to raise up even remotely close to the Planck scale while obtaining macroscopic structure. The fine-tuning problems associated with the electroweak breaking scale and the cosmological constant therefore appear to be qualitatively different from the perspective of obtaining a habitable universe.« less

Mapping daily evapotranspiration at field scales over rainfed and irrigated agricultural areas using remote sensing data fusion

USDA-ARS?s Scientific Manuscript database

A continuous monitoring of daily evapotranspiration (ET) at field scale can be achieved by combining thermal infrared remote sensing data information from multiple satellite platforms. Here, an integrated approach to field scale ET mapping is described, combining multi-scale surface energy balance e...

Methods for Improving Fine-Scale Applications of the WRF-CMAQ Modeling System

EPA Science Inventory

Presentation on the work in AMAD to improve fine-scale (e.g. 4km and 1km) WRF-CMAQ simulations. Includes iterative analysis, updated sea surface temperature and snow cover fields, and inclusion of impervious surface information (urban parameterization).

Generation and emplacement of fine-grained ejecta in planetary impacts

USGS Publications Warehouse

Ghent, R.R.; Gupta, V.; Campbell, B.A.; Ferguson, S.A.; Brown, J.C.W.; Fergason, R.L.; Carter, L.M.

2010-01-01

We report here on a survey of distal fine-grained ejecta deposits on the Moon, Mars, and Venus. On all three planets, fine-grained ejecta form circular haloes that extend beyond the continuous ejecta and other types of distal deposits such as run-out lobes or ramparts. Using Earth-based radar images, we find that lunar fine-grained ejecta haloes represent meters-thick deposits with abrupt margins, and are depleted in rocks 1cm in diameter. Martian haloes show low nighttime thermal IR temperatures and thermal inertia, indicating the presence of fine particles estimated to range from ???10??m to 10mm. Using the large sample sizes afforded by global datasets for Venus and Mars, and a complete nearside radar map for the Moon, we establish statistically robust scaling relationships between crater radius R and fine-grained ejecta run-out r for all three planets. On the Moon, ???R-0.18 for craters 5-640km in diameter. For Venus, radar-dark haloes are larger than those on the Moon, but scale as ???R-0.49, consistent with ejecta entrainment in Venus' dense atmosphere. On Mars, fine-ejecta haloes are larger than lunar haloes for a given crater size, indicating entrainment of ejecta by the atmosphere or vaporized subsurface volatiles, but scale as R-0.13, similar to the ballistic lunar scaling. Ejecta suspension in vortices generated by passage of the ejecta curtain is predicted to result in ejecta run-out that scales with crater size as R1/2, and the wind speeds so generated may be insufficient to transport particles at the larger end of the calculated range. The observed scaling and morphology of the low-temperature haloes leads us rather to favor winds generated by early-stage vapor plume expansion as the emplacement mechanism for low-temperature halo materials. ?? 2010 Elsevier Inc.

Soil moisture variability across different scales in an Indian watershed for satellite soil moisture product validation

NASA Astrophysics Data System (ADS)

Singh, Gurjeet; Panda, Rabindra K.; Mohanty, Binayak P.; Jana, Raghavendra B.

2016-05-01

Strategic ground-based sampling of soil moisture across multiple scales is necessary to validate remotely sensed quantities such as NASA's Soil Moisture Active Passive (SMAP) product. In the present study, in-situ soil moisture data were collected at two nested scale extents (0.5 km and 3 km) to understand the trend of soil moisture variability across these scales. This ground-based soil moisture sampling was conducted in the 500 km2 Rana watershed situated in eastern India. The study area is characterized as sub-humid, sub-tropical climate with average annual rainfall of about 1456 mm. Three 3x3 km square grids were sampled intensively once a day at 49 locations each, at a spacing of 0.5 km. These intensive sampling locations were selected on the basis of different topography, soil properties and vegetation characteristics. In addition, measurements were also made at 9 locations around each intensive sampling grid at 3 km spacing to cover a 9x9 km square grid. Intensive fine scale soil moisture sampling as well as coarser scale samplings were made using both impedance probes and gravimetric analyses in the study watershed. The ground-based soil moisture samplings were conducted during the day, concurrent with the SMAP descending overpass. Analysis of soil moisture spatial variability in terms of areal mean soil moisture and the statistics of higher-order moments, i.e., the standard deviation, and the coefficient of variation are presented. Results showed that the standard deviation and coefficient of variation of measured soil moisture decreased with extent scale by increasing mean soil moisture.

Introducing close-range photogrammetry for characterizing forest understory plant diversity and surface fuel structure at fine scales

Treesearch

Benjamin C. Bright; E. Louise Loudermilk; Scott M. Pokswinski; Andrew T. Hudak; Joseph J. O' Brien

2016-01-01

Methods characterizing fine-scale fuels and plant diversity can advance understanding of plant-fire interactions across scales and help in efforts to monitor important ecosystems such as longleaf pine (Pinus palustris Mill.) forests of the southeastern United States. Here, we evaluate the utility of close-range photogrammetry for measuring fuels and plant...

Fine-scale multi-species aggregations of oceanic zooplankton

NASA Astrophysics Data System (ADS)

Haury, L. R.; Wiebe, P. H.

1982-07-01

Sixteen Longhurst-Hardy Plankton Recorder tows were taken at different depths in the northwest Atlantic for analysis of fine-scale horizontal patchiness. Abundant species were non-randomly distributed in patches with scales of tens to hundreds of meters. Positive correlations between species abundances dominated, indicating that the patches were multi-species associations. Most horizontal pattern appeared to be of biological origin.

Analysing and correcting the differences between multi-source and multi-scale spatial remote sensing observations.

PubMed

Dong, Yingying; Luo, Ruisen; Feng, Haikuan; Wang, Jihua; Zhao, Jinling; Zhu, Yining; Yang, Guijun

2014-01-01

Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation.

Analysing and Correcting the Differences between Multi-Source and Multi-Scale Spatial Remote Sensing Observations

PubMed Central

Dong, Yingying; Luo, Ruisen; Feng, Haikuan; Wang, Jihua; Zhao, Jinling; Zhu, Yining; Yang, Guijun

2014-01-01

Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation. PMID:25405760

Background aerosol over the Himalayas and Tibetan Plateau: observed characteristics of aerosol mass loading

NASA Astrophysics Data System (ADS)

Liu, B.; Cong, Z.; Wang, Y.; Xin, J.; Wan, X.; Pan, Y.; Liu, Z.; Wang, Y.; Zhang, G.; Kang, S.

2016-12-01

To investigate the atmospheric aerosols of the Himalayas and Tibetan Plateau (HTP), an observation network was established within the region's various ecosystems, including at Ngari, Qomolangma (QOMS), Nam Co, and SouthEastern Tibetan (SET) stations. In this paper we illustrate aerosol mass loadings by integrating in situ measurements with satellite and ground-based remote sensing datasets for the 2011-2013 period, on both local and large scales. Mass concentrations of these surface atmospheric aerosols were relatively low and varied with land cover, showing a general tendency of Ngari and QOMS (barren sites) > Nam Co (grassland site) > SET (forest site). Bimodal mass distributions of size-segregated particles were found at all sites, with a relatively small peak in accumulation mode and a more notable peak in coarse mode. Diurnal variations in fine aerosol masses generally displayed a bi-peak pattern at the QOMS, Nam Co and SET stations and a single-peak pattern at the Ngari station, controlled by the effects of local geomorphology, mountain-valley breeze circulation and aerosol emissions. Combining surface aerosols data and atmospheric-column aerosol optical properties, the TSP mass and aerosol optical depth (AOD) of the Multi-angle Imaging Spectroradiometer (MISR) generally decreased as land cover changed from barren to forest, in inverse relation to the PM2.5 ratios. The seasonality of aerosol mass parameters was land-cover dependent. Over forest and grassland areas, TSP mass, PM2.5 mass, MISR-AOD and fine-mode AOD were higher in spring and summer, followed by relatively lower values in autumn and winter. At the barren site (the QOMS station), there were inconsistent seasonal variations between surface TSP mass (PM2.5 mass) and atmospheric column AOD (fine-mode AOD). Our findings implicate that, HTP aerosol masses (especially their reginal characteristics and fine particle emissions) need to be treated sensitively in relation to assessments of their climatic effect

Using High Resolution Commercial Satellite Imagery to Quantify Spatial Features of Urban Areas and their Relationship to Quality of Life Indicators in Accra, Ghana

NASA Astrophysics Data System (ADS)

Sandborn, A.; Engstrom, R.; Yu, Q.

2014-12-01

Mapping urban areas via satellite imagery is an important task for detecting and anticipating land cover and land use change at multiple scales. As developing countries experience substantial urban growth and expansion, remotely sensed based estimates of population and quality of life indicators can provide timely and spatially explicit information to researchers and planners working to determine how cities are changing. In this study, we use commercial high spatial resolution satellite imagery in combination with fine resolution census data to determine the ability of using remotely sensed data to reveal the spatial patterns of quality of life in Accra, Ghana. Traditionally, spectral characteristics are used on a per-pixel basis to determine land cover; however, in this study, we test a new methodology that quantifies spatial characteristics using a variety of spatial features observed in the imagery to determine the properties of an urban area. The spatial characteristics used in this study include histograms of oriented gradients, PanTex, Fourier transform, and line support regions. These spatial features focus on extracting structural and textural patterns of built-up areas, such as homogeneous building orientations and straight line indices. Information derived from aggregating the descriptive statistics of the spatial features at both the fine-resolution census unit and the larger neighborhood level are then compared to census derived quality of life indicators including information about housing, education, and population estimates. Preliminary results indicate that there are correlations between straight line indices and census data including available electricity and literacy rates. Results from this study will be used to determine if this methodology provides a new and improved way to measure a city structure in developing cities and differentiate between residential and commercial land use zones, as well as formal versus informal housing areas.

Pressure Measurements Using an Airborne Differential Absorption Lidar. Part 1; Analysis of the Systematic Error Sources

NASA Technical Reports Server (NTRS)

Flamant, Cyrille N.; Schwemmer, Geary K.; Korb, C. Laurence; Evans, Keith D.; Palm, Stephen P.

1999-01-01

Remote airborne measurements of the vertical and horizontal structure of the atmospheric pressure field in the lower troposphere are made with an oxygen differential absorption lidar (DIAL). A detailed analysis of this measurement technique is provided which includes corrections for imprecise knowledge of the detector background level, the oxygen absorption fine parameters, and variations in the laser output energy. In addition, we analyze other possible sources of systematic errors including spectral effects related to aerosol and molecular scattering interference by rotational Raman scattering and interference by isotopic oxygen fines.

Coastal sedimentation

NASA Technical Reports Server (NTRS)

Schubel, J. R.

1980-01-01

Several important coastal sedimentation problems are identified. Application of existing or anticipated remote sensing techniques to examine these problems is considered. Specifically, coastal fine particle sediment systems, floods and hy hurricanes and sedimentation f of coastal systems, routes and rates of sediment transport on continental shelves, and dredging and dredged material disposal are discussed.

Characteristics of Fine Particulate Carbonaceous Aerosol at Two Remote Sites in Central Asia

EPA Science Inventory

Central Asia is a relatively understudied region of the world in terms of characterizing ambient particulate matter (PM) and quantifying source impacts of PM at receptor locations, although it is speculated to have an important role as a source region for long-range transport of ...

MODELING TOOLS FOR ASSESSING LANDSCAPE-LEVEL DETERMINANTS OF FISH PRODUCTION: EXAMPLES FROM WESTERN OREGON

EPA Science Inventory

Most studies addressing relationships between salmonids and factors that affect their freshwater production have focused on small areas and short time frames. Limits of understanding gained at fine spatiotemporal scales have become obvious, and aggregating fine-scale information ...

IMPLEMENTATION OF AN URBAN CANOPY PARAMETERIZATION FOR FINE-SCALE SIMULATIONS

EPA Science Inventory

The Pennsylvania State University/National Center for Atmospheric Research Mesoscale Model (MM5) (Grell et al. 1994) has been modified to include an urban canopy parameterization (UCP) for fine-scale urban simulations ( 1 - km horizontal grid spacing ). The UCP accounts for dr...

[Object-oriented segmentation and classification of forest gap based on QuickBird remote sensing image.

PubMed

Mao, Xue Gang; Du, Zi Han; Liu, Jia Qian; Chen, Shu Xin; Hou, Ji Yu

2018-01-01

Traditional field investigation and artificial interpretation could not satisfy the need of forest gaps extraction at regional scale. High spatial resolution remote sensing image provides the possibility for regional forest gaps extraction. In this study, we used object-oriented classification method to segment and classify forest gaps based on QuickBird high resolution optical remote sensing image in Jiangle National Forestry Farm of Fujian Province. In the process of object-oriented classification, 10 scales (10-100, with a step length of 10) were adopted to segment QuickBird remote sensing image; and the intersection area of reference object (RA or ) and intersection area of segmented object (RA os ) were adopted to evaluate the segmentation result at each scale. For segmentation result at each scale, 16 spectral characteristics and support vector machine classifier (SVM) were further used to classify forest gaps, non-forest gaps and others. The results showed that the optimal segmentation scale was 40 when RA or was equal to RA os . The accuracy difference between the maximum and minimum at different segmentation scales was 22%. At optimal scale, the overall classification accuracy was 88% (Kappa=0.82) based on SVM classifier. Combining high resolution remote sensing image data with object-oriented classification method could replace the traditional field investigation and artificial interpretation method to identify and classify forest gaps at regional scale.

Utility of an image-based canopy reflectance modeling tool for remote estimation and LAI and leaf chlorophyll content at regional scales

USDA-ARS?s Scientific Manuscript database

Radiance data recorded by remote sensors function as a unique source for monitoring the terrestrial biosphere and vegetation dynamics at a range of spatial and temporal scales. A key challenge is to relate the remote sensing signal to critical variables describing land surface vegetation canopies su...

Incorporating remotely sensed tree canopy cover data into broad scale assessments of wildlife habitat distribution and conservation

Treesearch

Sebastian Martinuzzi; Lee A. Vierling; William A. Gould; Kerri T. Vierling; Andrew T. Hudak

2009-01-01

Remote sensing provides critical information for broad scale assessments of wildlife habitat distribution and conservation. However, such efforts have been typically unable to incorporate information about vegetation structure, a variable important for explaining the distribution of many wildlife species. We evaluated the consequences of incorporating remotely sensed...

Research Advances on Radiation Transfer Modeling and Inversion for Multi-Scale Land Surface Remote Sensing

NASA Astrophysics Data System (ADS)

Liu, Q.

2011-09-01

At first, research advances on radiation transfer modeling on multi-scale remote sensing data are presented: after a general overview of remote sensing radiation transfer modeling, several recent research advances are presented, including leaf spectrum model (dPROS-PECT), vegetation canopy BRDF models, directional thermal infrared emission models(TRGM, SLEC), rugged mountains area radiation models, and kernel driven models etc. Then, new methodologies on land surface parameters inversion based on multi-source remote sensing data are proposed. The land surface Albedo, leaf area index, temperature/emissivity, and surface net radiation etc. are taken as examples. A new synthetic land surface parameter quantitative remote sensing product generation system is designed and the software system prototype will be demonstrated. At last, multi-scale field experiment campaigns, such as the field campaigns in Gansu and Beijing, China will be introduced briefly. The ground based, tower based, and airborne multi-angular measurement system have been built to measure the directional reflectance, emission and scattering characteristics from visible, near infrared, thermal infrared and microwave bands for model validation and calibration. The remote sensing pixel scale "true value" measurement strategy have been designed to gain the ground "true value" of LST, ALBEDO, LAI, soil moisture and ET etc. at 1-km2 for remote sensing product validation.

Analysis of Microhabitat Use for Two Trout Species Using a Combination of Remote Sensing and Passive Integrated transponder Tags

NASA Astrophysics Data System (ADS)

Lokteff, R.; Wheaton, J. M.; Roper, B.; DeMeurichy, K.; Randall, J.

2011-12-01

The Logan River and its tributaries in northern Utah sustain a significant population of the imperiled Bonneville cutthroat trout (Oncorhynchus clarki Utah) as well as invasive brown trout (Salmo trutta). In general, the upper reaches of the system are populated by cutthroat trout and the lower reaches by brown trout. Spawn Creek is a unique tributary in that it supports both of these species throughout the year. The purpose of this study is to identify differences in fine-scale microhabitat that explain utilization patterns of each species of fish. Passive integrated transponder (PIT) tags have been placed in trout over the last 3 years throughout Spawn Creek. Repeat GPS observations of these fish in their habitat during both spawning and non-spawning periods have been acquired over the last 4 years. Non-spawning activity has been captured using mobile PIT tag antennae. GPS observations of cutthroat trout spawning locations have also been recorded. From these observations both spawning and non-spawning "hotspots" have emerged, which appear to be highly correlated with specific microhabitat characteristics. The entire 2.5 km study reach on lower Spawn Creek has been scanned using ground-based light detection and ranging (LiDAR) which covers all observed "hotspots." LiDAR data provides sub-centimeter resolution point clouds from which detailed geometric measurements and topographic analyses can be used to reveal specific aspects of trout habitat. Where bathymetric data is needed, total station bathymetric surveys have been completed at sub-meter resolution. The combination of these data types at known "hotspot" locations provides an opportunity to quantify aspects of the physical environment at a uniquely fine scale relevant to individual fish. New metrics, as well as old metrics resolved at finer scales, will be presented to explain species and life-stage specific habitat "hotspots" in mountain streams.

The potential for LiDAR technology to map fire fuel hazard over large areas of Australian forest.

PubMed

Price, Owen F; Gordon, Christopher E

2016-10-01

Fuel load is a primary determinant of fire spread in Australian forests. In east Australian forests, litter and canopy fuel loads and hence fire hazard are thought to be highest at and beyond steady-state fuel loads 15-20 years post-fire. Current methods used to predict fuel loads often rely on course-scale vegetation maps and simple time-since-fire relationships which mask fine-scale processes influencing fuel loads. Here we use Light Detecting and Remote Sensing technology (LiDAR) and field surveys to quantify post-fire mid-story and crown canopy fuel accumulation and fire hazard in Dry Sclerophyll Forests of the Sydney Basin (Australia) at fine spatial-scales (20 × 20 m cell resolution). Fuel cover was quantified in three strata important for crown fire propagation (0.5-4 m, 4-15 m, >15 m) over a 144 km(2) area subject to varying fire fuel ages. Our results show that 1) LiDAR provided a precise measurement of fuel cover in each strata and a less precise but still useful predictor of surface fuels, 2) cover varied greatly within a mapped vegetation class of the same fuel age, particularly for elevated fuel, 3) time-since-fire was a poor predictor of fuel cover and crown fire hazard because fuel loads important for crown fire propagation were variable over a range of fire fuel ages between 2 and 38 years post-fire, and 4) fuel loads and fire hazard can be high in the years immediately following fire. Our results show the benefits of spatially and temporally specific in situ fuel sampling methods such as LiDAR, and are widely applicable for fire management actions which aim to decrease human and environmental losses due to wildfire. Copyright © 2016 Elsevier Ltd. All rights reserved.

Application of a predator-prey overlap metric to determine the impact of sub-grid scale feeding dynamics on ecosystem productivity

NASA Astrophysics Data System (ADS)

Greer, A. T.; Woodson, C. B.

2016-02-01

Because of the complexity and extremely large size of marine ecosystems, research attention has a strong focus on modelling the system through space and time to elucidate processes driving ecosystem state. One of the major weaknesses of current modelling approaches is the reliance on a particular grid cell size (usually 10's of km in the horizontal & water column mean) to capture the relevant processes, even though empirical research has shown that marine systems are highly structured on fine scales, and this structure can persist over relatively long time scales (days to weeks). Fine-scale features can have a strong influence on the predator-prey interactions driving trophic transfer. Here we apply a statistic, the AB ratio, used to quantify increased predator production due to predator-prey overlap on fine scales in a manner that is computationally feasible for larger scale models. We calculated the AB ratio for predator-prey distributions throughout the scientific literature, as well as for data obtained with a towed plankton imaging system, demonstrating that averaging across a typical model grid cell neglects the fine-scale predator-prey overlap that is an essential component of ecosystem productivity. Organisms from a range of trophic levels and oceanographic regions tended to overlap with their prey both in the horizontal and vertical dimensions. When predator swimming over a diel cycle was incorporated, the amount of production indicated by the AB ratio increased substantially. For the plankton image data, the AB ratio was higher with increasing sampling resolution, especially when prey were highly aggregated. We recommend that ecosystem models incorporate more fine-scale information both to more accurately capture trophic transfer processes and to capitalize on the increasing sampling resolution and data volume from empirical studies.

A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

NASA Astrophysics Data System (ADS)

Zhang, Ce; Pan, Xin; Li, Huapeng; Gardiner, Andy; Sargent, Isabel; Hare, Jonathon; Atkinson, Peter M.

2018-06-01

The contextual-based convolutional neural network (CNN) with deep architecture and pixel-based multilayer perceptron (MLP) with shallow structure are well-recognized neural network algorithms, representing the state-of-the-art deep learning method and the classical non-parametric machine learning approach, respectively. The two algorithms, which have very different behaviours, were integrated in a concise and effective way using a rule-based decision fusion approach for the classification of very fine spatial resolution (VFSR) remotely sensed imagery. The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers. In consequence, the proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination. Meanwhile, limitations of the CNN due to the adoption of convolutional filters such as the uncertainty in object boundary partition and loss of useful fine spatial resolution detail were compensated. The effectiveness of the ensemble MLP-CNN classifier was tested in both urban and rural areas using aerial photography together with an additional satellite sensor dataset. The MLP-CNN classifier achieved promising performance, consistently outperforming the pixel-based MLP, spectral and textural-based MLP, and the contextual-based CNN in terms of classification accuracy. This research paves the way to effectively address the complicated problem of VFSR image classification.

NeMO-Net & Fluid Lensing: The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment Using Fluid Lensing Augmentation of NASA EOS Data

NASA Technical Reports Server (NTRS)

Chirayath, Ved

2018-01-01

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low-resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.

Coral Reef Surveillance: Infrared-Sensitive Video Surveillance Technology as a New Tool for Diurnal and Nocturnal Long-Term Field Observations.

PubMed

Dirnwoeber, Markus; Machan, Rudolf; Herler, Juergen

2012-10-31

Direct field observations of fine-scaled biological processes and interactions of the benthic community of corals and associated reef organisms (e.g., feeding, reproduction, mutualistic or agonistic behavior, behavioral responses to changing abiotic factors) usually involve a disturbing intervention. Modern digital camcorders (without inflexible land-or ship-based cable connection) such as the GoPro camera enable undisturbed and unmanned, stationary close-up observations. Such observations, however, are also very time-limited (~3 h) and full 24 h-recordings throughout day and night, including nocturnal observations without artificial daylight illumination, are not possible. Herein we introduce the application of modern standard video surveillance technology with the main objective of providing a tool for monitoring coral reef or other sessile and mobile organisms for periods of 24 h and longer. This system includes nocturnal close-up observations with miniature infrared (IR)-sensitive cameras and separate high-power IR-LEDs. Integrating this easy-to-set up and portable remote-sensing equipment into coral reef research is expected to significantly advance our understanding of fine-scaled biotic processes on coral reefs. Rare events and long-lasting processes can easily be recorded, in situ -experiments can be monitored live on land, and nocturnal IR-observations reveal undisturbed behavior. The options and equipment choices in IR-sensitive surveillance technology are numerous and subject to a steadily increasing technical supply and quality at decreasing prices. Accompanied by short video examples, this report introduces a radio-transmission system for simultaneous recordings and real-time monitoring of multiple cameras with synchronized timestamps, and a surface-independent underwater-recording system.

Scaling Irregular Applications through Data Aggregation and Software Multithreading

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

Morari, Alessandro; Tumeo, Antonino; Chavarría-Miranda, Daniel

Bioinformatics, data analytics, semantic databases, knowledge discovery are emerging high performance application areas that exploit dynamic, linked data structures such as graphs, unbalanced trees or unstructured grids. These data structures usually are very large, requiring significantly more memory than available on single shared memory systems. Additionally, these data structures are difficult to partition on distributed memory systems. They also present poor spatial and temporal locality, thus generating unpredictable memory and network accesses. The Partitioned Global Address Space (PGAS) programming model seems suitable for these applications, because it allows using a shared memory abstraction across distributed-memory clusters. However, current PGAS languagesmore » and libraries are built to target regular remote data accesses and block transfers. Furthermore, they usually rely on the Single Program Multiple Data (SPMD) parallel control model, which is not well suited to the fine grained, dynamic and unbalanced parallelism of irregular applications. In this paper we present {\\bf GMT} (Global Memory and Threading library), a custom runtime library that enables efficient execution of irregular applications on commodity clusters. GMT integrates a PGAS data substrate with simple fork/join parallelism and provides automatic load balancing on a per node basis. It implements multi-level aggregation and lightweight multithreading to maximize memory and network bandwidth with fine-grained data accesses and tolerate long data access latencies. A key innovation in the GMT runtime is its thread specialization (workers, helpers and communication threads) that realize the overall functionality. We compare our approach with other PGAS models, such as UPC running using GASNet, and hand-optimized MPI code on a set of typical large-scale irregular applications, demonstrating speedups of an order of magnitude.« less

Resolving the Many Mysteries of Martian Soil: Lessons Learned from Apollo

NASA Astrophysics Data System (ADS)

Pieters, C. M.

1999-09-01

If it were not for the return of lunar soil, we would still not understand why the spectrum of lunar soil is so unusual. We observe the intricacies of particles in the laboratory, but have never been able to duplicate the effects of weathering processes accumulated over time. We are in a directly parallel situation with our current understanding of Martian soil from spectroscopic techniques. We know it contains a ferric component and we know something of its elemental composition; we know it is very fine grained; we know it is the cumulative weathering product of Martian lithologies, some of which are known from meteorites. A summary of several relevant lessons from studying lunar soils include: 1) Physical and chemical processes fractionate the soils with respect to local rocks. 2) Meteoritic contamination (largely from the constant rain of micrometeorites) cumulates in the soils. Lunar estimates are about 2-3 percent. 3) The fine fraction dominate observed optical properties, regardless of the presence of larger particles. Individual particles may accumulate coatings and rinds. 4) The spectral characteristics of the weathering products of iron dominate soil spectra. On the Moon it is highly reduced iron (typically 10's of nm in scale); on Mars it is highly oxidized (nano-phase?) iron. Modeling this 'gunk' or 'crud' is illusive. 5) Although weathering products dominate most spectra, signatures of the mineralogy of regional terrain can nevertheless be detected as subtle superimposed features. 6) Small-scale outcrops where soil has not been able to form or accumulate are critical markers of local lithology diversity. In planning exploration strategies using remote detectors, this latter lesson is particularly important and underlines the need for high resolution.

Coral Reef Surveillance: Infrared-Sensitive Video Surveillance Technology as a New Tool for Diurnal and Nocturnal Long-Term Field Observations

PubMed Central

Dirnwoeber, Markus; Machan, Rudolf; Herler, Juergen

2014-01-01

Direct field observations of fine-scaled biological processes and interactions of the benthic community of corals and associated reef organisms (e.g., feeding, reproduction, mutualistic or agonistic behavior, behavioral responses to changing abiotic factors) usually involve a disturbing intervention. Modern digital camcorders (without inflexible land-or ship-based cable connection) such as the GoPro camera enable undisturbed and unmanned, stationary close-up observations. Such observations, however, are also very time-limited (~3 h) and full 24 h-recordings throughout day and night, including nocturnal observations without artificial daylight illumination, are not possible. Herein we introduce the application of modern standard video surveillance technology with the main objective of providing a tool for monitoring coral reef or other sessile and mobile organisms for periods of 24 h and longer. This system includes nocturnal close-up observations with miniature infrared (IR)-sensitive cameras and separate high-power IR-LEDs. Integrating this easy-to-set up and portable remote-sensing equipment into coral reef research is expected to significantly advance our understanding of fine-scaled biotic processes on coral reefs. Rare events and long-lasting processes can easily be recorded, in situ-experiments can be monitored live on land, and nocturnal IR-observations reveal undisturbed behavior. The options and equipment choices in IR-sensitive surveillance technology are numerous and subject to a steadily increasing technical supply and quality at decreasing prices. Accompanied by short video examples, this report introduces a radio-transmission system for simultaneous recordings and real-time monitoring of multiple cameras with synchronized timestamps, and a surface-independent underwater-recording system. PMID:24829763

Altered fish community and feeding behaviour in close proximity to boat moorings in an urban estuary.

PubMed

Lanham, Brendan S; Vergés, Adriana; Hedge, Luke H; Johnston, Emma L; Poore, Alistair G B

2018-04-01

Coastal urbanization has led to large-scale transformation of estuaries, with artificial structures now commonplace. Boat moorings are known to reduce seagrass cover, but little is known about their effect on fish communities. We used underwater video to quantify abundance, diversity, composition and feeding behaviour of fish assemblages on two scales: with increasing distance from moorings on fine scales, and among locations where moorings were present or absent. Fish were less abundant in close proximity to boat moorings, and the species composition varied on fine scales, leading to lower predation pressure near moorings. There was no relationship at the location with seagrass. On larger scales, we detected no differences in abundance or community composition among locations where moorings were present or absent. These findings show a clear impact of moorings on fish and highlight the importance of fine-scale assessments over location-scale comparisons in the detection of the effects of artificial structures. Copyright © 2018 Elsevier Ltd. All rights reserved.

Development of Japanese experiment module remote manipulator system

NASA Technical Reports Server (NTRS)

Matsueda, Tatsuo; Kuwao, Fumihiro; Motohasi, Shoichi; Okamura, Ryo

1994-01-01

National Space Development Agency of Japan (NASDA) is developing the Japanese Experiment Module (JEM), as its contribution to the International Space Station. The JEM consists of the pressurized module (PM), the exposed facility (EF), the experiment logistics module pressurized section (ELM-PS), the experiment logistics module exposed section (ELM-ES) and the Remote Manipulator System (RMS). The JEMRMS services for the JEM EF, which is a space experiment platform, consists of the Main Arm (MA), the Small Fine Arm (SFA) and the RMS console. The MA handles the JEM EF payloads, the SFA and the JEM element, such as ELM-ES.

Development of a generalized multi-pixel and multi-parameter satellite remote sensing algorithm for aerosol properties

NASA Astrophysics Data System (ADS)

Hashimoto, M.; Nakajima, T.; Takenaka, H.; Higurashi, A.

2013-12-01

We develop a new satellite remote sensing algorithm to retrieve the properties of aerosol particles in the atmosphere. In late years, high resolution and multi-wavelength, and multiple-angle observation data have been obtained by grand-based spectral radiometers and imaging sensors on board the satellite. With this development, optimized multi-parameter remote sensing methods based on the Bayesian theory have become popularly used (Turchin and Nozik, 1969; Rodgers, 2000; Dubovik et al., 2000). Additionally, a direct use of radiation transfer calculation has been employed for non-linear remote sensing problems taking place of look up table methods supported by the progress of computing technology (Dubovik et al., 2011; Yoshida et al., 2011). We are developing a flexible multi-pixel and multi-parameter remote sensing algorithm for aerosol optical properties. In this algorithm, the inversion method is a combination of the MAP method (Maximum a posteriori method, Rodgers, 2000) and the Phillips-Twomey method (Phillips, 1962; Twomey, 1963) as a smoothing constraint for the state vector. Furthermore, we include a radiation transfer calculation code, Rstar (Nakajima and Tanaka, 1986, 1988), numerically solved each time in iteration for solution search. The Rstar-code has been directly used in the AERONET operational processing system (Dubovik and King, 2000). Retrieved parameters in our algorithm are aerosol optical properties, such as aerosol optical thickness (AOT) of fine mode, sea salt, and dust particles, a volume soot fraction in fine mode particles, and ground surface albedo of each observed wavelength. We simultaneously retrieve all the parameters that characterize pixels in each of horizontal sub-domains consisting the target area. Then we successively apply the retrieval method to all the sub-domains in the target area. We conducted numerical tests for the retrieval of aerosol properties and ground surface albedo for GOSAT/CAI imager data to test the algorithm for the land area. In this test, we simulated satellite-observed radiances for a sub-domain consisting of 5 by 5 pixels by the Rstar code assuming wavelengths of 380, 674, 870 and 1600 [nm], atmospheric condition of the US standard atmosphere, and the several aerosol and ground surface conditions. The result of the experiment showed that AOTs of fine mode and dust particles, soot fraction and ground surface albedo at the wavelength of 674 [nm] are retrieved within absolute value differences of 0.04, 0.01, 0.06 and 0.006 from the true value, respectively, for the case of dark surface, and also, for the case of blight surface, 0.06, 0.03, 0.04 and 0.10 from the true value, respectively. We will conduct more tests to study the information contents of parameters needed for aerosol and land surface remote sensing with different boundary conditions among sub-domains.

Assessing indicators of rangeland health with remote sensing in southeast Arizona

Treesearch

Jared Buono; Philip Heilman; David Williams; Phillip Guertin

2005-01-01

The goal of this study was to scale up ground-based range assessments to ranch and landscape scales in southeast Arizona using remote sensing and minimum amount of field data collection. Remotely sensed metrics of canopy cover, biomass, and mesquite composition were used to assess soil and site stability and biotic integrity. Ground-based assessments were conducted on...

Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings.

PubMed

Xu, Yiming; Smith, Scot E; Grunwald, Sabine; Abd-Elrahman, Amr; Wani, Suhas P; Nair, Vimala D

2017-09-11

Digital soil mapping (DSM) is gaining momentum as a technique to help smallholder farmers secure soil security and food security in developing regions. However, communications of the digital soil mapping information between diverse audiences become problematic due to the inconsistent scale of DSM information. Spatial downscaling can make use of accessible soil information at relatively coarse spatial resolution to provide valuable soil information at relatively fine spatial resolution. The objective of this research was to disaggregate the coarse spatial resolution soil exchangeable potassium (K ex ) and soil total nitrogen (TN) base map into fine spatial resolution soil downscaled map using weighted generalized additive models (GAMs) in two smallholder villages in South India. By incorporating fine spatial resolution spectral indices in the downscaling process, the soil downscaled maps not only conserve the spatial information of coarse spatial resolution soil maps but also depict the spatial details of soil properties at fine spatial resolution. The results of this study demonstrated difference between the fine spatial resolution downscaled maps and fine spatial resolution base maps is smaller than the difference between coarse spatial resolution base maps and fine spatial resolution base maps. The appropriate and economical strategy to promote the DSM technique in smallholder farms is to develop the relatively coarse spatial resolution soil prediction maps or utilize available coarse spatial resolution soil maps at the regional scale and to disaggregate these maps to the fine spatial resolution downscaled soil maps at farm scale.

FINE SCALE AIR QUALITY MODELING USING DISPERSION AND CMAQ MODELING APPROACHES: AN EXAMPLE APPLICATION IN WILMINGTON, DE

EPA Science Inventory

Characterization of spatial variability of air pollutants in an urban setting at fine scales is critical for improved air toxics exposure assessments, for model evaluation studies and also for air quality regulatory applications. For this study, we investigate an approach that su...

CFD MODELING OF FINE SCALE FLOW AND TRANSPORT IN THE HOUSTON METROPOLITAN AREA, TEXAS

EPA Science Inventory

Fine scale modeling of flows and air quality in Houston, Texas is being performed; the use of computational fluid dynamics (CFD) modeling is being applied to investigate the influence of morphologic structures on the within-grid transport and dispersion of sources in grid models ...

A new multi-angle remote sensing framework for scaling vegetation properties from tower-based spectro-radiometers to next generation "CubeSat"-satellites.

NASA Astrophysics Data System (ADS)

Hilker, T.; Hall, F. G.; Dyrud, L. P.; Slagowski, S.

2014-12-01

Frequent earth observations are essential for assessing the risks involved with global climate change, its feedbacks on carbon, energy and water cycling and consequences for live on earth. Often, satellite-remote sensing is the only practical way to provide such observations at comprehensive spatial scales, but relationships between land surface parameters and remotely sensed observations are mostly empirical and cannot easily be scaled across larger areas or over longer time intervals. For instance, optically based methods frequently depend on extraneous effects that are unrelated to the surface property of interest, including the sun-server geometry or background reflectance. As an alternative to traditional, mono-angle techniques, multi-angle remote sensing can help overcome some of these limitations by allowing vegetation properties to be derived from comprehensive reflectance models that describe changes in surface parameters based on physical principles and radiative transfer theory. Recent results have shown in theoretical and experimental research that multi-angle techniques can be used to infer and scale the photosynthetic rate of vegetation, its biochemical and structural composition robustly from remote sensing. Multi-angle remote sensing could therefore revolutionize estimates of the terrestrial carbon uptake as scaling of primary productivity may provide a quantum leap in understanding the spatial and temporal complexity of terrestrial earth science. Here, we introduce a framework of next generation tower-based instruments to a novel and unique constellation of nano-satellites (Figure 1) that will allow us to systematically scale vegetation parameters from stand to global levels. We provide technical insights, scientific rationale and present results. We conclude that future earth observation from multi-angle satellite constellations, supported by tower based remote sensing will open new opportunities for earth system science and earth system modeling.

TopoSCALE v.1.0: downscaling gridded climate data in complex terrain

NASA Astrophysics Data System (ADS)

Fiddes, J.; Gruber, S.

2014-02-01

Simulation of land surface processes is problematic in heterogeneous terrain due to the the high resolution required of model grids to capture strong lateral variability caused by, for example, topography, and the lack of accurate meteorological forcing data at the site or scale it is required. Gridded data products produced by atmospheric models can fill this gap, however, often not at an appropriate spatial resolution to drive land-surface simulations. In this study we describe a method that uses the well-resolved description of the atmospheric column provided by climate models, together with high-resolution digital elevation models (DEMs), to downscale coarse-grid climate variables to a fine-scale subgrid. The main aim of this approach is to provide high-resolution driving data for a land-surface model (LSM). The method makes use of an interpolation of pressure-level data according to topographic height of the subgrid. An elevation and topography correction is used to downscale short-wave radiation. Long-wave radiation is downscaled by deriving a cloud-component of all-sky emissivity at grid level and using downscaled temperature and relative humidity fields to describe variability with elevation. Precipitation is downscaled with a simple non-linear lapse and optionally disaggregated using a climatology approach. We test the method in comparison with unscaled grid-level data and a set of reference methods, against a large evaluation dataset (up to 210 stations per variable) in the Swiss Alps. We demonstrate that the method can be used to derive meteorological inputs in complex terrain, with most significant improvements (with respect to reference methods) seen in variables derived from pressure levels: air temperature, relative humidity, wind speed and incoming long-wave radiation. This method may be of use in improving inputs to numerical simulations in heterogeneous and/or remote terrain, especially when statistical methods are not possible, due to lack of observations (i.e. remote areas or future periods).

Relating structural growth environment to white spruce sapling establishment at the Forest-Tundra Ecotone

NASA Astrophysics Data System (ADS)

Maguire, A.; Boelman, N.; Griffin, K. L.; Jensen, J.; Hiers, E.; Johnson, D. M.; Vierling, L. A.; Eitel, J.

2017-12-01

The effect of climate change on treeline position at the latitudinal Forest-Tundra ecotone (FTE) is poorly understood. While the FTE is expansive (stretching 13,000 km acros the panarctic), understanding relationships between climate and tree function may depend on very fine scale processes. High resolution tools are therefore needed to appropriately characterize the leading (northernmost) edge of the FTE. We hypothesized that microstructural metrics obtainable from lidar remote sensing may explain variation in the physical growth environment that governs sapling establishment. To test our hypothesis, we used terrestrial laser scanning (TLS) to collect highly spatially resolved 3-D structural information of white spruce (Picea glauca) saplings and their aboveground growth environment at the leading edge of a FTE in northern Alaska and Northwest Territories, Canada. Coordinates of sapling locations were extracted from the 3-D TLS data. Within each sampling plot, 20 sets of coordinates were randomly selected from regions where no saplings were present. Ground roughness, canopy roughness, average aspect, average slope, average curvature, wind shelter index, and wetness indexwere extracted from point clouds within a variable radius from all coordinates. Generalized linear models (GLM) were fit to determine which microstructural metrics were most strongly associated with sapling establishment. Preliminary analyses of three plots suggest that vegetation roughness, wetness index, ground roughness, and slope were the most important terrain metrics governing sapling presence (Figure 1). Comprehensive analyses will include eight plots and GLMs optimized for scale at which structural parameters affect sapling establishment. Spatial autocorrelation of sample locations will be accounted for in models. Because these analyses address how the physical growth environment affects sapling establishment, model outputs will provide information for improving understanding of the ecological processes that regulate treeline dynamics. Moreover, establishing relationships between the remotely sensed structural growth environment and tree establishment provides new ways of spatially scaling across larger areas to study ecological change at the FTE.

Bundle block adjustment of large-scale remote sensing data with Block-based Sparse Matrix Compression combined with Preconditioned Conjugate Gradient

NASA Astrophysics Data System (ADS)

Zheng, Maoteng; Zhang, Yongjun; Zhou, Shunping; Zhu, Junfeng; Xiong, Xiaodong

2016-07-01

In recent years, new platforms and sensors in photogrammetry, remote sensing and computer vision areas have become available, such as Unmanned Aircraft Vehicles (UAV), oblique camera systems, common digital cameras and even mobile phone cameras. Images collected by all these kinds of sensors could be used as remote sensing data sources. These sensors can obtain large-scale remote sensing data which consist of a great number of images. Bundle block adjustment of large-scale data with conventional algorithm is very time and space (memory) consuming due to the super large normal matrix arising from large-scale data. In this paper, an efficient Block-based Sparse Matrix Compression (BSMC) method combined with the Preconditioned Conjugate Gradient (PCG) algorithm is chosen to develop a stable and efficient bundle block adjustment system in order to deal with the large-scale remote sensing data. The main contribution of this work is the BSMC-based PCG algorithm which is more efficient in time and memory than the traditional algorithm without compromising the accuracy. Totally 8 datasets of real data are used to test our proposed method. Preliminary results have shown that the BSMC method can efficiently decrease the time and memory requirement of large-scale data.

Evaluation of seabed mapping methods for fine-scale classification of extremely shallow benthic habitats - Application to the Venice Lagoon, Italy

NASA Astrophysics Data System (ADS)

Montereale Gavazzi, G.; Madricardo, F.; Janowski, L.; Kruss, A.; Blondel, P.; Sigovini, M.; Foglini, F.

2016-03-01

Recent technological developments of multibeam echosounder systems (MBES) allow mapping of benthic habitats with unprecedented detail. MBES can now be employed in extremely shallow waters, challenging data acquisition (as these instruments were often designed for deeper waters) and data interpretation (honed on datasets with resolution sometimes orders of magnitude lower). With extremely high-resolution bathymetry and co-located backscatter data, it is now possible to map the spatial distribution of fine scale benthic habitats, even identifying the acoustic signatures of single sponges. In this context, it is necessary to understand which of the commonly used segmentation methods is best suited to account for such level of detail. At the same time, new sampling protocols for precisely geo-referenced ground truth data need to be developed to validate the benthic environmental classification. This study focuses on a dataset collected in a shallow (2-10 m deep) tidal channel of the Lagoon of Venice, Italy. Using 0.05-m and 0.2-m raster grids, we compared a range of classifications, both pixel-based and object-based approaches, including manual, Maximum Likelihood Classifier, Jenks Optimization clustering, textural analysis and Object Based Image Analysis. Through a comprehensive and accurately geo-referenced ground truth dataset, we were able to identify five different classes of the substrate composition, including sponges, mixed submerged aquatic vegetation, mixed detritic bottom (fine and coarse) and unconsolidated bare sediment. We computed estimates of accuracy (namely Overall, User, Producer Accuracies and the Kappa statistic) by cross tabulating predicted and reference instances. Overall, pixel based segmentations produced the highest accuracies and the accuracy assessment is strongly dependent on the number of classes chosen for the thematic output. Tidal channels in the Venice Lagoon are extremely important in terms of habitats and sediment distribution, particularly within the context of the new tidal barrier being built. However, they had remained largely unexplored until now, because of the surveying challenges. The application of this remote sensing approach, combined with targeted sampling, opens a new perspective in the monitoring of benthic habitats in view of a knowledge-based management of natural resources in shallow coastal areas.

Research Advances on Radiation Transfer Modeling and Inversion for Multi-scale Land Surface Remote Sensing

NASA Astrophysics Data System (ADS)

Liu, Q.; Li, J.; Du, Y.; Wen, J.; Zhong, B.; Wang, K.

2011-12-01

As the remote sensing data accumulating, it is a challenge and significant issue how to generate high accurate and consistent land surface parameter product from the multi source remote observation and the radiation transfer modeling and inversion methodology are the theoretical bases. In this paper, recent research advances and unresolved issues are presented. At first, after a general overview, recent research advances on multi-scale remote sensing radiation transfer modeling are presented, including leaf spectrum model, vegetation canopy BRDF models, directional thermal infrared emission models, rugged mountains area radiation models, and kernel driven models etc. Then, new methodologies on land surface parameters inversion based on multi-source remote sensing data are proposed, taking the land surface Albedo, leaf area index, temperature/emissivity, and surface net radiation as examples. A new synthetic land surface parameter quantitative remote sensing product generation system is suggested and the software system prototype will be demonstrated. At last, multi-scale field experiment campaigns, such as the field campaigns in Gansu and Beijing, China are introduced briefly. The ground based, tower based, and airborne multi-angular measurement system have been built to measure the directional reflectance, emission and scattering characteristics from visible, near infrared, thermal infrared and microwave bands for model validation and calibration. The remote sensing pixel scale "true value" measurement strategy have been designed to gain the ground "true value" of LST, ALBEDO, LAI, soil moisture and ET etc. at 1-km2 for remote sensing product validation.

Fine root dynamics and trace gas fluxes in two lowland tropical forest soils.

Treesearch

WHENDEE L. SILVER; ANDREW W. THOMPSON; MEGAN E . MCGRODDY; RUTH K. VARNER; JADSON D. DIAS; HUDSON SILVA; CRILL PATRICK M.; MICHAEL KELLER

2005-01-01

Fine root dynamics have the potential to contribute significantly to ecosystem-scale biogeochemical cycling, including the production and emission of greenhouse gases. This is particularly true in tropical forests which are often characterized as having large fine root biomass and rapid rates of root production and decomposition. We examined patterns in fine root...

Linking Fine-Scale Observations and Model Output with Imagery at Multiple Scales

NASA Astrophysics Data System (ADS)

Sadler, J.; Walthall, C. L.

2014-12-01

The development and implementation of a system for seasonal worldwide agricultural yield estimates is underway with the international Group on Earth Observations GeoGLAM project. GeoGLAM includes a research component to continually improve and validate its algorithms. There is a history of field measurement campaigns going back decades to draw upon for ways of linking surface measurements and model results with satellite observations. Ground-based, in-situ measurements collected by interdisciplinary teams include yields, model inputs and factors affecting scene radiation. Data that is comparable across space and time with careful attention to calibration is essential for the development and validation of agricultural applications of remote sensing. Data management to ensure stewardship, availability and accessibility of the data are best accomplished when considered an integral part of the research. The expense and logistical challenges of field measurement campaigns can be cost-prohibitive and because of short funding cycles for research, access to consistent, stable study sites can be lost. The use of a dedicated staff for baseline data needed by multiple investigators, and conducting measurement campaigns using existing measurement networks such as the USDA Long Term Agroecosystem Research network can fulfill these needs and ensure long-term access to study sites.

Electromagnetic liquid pistons for capillarity-based pumping.

PubMed

Malouin, Bernard A; Vogel, Michael J; Olles, Joseph D; Cheng, Lili; Hirsa, Amir H

2011-02-07

The small scales associated with lab-on-a-chip technologies lend themselves well to capillarity-dominated phenomena. We demonstrate a new capillarity-dominated system where two adjoining ferrofluid droplets can behave as an electronically-controlled oscillator or switch by an appropriate balance of magnetic, capillary, and inertial forces. Their oscillatory motion can be exploited to displace a surrounding liquid (akin to an axial piston pump), forming electromagnetic "liquid pistons." Such ferrofluid pistons can pump a precise volume of liquid via finely tunable amplitudes (cf. pump stroke) or resonant frequencies (cf. pump speed) with no solid moving parts for long-term operation without wear in a small device. Furthermore, the rapid propagation of electromagnetic fields and the favorable scaling of capillary forces with size permit micron sized devices with very fast operating speeds (∼kHz). The pumping dynamics and performance of these liquid pistons is explored, with experimental measurements showing good agreement with a spherical cap model. While these liquid pistons may find numerous applications in micro- and mesoscale fluidic devices (e.g., remotely activated drug delivery), here we demonstrate the use of these liquid pistons in capillarity-dominated systems for chip-level, fast-acting adaptive liquid lenses with nearly perfect spherical interfaces.

Using spatio-temporal modeling to predict long-term exposure to black smoke at fine spatial and temporal scale

NASA Astrophysics Data System (ADS)

Dadvand, Payam; Rushton, Stephen; Diggle, Peter J.; Goffe, Louis; Rankin, Judith; Pless-Mulloli, Tanja

2011-01-01

Whilst exposure to air pollution is linked to a wide range of adverse health outcomes, assessing levels of this exposure has remained a challenge. This study reports a modeling approach for the estimation of weekly levels of ambient black smoke (BS) at residential postcodes across Northeast England (2055 km 2) over a 12 year period (1985-1996). A two-stage modeling strategy was developed using monitoring data on BS together with a range of covariates including data on traffic, population density, industrial activity, land cover (remote sensing), and meteorology. The first stage separates the temporal trend in BS for the region as a whole from within-region spatial variation and the second stage is a linear model which predicts BS levels at all locations in the region using spatially referenced covariate data as predictors and the regional predicted temporal trend as an offset. Traffic and land cover predictors were included in the final model, which predicted 70% of the spatio-temporal variation in BS across the study region over the study period. This modeling approach appears to provide a robust way of estimating exposure to BS at an inter-urban scale.

Calibration of the MSL/ChemCam/LIBS Remote Sensing Composition Instrument

NASA Technical Reports Server (NTRS)

Wiens, R. C.; Maurice S.; Bender, S.; Barraclough, B. L.; Cousin, A.; Forni, O.; Ollila, A.; Newsom, H.; Vaniman, D.; Clegg, S.;

Downscaling of Remotely Sensed Land Surface Temperature with multi-sensor based products

NASA Astrophysics Data System (ADS)

Jeong, J.; Baik, J.; Choi, M.

2016-12-01

Remotely sensed satellite data provides a bird's eye view, which allows us to understand spatiotemporal behavior of hydrologic variables at global scale. Especially, geostationary satellite continuously observing specific regions is useful to monitor the fluctuations of hydrologic variables as well as meteorological factors. However, there are still problems regarding spatial resolution whether the fine scale land cover can be represented with the spatial resolution of the satellite sensor, especially in the area of complex topography. To solve these problems, many researchers have been trying to establish the relationship among various hydrological factors and combine images from multi-sensor to downscale land surface products. One of geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS), has Meteorological Imager (MI) and Geostationary Ocean Color Imager (GOCI). MI performing the meteorological mission produce Rainfall Intensity (RI), Land Surface Temperature (LST), and many others every 15 minutes. Even though it has high temporal resolution, low spatial resolution of MI data is treated as major research problem in many studies. This study suggests a methodology to downscale 4 km LST datasets derived from MI in finer resolution (500m) by using GOCI datasets in Northeast Asia. Normalized Difference Vegetation Index (NDVI) recognized as variable which has significant relationship with LST are chosen to estimate LST in finer resolution. Each pixels of NDVI and LST are separated according to land cover provided from MODerate resolution Imaging Spectroradiometer (MODIS) to achieve more accurate relationship. Downscaled LST are compared with LST observed from Automated Synoptic Observing System (ASOS) for assessing its accuracy. The downscaled LST results of this study, coupled with advantage of geostationary satellite, can be applied to observe hydrologic process efficiently.

Classification of Prairie basins by their hysteretic connected functions

NASA Astrophysics Data System (ADS)

Shook, K.; Pomeroy, J. W.

2017-12-01

Diagnosing climate change impacts in the post-glacial landscapes of the North American Prairies through hydrological modelling is made difficult by drainage basin physiography. The region is cold, dry and flat with poorly developed stream networks, and so the basin area that is hydrologically connected to the stream outlet varies with basin depressional storage. The connected area controls the contributing area for runoff reaching the stream outlet. As depressional storage fills, ponds spill from one to another; the chain of spilling ponds allows water to flow over the landscape and increases the connected area of the basin. As depressional storage decreases, the connected fraction drops dramatically. Detailed, fine-scale models and remote sensing have shown that the relationship between connected area and the depressional storage is hysteretic in Prairie basins and that the nature of hysteresis varies with basin physiography. This hysteresis needs to be represented in hydrological models to calculate contributing area, and therefore streamflow hydrographs. Parameterisations of the hysteresis are needed for large-scale models used for climate change diagnosis. However, use of parameterisations of hysteresis requires guidance on how to represent them for a particular basin. This study shows that it is possible to relate the shape of hysteretic functions as determined by detailed models to the overall physiography of the basin, such as the fraction of the basin below the outlet, and remote sensing estimates of depressional storage, using the size distribution and location of maximum ponded water areas. By classifying basin physiography, the hysteresis of connected area - storage relationships can be estimated for basins that do not have high-resolution topographic data, and without computationally-expensive high-resolution modelling.

Advanced Ecosystem Mapping Techniques for Large Arctic Study Domains Using Calibrated High-Resolution Imagery

NASA Astrophysics Data System (ADS)

Macander, M. J.; Frost, G. V., Jr.

2015-12-01

Regional-scale mapping of vegetation and other ecosystem properties has traditionally relied on medium-resolution remote sensing such as Landsat (30 m) and MODIS (250 m). Yet, the burgeoning availability of high-resolution (<=2 m) imagery and ongoing advances in computing power and analysis tools raises the prospect of performing ecosystem mapping at fine spatial scales over large study domains. Here we demonstrate cutting-edge mapping approaches over a ~35,000 km² study area on Alaska's North Slope using calibrated and atmospherically-corrected mosaics of high-resolution WorldView-2 and GeoEye-1 imagery: (1) an a priori spectral approach incorporating the Satellite Imagery Automatic Mapper (SIAM) algorithms; (2) image segmentation techniques; and (3) texture metrics. The SIAM spectral approach classifies radiometrically-calibrated imagery to general vegetation density categories and non-vegetated classes. The SIAM classes were developed globally and their applicability in arctic tundra environments has not been previously evaluated. Image segmentation, or object-based image analysis, automatically partitions high-resolution imagery into homogeneous image regions that can then be analyzed based on spectral, textural, and contextual information. We applied eCognition software to delineate waterbodies and vegetation classes, in combination with other techniques. Texture metrics were evaluated to determine the feasibility of using high-resolution imagery to algorithmically characterize periglacial surface forms (e.g., ice-wedge polygons), which are an important physical characteristic of permafrost-dominated regions but which cannot be distinguished by medium-resolution remote sensing. These advanced mapping techniques provide products which can provide essential information supporting a broad range of ecosystem science and land-use planning applications in northern Alaska and elsewhere in the circumpolar Arctic.

Fine-Granularity Functional Interaction Signatures for Characterization of Brain Conditions

PubMed Central

Hu, Xintao; Zhu, Dajiang; Lv, Peili; Li, Kaiming; Han, Junwei; Wang, Lihong; Shen, Dinggang; Guo, Lei; Liu, Tianming

2014-01-01

In the human brain, functional activity occurs at multiple spatial scales. Current studies on functional brain networks and their alterations in brain diseases via resting-state functional magnetic resonance imaging (rs-fMRI) are generally either at local scale (regionally confined analysis and inter-regional functional connectivity analysis) or at global scale (graph theoretic analysis). In contrast, inferring functional interaction at fine-granularity sub-network scale has not been adequately explored yet. Here our hypothesis is that functional interaction measured at fine-granularity subnetwork scale can provide new insight into the neural mechanisms of neurological and psychological conditions, thus offering complementary information for healthy and diseased population classification. In this paper, we derived fine-granularity functional interaction (FGFI) signatures in subjects with Mild Cognitive Impairment (MCI) and Schizophrenia by diffusion tensor imaging (DTI) and rsfMRI, and used patient-control classification experiments to evaluate the distinctiveness of the derived FGFI features. Our experimental results have shown that the FGFI features alone can achieve comparable classification performance compared with the commonly used inter-regional connectivity features. However, the classification performance can be substantially improved when FGFI features and inter-regional connectivity features are integrated, suggesting the complementary information achieved from the FGFI signatures. PMID:23319242

FINE STRUCTURES AND OVERLYING LOOPS OF CONFINED SOLAR FLARES

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

Yang, Shuhong; Zhang, Jun; Xiang, Yongyuan, E-mail: shuhongyang@nao.cas.cn

2014-10-01

Using the Hα observations from the New Vacuum Solar Telescope at the Fuxian Solar Observatory, we focus on the fine structures of three confined flares and the issue why all the three flares are confined instead of eruptive. All the three confined flares take place successively at the same location and have similar morphologies, so can be termed homologous confined flares. In the simultaneous images obtained by the Solar Dynamics Observatory, many large-scale coronal loops above the confined flares are clearly observed in multi-wavelengths. At the pre-flare stage, two dipoles emerge near the negative sunspot, and the dipolar patches aremore » connected by small loops appearing as arch-shaped Hα fibrils. There exists a reconnection between the small loops, and thus the Hα fibrils change their configuration. The reconnection also occurs between a set of emerging Hα fibrils and a set of pre-existing large loops, which are rooted in the negative sunspot, a nearby positive patch, and some remote positive faculae, forming a typical three-legged structure. During the flare processes, the overlying loops, some of which are tracked by activated dark materials, do not break out. These direct observations may illustrate the physical mechanism of confined flares, i.e., magnetic reconnection between the emerging loops and the pre-existing loops triggers flares and the overlying loops prevent the flares from being eruptive.« less

Remote sensing of the Canadian Arctic: Modelling biophysical variables

NASA Astrophysics Data System (ADS)

Liu, Nanfeng

It is anticipated that Arctic vegetation will respond in a variety of ways to altered temperature and precipitation patterns expected with climate change, including changes in phenology, productivity, biomass, cover and net ecosystem exchange. Remote sensing provides data and data processing methodologies for monitoring and assessing Arctic vegetation over large areas. The goal of this research was to explore the potential of hyperspectral and high spatial resolution multispectral remote sensing data for modelling two important Arctic biophysical variables: Percent Vegetation Cover (PVC) and the fraction of Absorbed Photosynthetically Active Radiation (fAPAR). A series of field experiments were conducted to collect PVC and fAPAR at three Canadian Arctic sites: (1) Sabine Peninsula, Melville Island, NU; (2) Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, NU; and (3) Apex River Watershed (ARW), Baffin Island, NU. Linear relationships between biophysical variables and Vegetation Indices (VIs) were examined at different spatial scales using field spectra (for the Sabine Peninsula site) and high spatial resolution satellite data (for the CBAWO and ARW sites). At the Sabine Peninsula site, hyperspectral VIs exhibited a better performance for modelling PVC than multispectral VIs due to their capacity for sampling fine spectral features. The optimal hyperspectral bands were located at important spectral features observed in Arctic vegetation spectra, including leaf pigment absorption in the red wavelengths and at the red-edge, leaf water absorption in the near infrared, and leaf cellulose and lignin absorption in the shortwave infrared. At the CBAWO and ARW sites, field PVC and fAPAR exhibited strong correlations (R2 > 0.70) with the NDVI (Normalized Difference Vegetation Index) derived from high-resolution WorldView-2 data. Similarly, high spatial resolution satellite-derived fAPAR was correlated to MODIS fAPAR (R2 = 0.68), with a systematic overestimation of 0.08, which was attributed to PAR absorption by soil that could not be excluded from the fAPAR calculation. This research clearly demonstrates that high spectral and spatial resolution remote sensing VIs can be used to successfully model Arctic biophysical variables. The methods and results presented in this research provided a guide for future studies aiming to model other Arctic biophysical variables through remote sensing data.

Remote Evaluation of the Coherence of Indirect Manipulation Interface Systems For Agent-Mediated Legacy Data

DTIC Science & Technology

2000-05-21

High School Chemistry teacher, for my first thorough appreciation and fine immersion in the scientific method. Mrs. Linda Giffin, Eleventh-Grade...Scott Renner, and Len Seligman . 1997. A consumer viewpoint on `mediator languages -- a proposal for a standard`. SIGMOD Record 26, no. 1: 45-46. http

Downscaling soil moisture over regions that include multiple coarse-resolution grid cells

USDA-ARS?s Scientific Manuscript database

Many applications require soil moisture estimates over large spatial extents (30-300 km) and at fine-resolutions (10-30 m). Remote-sensing methods can provide soil moisture estimates over very large spatial extents (continental to global) at coarse resolutions (10-40 km), but their output must be d...

Spectral behavior of hydrated sulfate salts: implications for Europa mission spectrometer design

NASA Technical Reports Server (NTRS)

Dalton, James Bradley 3rd

2003-01-01

Remote sensing of the surface of Europa with near-infrared instruments has suggested the presence of hydrated materials, including sulfate salts. Attention has been focused on these salts for the information they might yield regarding the evolution of a putative interior ocean, and the evaluation of its astrobiological potential. These materials exhibit distinct infrared absorption features due to bound water. The interactions of this water with the host molecules lead to fine structure that can be used to discriminate among these materials on the basis of their spectral behavior. This fine structure is even more pronounced at the low temperatures prevalent on icy satellites. Examination of hydrated sulfate salt spectra measured under cryogenic temperature conditions provides realistic constraints for future remote-sensing missions to Europa. In particular, it suggests that a spectrometer system capable of 2-5 nm spectral resolution or better, with a spatial resolution approaching 100 m, would be able to differentiate among proposed hydrated surface materials, if present, and constrain their distributions across the surface. Such information would provide valuable insights into the evolutionary history of Europa.

Spectral behavior of hydrated sulfate salts: implications for Europa mission spectrometer design.

PubMed

Dalton, James Bradley

2003-01-01

Remote sensing of the surface of Europa with near-infrared instruments has suggested the presence of hydrated materials, including sulfate salts. Attention has been focused on these salts for the information they might yield regarding the evolution of a putative interior ocean, and the evaluation of its astrobiological potential. These materials exhibit distinct infrared absorption features due to bound water. The interactions of this water with the host molecules lead to fine structure that can be used to discriminate among these materials on the basis of their spectral behavior. This fine structure is even more pronounced at the low temperatures prevalent on icy satellites. Examination of hydrated sulfate salt spectra measured under cryogenic temperature conditions provides realistic constraints for future remote-sensing missions to Europa. In particular, it suggests that a spectrometer system capable of 2-5 nm spectral resolution or better, with a spatial resolution approaching 100 m, would be able to differentiate among proposed hydrated surface materials, if present, and constrain their distributions across the surface. Such information would provide valuable insights into the evolutionary history of Europa.

Use of remote sensing technology for inventorying and planning utilization of land resources in South Dakota

NASA Technical Reports Server (NTRS)

1974-01-01

A comprehensive land use planning process model is being developed in Meade County, South Dakota, using remote sensing technology. The proper role of remote sensing in the land use planning process is being determined by interaction of remote sensing specialists with local land use planners. The data that were collected by remote sensing techniques are as follows: (1) level I land use data interpreted at a scale of 1:250,000 from false color enlargement prints of ERTS-1 color composite transparencies; (2) detailed land use data interpreted at a scale of 1:24,000 from enlargement color prints of high altitude RB-57 photography; and (3) general soils map interpreted at a scale of 1:250,000 from false color enlargement prints of ERTS-1 color composite transparencies. In addition to use of imagery as an interpretation aid, the utility of using photographs as base maps was demonstrated.

A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model. 3; Disaggregation

NASA Technical Reports Server (NTRS)

Caulfield, John; Crosson, William L.; Inguva, Ramarao; Laymon, Charles A.; Schamschula, Marius

1998-01-01

This is a followup on the preceding presentation by Crosson and Schamschula. The grid size for remote microwave measurements is much coarser than the hydrological model computational grids. To validate the hydrological models with measurements we propose mechanisms to disaggregate the microwave measurements to allow comparison with outputs from the hydrological models. Weighted interpolation and Bayesian methods are proposed to facilitate the comparison. While remote measurements occur at a large scale, they reflect underlying small-scale features. We can give continuing estimates of the small scale features by correcting the simple 0th-order, starting with each small-scale model with each large-scale measurement using a straightforward method based on Kalman filtering.

Predictive Modelling to Identify Near-Shore, Fine-Scale Seabird Distributions during the Breeding Season

PubMed Central

Warwick-Evans, Victoria C.; Atkinson, Philip W.; Robinson, Leonie A.; Green, Jonathan A.

2016-01-01

During the breeding season seabirds are constrained to coastal areas and are restricted in their movements, spending much of their time in near-shore waters either loafing or foraging. However, in using these areas they may be threatened by anthropogenic activities such as fishing, watersports and coastal developments including marine renewable energy installations. Although many studies describe large scale interactions between seabirds and the environment, the drivers behind near-shore, fine-scale distributions are not well understood. For example, Alderney is an important breeding ground for many species of seabird and has a diversity of human uses of the marine environment, thus providing an ideal location to investigate the near-shore fine-scale interactions between seabirds and the environment. We used vantage point observations of seabird distribution, collected during the 2013 breeding season in order to identify and quantify some of the environmental variables affecting the near-shore, fine-scale distribution of seabirds in Alderney’s coastal waters. We validate the models with observation data collected in 2014 and show that water depth, distance to the intertidal zone, and distance to the nearest seabird nest are key predictors in the distribution of Alderney’s seabirds. AUC values for each species suggest that these models perform well, although the model for shags performed better than those for auks and gulls. While further unexplained underlying localised variation in the environmental conditions will undoubtedly effect the fine-scale distribution of seabirds in near-shore waters we demonstrate the potential of this approach in marine planning and decision making. PMID:27031616

Hope for the Forests? Habitat Resiliency Illustrated in the Face of Climate Change Using Fine-Scale Modeling

NASA Astrophysics Data System (ADS)

Flint, L. E.; Flint, A. L.; Weiss, S. B.; Micheli, E. R.

2010-12-01

In the face of rapid climate change, fine-scale predictions of landscape change are of extreme interest to land managers that endeavor to develop long term adaptive strategies for maintaining biodiversity and ecosystem services. Global climate model (GCM) outputs, which generally focus on estimated increases in air temperature, are increasingly applied to species habitat distribution models. For sensitive species subject to climate change, habitat models predict significant migration (either northward or towards higher elevations), or complete extinction. Current studies typically rely on large spatial scale GCM projections (> 10 km) of changes in precipitation and air temperature: at this scale, these models necessarily neglect subtleties of topographic shading, geomorphic expression of the landscape, and fine-scale differences in soil properties - data that is readily available at meaningful local scales. Recent advances in modeling take advantage of available soils, geology, and topographic data to construct watershed-scale scenarios using GCM inputs and result in improved correlations of vegetation distribution with temperature. For this study, future climate projections were downscaled to 270-m and applied to a physically-based hydrologic model to calculate future changes in recharge, runoff, and climatic water deficit (CWD) for basins draining into the northern San Francisco Bay. CWD was analyzed for mapped vegetation types to evaluate the range of CWD for historic time periods in comparison to future time periods. For several forest communities (including blue oak woodlands, montane hardwoods, douglas-fir, and coast redwood) existing landscape area exhibiting suitable CWD diminishes by up 80 percent in the next century, with a trend towards increased CWD throughout the region. However, no forest community loses all suitable habitat, with islands of potential habitat primarily remaining on north facing slopes and deeper soils. Creation of new suitable habitat is also predicted throughout the region. Results have direct application to management issues of habitat connectivity, forest land protection and acquisition, and active management solutions such as transplanting or assisted migration. Although this analysis considers only one driver of forest habitat distribution, consideration of hydrologic derivatives at a fine scale explains current forest community distributions and provides a far more informed perspective on potential future forest distributions. Results demonstrate the utility of fine-scale modeling and provide landscape managers and conservation agencies valuable management tools in fine-scale future forest scenarios and a framework for evaluating forest resiliency in a changing climate.

Assessment of meteorological uncertainties as they apply to the ASCENDS mission

NASA Astrophysics Data System (ADS)

Snell, H. E.; Zaccheo, S.; Chase, A.; Eluszkiewicz, J.; Ott, L. E.; Pawson, S.

2011-12-01

Many environment-oriented remote sensing and modeling applications require precise knowledge of the atmospheric state (temperature, pressure, water vapor, surface pressure, etc.) on a fine spatial grid with a comprehensive understanding of the associated errors. Coincident atmospheric state measurements may be obtained via co-located remote sensing instruments or by extracting these data from ancillary models. The appropriate technique for a given application depends upon the required accuracy. State-of-the-art mesoscale/regional numerical weather prediction (NWP) models operate on spatial scales of a few kilometers resolution, and global scale NWP models operate on scales of tens of kilometers. Remote sensing measurements may be made on spatial scale comparable to the measurement of interest. These measurements normally require a separate sensor, which increases the overall size, weight, power and complexity of the satellite payload. Thus, a comprehensive understanding of the errors associated with each of these approaches is a critical part of the design/characterization of a remote-sensing system whose measurement accuracy depends on knowledge of the atmospheric state. One of the requirements as part of the overall ASCENDS (Active Sensing of CO2 Emissions over Nights, Days, and Seasons) mission development is to develop a consistent set of atmospheric state variables (vertical temperature and water vapor profiles, and surface pressure) for use in helping to constrain overall retrieval error budget. If the error budget requires tighter uncertainties on ancillary atmospheric parameters than can be provided by NWP models and analyses, additional sensors may be required to reduce the overall measurement error and meet mission requirements. To this end we have used NWP models and reanalysis information to generate a set of atmospheric profiles which contain reasonable variability. This data consists of a "truth" set and a companion "measured" set of profiles. The truth set contains climatologically-relevant profiles of pressure, temperature and humidity with an accompanying surface pressure. The measured set consists of some number of instances of the truth set which have been perturbed to represent realistic measurement uncertainty for the truth profile using measurement error covariance matrices. The primary focus has been to develop matrices derived using information about the profile retrieval accuracy as documented for on-orbit sensor systems including AIRS, AMSU, ATMS, and CrIS. Surface pressure variability and uncertainty was derived from globally-compiled station pressure information. We generated an additional measurement set of profiles which represent the overall error within NWP models. These profile sets will allow for comprehensive trade studies for sensor system design and provide a basis for setting measurement requirements for co-located temperature, humidity sounders, determine the utility of NWP data to either replace or supplement collocated measurements, and to assess the overall end-to-end system performance of the sensor system. In this presentation we discuss the process by which we created these data sets and show their utility in performing trade studies for sensor system concepts and designs.

Dissociative Experiences, Creative Imagination, and Artistic Production in Students of Fine Arts

ERIC Educational Resources Information Center

Perez-Fabello, Maria Jose; Campos, Alfredo

2011-01-01

The current research was designed to assess the influence of dissociative experiences and creative imagination on the artistic production of Fine Arts students of the University of Vigo (Spain). The sample consisted of 81 students who were administered the Creative Imagination Scale and The Dissociative Experiences Scale. To measure artistic…

Parallelization of fine-scale computation in Agile Multiscale Modelling Methodology

NASA Astrophysics Data System (ADS)

Macioł, Piotr; Michalik, Kazimierz

2016-10-01

Nowadays, multiscale modelling of material behavior is an extensively developed area. An important obstacle against its wide application is high computational demands. Among others, the parallelization of multiscale computations is a promising solution. Heterogeneous multiscale models are good candidates for parallelization, since communication between sub-models is limited. In this paper, the possibility of parallelization of multiscale models based on Agile Multiscale Methodology framework is discussed. A sequential, FEM based macroscopic model has been combined with concurrently computed fine-scale models, employing a MatCalc thermodynamic simulator. The main issues, being investigated in this work are: (i) the speed-up of multiscale models with special focus on fine-scale computations and (ii) on decreasing the quality of computations enforced by parallel execution. Speed-up has been evaluated on the basis of Amdahl's law equations. The problem of `delay error', rising from the parallel execution of fine scale sub-models, controlled by the sequential macroscopic sub-model is discussed. Some technical aspects of combining third-party commercial modelling software with an in-house multiscale framework and a MPI library are also discussed.

Commercial use of remote sensing in agriculture: a case study

NASA Astrophysics Data System (ADS)

Gnauck, Gary E.

1999-12-01

Over 25 years of research have clearly shown that an analysis of remote sensing imagery can provide information on agricultural crops. Most of this research has been funded by and directed toward the needs of government agencies. Commercial use of agricultural remote sensing has been limited to very small-scale operations supplying remote sensing services to a few selected customers. Datron/Transco Inc. undertook an internally funded remote sensing program directed toward the California cash crop industry (strawberries, lettuce, tomatoes, other fresh vegetables and cotton). The objectives of this program were twofold: (1) to assess the need and readiness of agricultural land managers to adopt remote sensing as a management tool, and (2) determine what technical barriers exist to large-scale implementation of this technology on a commercial basis. The program was divided into three phases: Planning, Engineering Test and Evaluation, and Commercial Operations. Findings: Remote sensing technology can deliver high resolution multispectral imagery with rapid turnaround, that can provide information on crop stress insects, disease and various soil parameters. The limiting factors to the use of remote sensing in agriculture are a lack of familiarization by the land managers, difficulty in translating 'information' into increased revenue or reduced cost for the land manager, and the large economies of scale needed to make the venture commercially viable.

Spatial and Temporal Scaling of Thermal Infrared Remote Sensing Data

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Goel, Narendra S.

1995-01-01

Although remote sensing has a central role to play in the acquisition of synoptic data obtained at multiple spatial and temporal scales to facilitate our understanding of local and regional processes as they influence the global climate, the use of thermal infrared (TIR) remote sensing data in this capacity has received only minimal attention. This results from some fundamental challenges that are associated with employing TIR data collected at different space and time scales, either with the same or different sensing systems, and also from other problems that arise in applying a multiple scaled approach to the measurement of surface temperatures. In this paper, we describe some of the more important problems associated with using TIR remote sensing data obtained at different spatial and temporal scales, examine why these problems appear as impediments to using multiple scaled TIR data, and provide some suggestions for future research activities that may address these problems. We elucidate the fundamental concept of scale as it relates to remote sensing and explore how space and time relationships affect TIR data from a problem-dependency perspective. We also describe how linearity and non-linearity observation versus parameter relationships affect the quantitative analysis of TIR data. Some insight is given on how the atmosphere between target and sensor influences the accurate measurement of surface temperatures and how these effects will be compounded in analyzing multiple scaled TIR data. Last, we describe some of the challenges in modeling TIR data obtained at different space and time scales and discuss how multiple scaled TIR data can be used to provide new and important information for measuring and modeling land-atmosphere energy balance processes.

Characterizing Methane Emission Response to the Past 60 Years of Permafrost Thaw in Thermokarst Lakes

NASA Astrophysics Data System (ADS)

Meyer, F. J.; Walter Anthony, K. M.; Regmi, P.; Engram, M. J.; Wirth, L.; Grosse, G.

2016-12-01

In this NASA ABoVE-funded project, we combine geospatial data products derived from airborne and spaceborne remote sensing (RS) data with targeted field observations and modeling in order to quantify ecosystem responses to Arctic and boreal environmental change. Specifically, we quantify methane (CH4) ebullition (bubbling) emissions associated with 60 years of permafrost thaw in thousands of Alaskan and NW Canadian lakes by direct observation with RS systems. To achieve our goals, we have developed statistically-significant models that are using SAR, optical and infrared RS data in order to detect and quantify CH4 ebullition emissions at intra-, whole- and regional-lake scales. We also established a relationship between observed CH4 ebullition and average annual soil organic carbon (SOC) inputs to a handful of Alaskan lakes via thermokarst-margin expansion during recent decades using field data, radiocarbon dating and modeling. Our paper we will provide an overview of the goals, datasets, and methods used for the various components of this project. We will present on (1) the collection of new and synthesis of existing field data on CH4 ebullition, thaw-bulbs and SOC; (2) the analysis of existing data from aerial surveys, SAR and optical RS of CH4 in lake ice; (3) the orthorectification of historic aerial photos for comparison to high-resolution satellite imagery to produce fine-scale regional maps of lake area change, (4) the modelling of permafrost SOC quantities eroded into lakes; (5) the radiocarbon dating of CH4 and SOC, (6) GIS modeling to produce multi-temporal regional maps of historic lake area change, associated CH4 emissions, and permafrost SOC stocks; and (7) outreach to stakeholders at Alaska village and rural community field sites. To demonstrate the scientific relevance of our work we will also showcase a set of research results that we have been able to achieve so far. These will include (1) first regional-scale RS-based estimates of lake-borne CH4 ebullition emissions; (2) regional scale estimates of lake area change from an analysis of 50 years of remote sensing data; and (3) regression models linking lake area change to CH4 emissions.

A high throughput geocomputing system for remote sensing quantitative retrieval and a case study

NASA Astrophysics Data System (ADS)

Xue, Yong; Chen, Ziqiang; Xu, Hui; Ai, Jianwen; Jiang, Shuzheng; Li, Yingjie; Wang, Ying; Guang, Jie; Mei, Linlu; Jiao, Xijuan; He, Xingwei; Hou, Tingting

2011-12-01

The quality and accuracy of remote sensing instruments have been improved significantly, however, rapid processing of large-scale remote sensing data becomes the bottleneck for remote sensing quantitative retrieval applications. The remote sensing quantitative retrieval is a data-intensive computation application, which is one of the research issues of high throughput computation. The remote sensing quantitative retrieval Grid workflow is a high-level core component of remote sensing Grid, which is used to support the modeling, reconstruction and implementation of large-scale complex applications of remote sensing science. In this paper, we intend to study middleware components of the remote sensing Grid - the dynamic Grid workflow based on the remote sensing quantitative retrieval application on Grid platform. We designed a novel architecture for the remote sensing Grid workflow. According to this architecture, we constructed the Remote Sensing Information Service Grid Node (RSSN) with Condor. We developed a graphic user interface (GUI) tools to compose remote sensing processing Grid workflows, and took the aerosol optical depth (AOD) retrieval as an example. The case study showed that significant improvement in the system performance could be achieved with this implementation. The results also give a perspective on the potential of applying Grid workflow practices to remote sensing quantitative retrieval problems using commodity class PCs.

Inferring landscape-scale land-use impacts on rivers using data from mesocosm experiments and artificial neural networks.

PubMed

Magierowski, Regina H; Read, Steve M; Carter, Steven J B; Warfe, Danielle M; Cook, Laurie S; Lefroy, Edward C; Davies, Peter E

2015-01-01

Identifying land-use drivers of changes in river condition is complicated by spatial scale, geomorphological context, land management, and correlations among responding variables such as nutrients and sediments. Furthermore, variations in standard metrics, such as substratum composition, do not necessarily relate causally to ecological impacts. Consequently, the absence of a significant relationship between a hypothesised driver and a dependent variable does not necessarily indicate the absence of a causal relationship. We conducted a gradient survey to identify impacts of catchment-scale grazing by domestic livestock on river macroinvertebrate communities. A standard correlative approach showed that community structure was strongly related to the upstream catchment area under grazing. We then used data from a stream mesocosm experiment that independently quantified the impacts of nutrients and fine sediments on macroinvertebrate communities to train artificial neural networks (ANNs) to assess the relative influence of nutrients and fine sediments on the survey sites from their community composition. The ANNs developed to predict nutrient impacts did not find a relationship between nutrients and catchment area under grazing, suggesting that nutrients were not an important factor mediating grazing impacts on community composition, or that these ANNs had no generality or insufficient power at the landscape-scale. In contrast, ANNs trained to predict the impacts of fine sediments indicated a significant relationship between fine sediments and catchment area under grazing. Macroinvertebrate communities at sites with a high proportion of land under grazing were thus more similar to those resulting from high fine sediments in a mesocosm experiment than to those resulting from high nutrients. Our study confirms that 1) fine sediment is an important mediator of land-use impacts on river macroinvertebrate communities, 2) ANNs can successfully identify subtle effects and separate the effects of correlated variables, and 3) data from small-scale experiments can generate relationships that help explain landscape-scale patterns.

Inferring Landscape-Scale Land-Use Impacts on Rivers Using Data from Mesocosm Experiments and Artificial Neural Networks

PubMed Central

Magierowski, Regina H.; Read, Steve M.; Carter, Steven J. B.; Warfe, Danielle M.; Cook, Laurie S.; Lefroy, Edward C.; Davies, Peter E.

2015-01-01

Identifying land-use drivers of changes in river condition is complicated by spatial scale, geomorphological context, land management, and correlations among responding variables such as nutrients and sediments. Furthermore, variations in standard metrics, such as substratum composition, do not necessarily relate causally to ecological impacts. Consequently, the absence of a significant relationship between a hypothesised driver and a dependent variable does not necessarily indicate the absence of a causal relationship. We conducted a gradient survey to identify impacts of catchment-scale grazing by domestic livestock on river macroinvertebrate communities. A standard correlative approach showed that community structure was strongly related to the upstream catchment area under grazing. We then used data from a stream mesocosm experiment that independently quantified the impacts of nutrients and fine sediments on macroinvertebrate communities to train artificial neural networks (ANNs) to assess the relative influence of nutrients and fine sediments on the survey sites from their community composition. The ANNs developed to predict nutrient impacts did not find a relationship between nutrients and catchment area under grazing, suggesting that nutrients were not an important factor mediating grazing impacts on community composition, or that these ANNs had no generality or insufficient power at the landscape-scale. In contrast, ANNs trained to predict the impacts of fine sediments indicated a significant relationship between fine sediments and catchment area under grazing. Macroinvertebrate communities at sites with a high proportion of land under grazing were thus more similar to those resulting from high fine sediments in a mesocosm experiment than to those resulting from high nutrients. Our study confirms that 1) fine sediment is an important mediator of land-use impacts on river macroinvertebrate communities, 2) ANNs can successfully identify subtle effects and separate the effects of correlated variables, and 3) data from small-scale experiments can generate relationships that help explain landscape-scale patterns. PMID:25775245

Deep learning decision fusion for the classification of urban remote sensing data

NASA Astrophysics Data System (ADS)

Abdi, Ghasem; Samadzadegan, Farhad; Reinartz, Peter

2018-01-01

Multisensor data fusion is one of the most common and popular remote sensing data classification topics by considering a robust and complete description about the objects of interest. Furthermore, deep feature extraction has recently attracted significant interest and has become a hot research topic in the geoscience and remote sensing research community. A deep learning decision fusion approach is presented to perform multisensor urban remote sensing data classification. After deep features are extracted by utilizing joint spectral-spatial information, a soft-decision made classifier is applied to train high-level feature representations and to fine-tune the deep learning framework. Next, a decision-level fusion classifies objects of interest by the joint use of sensors. Finally, a context-aware object-based postprocessing is used to enhance the classification results. A series of comparative experiments are conducted on the widely used dataset of 2014 IEEE GRSS data fusion contest. The obtained results illustrate the considerable advantages of the proposed deep learning decision fusion over the traditional classifiers.

SmartAQnet: remote and in-situ sensing of urban air quality

NASA Astrophysics Data System (ADS)

Budde, Matthias; Riedel, Till; Beigl, Michael; Schäfer, Klaus; Emeis, Stefan; Cyrys, Josef; Schnelle-Kreis, Jürgen; Philipp, Andreas; Ziegler, Volker; Grimm, Hans; Gratza, Thomas

2017-10-01

Air quality and the associated subjective and health-related quality of life are among the important topics of urban life in our time. However, it is very difficult for many cities to take measures to accommodate today's needs concerning e.g. mobility, housing and work, because a consistent fine-granular data and information on causal chains is largely missing. This has the potential to change, as today, both large-scale basic data as well as new promising measuring approaches are becoming available. The project "SmartAQnet", funded by the German Federal Ministry of Transport and Digital Infrastructure (BMVI), is based on a pragmatic, data driven approach, which for the first time combines existing data sets with a networked mobile measurement strategy in the urban space. By connecting open data, such as weather data or development plans, remote sensing of influencing factors, and new mobile measurement approaches, such as participatory sensing with low-cost sensor technology, "scientific scouts" (autonomous, mobile smart dust measurement device that is auto-calibrated to a high-quality reference instrument within an intelligent monitoring network) and demand-oriented measurements by light-weight UAVs, a novel measuring and analysis concept is created within the model region of Augsburg, Germany. In addition to novel analytics, a prototypical technology stack is planned which, through modern analytics methods and Big Data and IoT technologies, enables application in a scalable way.

Application of PALSAR-2 Remote Sensing Data for Landslide Hazard Mapping in Kelantan River Basin, Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Beiranvand Pour, Amin; Hashim, Mazlan

2016-06-01

Yearly, several landslides ensued during heavy monsoons rainfall in Kelantan river basin, peninsular Malaysia, which are obviously connected to geological structures and topographical features of the region. In this study, the recently launched Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) onboard the Advanced Land Observing Satellite-2 (ALOS-2), remote sensing data were used to map geological structural and topographical features in the Kelantan river basin for identification of high potential risk and susceptible zones for landslides. Adaptive Local Sigma filter was selected and applied to accomplish speckle reduction and preserving both edges and features in PALSAR-2 fine mode observation images. Different polarization images were integrated to enhance geological structures. Additionally, directional filters were applied to the PALSAR-2 Local Sigma resultant image for edge enhancement and detailed identification of linear features. Several faults, drainage patterns and lithological contact layers were identified at regional scale. In order to assess the results, fieldwork and GPS survey were conducted in the landslide affected zones in the Kelantan river basin. Results demonstrate the most of the landslides were associated with N-S, NNW-SSE and NE-SW trending faults, angulated drainage pattern and metamorphic and Quaternary units. Consequently, structural and topographical geology maps were produced for Kelantan river basin using PALSAR-2 data, which could be broadly applicable for landslide hazard mapping.

Temporal and Fine-Grained Pedestrian Action Recognition on Driving Recorder Database

PubMed Central

Satoh, Yutaka; Aoki, Yoshimitsu; Oikawa, Shoko; Matsui, Yasuhiro

2018-01-01

The paper presents an emerging issue of fine-grained pedestrian action recognition that induces an advanced pre-crush safety to estimate a pedestrian intention in advance. The fine-grained pedestrian actions include visually slight differences (e.g., walking straight and crossing), which are difficult to distinguish from each other. It is believed that the fine-grained action recognition induces a pedestrian intention estimation for a helpful advanced driver-assistance systems (ADAS). The following difficulties have been studied to achieve a fine-grained and accurate pedestrian action recognition: (i) In order to analyze the fine-grained motion of a pedestrian appearance in the vehicle-mounted drive recorder, a method to describe subtle change of motion characteristics occurring in a short time is necessary; (ii) even when the background moves greatly due to the driving of the vehicle, it is necessary to detect changes in subtle motion of the pedestrian; (iii) the collection of large-scale fine-grained actions is very difficult, and therefore a relatively small database should be focused. We find out how to learn an effective recognition model with only a small-scale database. Here, we have thoroughly evaluated several types of configurations to explore an effective approach in fine-grained pedestrian action recognition without a large-scale database. Moreover, two different datasets have been collected in order to raise the issue. Finally, our proposal attained 91.01% on National Traffic Science and Environment Laboratory database (NTSEL) and 53.23% on the near-miss driving recorder database (NDRDB). The paper has improved +8.28% and +6.53% from baseline two-stream fusion convnets. PMID:29461473

Estimating Evaporative Fraction From Readily Obtainable Variables in Mangrove Forests of the Everglades, U.S.A.

NASA Technical Reports Server (NTRS)

Yagci, Ali Levent; Santanello, Joseph A.; Jones, John; Barr, Jordan

2017-01-01

A remote-sensing-based model to estimate evaporative fraction (EF) the ratio of latent heat (LE; energy equivalent of evapotranspiration -ET-) to total available energy from easily obtainable remotely-sensed and meteorological parameters is presented. This research specifically addresses the shortcomings of existing ET retrieval methods such as calibration requirements of extensive accurate in situ micro-meteorological and flux tower observations, or of a large set of coarse-resolution or model-derived input datasets. The trapezoid model is capable of generating spatially varying EF maps from standard products such as land surface temperature [T(sub s)] normalized difference vegetation index (NDVI)and daily maximum air temperature [T(sub a)]. The 2009 model results were validated at an eddy-covariance tower (Fluxnet ID: US-Skr) in the Everglades using T(sub s) and NDVI products from Landsat as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results indicate that the model accuracy is within the range of instrument uncertainty, and is dependent on the spatial resolution and selection of end-members (i.e. wet/dry edge). The most accurate results were achieved with the T(sub s) from Landsat relative to the T(sub s) from the MODIS flown on the Terra and Aqua platforms due to the fine spatial resolution of Landsat (30 m). The bias, mean absolute percentage error and root mean square percentage error were as low as 2.9% (3.0%), 9.8% (13.3%), and 12.1% (16.1%) for Landsat-based (MODIS-based) EF estimates, respectively. Overall, this methodology shows promise for bridging the gap between temporally limited ET estimates at Landsat scales and more complex and difficult to constrain global ET remote-sensing models.

Estimating evaporative fraction from readily obtainable variables in mangrove forests of the Everglades, U.S.A.

USGS Publications Warehouse

Yagci, Ali Levent; Santanello, Joseph A.; Jones, John W.; Barr, Jordan G.

2017-01-01

A remote-sensing-based model to estimate evaporative fraction (EF) – the ratio of latent heat (LE; energy equivalent of evapotranspiration –ET–) to total available energy – from easily obtainable remotely-sensed and meteorological parameters is presented. This research specifically addresses the shortcomings of existing ET retrieval methods such as calibration requirements of extensive accurate in situ micrometeorological and flux tower observations or of a large set of coarse-resolution or model-derived input datasets. The trapezoid model is capable of generating spatially varying EF maps from standard products such as land surface temperature (Ts) normalized difference vegetation index (NDVI) and daily maximum air temperature (Ta). The 2009 model results were validated at an eddy-covariance tower (Fluxnet ID: US-Skr) in the Everglades using Ts and NDVI products from Landsat as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results indicate that the model accuracy is within the range of instrument uncertainty, and is dependent on the spatial resolution and selection of end-members (i.e. wet/dry edge). The most accurate results were achieved with the Ts from Landsat relative to the Ts from the MODIS flown on the Terra and Aqua platforms due to the fine spatial resolution of Landsat (30 m). The bias, mean absolute percentage error and root mean square percentage error were as low as 2.9% (3.0%), 9.8% (13.3%), and 12.1% (16.1%) for Landsat-based (MODIS-based) EF estimates, respectively. Overall, this methodology shows promise for bridging the gap between temporally limited ET estimates at Landsat scales and more complex and difficult to constrain global ET remote-sensing models.

Mapping multi-scale vascular plant richness in a forest landscape with integrated LiDAR and hyperspectral remote-sensing.

PubMed

Hakkenberg, C R; Zhu, K; Peet, R K; Song, C

2018-02-01

The central role of floristic diversity in maintaining habitat integrity and ecosystem function has propelled efforts to map and monitor its distribution across forest landscapes. While biodiversity studies have traditionally relied largely on ground-based observations, the immensity of the task of generating accurate, repeatable, and spatially-continuous data on biodiversity patterns at large scales has stimulated the development of remote-sensing methods for scaling up from field plot measurements. One such approach is through integrated LiDAR and hyperspectral remote-sensing. However, despite their efficiencies in cost and effort, LiDAR-hyperspectral sensors are still highly constrained in structurally- and taxonomically-heterogeneous forests - especially when species' cover is smaller than the image resolution, intertwined with neighboring taxa, or otherwise obscured by overlapping canopy strata. In light of these challenges, this study goes beyond the remote characterization of upper canopy diversity to instead model total vascular plant species richness in a continuous-cover North Carolina Piedmont forest landscape. We focus on two related, but parallel, tasks. First, we demonstrate an application of predictive biodiversity mapping, using nonparametric models trained with spatially-nested field plots and aerial LiDAR-hyperspectral data, to predict spatially-explicit landscape patterns in floristic diversity across seven spatial scales between 0.01-900 m 2 . Second, we employ bivariate parametric models to test the significance of individual, remotely-sensed predictors of plant richness to determine how parameter estimates vary with scale. Cross-validated results indicate that predictive models were able to account for 15-70% of variance in plant richness, with LiDAR-derived estimates of topography and forest structural complexity, as well as spectral variance in hyperspectral imagery explaining the largest portion of variance in diversity levels. Importantly, bivariate tests provide evidence of scale-dependence among predictors, such that remotely-sensed variables significantly predict plant richness only at spatial scales that sufficiently subsume geolocational imprecision between remotely-sensed and field data, and best align with stand components including plant size and density, as well as canopy gaps and understory growth patterns. Beyond their insights into the scale-dependent patterns and drivers of plant diversity in Piedmont forests, these results highlight the potential of remotely-sensible essential biodiversity variables for mapping and monitoring landscape floristic diversity from air- and space-borne platforms. © 2017 by the Ecological Society of America.

Probabilistic Assessment of Soil Moisture using C-band Quad-polarized Remote Sensing Data from RISAT1

NASA Astrophysics Data System (ADS)

Pal, Manali; Suman, Mayank; Das, Sarit Kumar; Maity, Rajib

2017-04-01

Information on spatio-temporal distribution of surface Soil Moisture Content (SMC) is essential in several hydrological, meteorological and agricultural applications. There has been increasing importance of microwave active remote sensing data for large-scale estimation of surface SMC because of its ability to monitor spatial and temporal variation of surface SMC at regional, continental and global scale at a reasonably fine spatial and temporal resolution. The use of Synthetic Aperture Radar (SAR) is highly potential for catchment-scale applications due to high spatial resolution (˜10-20 m) both for vegetated and bare soil surface as well as because of its all-weather and day and night characteristics. However, one prime disadvantage of SAR is that their signal is subjective to SMC along with Land Use Land Cover (LULC) and surface roughness conditions, making the retrieval of SMC from SAR data an "ill-posed" problem. Moreover, the quantification of uncertainty due to inappropriate surface roughness characterization, soil texture, inversion techniques etc. even in the latest established retrieval methods, is little explored. This paper reports a recently developed method to estimate the surface SMC with probabilistic assessment of uncertainty associated with the estimation (Pal et al., 2016). Quad-polarized SAR data from Radar Imaging Satellite1 (RISAT1), launched in 2012 by Indian Space Research Organization (ISRO) and information on LULC regarding bareland and vegetated land (<30 cm height) are used in estimation using the potential of multivariate probabilistic assessment through copulas. The salient features of the study are: 1) development of a combined index to understand the role of all the quad-polarized backscattering coefficients and soil texture information in SMC estimation; 2) applicability of the model for different incidence angles using normalized incidence angle theory proposed by Zibri et al. (2005); and 3) assessment of uncertainty range of the estimated SMC. Supervised Principal Component Analysis (SPCA) is used for development of combined index and Frank copula is found to be the best-fit copula. The developed model is validated with the field soil moisture values over 334 monitoring points within the study area and used for development of a soil moisture map. While the performance is promising, the model is applicable only for bare and vegetated land. References: Pal, M., Maity, R., Suman, M., Das, S.K., Patel, P., and Srivastava, H.S., (2016). "Satellite-Based Probabilistic Assessment of Soil Moisture Using C-Band Quad-Polarized RISAT1 Data." IEEE Transactions on Geoscience and Remote Sensing, In Press, doi:10.1109/TGRS.2016.2623378. Zribi, M., Baghdadi, N., Holah, N., and Fafin, O., (2005)."New methodology for soil surface moisture estimation and its application to ENVISAT-ASAR multi-incidence data inversion." Remote Sensing of Environment, vol. 96, nos. 3-4, pp. 485-496.

Development of fine-resolution analyses and expanded large-scale forcing properties. Part II: Scale-awareness and application to single-column model experiments

DOE PAGES

Feng, Sha; Vogelmann, Andrew M.; Li, Zhijin; ...

2015-01-20

Fine-resolution three-dimensional fields have been produced using the Community Gridpoint Statistical Interpolation (GSI) data assimilation system for the U.S. Department of Energy’s Atmospheric Radiation Measurement Program (ARM) Southern Great Plains region. The GSI system is implemented in a multi-scale data assimilation framework using the Weather Research and Forecasting model at a cloud-resolving resolution of 2 km. From the fine-resolution three-dimensional fields, large-scale forcing is derived explicitly at grid-scale resolution; a subgrid-scale dynamic component is derived separately, representing subgrid-scale horizontal dynamic processes. Analyses show that the subgrid-scale dynamic component is often a major component over the large-scale forcing for grid scalesmore » larger than 200 km. The single-column model (SCM) of the Community Atmospheric Model version 5 (CAM5) is used to examine the impact of the grid-scale and subgrid-scale dynamic components on simulated precipitation and cloud fields associated with a mesoscale convective system. It is found that grid-scale size impacts simulated precipitation, resulting in an overestimation for grid scales of about 200 km but an underestimation for smaller grids. The subgrid-scale dynamic component has an appreciable impact on the simulations, suggesting that grid-scale and subgrid-scale dynamic components should be considered in the interpretation of SCM simulations.« less

Carbon nanotube growth density control

NASA Technical Reports Server (NTRS)

Delzeit, Lance D. (Inventor); Schipper, John F. (Inventor)

2010-01-01

Method and system for combined coarse scale control and fine scale control of growth density of a carbon nanotube (CNT) array on a substrate, using a selected electrical field adjacent to a substrate surface for coarse scale density control (by one or more orders of magnitude) and a selected CNT growth temperature range for fine scale density control (by multiplicative factors of less than an order of magnitude) of CNT growth density. Two spaced apart regions on a substrate may have different CNT growth densities and/or may use different feed gases for CNT growth.

High Resolutions Studies of the Structure of the Solar Atmosphere

DTIC Science & Technology

1992-06-30

Pairs in the Solar Wind", submitted to J. Geophys. Res., July 20, 1992. M. Karovska , F. Blundell and S. R. Habbal, "Fine Scale Structure of Active...Regions", manuscript in preparation. M. Karovska , F. Blundell and S. R. Habbal, "Fine Scale Structure of the Solar Limb in a Coronal Hole", manuscript in

Using simulated 3D surface fuelbeds and terrestrial laser scan data to develop inputs to fire behavior models

Treesearch

Eric Rowell; E. Louise Loudermilk; Carl Seielstad; Joseph O' Brien

2016-01-01

Understanding fine-scale variability in understory fuels is increasingly important as physics-based fire behavior modelsdrive needs for higher-resolution data. Describing fuelbeds 3Dly is critical in determining vertical and horizontal distributions offuel elements and the mass, especially in frequently burned pine ecosystems where fine-scale...

Fine scale population genetic structure and within tree distribution of mating types of Venturia effusa, cause of pecan scab in the USA

USDA-ARS?s Scientific Manuscript database

Scab (caused by Venturia effusa) is the major disease of pecan in the southeastern USA. There is no information available on the fine scale population genetic diversity. Four cv. Wichita trees (populations) were sampled hierarchically. Within each tree canopy, 4 approximately evenly spaced terminals...

Fine-scale genetic structure of whitebark pine (Pinus albicaulis) associations with watershed and growth form

Treesearch

Deborah L. Rogers; Constance I. Millar; Robert D. Westfall

1999-01-01

The fine-scale genetic structure of a subalpine conifer, whitebark pine (Pinus albicaulis Engelm.), was studied at nested geographic levels from watershed to adjacent stems in the eastern Sierra Nevada Range of California. A combination of several characteristics contributed to unpredicted genetic structure in this species. This includes being one of...

Clonal growth and fine-scale genetic structure in tanoak (Notholithocarpus densiflorus: Fagaceae)

Treesearch

Richard S. Dodd; Wasima Mayer; Alejandro Nettel; Zara Afzal-Rafii

2013-01-01

The combination of sprouting and reproduction by seed can have important consequences on fine-scale spatial distribution of genetic structure (SGS). SGS is an important consideration for species’ restoration because it determines the minimum distance among seed trees to maximize genetic diversity while not prejudicing locally adapted genotypes. Local environmental...

Development of the fine-particle agglomerator

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

Feldman, P.; Balasic, P.

1999-07-01

This paper presents the current status of the commercial development of a new technology to more efficiently control fine particulate emissions. The technology is based on an invention by Environmental Elements Corporation (EEC) which utilizes laminar flow to promote contact of fine submicron particles with larger particles to form agglomerates prior to their removal in a conventional particulate control device, such as an ESP. As agglomerates the particles are easily captured in the control device, whereas a substantial amount would pass through if allowed to remain as fine particles. EEC has developed the laminar-flow agglomerator technology through the laboratory proof-of-conceptmore » stage, which was funded by a DOE SBIR grant, to pilot-scale and full-scale demonstrations.« less

Ship detection in panchromatic images: a new method and its DSP implementation

NASA Astrophysics Data System (ADS)

Yao, Yuan; Jiang, Zhiguo; Zhang, Haopeng; Wang, Mengfei; Meng, Gang

2016-03-01

In this paper, a new ship detection method is proposed after analyzing the characteristics of panchromatic remote sensing images and ship targets. Firstly, AdaBoost(Adaptive Boosting) classifiers trained by Haar features are utilized to make coarse detection of ship targets. Then LSD (Line Segment Detector) is adopted to extract the line features in target slices to make fine detection. Experimental results on a dataset of panchromatic remote sensing images with a spatial resolution of 2m show that the proposed algorithm can achieve high detection rate and low false alarm rate. Meanwhile, the algorithm can meet the needs of practical applications on DSP (Digital Signal Processor).

Early Forest Fire Detection Using Radio-Acoustic Sounding System

PubMed Central

Sahin, Yasar Guneri; Ince, Turker

2009-01-01

Automated early fire detection systems have recently received a significant amount of attention due to their importance in protecting the global environment. Some emergent technologies such as ground-based, satellite-based remote sensing and distributed sensor networks systems have been used to detect forest fires in the early stages. In this study, a radio-acoustic sounding system with fine space and time resolution capabilities for continuous monitoring and early detection of forest fires is proposed. Simulations show that remote thermal mapping of a particular forest region by the proposed system could be a potential solution to the problem of early detection of forest fires. PMID:22573967

Fine-scale human genetic structure in Western France.

PubMed

Karakachoff, Matilde; Duforet-Frebourg, Nicolas; Simonet, Floriane; Le Scouarnec, Solena; Pellen, Nadine; Lecointe, Simon; Charpentier, Eric; Gros, Françoise; Cauchi, Stéphane; Froguel, Philippe; Copin, Nane; Le Tourneau, Thierry; Probst, Vincent; Le Marec, Hervé; Molinaro, Sabrina; Balkau, Beverley; Redon, Richard; Schott, Jean-Jacques; Blum, Michael Gb; Dina, Christian

2015-06-01

The difficulties arising from association analysis with rare variants underline the importance of suitable reference population cohorts, which integrate detailed spatial information. We analyzed a sample of 1684 individuals from Western France, who were genotyped at genome-wide level, from two cohorts D.E.S.I.R and CavsGen. We found that fine-scale population structure occurs at the scale of Western France, with distinct admixture proportions for individuals originating from the Brittany Region and the Vendée Department. Genetic differentiation increases with distance at a high rate in these two parts of Northwestern France and linkage disequilibrium is higher in Brittany suggesting a lower effective population size. When looking for genomic regions informative about Breton origin, we found two prominent associated regions that include the lactase region and the HLA complex. For both the lactase and the HLA regions, there is a low differentiation between Bretons and Irish, and this is also found at the genome-wide level. At a more refined scale, and within the Pays de la Loire Region, we also found evidence of fine-scale population structure, although principal component analysis showed that individuals from different departments cannot be confidently discriminated. Because of the evidence for fine-scale genetic structure in Western France, we anticipate that rare and geographically localized variants will be identified in future full-sequence analyses.

Demonstrating the Uneven Importance of Fine-Scale Forest Structure on Snow Distributions using High Resolution Modeling

NASA Astrophysics Data System (ADS)

Broxton, P. D.; Harpold, A. A.; van Leeuwen, W.; Biederman, J. A.

2016-12-01

Quantifying the amount of snow in forested mountainous environments, as well as how it may change due to warming and forest disturbance, is critical given its importance for water supply and ecosystem health. Forest canopies affect snow accumulation and ablation in ways that are difficult to observe and model. Furthermore, fine-scale forest structure can accentuate or diminish the effects of forest-snow interactions. Despite decades of research demonstrating the importance of fine-scale forest structure (e.g. canopy edges and gaps) on snow, we still lack a comprehensive understanding of where and when forest structure has the largest impact on snowpack mass and energy budgets. Here, we use a hyper-resolution (1 meter spatial resolution) mass and energy balance snow model called the Snow Physics and Laser Mapping (SnowPALM) model along with LIDAR-derived forest structure to determine where spatial variability of fine-scale forest structure has the largest influence on large scale mass and energy budgets. SnowPALM was set up and calibrated at sites representing diverse climates in New Mexico, Arizona, and California. Then, we compared simulations at different model resolutions (i.e. 1, 10, and 100 m) to elucidate the effects of including versus not including information about fine scale canopy structure. These experiments were repeated for different prescribed topographies (i.e. flat, 30% slope north, and south-facing) at each site. Higher resolution simulations had more snow at lower canopy cover, with the opposite being true at high canopy cover. Furthermore, there is considerable scatter, indicating that different canopy arrangements can lead to different amounts of snow, even when the overall canopy coverage is the same. This modeling is contributing to the development of a high resolution machine learning algorithm called the Snow Water Artificial Network (SWANN) model to generate predictions of snow distributions over much larger domains, which has implications for improving land surface models that do not currently resolve or parameterize fine-scale canopy structure. In addition, these findings have implications for understanding the potential of different forest management strategies (i.e. thinning) based on local topography and climate to maximize the amount and retention of snow.

Effect of environmental conditions on the relationship between solar-induced fluorescence and gross primary productivity at an OzFlux grassland site

NASA Astrophysics Data System (ADS)

Verma, Manish; Schimel, David; Evans, Bradley; Frankenberg, Christian; Beringer, Jason; Drewry, Darren T.; Magney, Troy; Marang, Ian; Hutley, Lindsay; Moore, Caitlin; Eldering, Annmarie

2017-03-01

Recent studies have utilized coarse spatial and temporal resolution remotely sensed solar-induced fluorescence (SIF) for modeling terrestrial gross primary productivity (GPP) at regional scales. Although these studies have demonstrated the potential of SIF, there have been concerns about the ecophysiological basis of the relationship between SIF and GPP in different environmental conditions. Launched in 2014, the Orbiting Carbon Observatory-2 (OCO-2) has enabled fine-scale (1.3 by 2.5 km) retrievals of SIF that are comparable with measurements recorded at eddy covariance towers. In this study, we examine the effect of environmental conditions on the relationship of OCO-2 SIF with tower GPP over the course of a growing season at a well-characterized natural grassland site. Combining OCO-2 SIF and eddy covariance tower data with a canopy radiative transfer and an ecosystem model, we also assess the potential of OCO-2 SIF to constrain the estimates of Vcmax, one of the most important parameters in ecosystem models. Based on the results, we suggest that although environmental conditions play a role in determining the nature of relationship between SIF and GPP, overall, the linear relationship is more robust at ecosystem scale than the theory based on leaf-level processes might suggest. Our study also shows that the ability of SIF to constrain Vcmax is weak at the selected site.

Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach

NASA Astrophysics Data System (ADS)

Bellón, Beatriz; Bégué, Agnès; Lo Seen, Danny; Lebourgeois, Valentine; Evangelista, Balbino Antônio; Simões, Margareth; Demonte Ferraz, Rodrigo Peçanha

2018-06-01

Cropping systems' maps at fine scale over large areas provide key information for further agricultural production and environmental impact assessments, and thus represent a valuable tool for effective land-use planning. There is, therefore, a growing interest in mapping cropping systems in an operational manner over large areas, and remote sensing approaches based on vegetation index time series analysis have proven to be an efficient tool. However, supervised pixel-based approaches are commonly adopted, requiring resource consuming field campaigns to gather training data. In this paper, we present a new object-based unsupervised classification approach tested on an annual MODIS 16-day composite Normalized Difference Vegetation Index time series and a Landsat 8 mosaic of the State of Tocantins, Brazil, for the 2014-2015 growing season. Two variants of the approach are compared: an hyperclustering approach, and a landscape-clustering approach involving a previous stratification of the study area into landscape units on which the clustering is then performed. The main cropping systems of Tocantins, characterized by the crop types and cropping patterns, were efficiently mapped with the landscape-clustering approach. Results show that stratification prior to clustering significantly improves the classification accuracies for underrepresented and sparsely distributed cropping systems. This study illustrates the potential of unsupervised classification for large area cropping systems' mapping and contributes to the development of generic tools for supporting large-scale agricultural monitoring across regions.

Grain-Scale Analyses of Curiosity Data at Marias Pass, Gale Crater, Mars: Methods Comparison and Depositional Interpretation

NASA Astrophysics Data System (ADS)

Sacks, L. E.; Edgar, L. A.; Edwards, C. S.; Anderson, R. B.

2016-12-01

Images acquired by the Mars Hand Lens Imager (MAHLI) and the ChemCam Remote Micro Imager (RMI) onboard the Mars Science Laboratory (MSL) Curiosity rover provide grain-scale data that are critical for interpreting sedimentary deposits. At the location informally known as Marias Pass, Curiosity used both cameras to image the nine rock targets used in this study. We used manual point-counts to measure grain size distributions from those images to compare the abilities of the two cameras. The manually derived results were compared to automated grain size data obtained using pyDGS (Digital Grain Size), an open-source python program. Grain size analyses were used to test the lacustrine and aeolian depositional hypotheses for the Murray and Stimson formations at Marias Pass. Results indicate that the MAHLI and RMI instruments, despite their different fields of view and properties, provide comparable grain size measurements. Additionally, pyDGS does not account for grains smaller than a few pixels and thus does not report representative grain size data and should not be used on images with a large fraction of unresolved grains. Finally, the data collected at Marias Pass are consistent with the existing interpretations of the Murray and Stimson formations. The fine-grained results of the Murray formation analyses support lacustrine deposition, while the mean grain size of the Stimson formation is fine to medium sized sand, consistent with aeolian deposition. However, directly above the contact with the Murray formation, larger rip-up clasts of the Murray formation are present in the Stimson formation. It is possible that water was involved at this stage of erosion and re-deposition, prior to aeolian deposition. Additionally, the grain-scale analyses conducted in this study show that the Dust Removal Tool on Curiosity should be used prior to capturing images for grain-scale analysis. Two images of the target informally named Ronan, taken before and after brushing, resulted in dramatically different grain size results, suggesting that the common, thin layer of dust obscured the true grain size distribution. These grain-scale analyses at Marias Pass have important implications for the collection and processing of image data, as well as the depositional environments recorded in Gale crater. Funded by NSF Grant AST-1461200

Species Associations in a Species-Rich Subtropical Forest Were Not Well-Explained by Stochastic Geometry of Biodiversity

PubMed Central

Wang, Qinggang; Bao, Dachuan; Guo, Yili; Lu, Junmeng; Lu, Zhijun; Xu, Yaozhan; Zhang, Kuihan; Liu, Haibo; Meng, Hongjie; Jiang, Mingxi; Qiao, Xiujuan; Huang, Handong

2014-01-01

The stochastic dilution hypothesis has been proposed to explain species coexistence in species-rich communities. The relative importance of the stochastic dilution effects with respect to other effects such as competition and habitat filtering required to be tested. In this study, using data from a 25-ha species-rich subtropical forest plot with a strong topographic structure at Badagongshan in central China, we analyzed overall species associations and fine-scale species interactions between 2,550 species pairs. The result showed that: (1) the proportion of segregation in overall species association analysis at 2 m neighborhood in this plot followed the prediction of the stochastic dilution hypothesis that segregations should decrease with species richness but that at 10 m neighborhood was higher than the prediction. (2) The proportion of no association type was lower than the expectation of stochastic dilution hypothesis. (3) Fine-scale species interaction analyses using Heterogeneous Poisson processes as null models revealed a high proportion (47%) of significant species effects. However, the assumption of separation of scale of this method was not fully met in this plot with a strong fine-scale topographic structure. We also found that for species within the same families, fine-scale positive species interactions occurred more frequently and negative ones occurred less frequently than expected by chance. These results suggested effects of environmental filtering other than species interaction in this forest. (4) We also found that arbor species showed a much higher proportion of significant fine-scale species interactions (66%) than shrub species (18%). We concluded that the stochastic dilution hypothesis only be partly supported and environmental filtering left discernible spatial signals in the spatial associations between species in this species-rich subtropical forest with a strong topographic structure. PMID:24824996

Species associations in a species-rich subtropical forest were not well-explained by stochastic geometry of biodiversity.

PubMed

Wang, Qinggang; Bao, Dachuan; Guo, Yili; Lu, Junmeng; Lu, Zhijun; Xu, Yaozhan; Zhang, Kuihan; Liu, Haibo; Meng, Hongjie; Jiang, Mingxi; Qiao, Xiujuan; Huang, Handong

2014-01-01

The stochastic dilution hypothesis has been proposed to explain species coexistence in species-rich communities. The relative importance of the stochastic dilution effects with respect to other effects such as competition and habitat filtering required to be tested. In this study, using data from a 25-ha species-rich subtropical forest plot with a strong topographic structure at Badagongshan in central China, we analyzed overall species associations and fine-scale species interactions between 2,550 species pairs. The result showed that: (1) the proportion of segregation in overall species association analysis at 2 m neighborhood in this plot followed the prediction of the stochastic dilution hypothesis that segregations should decrease with species richness but that at 10 m neighborhood was higher than the prediction. (2) The proportion of no association type was lower than the expectation of stochastic dilution hypothesis. (3) Fine-scale species interaction analyses using Heterogeneous Poisson processes as null models revealed a high proportion (47%) of significant species effects. However, the assumption of separation of scale of this method was not fully met in this plot with a strong fine-scale topographic structure. We also found that for species within the same families, fine-scale positive species interactions occurred more frequently and negative ones occurred less frequently than expected by chance. These results suggested effects of environmental filtering other than species interaction in this forest. (4) We also found that arbor species showed a much higher proportion of significant fine-scale species interactions (66%) than shrub species (18%). We concluded that the stochastic dilution hypothesis only be partly supported and environmental filtering left discernible spatial signals in the spatial associations between species in this species-rich subtropical forest with a strong topographic structure.

Spatial Patterns in Water Temperature in Pacific Northwest Rivers: Diversity at Multiple Scales and Potential Influence of Climate Change

NASA Astrophysics Data System (ADS)

Torgersen, C. E.; Fullerton, A.; Lawler, J. J.; Ebersole, J. L.; Leibowitz, S. G.; Steel, E. A.; Beechie, T. J.; Faux, R.

2016-12-01

Understanding spatial patterns in water temperature will be essential for evaluating vulnerability of aquatic biota to future climate and for identifying and protecting diverse thermal habitats. We used high-resolution remotely sensed water temperature data for over 16,000 km of 2nd to 7th-order rivers throughout the Pacific Northwest and California to evaluate spatial patterns of summertime water temperature at multiple spatial scales. We found a diverse and geographically distributed suite of whole-river patterns. About half of rivers warmed asymptotically in a downstream direction, whereas the rest exhibited complex and unique spatial patterns. Patterns were associated with both broad-scale hydroclimatic variables as well as characteristics unique to each basin. Within-river thermal heterogeneity patterns were highly river-specific; across rivers, median size and spacing of cool patches <15 °C were around 250 m. Patches of this size are large enough for juvenile salmon rearing and for resting during migration, and the distance between patches is well within the movement capabilities of both juvenile and adult salmon. We found considerable thermal heterogeneity at fine spatial scales that may be important to fish that would be missed if data were analyzed at coarser scales. We estimated future thermal heterogeneity and concluded that climate change will cause warmer temperatures overall, but that thermal heterogeneity patterns may remain similar in the future for many rivers. We demonstrated considerable spatial complexity in both current and future water temperature, and resolved spatial patterns that could not have been perceived without spatially continuous data.

Small Traditional Human Communities Sustain Genomic Diversity over Microgeographic Scales despite Linguistic Isolation

PubMed Central

Cox, Murray P.; Hudjashov, Georgi; Sim, Andre; Savina, Olga; Karafet, Tatiana M.; Sudoyo, Herawati; Lansing, J. Stephen

2016-01-01

At least since the Neolithic, humans have largely lived in networks of small, traditional communities. Often socially isolated, these groups evolved distinct languages and cultures over microgeographic scales of just tens of kilometers. Population genetic theory tells us that genetic drift should act quickly in such isolated groups, thus raising the question: do networks of small human communities maintain levels of genetic diversity over microgeographic scales? This question can no longer be asked in most parts of the world, which have been heavily impacted by historical events that make traditional society structures the exception. However, such studies remain possible in parts of Island Southeast Asia and Oceania, where traditional ways of life are still practiced. We captured genome-wide genetic data, together with linguistic records, for a case–study system—eight villages distributed across Sumba, a small, remote island in eastern Indonesia. More than 4,000 years after these communities were established during the Neolithic period, most speak different languages and can be distinguished genetically. Yet their nuclear diversity is not reduced, instead being comparable to other, even much larger, regional groups. Modeling reveals a separation of time scales: while languages and culture can evolve quickly, creating social barriers, sporadic migration averaged over many generations is sufficient to keep villages linked genetically. This loosely-connected network structure, once the global norm and still extant on Sumba today, provides a living proxy to explore fine-scale genome dynamics in the sort of small traditional communities within which the most recent episodes of human evolution occurred. PMID:27274003

Modeling the height of young forests regenerating from recent disturbances in Mississippi using Landsat and ICESat data

Treesearch

Ainong Li; Chengquan Huang; Guoqing Sun; Hua Shi; Chris Toney; Zhiliang Zhu; Matthew G. Rollins; Samuel N. Goward; Jeffrey G. Masek

2011-01-01

Many forestry and earth science applications require spatially detailed forest height data sets. Among the various remote sensing technologies, lidar offers the most potential for obtaining reliable height measurement. However, existing and planned spaceborne lidar systems do not have the capability to produce spatially contiguous, fine resolution forest height maps...

Adjusting Curvatures Of Large Mirrors And Lenses

NASA Technical Reports Server (NTRS)

Birnbaum, Morris M.

1992-01-01

Actuators apply stresses to generate distortions counteracting undesired distortions in technique for adjusting curvature of large focusing mirror or lens. Motor-and-gear assemblies under remote control vary squeeze of ring clamp and push or pull of hollow shaft to make fine adjustments in curvature of mirror. Applicable to large astronomical-telescope mirrors with diameters of 60 cm or more.

Optimal Remote Sensing with Small Unmanned Aircraft Systems and Risk Management

NASA Astrophysics Data System (ADS)

Stark, Brandon

Over the past decade, the rapid rise of Unmanned Aircraft Systems (UASs) has blossomed into a new component of the aviation industry. Though regulations within the United States lagged, the promise of the ability of Small Unmanned Aircraft Systems (SUASs), or those UAS that weigh less than 55 lbs, has driven significant advances in small scale aviation technology. The dream of a small, low-cost aerial platform that can fly anywhere and keep humans safely away from the `dull, dangerous and dirty' jobs, has encouraged many to examine the possibilities of utilizing SUAS in new and transformative ways, especially as a new tool in remote sensing. However, as with any new tool, there remains significant challenges in realizing the full potential of SUAS-based remote sensing. Within this dissertation, two specific challenges are addressed: validating the use of SUAS as a remote sensing platform and improving the safety and management of SUAS. The use of SUAS in remote sensing is a relatively new challenge and while it has many similarities to other remote sensing platforms, the dynamic nature of its operation makes it unique. In this dissertation, a closer look at the methodology of using SUAS reveals that while many view SUAS as an alternative to satellite imagery, this is an incomplete view and that the current common implementation introduces a new source of error that has significant implications on the reliability of the data collected. It can also be seen that a new approach to remote sensing with an SUAS can be developed by addressing the spatial, spectral and temporal factors that can now be more finely adjusted with the use of SUAS. However, to take the full advantage of the potential of SUAS, they must uphold the promise of improved safety. This is not a trivial challenge, especially for the integration into the National Airspace System (NAS) and for the safety management and oversight of diverse UAS operations. In this dissertation, the challenge of integrating SUAS in the NAS is addressed by presenting an analysis of enabling flight operations at night, developing a swarm safety management system for improving SUAS robustness, investigating the use of new technology on SUAS to improve air safety, and developing a novel framework to better understand human-SUAS interaction. Addressing the other side of safety, this dissertation discusses the struggle of large diverse organizations to balance acceptance, safety and oversight for UAS operations and the development of a novel implementation of a UAS Safety Management System.

Fine Structure of Anomalously Intense Pulses of PSR J0814+7429 Radio Emission in the Decameter Range

NASA Astrophysics Data System (ADS)

Skoryk, A. O.; Ulyanov, O. M.; Zakharenko, V. V.; Shevtsova, A. I.; Vasylieva, I. Y.; Plakhov, M. S.; Kravtsov, I. M.

2017-06-01

Purpose: The fine structure of the anomalously intense pulses of PSR J0814+7429 (B0809+74) has been studied. The pulsar radio emission fine structure is investigated to determine its parameters in the lowest part of spectrum available for groundbased observations. Design/methodology/approach: The scattering measure in the interstellar plasma have been estimated using the spectral and correlation analyses of pulsar data recorded by the UTR-2 radio telescope. Results: Two characteristic time scales of the anomalously intense pulses fine structure of the PSR J0814+7429 radio emission have been found. The strongest pulses of this pulsar in the decameter range can have a duration of about t 2÷3 ms. These pulses are emitted in short series. In some cases, they are emitted over the low-intensity plateau consisting of the “long” subpulse component. Conclusions: The narrowest correlation scale of pulsar J0814+7429 radio emission corresponds to the doubled scattering time constant of the interstellar medium impulse response. Broader scale of the fine structure of its radio emission can be explained by the radiation of a short series of narrow pulses or relatively broad pulses inside this pulsar magnetosphere.

Phenomenology of NMSSM in TeV scale mirage mediation

NASA Astrophysics Data System (ADS)

Hagimoto, Kei; Kobayashi, Tatsuo; Makino, Hiroki; Okumura, Ken-ichi; Shimomura, Takashi

2016-02-01

We study the next-to-minimal supersymmetric standard model (NMSSM) with the TeV scale mirage mediation, which is known as a solution for the little hierarchy problem in supersymmetry. Our previous study showed that 125 GeV Higgs boson is realized with {O} (10)% fine-tuning for 1.5 TeV gluino (1 TeV stop) mass. The μ term could be as large as 500 GeV without sacrificing the fine-tuning thanks to a cancellation mechanism. The singlet-doublet mixing is suppressed by tan β. In this paper, we further extend this analysis. We argue that approximate scale symmetries play a role behind the suppression of the singlet-doublet mixing. They reduce the mixing matrix to a simple form that is useful to understand the results of the numerical analysis. We perform a comprehensive analysis of the fine-tuning including the singlet sector by introducing a simple formula for the fine-tuning measure. This shows that the singlet mass of the least fine-tuning is favored by the LEP anomaly for moderate tan β. We also discuss prospects for the precision measurements of the Higgs couplings at LHC and ILC and direct/indirect dark matter searches in the model.

Abiotic and biotic controls of spatial pattern at alpine treeline

USGS Publications Warehouse

Malanson, George P.; Xiao, Ningchuan; Alftine, K.J.; Bekker, Mathew; Butler, David R.; Brown, Daniel G.; Cairns, David M.; Fagre, Daniel; Walsh, Stephen J.

2000-01-01

At alpine treeline, trees and krummholz forms affect the environment in ways that increase their growth and reproduction. We assess the way in which these positive feedbacks combine in spatial patterns to alter the environment in the neighborhood of existing plants. The research is significant because areas of alpine tundra are susceptible to encroachment by woody species as climate changes. Moreover, understanding the general processes of plant invasion is important. The importance of spatial pattern has been recognized, but the spatial pattern of positive feedbacks per se has not been explored in depth. We present a linked set of models of vegetation change at an alpine forest-tundra ecotone. Our aim is to create models that are as simple as possible in order to test specific hypotheses. We present results from a model of the resource averaging hypothesis and the positive feedback switch hypothesis of treelines. We compare the patterns generated by the models to patterns observed in fine scale remotely sensed data.

Acoustic telemetry and network analysis reveal the space use of multiple reef predators and enhance marine protected area design

PubMed Central

Lea, James S. E.; Humphries, Nicolas E.; von Brandis, Rainer G.; Clarke, Christopher R.; Sims, David W.

2016-01-01

Marine protected areas (MPAs) are commonly employed to protect ecosystems from threats like overfishing. Ideally, MPA design should incorporate movement data from multiple target species to ensure sufficient habitat is protected. We used long-term acoustic telemetry and network analysis to determine the fine-scale space use of five shark and one turtle species at a remote atoll in the Seychelles, Indian Ocean, and evaluate the efficacy of a proposed MPA. Results revealed strong, species-specific habitat use in both sharks and turtles, with corresponding variation in MPA use. Defining the MPA's boundary from the edge of the reef flat at low tide instead of the beach at high tide (the current best in Seychelles) significantly increased the MPA's coverage of predator movements by an average of 34%. Informed by these results, the larger MPA was adopted by the Seychelles government, demonstrating how telemetry data can improve shark spatial conservation by affecting policy directly. PMID:27412274

High-resolution digital brain atlases: a Hubble telescope for the brain.

PubMed

Jones, Edward G; Stone, James M; Karten, Harvey J

2011-05-01

We describe implementation of a method for digitizing at microscopic resolution brain tissue sections containing normal and experimental data and for making the content readily accessible online. Web-accessible brain atlases and virtual microscopes for online examination can be developed using existing computer and internet technologies. Resulting databases, made up of hierarchically organized, multiresolution images, enable rapid, seamless navigation through the vast image datasets generated by high-resolution scanning. Tools for visualization and annotation of virtual microscope slides enable remote and universal data sharing. Interactive visualization of a complete series of brain sections digitized at subneuronal levels of resolution offers fine grain and large-scale localization and quantification of many aspects of neural organization and structure. The method is straightforward and replicable; it can increase accessibility and facilitate sharing of neuroanatomical data. It provides an opportunity for capturing and preserving irreplaceable, archival neurohistological collections and making them available to all scientists in perpetuity, if resources could be obtained from hitherto uninterested agencies of scientific support. © 2011 New York Academy of Sciences.

Radio Frequencies Emitted by Mobile Granular Materials: A Basis for Remote Sensing of Sand and Dust Activity on Mars and Earth

NASA Technical Reports Server (NTRS)

Marshall, J.; Farrell, W.; Houser, G.; Bratton, C.

1999-01-01

In recent laboratory experiments, measurements were made of microsecond radio-wave (RF) bursts emitted by grains of sand as they energetically circulated in a closed, electrically ungrounded chamber. The bursts appeared to result from nanoscale electrical discharging from grain surfaces. Both the magnitude and wave form of the RF pulses varied with the type of material undergoing motion. The release of RF from electrical discharging is a well-known phenomenon, but it is generally measured on much larger energy scales (e.g., in association with lightning or electrical motors). This phenomenon might be used to detect, on planetary surfaces, the motion and composition of sand moving over dunes, the turbulent motion of fine particles in dust storms, highly-energetic grain and rock collisions in volcanic eruptions, and frictional grinding of granular materials in dry debris flows, landslides, and avalanches. The occurrence of these discharges has been predicted from theoretical considerations Additional information is contained in the original.

The environment of south-central Tunisia as observed on Landsat scene 206/036

USGS Publications Warehouse

Grolier, M.J.; Schultejann, P.A.

1982-01-01

One Landsat image in south-central Tunisia was analyzed to demonstrate the application of remote-sensing technology to regional development. A preliminary analysis included I) major landscape features; 2) gypsum-encrusted soils; and 3) phosphate-bearing beds exposed in the Gafsa mining district. The products specifically used for this report include: 1) A false-color composite (FCC), which had been linearly stretched to enhance contrast, and to which a modulation transfer function correction (a high-pass filter 3 pixels by 3 pixels wide) had been applied to enhance fine topographic relief. 2) A sinusoidally stretched false-color composite, on which mappable gypsum-encrusted soils and saline soils are detectable in greater detail than on the existing soil map of Tunisia at 1:500,000 scale. 3) A sinusoidally stretched band-ratio false-color composite, from which a thematic map of most phosphate-bearing beds in the Gafsa mining district was prepared. Recommendations for future Landsat image interpretation in Tunisia are offered.

Examining fire-induced forest changes using novel remote sensing technique: a case study in a mixed pine-oak forest

NASA Astrophysics Data System (ADS)

Meng, R.; Wu, J.; Zhao, F. R.; Cook, B.; Hanavan, R. P.; Serbin, S.

2017-12-01

Fire-induced forest changes has long been a central focus for forest ecology and global carbon cycling studies, and is becoming a pressing issue for global change biologists particularly with the projected increases in the frequency and intensity of fire with a warmer and drier climate. Compared with time-consuming and labor intensive field-based approaches, remote sensing offers a promising way to efficiently assess fire effects and monitor post-fire forest responses across a range of spatial and temporal scales. However, traditional remote sensing studies relying on simple optical spectral indices or coarse resolution imagery still face a number of technical challenges, including confusion or contamination of the signal by understory dynamics and mixed pixels with moderate to coarse resolution data (>= 30 m). As such, traditional remote sensing may not meet the increasing demand for more ecologically-meaningful monitoring and quantitation of fire-induced forest changes. Here we examined the use of novel remote sensing technique (i.e. airborne imaging spectroscopy and LiDAR measurement, very high spatial resolution (VHR) space-borne multi-spectral measurement, and high temporal-spatial resolution UAS-based (Unmanned Aerial System) imagery), in combination with field and phenocam measurements to map forest burn severity across spatial scales, quantify crown-scale post-fire forest recovery rate, and track fire-induced phenology changes in the burned areas. We focused on a mixed pine-oak forest undergoing multiple fire disturbances for the past several years in Long Island, NY as a case study. We demonstrate that (1) forest burn severity mapping from VHR remote sensing measurement can capture crown-scale heterogeneous fire patterns over large-scale; (2) the combination of VHR optical and structural measurements provides an efficient means to remotely sense species-level post-fire forest responses; (3) the UAS-based remote sensing enables monitoring of fire-induced forest phenology changes at unprecedented temporal and spatial resolutions. This work provides the methodological approach monitor fire-induced forest changes in a spatially explicit manner across scales, with important implications for fire-related forest management and for constraining/benchmarking process models.

Scale-dependent effects of nonnative plant invasion on host-seeking tick abundance

PubMed Central

Adalsteinsson, Solny A.; D’Amico, Vincent; Shriver, W. Gregory; Brisson, Dustin; Buler, Jeffrey J.

2016-01-01

Nonnative, invasive shrubs can affect human disease risk through direct and indirect effects on vector populations. Multiflora rose (Rosa multiflora) is a common invader within eastern deciduous forests where tick-borne disease (e.g. Lyme disease) rates are high. We tested whether R. multiflora invasion affects blacklegged tick (Ixodes scapularis) abundance, and at what scale. We sampled host-seeking ticks at two spatial scales: fine-scale, within R. multiflora-invaded forest fragments; and patch scale, among R. multiflora-invaded and R. multiflora-free forest fragments. At a fine scale, we trapped 2.3 times more ticks under R. multiflora compared to paired traps 25 m away from R. multiflora. At the patch scale, we trapped 3.2 times as many ticks in R. multiflora-free forests compared to R. multiflora-invaded forests. Thus, ticks are concentrated beneath R. multiflora within invaded forests, but uninvaded forests support significantly more ticks. Among all covariates tested, leaf litter volume was the best predictor of tick abundance; at the patch scale, R. multiflora-invaded forests had less leaf litter than uninvaded forests. We suggest that leaf litter availability at the patch-scale plays a greater role in constraining tick abundance than the fine-scale, positive effect of invasive shrubs. PMID:27088044

Patterns of resting state connectivity in human primary visual cortical areas: a 7T fMRI study.

PubMed

Raemaekers, Mathijs; Schellekens, Wouter; van Wezel, Richard J A; Petridou, Natalia; Kristo, Gert; Ramsey, Nick F

2014-01-01

The nature and origin of fMRI resting state fluctuations and connectivity are still not fully known. More detailed knowledge on the relationship between resting state patterns and brain function may help to elucidate this matter. We therefore performed an in depth study of how resting state fluctuations map to the well known architecture of the visual system. We investigated resting state connectivity at both a fine and large scale within and across visual areas V1, V2 and V3 in ten human subjects using a 7Tesla scanner. We found evidence for several coexisting and overlapping connectivity structures at different spatial scales. At the fine-scale level we found enhanced connectivity between the same topographic locations in the fieldmaps of V1, V2 and V3, enhanced connectivity to the contralateral functional homologue, and to a lesser extent enhanced connectivity between iso-eccentric locations within the same visual area. However, by far the largest proportion of the resting state fluctuations occurred within large-scale bilateral networks. These large-scale networks mapped to some extent onto the architecture of the visual system and could thereby obscure fine-scale connectivity. In fact, most of the fine-scale connectivity only became apparent after the large-scale network fluctuations were filtered from the timeseries. We conclude that fMRI resting state fluctuations in the visual cortex may in fact be a composite signal of different overlapping sources. Isolating the different sources could enhance correlations between BOLD and electrophysiological correlates of resting state activity. © 2013 Elsevier Inc. All rights reserved.

Ship detection using STFT sea background statistical modeling for large-scale oceansat remote sensing image

NASA Astrophysics Data System (ADS)

Wang, Lixia; Pei, Jihong; Xie, Weixin; Liu, Jinyuan

2018-03-01

Large-scale oceansat remote sensing images cover a big area sea surface, which fluctuation can be considered as a non-stationary process. Short-Time Fourier Transform (STFT) is a suitable analysis tool for the time varying nonstationary signal. In this paper, a novel ship detection method using 2-D STFT sea background statistical modeling for large-scale oceansat remote sensing images is proposed. First, the paper divides the large-scale oceansat remote sensing image into small sub-blocks, and 2-D STFT is applied to each sub-block individually. Second, the 2-D STFT spectrum of sub-blocks is studied and the obvious different characteristic between sea background and non-sea background is found. Finally, the statistical model for all valid frequency points in the STFT spectrum of sea background is given, and the ship detection method based on the 2-D STFT spectrum modeling is proposed. The experimental result shows that the proposed algorithm can detect ship targets with high recall rate and low missing rate.

Regional scale patterns of fine root lifespan and turnover under current and future climate

Treesearch

M. Luke McCormack; David M. Eissenstat; Anantha M. Prasad; Erica A. Smithwick

2013-01-01

Fine root dynamics control a dominant flux of carbon from plants and into soils and mediate potential uptake and cycling of nutrients and water in terrestrial ecosystems. Understanding of these patterns is needed to accurately describe critical processes like productivity and carbon storage from ecosystem to global scales. However, limited observations of root dynamics...

Multiple Time Series Node Synchronization Utilizing Ambient Reference

DTIC Science & Technology

2014-12-31

assessment, is the need for fine scale synchronization among communicating nodes and across multiple domains. The severe requirements that Special...processing targeted to performance assessment, is the need for fine scale synchronization among communicating nodes and across multiple domains. The...research community and it is well documented and characterized. The datasets considered from this project (listed below) were used to derive the

A Study on Developing Learning Strategies in Violin Education

ERIC Educational Resources Information Center

Afacan, Senol; Cilden, Seyda

2018-01-01

This study was conducted for the purpose of developing a valid and reliable learning strategies scale for students receiving violin education in Departments of Music at Fine Arts High Schools. The scale was applied to 391 violin students receiving education in the 11th and 12th grades in Departments of Music at Fine Arts High Schools in the…

Fine scale variations of surface water chemistry in an ephemeral to perennial drainage network

Treesearch

Margaret A. Zimmer; Scott W. Bailey; Kevin J. McGuire; Thomas D. Bullen

2013-01-01

Although temporal variation in headwater stream chemistry has long been used to document baseline conditions and response to environmental drivers, less attention is paid to fine scale spatial variations that could yield clues to processes controlling stream water sources. We documented spatial and temporal variation in water composition in a headwater catchment (41 ha...

Assessment of fine-scale resource selection and spatially explicit habitat suitability modelling for a re-introduced tiger (Panthera tigris) population in central India

PubMed Central

Sarkar, Mriganka Shekhar; Johnson, Jeyaraj A.; Sen, Subharanjan

2017-01-01

Background Large carnivores influence ecosystem functions at various scales. Thus, their local extinction is not only a species-specific conservation concern, but also reflects on the overall habitat quality and ecosystem value. Species-habitat relationships at fine scale reflect the individuals’ ability to procure resources and negotiate intraspecific competition. Such fine scale habitat choices are more pronounced in large carnivores such as tiger (Panthera tigris), which exhibits competitive exclusion in habitat and mate selection strategies. Although landscape level policies and conservation strategies are increasingly promoted for tiger conservation, specific management interventions require knowledge of the habitat correlates at fine scale. Methods We studied nine radio-collared individuals of a successfully reintroduced tiger population in Panna Tiger Reserve, central India, focussing on the species-habitat relationship at fine scales. With 16 eco-geographical variables, we performed Manly’s selection ratio and K-select analyses to define population-level and individual-level variation in resource selection, respectively. We analysed the data obtained during the exploratory period of six tigers and during the settled period of eight tigers separately, and compared the consequent results. We further used the settled period characteristics to model and map habitat suitability based on the Mahalanobis D2 method and the Boyce index. Results There was a clear difference in habitat selection by tigers between the exploratory and the settled period. During the exploratory period, tigers selected dense canopy and bamboo forests, but also spent time near villages and relocated village sites. However, settled tigers predominantly selected bamboo forests in complex terrain, riverine forests and teak-mixed forest, and totally avoided human settlements and agriculture areas. There were individual variations in habitat selection between exploratory and settled periods. Based on threshold limits of habitat selection by the Boyce Index, we established that 83% of core and 47% of buffer areas are now suitable habitats for tiger in this reserve. Discussion Tiger management often focuses on large-scale measures, but this study for the first time highlights the behaviour and fine-scale individual-specific habitat selection strategies. Such knowledge is vital for management of critical tiger habitats and specifically for the success of reintroduction programs. Our spatially explicit habitat suitability map provides a baseline for conservation planning and optimizing carrying capacity of the tiger population in this reserve. PMID:29114438

Assessment of fine-scale resource selection and spatially explicit habitat suitability modelling for a re-introduced tiger (Panthera tigris) population in central India.

PubMed

Sarkar, Mriganka Shekhar; Krishnamurthy, Ramesh; Johnson, Jeyaraj A; Sen, Subharanjan; Saha, Goutam Kumar

2017-01-01

Large carnivores influence ecosystem functions at various scales. Thus, their local extinction is not only a species-specific conservation concern, but also reflects on the overall habitat quality and ecosystem value. Species-habitat relationships at fine scale reflect the individuals' ability to procure resources and negotiate intraspecific competition. Such fine scale habitat choices are more pronounced in large carnivores such as tiger ( Panthera tigris ), which exhibits competitive exclusion in habitat and mate selection strategies. Although landscape level policies and conservation strategies are increasingly promoted for tiger conservation, specific management interventions require knowledge of the habitat correlates at fine scale. We studied nine radio-collared individuals of a successfully reintroduced tiger population in Panna Tiger Reserve, central India, focussing on the species-habitat relationship at fine scales. With 16 eco-geographical variables, we performed Manly's selection ratio and K-select analyses to define population-level and individual-level variation in resource selection, respectively. We analysed the data obtained during the exploratory period of six tigers and during the settled period of eight tigers separately, and compared the consequent results. We further used the settled period characteristics to model and map habitat suitability based on the Mahalanobis D 2 method and the Boyce index. There was a clear difference in habitat selection by tigers between the exploratory and the settled period. During the exploratory period, tigers selected dense canopy and bamboo forests, but also spent time near villages and relocated village sites. However, settled tigers predominantly selected bamboo forests in complex terrain, riverine forests and teak-mixed forest, and totally avoided human settlements and agriculture areas. There were individual variations in habitat selection between exploratory and settled periods. Based on threshold limits of habitat selection by the Boyce Index, we established that 83% of core and 47% of buffer areas are now suitable habitats for tiger in this reserve. Tiger management often focuses on large-scale measures, but this study for the first time highlights the behaviour and fine-scale individual-specific habitat selection strategies. Such knowledge is vital for management of critical tiger habitats and specifically for the success of reintroduction programs. Our spatially explicit habitat suitability map provides a baseline for conservation planning and optimizing carrying capacity of the tiger population in this reserve.

Mapping Fearscapes of a Mammalian Herbivore using Terrestrial LiDAR and UAV Imagery

NASA Astrophysics Data System (ADS)

Olsoy, P.; Nobler, J. D.; Forbey, J.; Rachlow, J. L.; Burgess, M. A.; Glenn, N. F.; Shipley, L. A.

2013-12-01

Concealment allows prey animals to remain hidden from a predator and can influence both real and perceived risks of predation. The heterogeneous nature of vegetative structure can create a variable landscape of concealment - a 'fearscape' - that may influence habitat quality and use by prey. Traditional measurements of concealment rely on a limited number of distances, heights, and vantage points, resulting in small snapshots of concealment available to a prey animal. Our objective was to demonstrate the benefits of emerging remote sensing techniques to map fearscapes for pygmy rabbits (Brachylagus idahoensis) in sagebrush steppe habitat across a continuous range of scales. Specifically, we used vegetation height rasters derived from terrestrial laser scanning (TLS) to create viewsheds from multiple vantage points, representing predator visibility. The sum of all the viewsheds modeled horizontal concealment of prey at both the shrub and patch scales. We also used a small, unmanned aerial vehicle (UAV) to determine vertical concealment at a habitat scale. Terrestrial laser scanning provided similar estimates of horizontal concealment at the shrub scale when compared to photographic methods (R2 = 0.85). Both TLS and UAV provide the potential to quantify concealment of prey from multiple distances, heights, or vantage points, allowing the creation of a manipulable fearscape map that can be correlated with habitat use by prey animals. The predictive power of such a map also could identify shrubs or patches for fine scale nutritional and concealment analysis for future investigation and conservation efforts. Fearscape map at the mound-scale. Viewsheds were calculated from 100 equally spaced observer points located 4 m from the closest on-mound sagebrush of interest. Red areas offer low concealment, while green areas provide high concealment.

The East River, Colorado Community Watershed: Hydrobiogeochemical Studies Spanning Scales and Disciplines

NASA Astrophysics Data System (ADS)

Williams, K. H.; Brown, W. S.; Carroll, R. W. H.; Dafflon, B.; Dong, W.; Hubbard, S. S.; Leger, E.; Li, L.; Maxwell, R. M.; Rowland, J. C.; Steltzer, H.; Tokunaga, T. K.; Wainwright, H. M.

2017-12-01

The Lawrence Berkeley National Laboratory and its collaborating institutions have recently established a "Community Watershed" in the headwaters of the East River near Crested Butte, Colorado (USA) designed to quantify processes impacting the ability of mountainous systems to retain and release water, nutrients, carbon, and metals. The East River Community Watershed spans a range of scales from hillslope to catena to catchment, with surface water and groundwater linking a diversity of geomorphic compartments. Research is highly multi-disciplinary involving hydrologists, plant ecologists, geochemists, geomorphologists, microbiologists, and climate scientists. Research is focused on both mechanistic and empirical studies designed to assess the impact of climate perturbations, such as early snowmelt, on coupled ecohydrological and biogeochemical processes as they relate to both water availability and water quality. Stakeholder participation provides feedback and support on environmental monitoring as well as a direct link to management planning decisions being conducted as part of the Colorado Water Plan. Data collection activities and monitoring infrastructure are emplaced within the catchment in such a way as to assess the aggregate impact of fine scale processes on catchment scale behavior. Monitoring occurs over diversity of time scales from minutes to months to years, with observational data being used to populate and constrain reactive transport models describing water and nutrient flows across the aforementioned scales of enquiry. Strong infrastructural investments in both data and monitoring networks include dispersed stream gaging and water sampling, meteorological station networks, elevation dependent fluxes of carbon, water, and plant phenological behavior, as well as remote sensing datasets designed to establish baseline data required to assess the impacts of both natural and simulated climate perturbations.

Strontium isotopes delineate fine-scale natal origins and migration histories of Pacific salmon

USGS Publications Warehouse

Brennan, Sean R.; Zimmerman, Christian E.; Fernandez, Diego P.; Cerling, Thure E.; McPhee, Megan V.; Wooller, Matthew J.

2015-01-01

Highly migratory organisms present major challenges to conservation efforts. This is especially true for exploited anadromous fish species, which exhibit long-range dispersals from natal sites, complex population structures, and extensive mixing of distinct populations during exploitation. By tracing the migratory histories of individual Chinook salmon caught in fisheries using strontium isotopes, we determined the relative production of natal habitats at fine spatial scales and different life histories. Although strontium isotopes have been widely used in provenance research, we present a new robust framework to simultaneously assess natal sources and migrations of individuals within fishery harvests through time. Our results pave the way for investigating how fine-scale habitat production and life histories of salmon respond to perturbations—providing crucial insights for conservation.

Studies and Application of Remote Sensing Retrieval Method of Soil Moisture Content in Land Parcel Units in Irrigation Area

NASA Astrophysics Data System (ADS)

Zhu, H.; Zhao, H. L.; Jiang, Y. Z.; Zang, W. B.

2018-05-01

Soil moisture is one of the important hydrological elements. Obtaining soil moisture accurately and effectively is of great significance for water resource management in irrigation area. During the process of soil moisture content retrieval with multiremote sensing data, multi- remote sensing data always brings multi-spatial scale problems which results in inconformity of soil moisture content retrieved by remote sensing in different spatial scale. In addition, agricultural water use management has suitable spatial scale of soil moisture information so as to satisfy the demands of dynamic management of water use and water demand in certain unit. We have proposed to use land parcel unit as the minimum unit to do soil moisture content research in agricultural water using area, according to soil characteristics, vegetation coverage characteristics in underlying layer, and hydrological characteristic into the basis of study unit division. We have proposed division method of land parcel units. Based on multi thermal infrared and near infrared remote sensing data, we calculate the ndvi and tvdi index and make a statistical model between the tvdi index and soil moisture of ground monitoring station. Then we move forward to study soil moisture remote sensing retrieval method on land parcel unit scale. And the method has been applied in Hetao irrigation area. Results show that compared with pixel scale the soil moisture content in land parcel unit scale has displayed stronger correlation with true value. Hence, remote sensing retrieval method of soil moisture content in land parcel unit scale has shown good applicability in Hetao irrigation area. We converted the research unit into the scale of land parcel unit. Using the land parcel units with unified crops and soil attributes as the research units more complies with the characteristics of agricultural water areas, avoids the problems such as decomposition of mixed pixels and excessive dependence on high-resolution data caused by the research units of pixels, and doesn't involve compromises in the spatial scale and simulating precision like the grid simulation. When the application needs are met, the production efficiency of products can also be improved at a certain degree.

Occurrence of weak, sub-micron, tropospheric aerosol events at high Arctic latitudes

NASA Astrophysics Data System (ADS)

O'Neill, N. T.; Pancrati, O.; Baibakov, K.; Eloranta, E.; Batchelor, R. L.; Freemantle, J.; McArthur, L. J. B.; Strong, K.; Lindenmaier, R.

2008-07-01

Numerous fine mode (sub-micron) aerosol optical events were observed during the summer of 2007 at the High Arctic atmospheric observatory (PEARL) located at Eureka, Nunavut, Canada. Half of these events could be traced to forest fires in southern and eastern Russia and the Northwest Territories of Canada. The most notable findings were that (a) a combination of ground-based measurements (passive sunphotometry, high spectral resolution lidar) could be employed to determine that weak (near sub-visual) fine mode events had occurred, and (b) this data combined with remote sensing imagery products (MODIS, OMI-AI, FLAMBE fire sources), Fourier transform spectroscopy and back trajectories could be employed to identify the smoke events.

Regional model studies of the atmospheric dispersion of fine volcanic ash after the eruption of Eyjafjallajoekull

NASA Astrophysics Data System (ADS)

Langmann, B.; Hort, M. K.

2010-12-01

During the eruption of Eyjafjallajoekull on Iceland in April/May 2010 air traffic over Europe was repeatedly interrupted because of volcanic ash in the atmosphere. This completely unusual situation in Europe leads to the demand of improved crisis management, e.g. European wide regulations of volcanic ash thresholds and improved forecasts of theses thresholds. However, the quality of the forecast of fine volcanic ash concentrations in the atmosphere depends to a great extent on a realistic description of the erupted mass flux of fine ash particles, which is rather uncertain. Numerous aerosol measurements (ground based and satellite remote sensing, and in situ measurements) all over Europe have tracked the volcanic ash clouds during the eruption of Eyjafjallajoekull offering the possibility for an interdisciplinary effort between volcanologists and aerosol researchers to analyse the release and dispersion of fine volcanic ash in order to better understand the needs for realistic volcanic ash forecasts. This contribution describes the uncertainties related to the amount of fine volcanic ash released from Eyjafjallajoekull and its influence on the dispersion of volcanic ash over Europe by numerical modeling. We use the three-dimensional Eulerian atmosphere-chemistry/aerosol model REMOTE (Langmann et al., 2008) to simulate the distribution of volcanic ash as well as its deposition after the eruptions of Eyjafjallajoekull during April and May 2010. The model has been used before to simulate the fate of the volcanic ash after the volcanic eruptions of Kasatochi in 2008 (Langmann et al., 2010) and Mt. Pinatubo in 1991. Comparing our model results with available measurements for the Eyjafjallajoekull eruption we find a quite good agreement with available ash concentrations data measured over Europe as well as with the results from other models. Langmann, B., K. Zakšek and M. Hort, Atmospheric distribution and removal of volcanic ash after the eruption of Kasatochi volcano: A regional model study, J. Geophys. Res., 115, D00L06, doi:10.1029/2009JD013298, 2010. Langmann, B., S. Varghese, E. Marmer, E. Vignati, J. Wilson, P. Stier and C. O’Dowd, Aerosol distribution over Europe: A model evaluation study with detailed aerosol microphysics, Atmos. Chem. Phys. 8, 1591-1607, 2008.

Moving forward on remote sensing of soil salinity at regional scale

USDA-ARS?s Scientific Manuscript database

Soil salinity undermines global agriculture by reducing crop yield and soil quality. Irrigation management can help control salinity levels within the root-zone. To best allocate water resources, accurate regional-scale inventories are needed. Two remote sensing approaches are currently used to moni...

Scale in Remote Sensing and GIS: An Advancement in Methods Towards a Science of Scale

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.

1998-01-01

The term "scale", both in space and time, is central to remote sensing and geographic information systems (GIS). The emergence and widespread use of GIS technologies, including remote sensing, has generated significant interest in addressing scale as a generic topic, and in the development and implementation of techniques for dealing explicitly with the vicissitudes of scale as a multidisciplinary issue. As science becomes more complex and utilizes databases that are capable of performing complex space-time data analyses, it becomes paramount that we develop the tools and techniques needed to operate at multiple scales, to work with data whose scales are not necessarily ideal, and to produce results that can be aggregated or disaggregated in ways that suit the decision-making process. Contemporary science is constantly coping with compromises, and the data available for a particular study rarely fit perfectly with the scales at which the processes being investigated operate, or the scales that policy-makers require to make sound, rational decisions. This presentation discusses some of the problems associated with scale as related to remote sensing and GIS, and describes some of the questions that need to be addressed in approaching the development of a multidisciplinary "science of scale". Techniques for dealing with multiple scaled data that have been developed or explored recently are described as a means for recognizing scale as a generic issue, along with associated theory and tools that can be of simultaneous value to a large number of disciplines. These can be used to seek answers to a host of interrelated questions in the interest of providing a formal structure for the management and manipulation of scale and its universality as a key concept from a multidisciplinary perspective.

Scale in Remote Sensing and GIS: An Advancement in Methods Towards a Science of Scale

NASA Technical Reports Server (NTRS)

Quattrochi, D. A.

1998-01-01

The term "scale", both in space and time, is central to remote sensing and Geographic Information Systems (GIS). The emergence and widespread use of GIS technologies, including remote sensing, has generated significant interest in addressing scale as a generic topic, and in the development and implementation of techniques for dealing explicitly with the vicissitudes of scale as a multidisciplinary issue. As science becomes more complex and utilizes databases that are capable of performing complex space-time data analyses, it becomes paramount that we develop the tools and techniques needed to operate at multiple scales, to work with data whose scales are not necessarily ideal, and to produce results that can be aggregated or disaggregated ways that suit the decision-making process. Contemporary science is constantly coping with compromises, and the data available for a particular study rarely fit perfectly with the scales at which the processes being investigated operate, or the scales that policy-makers require to make sound, rational decisions. This presentation discusses some of the problems associated with scale as related to remote sensing and GIS, and describes some of the questions that need to be addressed in approaching the development of a multidisciplinary "science of scale". Techniques for dealing with multiple scaled data that have been developed or explored recently are described as a means for recognizing scale as a generic issue, along with associated theory and tools that can be of simultaneous value to a large number of disciplines. These can be used to seek answers to a host of interrelated questions in the interest of providing a formal structure for the management and manipulation of scale and its universality as a key concept from a multidisciplinary perspective.

Modeling nutrient in-stream processes at the watershed scale using Nutrient Spiralling metrics

NASA Astrophysics Data System (ADS)

Marcé, R.; Armengol, J.

2009-01-01

One of the fundamental problems of using large-scale biogeochemical models is the uncertainty involved in aggregating the components of fine-scale deterministic models in watershed applications, and in extrapolating the results of field-scale measurements to larger spatial scales. Although spatial or temporal lumping may reduce the problem, information obtained during fine-scale research may not apply to lumped categories. Thus, the use of knowledge gained through fine-scale studies to predict coarse-scale phenomena is not straightforward. In this study, we used the nutrient uptake metrics defined in the Nutrient Spiralling concept to formulate the equations governing total phosphorus in-stream fate in a watershed-scale biogeochemical model. The rationale of this approach relies on the fact that the working unit for the nutrient in-stream processes of most watershed-scale models is the reach, the same unit used in field research based on the Nutrient Spiralling concept. Automatic calibration of the model using data from the study watershed confirmed that the Nutrient Spiralling formulation is a convenient simplification of the biogeochemical transformations involved in total phosphorus in-stream fate. Following calibration, the model was used as a heuristic tool in two ways. First, we compared the Nutrient Spiralling metrics obtained during calibration with results obtained during field-based research in the study watershed. The simulated and measured metrics were similar, suggesting that information collected at the reach scale during research based on the Nutrient Spiralling concept can be directly incorporated into models, without the problems associated with upscaling results from fine-scale studies. Second, we used results from our model to examine some patterns observed in several reports on Nutrient Spiralling metrics measured in impaired streams. Although these two exercises involve circular reasoning and, consequently, cannot validate any hypothesis, this is a powerful example of how models can work as heuristic tools to compare hypotheses and stimulate research in ecology.

Generalizing roughness: experiments with flow-oriented roughness

NASA Astrophysics Data System (ADS)

Trevisani, Sebastiano

2015-04-01

Surface texture analysis applied to High Resolution Digital Terrain Models (HRDTMs) improves the capability to characterize fine-scale morphology and permits the derivation of useful morphometric indexes. An important indicator to be taken into account in surface texture analysis is surface roughness, which can have a discriminant role in the detection of different geomorphic processes and factors. The evaluation of surface roughness is generally performed considering it as an isotropic surface parameter (e.g., Cavalli, 2008; Grohmann, 2011). However, surface texture has often an anisotropic character, which means that surface roughness could change according to the considered direction. In some applications, for example involving surface flow processes, the anisotropy of roughness should be taken into account (e.g., Trevisani, 2012; Smith, 2014). Accordingly, we test the application of a flow-oriented directional measure of roughness, computed considering surface gravity-driven flow. For the calculation of flow-oriented roughness we use both classical variogram-based roughness (e.g., Herzfeld,1996; Atkinson, 2000) as well as an ad-hoc developed robust modification of variogram (i.e. MAD, Trevisani, 2014). The presented approach, based on a D8 algorithm, shows the potential impact of considering directionality in the calculation of roughness indexes. The use of flow-oriented roughness could improve the definition of effective proxies of impedance to flow. Preliminary results on the integration of directional roughness operators with morphometric-based models, are promising and can be extended to more complex approaches. Atkinson, P.M., Lewis, P., 2000. Geostatistical classification for remote sensing: an introduction. Computers & Geosciences 26, 361-371. Cavalli, M. & Marchi, L. 2008, "Characterization of the surface morphology of an alpine alluvial fan using airborne LiDAR", Natural Hazards and Earth System Science, vol. 8, no. 2, pp. 323-333. Grohmann, C.H., Smith, M.J., Riccomini, C., 2011. Multiscale Analysis of Topographic Surface Roughness in the Midland Valley, Scotland. IEEE Transactions on Geoscience and Remote Sensing 49, 1220-1213. Herzfeld, U.C., Higginson, C.A., 1996. Automated geostatistical seafloor classification - Principles, parameters, feature vectors, and discrimination criteria. Computers and Geosciences, 22 (1), pp. 35-52. Smith, M.W. 2014, "Roughness in the Earth Sciences", Earth-Science Reviews, vol. 136, pp. 202-225. Trevisani, S., Cavalli, M. & Marchi, L. 2012, "Surface texture analysis of a high-resolution DTM: Interpreting an alpine basin", Geomorphology, vol. 161-162, pp. 26-39. Trevisani S., Rocca M., 2014. Geomorphometric analysis of fine-scale morphology for extensive areas: a new surface-texture operator. Geophysical Research Abstracts, Vol. 16, EGU2014-5612, 2014. EGU General Assembly 2014.

Analysis of fine-mode aerosol retrieval capabilities by different passive remote sensing instrument designs.

PubMed

Knobelspiesse, Kirk; Cairns, Brian; Mishchenko, Michael; Chowdhary, Jacek; Tsigaridis, Kostas; van Diedenhoven, Bastiaan; Martin, William; Ottaviani, Matteo; Alexandrov, Mikhail

2012-09-10

Remote sensing of aerosol optical properties is difficult, but multi-angle, multi-spectral, polarimetric instruments have the potential to retrieve sufficient information about aerosols that they can be used to improve global climate models. However, the complexity of these instruments means that it is difficult to intuitively understand the relationship between instrument design and retrieval success. We apply a Bayesian statistical technique that relates instrument characteristics to the information contained in an observation. Using realistic simulations of fine size mode dominated spherical aerosols, we investigate three instrument designs. Two of these represent instruments currently in orbit: the Multiangle Imaging SpectroRadiometer (MISR) and the POLarization and Directionality of the Earths Reflectances (POLDER). The third is the Aerosol Polarimetry Sensor (APS), which failed to reach orbit during recent launch, but represents a viable design for future instruments. The results show fundamental differences between the three, and offer suggestions for future instrument design and the optimal retrieval strategy for current instruments. Generally, our results agree with previous validation efforts of POLDER and airborne prototypes of APS, but show that the MISR aerosol optical thickness uncertainty characterization is possibly underestimated.

Wind turbine extraction from high spatial resolution remote sensing images based on saliency detection

NASA Astrophysics Data System (ADS)

Chen, Jingbo; Yue, Anzhi; Wang, Chengyi; Huang, Qingqing; Chen, Jiansheng; Meng, Yu; He, Dongxu

2018-01-01

The wind turbine is a device that converts the wind's kinetic energy into electrical power. Accurate and automatic extraction of wind turbine is instructive for government departments to plan wind power plant projects. A hybrid and practical framework based on saliency detection for wind turbine extraction, using Google Earth image at spatial resolution of 1 m, is proposed. It can be viewed as a two-phase procedure: coarsely detection and fine extraction. In the first stage, we introduced a frequency-tuned saliency detection approach for initially detecting the area of interest of the wind turbines. This method exploited features of color and luminance, was simple to implement, and was computationally efficient. Taking into account the complexity of remote sensing images, in the second stage, we proposed a fast method for fine-tuning results in frequency domain and then extracted wind turbines from these salient objects by removing the irrelevant salient areas according to the special properties of the wind turbines. Experiments demonstrated that our approach consistently obtains higher precision and better recall rates. Our method was also compared with other techniques from the literature and proves that it is more applicable and robust.

Development of a remote sensing algorithm to retrieve atmospheric aerosol properties using multiwavelength and multipixel information

NASA Astrophysics Data System (ADS)

Hashimoto, Makiko; Nakajima, Teruyuki

2017-06-01

We developed a satellite remote sensing algorithm to retrieve the aerosol optical properties using satellite-received radiances for multiple wavelengths and pixels. Our algorithm utilizes spatial inhomogeneity of surface reflectance to retrieve aerosol properties, and the main target is urban aerosols. This algorithm can simultaneously retrieve aerosol optical thicknesses (AOT) for fine- and coarse-mode aerosols, soot volume fraction in fine-mode aerosols (SF), and surface reflectance over heterogeneous surfaces such as urban areas that are difficult to obtain by conventional pixel-by-pixel methods. We applied this algorithm to radiances measured by the Greenhouse Gases Observing Satellite/Thermal and Near Infrared Sensor for Carbon Observations-Cloud and Aerosol Image (GOSAT/TANSO-CAI) at four wavelengths and were able to retrieve the aerosol parameters in several urban regions and other surface types. A comparison of the retrieved AOTs with those from the Aerosol Robotic Network (AERONET) indicated retrieval accuracy within ±0.077 on average. It was also found that the column-averaged SF and the aerosol single scattering albedo (SSA) underwent seasonal changes as consistent with the ground surface measurements of SSA and black carbon at Beijing, China.

Modeling dynamics of western juniper under climate change in a semiarid ecosystem

NASA Astrophysics Data System (ADS)

Shrestha, R.; Glenn, N. F.; Flores, A. N.

2013-12-01

Modeling future vegetation dynamics in response to climate change and disturbances such as fire relies heavily on model parameterization. Fine-scale field-based measurements can provide the necessary parameters for constraining models at a larger scale. But the time- and labor-intensive nature of field-based data collection leads to sparse sampling and significant spatial uncertainties in retrieved parameters. In this study we quantify the fine-scale carbon dynamics and uncertainty of juniper woodland in the Reynolds Creek Experimental Watershed (RCEW) in southern Idaho, which is a proposed critical zone observatory (CZO) site for soil carbon processes. We leverage field-measured vegetation data along with airborne lidar and timeseries Landsat imagery to initialize a state-and-transition model (VDDT) and a process-based fire-model (FlamMap) to examine the vegetation dynamics in response to stochastic fire events and climate change. We utilize recently developed and novel techniques to measure biomass and canopy characteristics of western juniper at the individual tree scale using terrestrial and airborne laser scanning techniques in RCEW. These fine-scale data are upscaled across the watershed for the VDDT and FlamMap models. The results will immediately improve our understanding of fine-scale dynamics and carbon stocks and fluxes of woody vegetation in a semi-arid ecosystem. Moreover, quantification of uncertainty will also provide a basis for generating ensembles of spatially-explicit alternative scenarios to guide future land management decisions in the region.

Coupling physics and biogeochemistry thanks to high-resolution observations of the phytoplankton community structure in the northwestern Mediterranean Sea

NASA Astrophysics Data System (ADS)

Marrec, Pierre; Grégori, Gérald; Doglioli, Andrea M.; Dugenne, Mathilde; Della Penna, Alice; Bhairy, Nagib; Cariou, Thierry; Hélias Nunige, Sandra; Lahbib, Soumaya; Rougier, Gilles; Wagener, Thibaut; Thyssen, Melilotus

2018-03-01

Fine-scale physical structures and ocean dynamics strongly influence and regulate biogeochemical and ecological processes. These processes are particularly challenging to describe and understand because of their ephemeral nature. The OSCAHR (Observing Submesoscale Coupling At High Resolution) campaign was conducted in fall 2015 in which a fine-scale structure (1-10 km/1-10 days) in the northwestern Mediterranean Ligurian subbasin was pre-identified using both satellite and numerical modeling data. Along the ship track, various variables were measured at the surface (temperature, salinity, chlorophyll a and nutrient concentrations) with ADCP current velocity. We also deployed a new model of the CytoSense automated flow cytometer (AFCM) optimized for small and dim cells, for near real-time characterization of the surface phytoplankton community structure of surface waters with a spatial resolution of a few kilometers and an hourly temporal resolution. For the first time with this optimized version of the AFCM, we were able to fully resolve Prochlorococcus picocyanobacteria in addition to the easily distinguishable Synechococcus. The vertical physical dynamics and biogeochemical properties of the studied area were investigated by continuous high-resolution CTD profiles thanks to a moving vessel profiler (MVP) during the vessel underway associated with a high-resolution pumping system deployed during fixed stations allowing sampling of the water column at a fine resolution (below 1 m). The observed fine-scale feature presented a cyclonic structure with a relatively cold core surrounded by warmer waters. Surface waters were totally depleted in nitrate and phosphate. In addition to the doming of the isopycnals by the cyclonic circulation, an intense wind event induced Ekman pumping. The upwelled subsurface cold nutrient-rich water fertilized surface waters and was marked by an increase in Chl a concentration. Prochlorococcus and pico- and nano-eukaryotes were more abundant in cold core waters, while Synechococcus dominated in warm boundary waters. Nanoeukaryotes were the main contributors ( > 50 %) in terms of pigment content (red fluorescence) and biomass. Biological observations based on the mean cell's red fluorescence recorded by AFCM combined with physical properties of surface waters suggest a distinct origin for two warm boundary waters. Finally, the application of a matrix growth population model based on high-frequency AFCM measurements in warm boundary surface waters provides estimates of in situ growth rate and apparent net primary production for Prochlorococcus (μ = 0.21 d-1, NPP = 0.11 mg C m-3 d-1) and Synechococcus (μ = 0.72 d-1, NPP = 2.68 mg C m-3 d-1), which corroborate their opposite surface distribution pattern. The innovative adaptive strategy applied during OSCAHR with a combination of several multidisciplinary and complementary approaches involving high-resolution in situ observations and sampling, remote-sensing and model simulations provided a deeper understanding of the marine biogeochemical dynamics through the first trophic levels.

Estimation of fine-scale recombination intensity variation in the white-echinus interval of D. melanogaster

PubMed Central

Singh, Nadia D.; Aquadro, Charles F.; Clark, Andrew G.

2009-01-01

Accurate assessment of local recombination rate variation is crucial for understanding the recombination process and for determining the impact of natural selection on linked sites. In Drosophila, local recombination intensity has been estimated primarily by statistical approaches, estimating the local slope of the relationship between the physical and genetic maps. However, these estimates are limited in resolution, and as a result, the physical scale at which recombination intensity varies in Drosophila is largely unknown. While there is some evidence suggesting as much as a 40-fold variation in crossover rate at a local scale in D. pseudoobscura, little is known about the fine-scale structure of recombination rate variation in D. melanogaster. Here, we experimentally examine the fine-scale distribution of crossover events in a 1.2 Mb region on the D. melanogaster X chromosome using a classic genetic mapping approach. Our results show that crossover frequency is significantly heterogeneous within this region, varying ~ 3.5 fold. Simulations suggest that this degree of heterogeneity is sufficient to affect levels of standing nucleotide diversity, although the magnitude of this effect is small. We recover no statistical association between empirical estimates of nucleotide diversity and recombination intensity, which is likely due to the limited number of loci sampled in our population genetic dataset. However, codon bias is significantly negatively correlated with fine-scale recombination intensity estimates, as expected. Our results shed light on the relevant physical scale to consider in evolutionary analyses relating to recombination rate, and highlight the motivations to increase the resolution of the recombination map in Drosophila. PMID:19504037

Improving crop condition monitoring at field scale by using optimal Landsat and MODIS images

USDA-ARS?s Scientific Manuscript database

Satellite remote sensing data at coarse resolution (kilometers) have been widely used in monitoring crop condition for decades. However, crop condition monitoring at field scale requires high resolution data in both time and space. Although a large number of remote sensing instruments with different...

A New Multibeam Sonar Technique for Evaluating Fine-Scale Fish Behavior Near Hydroelectric Dam Guidance Structures

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

Johnson, Robert L.; Simmons, Mary Ann; Simmons, Carver S.

2002-03-07

This book chapter describes a Dual-Head Multibeam Sonar (DHMS) system developed by Battelle and deployed at two dam sites on the Snake and Columbia rivers in Washington State to evaluate the fine-scale (

Data, data everywhere: detecting spatial patterns in fine-scale ecological information collected across a continent

Treesearch

Kevin M. Potter; Frank H. Koch; Christopher M. Oswalt; Basil V. Iannone

2016-01-01

Context Fine-scale ecological data collected across broad regions are becoming increasingly available. Appropriate geographic analyses of these data can help identify locations of ecological concern. Objectives We present one such approach, spatial association of scalable hexagons (SASH), whichidentifies locations where ecological phenomena occur at greater...

Light stops and fine-tuning in MSSM

NASA Astrophysics Data System (ADS)

Çiçi, Ali; Kırca, Zerrin; Ün, Cem Salih

2018-01-01

We discuss the fine-tuning issue within the MSSM framework. Following the idea that the fine-tuning can measure effects of some missing mechanism, we impose non-universal gaugino masses at the GUT scale, and explore the low scale implications. We realize that the fine-tuning parametrized with Δ _{EW} can be as low as zero. We consider the stop mass with a special importance and focus on the mass scales as m_{\\tilde{t}} ≤ 700 GeV, which are excluded by the current experiments when the stop decays into a neutralino along with a top quark or a chargino along with a bottom quark. We find that the stop mass can be as low as about 250 GeV with Δ _{EW} ˜ 50. We find that the solutions in this region can be exluded only up to 60% when stop decays into a neutralino-top quark, and 50% when it decays into a chargino-b quark. Setting 65% CL to be potential exclusion and 95% to be pure exclusion limit such solutions will be tested in near future experiments, which are conducted with higher luminosity. In addition to stop, the region with low fine-tuning and light stops predicts masses for the other supersymmetric particles such as m_{\\tilde{b}} ≳ 700 GeV, m_{\\tilde{τ }} ≳ 1 TeV, m_{\\tilde{χ }1^{± }} ≳ 120 GeV. The details for the mass scales and decay rates are also provided by tables of benchmark points.

Unmanned Aerial System Aids Dry-season Stream Temperature Sensing

NASA Astrophysics Data System (ADS)

Chung, M.; Detweiler, C.; Higgins, J.; Ore, J. P.; Dralle, D.; Thompson, S. E.

2016-12-01

In freshwater ecosystems, temperature affects biogeochemistry and ecology, and is thus a primary physical determinant of habitat quality. Measuring temperatures in spatially heterogeneous water bodies poses a serious challenge to researchers due to constraints associated with currently available methods: in situ loggers record temporally continuous temperature measurements but are limited to discrete spatial locations, while distributed temperature and remote sensing provide fine-resolution spatial measurements that are restricted to only two-dimensions (i.e. streambed and surface, respectively). Using a commercially available quadcopter equipped with a 6m cable and temperature-pressure sensor system, we measured stream temperatures at two confluences at the South Fork Eel River, where cold water inputs from the tributary to the mainstem create thermal refugia for juvenile salmonids during the dry season. As a mobile sensing platform, unmanned aerial systems (UAS) can facilitate quick and repeated sampling with minimal disturbance to the ecosystem, and their datasets can be interpolated to create a three-dimensional thermal map of a water body. The UAS-derived data was compared to data from in situ data loggers to evaluate whether the UAS is better able to capture fine-scale temperature dynamics at each confluence. The UAS has inherent limitations defined by battery life and flight times, as well as operational constraints related to maneuverability under wind and streamflow conditions. However, the platform is able to serve as an additional field tool for researchers to capture complex thermal structures in water bodies.

Effects of large-scale deforestation on precipitation in the monsoon regions: remote versus local effects.

PubMed

Devaraju, N; Bala, Govindasamy; Modak, Angshuman

2015-03-17

In this paper, using idealized climate model simulations, we investigate the biogeophysical effects of large-scale deforestation on monsoon regions. We find that the remote forcing from large-scale deforestation in the northern middle and high latitudes shifts the Intertropical Convergence Zone southward. This results in a significant decrease in precipitation in the Northern Hemisphere monsoon regions (East Asia, North America, North Africa, and South Asia) and moderate precipitation increases in the Southern Hemisphere monsoon regions (South Africa, South America, and Australia). The magnitude of the monsoonal precipitation changes depends on the location of deforestation, with remote effects showing a larger influence than local effects. The South Asian Monsoon region is affected the most, with 18% decline in precipitation over India. Our results indicate that any comprehensive assessment of afforestation/reforestation as climate change mitigation strategies should carefully evaluate the remote effects on monsoonal precipitation alongside the large local impacts on temperatures.

Fine-scale spatial genetic structure of common and declining bumble bees across an agricultural landscape.

PubMed

Dreier, Stephanie; Redhead, John W; Warren, Ian A; Bourke, Andrew F G; Heard, Matthew S; Jordan, William C; Sumner, Seirian; Wang, Jinliang; Carvell, Claire

2014-07-01

Land-use changes have threatened populations of many insect pollinators, including bumble bees. Patterns of dispersal and gene flow are key determinants of species' ability to respond to land-use change, but have been little investigated at a fine scale (<10 km) in bumble bees. Using microsatellite markers, we determined the fine-scale spatial genetic structure of populations of four common Bombus species (B. terrestris, B. lapidarius, B. pascuorum and B. hortorum) and one declining species (B. ruderatus) in an agricultural landscape in Southern England, UK. The study landscape contained sown flower patches representing agri-environment options for pollinators. We found that, as expected, the B. ruderatus population was characterized by relatively low heterozygosity, number of alleles and colony density. Across all species, inbreeding was absent or present but weak (FIS  = 0.01-0.02). Using queen genotypes reconstructed from worker sibships and colony locations estimated from the positions of workers within these sibships, we found that significant isolation by distance was absent in B. lapidarius, B. hortorum and B. ruderatus. In B. terrestris and B. pascuorum, it was present but weak; for example, in these two species, expected relatedness of queens founding colonies 1 m apart was 0.02. These results show that bumble bee populations exhibit low levels of spatial genetic structure at fine spatial scales, most likely because of ongoing gene flow via widespread queen dispersal. In addition, the results demonstrate the potential for agri-environment scheme conservation measures to facilitate fine-scale gene flow by creating a more even distribution of suitable habitats across landscapes. © 2014 The Authors. Molecular Ecology Published by John Wiley & Sons Ltd.

Mud deposit formation on the open coast of the larger Patos Lagoon-Cassino Beach system

NASA Astrophysics Data System (ADS)

Vinzon, S. B.; Winterwerp, J. C.; Nogueira, R.; de Boer, G. J.

2009-03-01

This paper proposes an explanation of the mud deposits on the inner Shelf of Cassino Beach, South Brazil, by using computational modeling. These mud deposits are mainly formed by sediments delivered from Patos Lagoon, a coastal lagoon connected to the Shelf, next to Cassino Beach. The deposits are characterized by (soft) mud layers of about 1 m thick and are found between the -5 and -20 isobaths. Two hydrodynamic models of the larger Patos Lagoon-Cassino Beach system were calibrated against water elevation measured for a 5 months period, and against currents and salinity measured for a week period. The circulation patterns and water exchange through the mouth were analyzed as a function of local and remote wind effects, and river discharges. The remote wind effect mainly governs the quantity of water exchange with the Lagoon through its effect on mean sea level as a result of Ekman dynamics, while river discharges are important for the salinity of the exchanged water masses. Local winds augment the export-import rates by set-up and set-down within the Lagoon, but their effects are much smaller than those of the remote wind. Currents patterns on the inner Shelf during water outflow revealed a recirculation zone south of the Lagoon, induced by the local geometry and bathymetry of the system. This recirculation zone coincides with observed locations of mud deposition. Water, hence suspended sediment export occurs when remote and local winds are from the N-E, which explains why fine sediment deposits are mainly found south of the Lagoon's breakwater. A sensitivity analysis with the numerical model quantified the contribution of the various mechanisms driving the transport and fate of the fine suspended sediments, i.e. the effects of remote and local wind, of the astronomical tide, of river discharge and fresh-salt water-induced density currents, and of earth rotation. It is concluded that gravitational circulation and earth rotation affects the further dispersion of the deposits largely, whereas the remote wind effect has the largest influence on the amount of sediment released from the Lagoon. It is noted that this paper analyzes the initial deposition patterns induced by current effects only. However, in reality, these deposits are further redistributed over the Shelf by wave effects—these are subject of a next study on the sediment dynamics of the larger Patos Lagoon-Cassino Beach system.

Microwave brightness temperature and thermal inertia - towards synergistic method of high-resolution soil moisture retrieval

NASA Astrophysics Data System (ADS)

Lukowski, Mateusz; Usowicz, Boguslaw; Sagan, Joanna; Szlazak, Radoslaw; Gluba, Lukasz; Rojek, Edyta

2017-04-01

Soil moisture is an important parameter in many environmental studies, as it influences the exchange of water and energy at the interface between the land surface and the atmosphere. Accurate assessment of the soil moisture spatial and temporal variations is crucial for numerous studies; starting from a small scale of single field, then catchment, mesoscale basin, ocean conglomeration, finally ending at the global water cycle. Despite numerous advantages, such as fine accuracy (undisturbed by clouds or daytime conditions) and good temporal resolution, passive microwave remote sensing of soil moisture, e.g. SMOS and SMAP, are not applicable to a small scale - simply because of too coarse spatial resolution. On the contrary, thermal infrared-based methods of soil moisture retrieval have a good spatial resolution, but are often disturbed by clouds and vegetation interferences or night effects. The methods that base on point measurements, collected in situ by monitoring stations or during field campaigns, are sometimes called "ground truth" and may serve as a reference for remote sensing, of course after some up-scaling and approximation procedures that are, unfortunately, potential source of error. Presented research concern attempt to synergistic approach that join two remote sensing methods: passive microwave and thermal infrared, supported by in situ measurements. Microwave brightness temperature of soil was measured by ELBARA, the radiometer at 1.4 GHz frequency, installed at 6 meters high tower at Bubnow test site in Poland. Thermal inertia around the tower was modelled using the statistical-physical model whose inputs were: soil physical properties, its water content, albedo and surface temperatures measured by an infrared pyrometer, directed at the same footprint as ELBARA. The results coming from this method were compared to in situ data obtained during several field campaigns and by the stationary agrometeorological stations. The approach seems to be reasonable, as both variables, brightness temperature and thermal inertia, strongly depend on soil moisture. Despite the fact that the presented research focused on modelling in the small size, 4 ha test site, the method is promising for larger scales as well, due to similarities between ELBARA and SMOS and between pyrometer and satellite imaging spectrometers (Landsat, Sentinel etc.). The approach will merge advantages: high accuracy of passive microwave sensing with a good spatial resolution of thermal infrared methods. The work was partially funded under two ESA projects: 1) "ELBARA_PD (Penetration Depth)" No. 4000107897/13/NL/KML, funded by the Government of Poland through an ESA-PECS contract (Plan for European Cooperating States). 2) "Technical Support for the fabrication and deployment of the radiometer ELBARA-III in Bubnow, Poland" No. 4000113360/15/NL/FF/gp.

Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement

PubMed Central

Zhu, Hong; Tang, Xinming; Xie, Junfeng; Song, Weidong; Mo, Fan; Gao, Xiaoming

2018-01-01

There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L0 gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements. PMID:29414893

Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement.

PubMed

Zhu, Hong; Tang, Xinming; Xie, Junfeng; Song, Weidong; Mo, Fan; Gao, Xiaoming

2018-02-07

There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L ₀ gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements.

PIXELS: Using field-based learning to investigate students' concepts of pixels and sense of scale

NASA Astrophysics Data System (ADS)

Pope, A.; Tinigin, L.; Petcovic, H. L.; Ormand, C. J.; LaDue, N.

2015-12-01

Empirical work over the past decade supports the notion that a high level of spatial thinking skill is critical to success in the geosciences. Spatial thinking incorporates a host of sub-skills such as mentally rotating an object, imagining the inside of a 3D object based on outside patterns, unfolding a landscape, and disembedding critical patterns from background noise. In this study, we focus on sense of scale, which refers to how an individual quantified space, and is thought to develop through kinesthetic experiences. Remote sensing data are increasingly being used for wide-reaching and high impact research. A sense of scale is critical to many areas of the geosciences, including understanding and interpreting remotely sensed imagery. In this exploratory study, students (N=17) attending the Juneau Icefield Research Program participated in a 3-hour exercise designed to study how a field-based activity might impact their sense of scale and their conceptions of pixels in remotely sensed imagery. Prior to the activity, students had an introductory remote sensing lecture and completed the Sense of Scale inventory. Students walked and/or skied the perimeter of several pixel types, including a 1 m square (representing a WorldView sensor's pixel), a 30 m square (a Landsat pixel) and a 500 m square (a MODIS pixel). The group took reflectance measurements using a field radiometer as they physically traced out the pixel. The exercise was repeated in two different areas, one with homogenous reflectance, and another with heterogeneous reflectance. After the exercise, students again completed the Sense of Scale instrument and a demographic survey. This presentation will share the effects and efficacy of the field-based intervention to teach remote sensing concepts and to investigate potential relationships between students' concepts of pixels and sense of scale.

Remotely detected high-field MRI of porous samples

NASA Astrophysics Data System (ADS)

Seeley, Juliette A.; Han, Song-I.; Pines, Alexander

2004-04-01

Remote detection of NMR is a novel technique in which an NMR-active sensor surveys an environment of interest and retains memory of that environment to be recovered at a later time in a different location. The NMR or MRI information about the sensor nucleus is encoded and stored as spin polarization at the first location and subsequently moved to a different physical location for optimized detection. A dedicated probe incorporating two separate radio frequency (RF)—circuits was built for this purpose. The encoding solenoid coil was large enough to fit around the bulky sample matrix, while the smaller detection solenoid coil had not only a higher quality factor, but also an enhanced filling factor since the coil volume comprised purely the sensor nuclei. We obtained two-dimensional (2D) void space images of two model porous samples with resolution less than 1.4 mm 2. The remotely reconstructed images demonstrate the ability to determine fine structure with image quality superior to their directly detected counterparts and show the great potential of NMR remote detection for imaging applications that suffer from low sensitivity due to low concentrations and filling factor.

The Atmospheric Tomography Mission (ATom): Comparing the Chemical Climatology of Reactive Species and Air Parcels from Measurements and Global Models

NASA Astrophysics Data System (ADS)

Prather, M. J.; Flynn, C.; Wennberg, P. O.; Kim, M. J.; Ryerson, T. B.; Hanisco, T. F.; Diskin, G. S.; Daube, B. C.; Commane, R.; McKain, K.; Apel, E. C.; Blake, N. J.; Blake, D. R.; Elkins, J. W.; Hall, S.; Steenrod, S.; Strahan, S. E.; Lamarque, J. F.; Fiore, A. M.; Horowitz, L. W.; Murray, L. T.; Mao, J.; Shindell, D. T.; Wofsy, S. C.

2017-12-01

The NASA Atmospheric Tomography Mission (ATom) is building a photochemical climatology of the remote troposphere based on objective sampling and profiling transects over the Pacific and Atlantic Oceans. These statistics provide direct tests of chemistry-climate models. The choice of species focuses on those controlling primary reactivity (a.k.a. oxidative state) of the troposphere, specifically chemical tendencies of O3 and CH4. These key species include, inter alia, O3, CH4, CO, C2H6, other alkanes, alkenes, aromatics, NOx, HNO3, HO2NO2, PAN, other organic nitrates, H2O, HCHO, H2O2, CH3OOH. Three of the four ATom deployments are now complete, and data from the first two (ATom-1 & -2) have been released as of this talk (see espoarchive.nasa.gov/archive/browse/atom). The statistical distributions of key species are presented as 1D and 2D probability densities (PDs) and we focus here on the tropical and mid-latitude regions of the Pacific during ATom-1 (Aug) and -2 (Feb). PDs are computed from ATom observations and 6 global chemistry models over the tropospheric depth (0-12 km) and longitudinal extent of the observations. All data are weighted to achieve equal mass-weighting by latitude regimes to account for spatial sampling biases. The models are used to calculate the reactivity in each ATom air parcel. Reweighting parcels with loss of CH4 or production of O3, for example, allows us to identify which air parcels are most influential, including assessment of the importance of fine pollution layers in the most remote troposphere. Another photochemical climatology developed from ATom, and used to test models, includes the effect of clouds on photolysis rates. The PDs and reactivity-weighted PDs reveal important seasonal differences and similarities between the two campaigns and also show which species may be most important in controlling reactivities. They clearly identify some very specific failings in the modeled climatologies and help us evaluate the chemical importance of fine-scale laminae with distinct chemical composition that are beyond model simulations.

Landscape-scale forest disturbance regimes in southern Peruvian Amazonia.

PubMed

Boyd, Doreen S; Hill, Ross A; Hopkinson, Chris; Baker, Timothy R

2013-10-01

Landscape-scale gap-size frequency distributions in tropical forests are a poorly studied but key ecological variable. Currently, a scale gap currently exists between local-scale field-based studies and those employing regional-scale medium-resolution satellite data. Data at landscape scales but of fine resolution would, however, facilitate investigation into a range of ecological questions relating to gap dynamics. These include whether canopy disturbances captured in permanent sample plots (PSPs) are representative of those in their surrounding landscape, and whether disturbance regimes vary with forest type. Here, therefore, we employ airborne LiDAR data captured over 142.5 km2 of mature, swamp, and regenerating forests in southeast Peru to assess the landscape-scale disturbance at a sampling resolution of up to 2 m. We find that this landscape is characterized by large numbers of small gaps; large disturbance events are insignificant and infrequent. Of the total number of gaps that are 2 m2 or larger in area, just 0.45% were larger than 100 m2, with a power-law exponent (alpha) value of the gap-size frequency distribution of 2.22. However, differences in disturbance regimes are seen among different forest types, with a significant difference in the alpha value of the gap-size frequency distribution observed for the swamp/regenerating forests compared with the mature forests at higher elevations. Although a relatively small area of the total forest of this region was investigated here, this study presents an unprecedented assessment of this landscape with respect to its gap dynamics. This is particularly pertinent given the range of forest types present in the landscape and the differences observed. The coupling of detailed insights into forest properties and growth provided by PSPs with the broader statistics of disturbance events using remote sensing is recommended as a strong basis for scaling-up estimates of landscape and regional-scale carbon balance.

Relationship between postural control and fine motor skills in preterm infants at 6 and 12 months adjusted age.

PubMed

Wang, Tien-Ni; Howe, Tsu-Hsin; Hinojosa, Jim; Weinberg, Sharon L

2011-01-01

We examined the relationship between postural control and fine motor skills of preterm infants at 6 and 12 mo adjusted age. The Alberta Infant Motor Scale was used to measure postural control, and the Peabody Developmental Motor Scales II was used to measure fine motor skills. The data analyzed were taken from 105 medical records from a preterm infant follow-up clinic at an urban academic medical center in south Taiwan. Using multiple regression analyses, we found that the development of postural control is related to the development of fine motor skills, especially in the group of preterm infants with delayed postural control. This finding supports the theoretical assumption of proximal-distal development used by many occupational therapists to guide intervention. Further research is suggested to corroborate findings.

Membrane properties change in fine-pore aeration diffusers: full-scale variations of transfer efficiency and headloss.

PubMed

Rosso, Diego; Libra, Judy A; Wiehe, Wolfgang; Stenstrom, Michael K

2008-05-01

Fine-pore diffusers are the most common aeration system in municipal wastewater treatment. Punched polymeric membranes are often used in fine-pore aeration due to their advantageous initial performance. These membranes are subject to fouling and scaling, resulting in increased headloss and reduced oxygen transfer efficiency, both contributing to increased plant energy costs. This paper describes and discusses the change in material properties for polymeric fine-pore diffusers, comparing new and used membranes. Three different diffuser technologies were tested and sample diffusers from two wastewater treatment facilities were analysed. The polymeric membranes analysed in this paper were composed of ethylene-propylene-diene monomer (EPDM), polyurethane, and silicon. Transfer efficiency is usually lower with longer times in operation, as older, dilated orifices produce larger bubbles, which are unfavourable to mass transfer. At the same time, headloss increases with time in operation, since membranes increase in rigidity and hardness, and fouling and scaling phenomena occur at the orifice opening. Change in polymer properties and laboratory test results correlate with the decrease in oxygen transfer efficiency.

Modelling Soil-Landscapes in Coastal California Hills Using Fine Scale Terrestrial Lidar

NASA Astrophysics Data System (ADS)

Prentice, S.; Bookhagen, B.; Kyriakidis, P. C.; Chadwick, O.

2013-12-01

Digital elevation models (DEMs) are the dominant input to spatially explicit digital soil mapping (DSM) efforts due to their increasing availability and the tight coupling between topography and soil variability. Accurate characterization of this coupling is dependent on DEM spatial resolution and soil sampling density, both of which may limit analyses. For example, DEM resolution may be too coarse to accurately reflect scale-dependent soil properties yet downscaling introduces artifactual uncertainty unrelated to deterministic or stochastic soil processes. We tackle these limitations through a DSM effort that couples moderately high density soil sampling with a very fine scale terrestrial lidar dataset (20 cm) implemented in a semiarid rolling hillslope domain where terrain variables change rapidly but smoothly over short distances. Our guiding hypothesis is that in this diffusion-dominated landscape, soil thickness is readily predicted by continuous terrain attributes coupled with catenary hillslope segmentation. We choose soil thickness as our keystone dependent variable for its geomorphic and hydrologic significance, and its tendency to be a primary input to synthetic ecosystem models. In defining catenary hillslope position we adapt a logical rule-set approach that parses common terrain derivatives of curvature and specific catchment area into discrete landform elements (LE). Variograms and curvature-area plots are used to distill domain-scale terrain thresholds from short range order noise characteristic of very fine-scale spatial data. The revealed spatial thresholds are used to condition LE rule-set inputs, rendering a catenary LE map that leverages the robustness of fine-scale terrain data to create a generalized interpretation of soil geomorphic domains. Preliminary regressions show that continuous terrain variables alone (curvature, specific catchment area) only partially explain soil thickness, and only in a subset of soils. For example, at spatial scales up 20, curvature explains 40% of soil thickness variance among soils <3 m deep, while soils >3 m deep show no clear relation to curvature. To further demonstration our geomorphic segmentation approach, we apply it to DEM domains where diffusion processes are less dominant than in our primary study area. Classified landform map derived from fine scale terrestrial lidar. Color classes depict hydrogeomorphic process domains in zero order watersheds.

Mediterranean maquis fuel model development and mapping to support fire modeling

NASA Astrophysics Data System (ADS)

Bacciu, V.; Arca, B.; Pellizzaro, G.; Salis, M.; Ventura, A.; Spano, D.; Duce, P.

2009-04-01

Fuel load data and fuel model maps represent a critical issue for fire spread and behaviour modeling. The availability of accurate input data at different spatial and temporal scales can allow detailed analysis and predictions of fire hazard and fire effects across a landscape. Fuel model data are used in spatially explicit fire growth models to attain fire behaviour information for fuel management in prescribed fires, fire management applications, firefighters training, smoke emissions, etc. However, fuel type characteristics are difficult to be parameterized due to their complexity and variability: live and dead materials with different size contribute in different ways to the fire spread and behaviour. In the last decades, a strong help was provided by the use of remote sensing imagery at high spatial and spectral resolution. Such techniques are able to capture fine scale fuel distributions for accurate fire growth projections. Several attempts carried out in Europe were devoted to fuel classification and map characterization. In Italy, fuel load estimation and fuel model definition are still critical issues to be addressed due to the lack of detailed information. In this perspective, the aim of the present work was to propose an integrated approach based on field data collection, fuel model development and fuel model mapping to provide fuel models for the Mediterranean maquis associations. Field data needed for the development of fuel models were collected using destructive and non destructive measurements in experimental plots located in Northern Sardinia (Italy). Statistical tests were used to identify the main fuel types that were classified into four custom fuel models. Subsequently, a supervised classification by the Maximum Likelihood algorithm was applied on IKONOS images to identify and map the different types of maquis vegetation. The correspondent fuel model was then associated to each vegetation type to obtain the fuel model map. The results show the potential of this approach in achieving a reasonable accuracy in fuel model development and mapping; fine scale fuel model maps can be potentially helpful to obtain realistic predictions of fire behaviour and fire effects.

Integrating fine-scale soil data into species distribution models: preparing Soil Survey Geographic (SSURGO) data from multiple counties

Treesearch

Matthew P. Peters; Louis R. Iverson; Anantha M. Prasad; Steve N. Matthews

2013-01-01

Fine-scale soil (SSURGO) data were processed at the county level for 37 states within the eastern United States, initially for use as predictor variables in a species distribution model called DISTRIB II. Values from county polygon files converted into a continuous 30-m raster grid were aggregated to 4-km cells and integrated with other environmental and site condition...

Heavy precipitation episodes in the Western Mediterranean : Use of a semi-Lagrangian advection model for the fine-scale description of upper-level troughs

NASA Astrophysics Data System (ADS)

Gauthier, N.; Claud, C.; Funatsu, B. M.; Chaboureau, J.-P.; Argence, S.; Lambert, D.; Richard, E.; Hauchecorne, A.; Arbogast, P.; Maynard, K.

2009-09-01

Heavy precipitation events over the Mediterranean Sea are generally associated with upper-level troughs. The mesoscale structures of such troughs are however not well reproduced by the atmospheric analyses due to inappropriate spatial resolution. We propose here to use a semi-Lagrangian advection model called MIMOSA (Modélisation Isentrope du transport Méso-échelle de l'Ozone Stratosphérique par Advection) initially developed to describe stratospheric filaments, to calculate fine-scale Potential Vorticity (PV) fields on isentropic surfaces near the tropopause. After a description of MIMOSA, we will focus on the model-generated PV fields for several high impact weather cases that occurred over the Western Mediterreanean Sea. We will demonstrate the ability of MIMOSA to resolve fine scale structures of upper-level troughs considering the Algiers' flash flood, which occurred on November 2001, and then a heavy precipitation event over southeast France on the 5-6 September 2005. Finally, with a PV inversion method, we will show the impact of the fine scales PV structures as depicted by MIMOSA to improve the numerical simulation of a « hurricane » that hit Italy in September 2006, both in terms of surface pressure and precipitation forecasts.

CNR LARA project, Italy: Airborne laboratory for environmental research

NASA Technical Reports Server (NTRS)

Bianchi, R.; Cavalli, R. M.; Fiumi, L.; Marino, C. M.; Pignatti, S.

1995-01-01

The increasing interest for the environmental problems and the study of the impact on the environment due to antropic activity produced an enhancement of remote sensing applications. The Italian National Research Council (CNR) established a new laboratory for airborne hyperspectral imaging, the LARA Project (Laboratorio Aero per Ricerche Ambientali - Airborne Laboratory for Environmental Research), equipping its airborne laboratory, a CASA-212, mainly with the Daedalus AA5000 MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) instrument. MIVIS's channels, spectral bandwidths, and locations are chosen to meet the needs of scientific research for advanced applications of remote sensing data. MIVIS can make significant contributions to solving problems in many diverse areas such as geologic exploration, land use studies, mineralogy, agricultural crop studies, energy loss analysis, pollution assessment, volcanology, forest fire management and others. The broad spectral range and the many discrete narrow channels of MIVIS provide a fine quantization of spectral information that permits accurate definition of absorption features from a variety of materials, allowing the extraction of chemical and physical information of our environment. The availability of such a hyperspectral imager, that will operate mainly in the Mediterranean area, at the present represents a unique opportunity for those who are involved in environmental studies and land-management to collect systematically large-scale and high spectral-spatial resolution data of this part of the world. Nevertheless, MIVIS deployments will touch other parts of the world, where a major interest from the international scientific community is present.

Thermal infrared (2.5-13.5 μm) spectroscopic remote sensing of igneous rock types on particulate planetary surfaces

USGS Publications Warehouse

Salisbury, John W.; Walter, Louis S.

1989-01-01

Fundamental molecular vibration bands are significantly diminished by scattering. Thus such bands in spectra of fine particulate regoliths (i.e., dominated by <5-μm particles), or regoliths displaying a similar scale of porosity, are difficult to use for mineralogical or rock type identification. Consequently, other spectral features have been sought that may be more useful in spectroscopic remote sensing of composition. We find that mineralogical information is retained in overtones and combination tones of the fundamental molecular vibrations in the 3.0- to 7.0-μm region, but that relatively few minerals have a sufficiently distinctive band structure to be unambiguously identified with currently available techniques. More significantly, identification of general rock type, as defined by the SCFM chemical index (SCFM = SiO2/SiO2 + CaO + FeO + MgO), is possible using spectral features associated with the principal Christiansen frequency and with a region of relative transparency between the Si-O stretching and bending bands. However, environmental factors may affect the appearance and wavelengths of these features. Finally, prominent absorption bands may result from the presence of relatively small amounts of water, hydroxyl or carbonate, because absorption bands exhibited by these materials in the 2.7- to 4.0-μm region, where silicate spectra are otherwise featureless, increase strongly in spectral contrast with decreasing particle size. Such materials are thus detectable in very small amounts in a particulate regolith composed predominantly of silicate minerals.

Multiscaling of vegetative indexes from remote sensing images obtained at different spatial resolutions

NASA Astrophysics Data System (ADS)

Alonso, Carmelo; Tarquis, Ana M.; Zuñiga, Ignacio; Benito, Rosa M.

2017-04-01

Vegetation indexes, such as Normalized Difference Vegetation Index (NDVI) and enhanced Vegetation index (EVI), can been used to estimate root zone soil moisture through high resolution remote sensing images. These indexes are based in red (R), near infrared (NIR) and blue (B) wavelengths data. In this work we have studied the scaling properties of both vegetation indexes analyzing the information contained in two satellite data: Landsat-7 and Ikonos. Because of the potential capacity for systematic observations at various scales, remote sensing technology extends possible data archives from present time to over several decades back. For this advantage, enormous efforts have been made by researchers and application specialists to delineate vegetation indexes from local scale to global scale by applying remote sensing imagery. To study the influence of the spatial resolution the vegetation indexes map estimated with Ikonos-2 coded in 8 bits, with a resolution of 4m, have been compared through a multifractal analysis with the ones obtained with Lansat-7 8 bits, of 30 m. resolution, on the same area of study. The scaling behaviour of NDVI and EVI presents several differences that will be discussed based on the multifractal parameters extracted from the analysis. REFERENCES Alonso, C., Tarquis, A. M., Benito, R. M. and Zuñiga, I. Correlation scaling properties between soil moisture and vegetation indices. Geophysical Research Abstracts, 11, EGU2009-13932, 2009. Alonso, C., Tarquis, A. M. and Benito, R. M. Comparison of fractal dimensions based on segmented NDVI fields obtained from different remote sensors. Geophysical Research Abstracts, 14, EGU2012-14342, 2012. Escribano Rodriguez, J., Alonso, C., Tarquis, A.M., Benito, R.M. and Hernandez Diaz-Ambrona, C. Comparison of NDVI fields obtained from different remote sensors. Geophysical Research Abstracts,15, EGU2013-14153, 2013. Lovejoy, S., Tarquis, A., Gaonac'h, H. and Schertzer, D. Single and multiscale remote sensing techniques, multifractals and MODIS derived vegetation and soil moisture, Vadose Zone J., 7, 533-546, 2008. Renosh, P. R., Schmitt, F. G., and Loisel, H.: Scaling analysis of ocean surface turbulent heterogeneities from satellite remote sensing: use of 2D structure functions. PLoS ONE, 10, e0126975, 2015. Tarquis, A.M., Platonov, A., Matulka, A., Grau, J., Sekula, E., Diez, M. and Redondo J. M. Application of multifractal analysis to the study of SAR features and oil spills on the ocean surface. Nonlin. Processes Geophys., 21, 439-450, 2014.

Representativeness of regional and global mass-balance measurement networks (Invited)

NASA Astrophysics Data System (ADS)

Cogley, J. G.; Moholdt, G.; Gardner, A. S.

2013-12-01

We showed in a recent publication that regional estimates of glacier mass budgets, obtained by interpolation from in-situ measurements, were markedly more negative than corresponding estimates by satellite gravimetry (GRACE) and satellite altimetry (ICESat) during 2003-2009. Examining the ICESat data in more detail, we found that in-situ records tend to be located in areas where glaciers are thinning more rapidly than as observed in their regional surroundings. Because neither GRACE nor ICESat can provide information for times before 2002-2003, and may not operate without interruption in the future, we explore possible explanations of and remedies for the identified bias in the in-situ network. Sparse spatial sampling, coupled with previously undetected spatial variability of mass balance at scales between the 10-km in-situ scale and the 350-km gravimetric scale, appears to be the leading explanation. Satisfactory remedies are not obvious. Selecting glaciers for in-situ measurement that are more representative will yield only incremental improvements. There appears to be no alternative to mass-balance modelling as a versatile tool for estimation of regional mass balance. However the meteorological data for forcing the surface components of glacier models have coarser resolution than is desirable and are themselves uncertain, especially in the remote regions where much of the glacier ice is found. Measurements of frontal (dynamic) mass changes are still difficult, and modelling of these changes remains underdeveloped in spite of recent advances. Thus research on a broad scale is called for in order to meet the challenge of producing more accurate hindcasts and projections of glacier mass budgets with fine spatial and temporal resolution.

Environmental analysis using integrated GIS and remotely sensed data - Some research needs and priorities

NASA Technical Reports Server (NTRS)

Davis, Frank W.; Quattrochi, Dale A.; Ridd, Merrill K.; Lam, Nina S.-N.; Walsh, Stephen J.

1991-01-01

This paper discusses some basic scientific issues and research needs in the joint processing of remotely sensed and GIS data for environmental analysis. Two general topics are treated in detail: (1) scale dependence of geographic data and the analysis of multiscale remotely sensed and GIS data, and (2) data transformations and information flow during data processing. The discussion of scale dependence focuses on the theory and applications of spatial autocorrelation, geostatistics, and fractals for characterizing and modeling spatial variation. Data transformations during processing are described within the larger framework of geographical analysis, encompassing sampling, cartography, remote sensing, and GIS. Development of better user interfaces between image processing, GIS, database management, and statistical software is needed to expedite research on these and other impediments to integrated analysis of remotely sensed and GIS data.

In the absence of a "landscape of fear": How lions, hyenas, and cheetahs coexist.

PubMed

Swanson, Alexandra; Arnold, Todd; Kosmala, Margaret; Forester, James; Packer, Craig

2016-12-01

Aggression by top predators can create a "landscape of fear" in which subordinate predators restrict their activity to low-risk areas or times of day. At large spatial or temporal scales, this can result in the costly loss of access to resources. However, fine-scale reactive avoidance may minimize the risk of aggressive encounters for subordinate predators while maintaining access to resources, thereby providing a mechanism for coexistence. We investigated fine-scale spatiotemporal avoidance in a guild of African predators characterized by intense interference competition. Vulnerable to food stealing and direct killing, cheetahs are expected to avoid both larger predators; hyenas are expected to avoid lions. We deployed a grid of 225 camera traps across 1,125 km 2 in Serengeti National Park, Tanzania, to evaluate concurrent patterns of habitat use by lions, hyenas, cheetahs, and their primary prey. We used hurdle models to evaluate whether smaller species avoided areas preferred by larger species, and we used time-to-event models to evaluate fine-scale temporal avoidance in the hours immediately surrounding top predator activity. We found no evidence of long-term displacement of subordinate species, even at fine spatial scales. Instead, hyenas and cheetahs were positively associated with lions except in areas with exceptionally high lion use. Hyenas and lions appeared to actively track each, while cheetahs appear to maintain long-term access to sites with high lion use by actively avoiding those areas just in the hours immediately following lion activity. Our results suggest that cheetahs are able to use patches of preferred habitat by avoiding lions on a moment-to-moment basis. Such fine-scale temporal avoidance is likely to be less costly than long-term avoidance of preferred areas: This may help explain why cheetahs are able to coexist with lions despite high rates of lion-inflicted mortality, and highlights reactive avoidance as a general mechanism for predator coexistence.

40 CFR 420.20 - Applicability; description of the sintering subcategory.

Code of Federal Regulations, 2010 CFR

2010-07-01

... resulting from sintering operations conducted by the heating of iron bearing wastes (mill scale and dust from blast furnaces and steelmaking furnaces) together with fine iron ore, limestone, and coke fines in...

40 CFR 420.20 - Applicability; description of the sintering subcategory.

Code of Federal Regulations, 2012 CFR

2012-07-01

... resulting from sintering operations conducted by the heating of iron bearing wastes (mill scale and dust from blast furnaces and steelmaking furnaces) together with fine iron ore, limestone, and coke fines in...

40 CFR 420.20 - Applicability; description of the sintering subcategory.

Code of Federal Regulations, 2013 CFR

2013-07-01

... resulting from sintering operations conducted by the heating of iron bearing wastes (mill scale and dust from blast furnaces and steelmaking furnaces) together with fine iron ore, limestone, and coke fines in...

40 CFR 420.20 - Applicability; description of the sintering subcategory.

Code of Federal Regulations, 2011 CFR

2011-07-01

... resulting from sintering operations conducted by the heating of iron bearing wastes (mill scale and dust from blast furnaces and steelmaking furnaces) together with fine iron ore, limestone, and coke fines in...

40 CFR 420.20 - Applicability; description of the sintering subcategory.

Code of Federal Regulations, 2014 CFR

2014-07-01

... resulting from sintering operations conducted by the heating of iron bearing wastes (mill scale and dust from blast furnaces and steelmaking furnaces) together with fine iron ore, limestone, and coke fines in...

An Assessment of Capacity, Gaps and Opportunities toward Building a Global Early Warning System for Flood Disasters

NASA Astrophysics Data System (ADS)

Hong, Y.; Adler, R.; Huffman, G.

2007-12-01

Many governmental emergency management agencies or non-governmental organizations need real-time information on emerging disasters for preparedness and response. However, progress in warnings for hydrologic disasters has been constrained by the difficulty of measuring spatiotemporal variability of rainfall fluxes continuously over space and time, due largely to insufficient ground monitoring networks, long delay in data transmission and absence of data sharing protocols among many geopolitically trans-boundary basins. In addition, in-situ gauging stations are often washed away by the very floods they are designed to monitor, making reconstruction of gauges a common post-flood activity around the world. In reality, remote sensing precipitation estimates may be the only source of rainfall information available over much of the globe, particularly for vulnerable countries in the tropics where abundant extreme rain storms and severe flooding events repeat every year. Building on progress in remote sensing technology, researchers have improved the accuracy, coverage, and resolution of rainfall estimates by combining imagery from infrared, passive microwave, and weather radar sensors. Today, remote sensing imagery acquired and processed in real time can provide near-real-time rainfall fluxes at relatively fine spatiotemporal scales (kilometers to tens of kilometers and 30-minute to 3-hour). These new suites of rainfall products have the potential to support daily decision-making in analysis of hydrologic hazards. This talk will address several key issues, including remote sensing rainfall retrieval and data assimilation, for hydrologists to develop alternative satellite-based flood warning systems that may supplement in-situ infrastructure when conventional data sources are denied due to natural or administrative causes. This talk will also assess a module-structure global flood prediction system that has been running at real-time by integrating remote sensing forcing data with simplified hydrological models, in an effort to offer a practical solution to the challenge of building cost-effective flood warning systems for the data-spares regions of the world. The real-time outlook of hazardous floods will quickly disseminate through an open-access web-interface to many agencies and organizations for their daily decision-making, with the potential to save human life and reduce economic impacts. The interactive Web interface will also show close-up maps of the disaster risks overlaid on population or integrated with the Google-Earth visualization tool.

Flexible nanogenerators

DOEpatents

Wang, Zhong L [Marietta, GA; Wang, Xudong [Atlanta, GA; Qin, Yong [Atlanta, GA; Yang, Rusen [Atlanta, GA

2011-07-19

A small scale electrical generator includes an elongated substrate and a first piezoelectric fine wire. The first piezoelectric fine wire is disposed along a surface of the substrate. The first piezoelectric fine wire has a first end and a spaced-apart second end. A first conductive contact secures the first end of the fine wire to a first portion of the substrate and a second conductive contact secures the second end of the fine wire to a second portion of the substrate. A fabric made of interwoven strands that includes fibers from which piezoelectric nanowires extend radially therefrom and conductive nanostructures extend therefrom is configured to generate electricity.

SOMAR-LES: A framework for multi-scale modeling of turbulent stratified oceanic flows

NASA Astrophysics Data System (ADS)

Chalamalla, Vamsi K.; Santilli, Edward; Scotti, Alberto; Jalali, Masoud; Sarkar, Sutanu

2017-12-01

A new multi-scale modeling technique, SOMAR-LES, is presented in this paper. Localized grid refinement gives SOMAR (the Stratified Ocean Model with Adaptive Resolution) access to small scales of the flow which are normally inaccessible to general circulation models (GCMs). SOMAR-LES drives a LES (Large Eddy Simulation) on SOMAR's finest grids, forced with large scale forcing from the coarser grids. Three-dimensional simulations of internal tide generation, propagation and scattering are performed to demonstrate this multi-scale modeling technique. In the case of internal tide generation at a two-dimensional bathymetry, SOMAR-LES is able to balance the baroclinic energy budget and accurately model turbulence losses at only 10% of the computational cost required by a non-adaptive solver running at SOMAR-LES's fine grid resolution. This relative cost is significantly reduced in situations with intermittent turbulence or where the location of the turbulence is not known a priori because SOMAR-LES does not require persistent, global, high resolution. To illustrate this point, we consider a three-dimensional bathymetry with grids adaptively refined along the tidally generated internal waves to capture remote mixing in regions of wave focusing. The computational cost in this case is found to be nearly 25 times smaller than that of a non-adaptive solver at comparable resolution. In the final test case, we consider the scattering of a mode-1 internal wave at an isolated two-dimensional and three-dimensional topography, and we compare the results with Legg (2014) numerical experiments. We find good agreement with theoretical estimates. SOMAR-LES is less dissipative than the closure scheme employed by Legg (2014) near the bathymetry. Depending on the flow configuration and resolution employed, a reduction of more than an order of magnitude in computational costs is expected, relative to traditional existing solvers.

Biodiversity Mapping via Natura 2000 Conservation Status and Ebv Assessment Using Airborne Laser Scanning in Alkali Grasslands

NASA Astrophysics Data System (ADS)

Zlinszky, A.; Deák, B.; Kania, A.; Schroiff, A.; Pfeifer, N.

2016-06-01

Biodiversity is an ecological concept, which essentially involves a complex sum of several indicators. One widely accepted such set of indicators is prescribed for habitat conservation status assessment within Natura 2000, a continental-scale conservation programme of the European Union. Essential Biodiversity Variables are a set of indicators designed to be relevant for biodiversity and suitable for global-scale operational monitoring. Here we revisit a study of Natura 2000 conservation status mapping via airbone LIDAR that develops individual remote sensing-derived proxies for every parameter required by the Natura 2000 manual, from the perspective of developing regional-scale Essential Biodiversity Variables. Based on leaf-on and leaf-off point clouds (10 pt/m2) collected in an alkali grassland area, a set of data products were calculated at 0.5 ×0.5 m resolution. These represent various aspects of radiometric and geometric texture. A Random Forest machine learning classifier was developed to create fuzzy vegetation maps of classes of interest based on these data products. In the next step, either classification results or LIDAR data products were selected as proxies for individual Natura 2000 conservation status variables, and fine-tuned based on field references. These proxies showed adequate performance and were summarized to deliver Natura 2000 conservation status with 80% overall accuracy compared to field references. This study draws attention to the potential of LIDAR for regional-scale Essential Biodiversity variables, and also holds implications for global-scale mapping. These are (i) the use of sensor data products together with habitat-level classification, (ii) the utility of seasonal data, including for non-seasonal variables such as grassland canopy structure, and (iii) the potential of fuzzy mapping-derived class probabilities as proxies for species presence and absence.

Building a better foundation: improving root-trait measurements to understand and model plant and ecosystem processes

DOE PAGES

McCormack, M. Luke; Guo, Dali; Iversen, Colleen M.; ...

2017-03-13

Trait-based approaches provide a useful framework to investigate plant strategies for resource acquisition, growth, and competition, as well as plant impacts on ecosystem processes. Despite significant progress capturing trait variation within and among stems and leaves, identification of trait syndromes within fine-root systems and between fine roots and other plant organs is limited. Here we discuss three underappreciated areas where focused measurements of fine-root traits can make significant contributions to ecosystem science. These include assessment of spatiotemporal variation in fine-root traits, integration of mycorrhizal fungi into fine-root-trait frameworks, and the need for improved scaling of traits measured on individual rootsmore » to ecosystem-level processes. Progress in each of these areas is providing opportunities to revisit how below-ground processes are represented in terrestrial biosphere models. Targeted measurements of fine-root traits with clear linkages to ecosystem processes and plant responses to environmental change are strongly needed to reduce empirical and model uncertainties. Further identifying how and when suites of root and whole-plant traits are coordinated or decoupled will ultimately provide a powerful tool for modeling plant form and function at local and global scales.« less

Building a better foundation: improving root-trait measurements to understand and model plant and ecosystem processes

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

McCormack, M. Luke; Guo, Dali; Iversen, Colleen M.

Trait-based approaches provide a useful framework to investigate plant strategies for resource acquisition, growth, and competition, as well as plant impacts on ecosystem processes. Despite significant progress capturing trait variation within and among stems and leaves, identification of trait syndromes within fine-root systems and between fine roots and other plant organs is limited. Here we discuss three underappreciated areas where focused measurements of fine-root traits can make significant contributions to ecosystem science. These include assessment of spatiotemporal variation in fine-root traits, integration of mycorrhizal fungi into fine-root-trait frameworks, and the need for improved scaling of traits measured on individual rootsmore » to ecosystem-level processes. Progress in each of these areas is providing opportunities to revisit how below-ground processes are represented in terrestrial biosphere models. Targeted measurements of fine-root traits with clear linkages to ecosystem processes and plant responses to environmental change are strongly needed to reduce empirical and model uncertainties. Further identifying how and when suites of root and whole-plant traits are coordinated or decoupled will ultimately provide a powerful tool for modeling plant form and function at local and global scales.« less

Design of a V/STOL propulsion system for a large-scale fighter model

NASA Technical Reports Server (NTRS)

Willis, W. S.

1981-01-01

Modifications were made to the existing Large-Scale STOL fighter model to simulate a V/STOL configuration. Modifications include the substitutions of two dimensional lift/cruise exhaust nozzles in the nacelles, and the addition of a third J97 engine in the fuselage to suppy a remote exhaust nozzle simulating a Remote Augmented Lift System. A preliminary design of the inlet and exhaust ducting for the third engine was developed and a detailed design was completed of the hot exhaust ducting and remote nozzle.

A low cost, high performance remotely controlled backhoe/excavator

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

Rizzo, J.

1995-12-31

This paper addresses a state of the art, low cost, remotely controlled backhoe/excavator system for remediation use at hazardous waste sites. The all weather, all terrain, Remote Dig-It is based on a simple, proven construction platform and incorporates state of the art sensors, control, telemetry and other subsystems derived from advanced underwater remotely operated vehicle systems. The system can be towed to a site without the use of a trailer, manually operated by an on board operator or operated via a fiber optic or optional RF communications link by a remotely positioned operator. A proportional control system is piggy backedmore » onto the standard manual control system. The control system improves manual operation, allows rapid manual/remote mode selection and provides fine manual or remote control of all functions. The system incorporates up to 4 separate video links, acoustic obstacle proximity sensors, and stereo audio pickups and an optional differential GPS navigation. Video system options include electronic panning and tilting within a distortion-corrected wide angle field of view. The backhoe/excavator subsystem has a quick disconnect interface feature which allows its use as a manipulator with a wide variety of end effectors and tools. The Remote Dig-It was developed to respond to the need for a low-cost, effective remediation system for use at sites containing hazardous materials. The prototype system was independently evaluated for this purpose by the Army at the Jefferson Proving Ground where it surpassed all performance goals. At the time of this writing, the Remote Dig-It system is currently the only backhoe/excavator which met the Army`s goals for remediation systems for use at hazardous waste sites and it costs a fraction of any known competing offerings.« less

Land cover mapping at sub-pixel scales

NASA Astrophysics Data System (ADS)

Makido, Yasuyo Kato

One of the biggest drawbacks of land cover mapping from remotely sensed images relates to spatial resolution, which determines the level of spatial details depicted in an image. Fine spatial resolution images from satellite sensors such as IKONOS and QuickBird are now available. However, these images are not suitable for large-area studies, since a single image is very small and therefore it is costly for large area studies. Much research has focused on attempting to extract land cover types at sub-pixel scale, and little research has been conducted concerning the spatial allocation of land cover types within a pixel. This study is devoted to the development of new algorithms for predicting land cover distribution using remote sensory imagery at sub-pixel level. The "pixel-swapping" optimization algorithm, which was proposed by Atkinson for predicting sub-pixel land cover distribution, is investigated in this study. Two limitations of this method, the arbitrary spatial range value and the arbitrary exponential model of spatial autocorrelation, are assessed. Various weighting functions, as alternatives to the exponential model, are evaluated in order to derive the optimum weighting function. Two different simulation models were employed to develop spatially autocorrelated binary class maps. In all tested models, Gaussian, Exponential, and IDW, the pixel swapping method improved classification accuracy compared with the initial random allocation of sub-pixels. However the results suggested that equal weight could be used to increase accuracy and sub-pixel spatial autocorrelation instead of using these more complex models of spatial structure. New algorithms for modeling the spatial distribution of multiple land cover classes at sub-pixel scales are developed and evaluated. Three methods are examined: sequential categorical swapping, simultaneous categorical swapping, and simulated annealing. These three methods are applied to classified Landsat ETM+ data that has been resampled to 210 meters. The result suggested that the simultaneous method can be considered as the optimum method in terms of accuracy performance and computation time. The case study employs remote sensing imagery at the following sites: tropical forests in Brazil and temperate multiple land mosaic in East China. Sub-areas for both sites are used to examine how the characteristics of the landscape affect the ability of the optimum technique. Three types of measurement: Moran's I, mean patch size (MPS), and patch size standard deviation (STDEV), are used to characterize the landscape. All results suggested that this technique could increase the classification accuracy more than traditional hard classification. The methods developed in this study can benefit researchers who employ coarse remote sensing imagery but are interested in detailed landscape information. In many cases, the satellite sensor that provides large spatial coverage has insufficient spatial detail to identify landscape patterns. Application of the super-resolution technique described in this dissertation could potentially solve this problem by providing detailed land cover predictions from the coarse resolution satellite sensor imagery.

Grid workflow validation using ontology-based tacit knowledge: A case study for quantitative remote sensing applications

NASA Astrophysics Data System (ADS)

Liu, Jia; Liu, Longli; Xue, Yong; Dong, Jing; Hu, Yingcui; Hill, Richard; Guang, Jie; Li, Chi

2017-01-01

Workflow for remote sensing quantitative retrieval is the ;bridge; between Grid services and Grid-enabled application of remote sensing quantitative retrieval. Workflow averts low-level implementation details of the Grid and hence enables users to focus on higher levels of application. The workflow for remote sensing quantitative retrieval plays an important role in remote sensing Grid and Cloud computing services, which can support the modelling, construction and implementation of large-scale complicated applications of remote sensing science. The validation of workflow is important in order to support the large-scale sophisticated scientific computation processes with enhanced performance and to minimize potential waste of time and resources. To research the semantic correctness of user-defined workflows, in this paper, we propose a workflow validation method based on tacit knowledge research in the remote sensing domain. We first discuss the remote sensing model and metadata. Through detailed analysis, we then discuss the method of extracting the domain tacit knowledge and expressing the knowledge with ontology. Additionally, we construct the domain ontology with Protégé. Through our experimental study, we verify the validity of this method in two ways, namely data source consistency error validation and parameters matching error validation.

Using Wavelet Bases to Separate Scales in Quantum Field Theory

NASA Astrophysics Data System (ADS)

Michlin, Tracie L.

This thesis investigates the use of Daubechies wavelets to separate scales in local quantum field theory. Field theories have an infinite number of degrees of freedom on all distance scales. Quantum field theories are believed to describe the physics of subatomic particles. These theories have no known mathematically convergent approximation methods. Daubechies wavelet bases can be used separate degrees of freedom on different distance scales. Volume and resolution truncations lead to mathematically well-defined truncated theories that can be treated using established methods. This work demonstrates that flow equation methods can be used to block diagonalize truncated field theoretic Hamiltonians by scale. This eliminates the fine scale degrees of freedom. This may lead to approximation methods and provide an understanding of how to formulate well-defined fine resolution limits.

Crown-Level Tree Species Classification Using Integrated Airborne Hyperspectral and LIDAR Remote Sensing Data

NASA Astrophysics Data System (ADS)

Wang, Z.; Wu, J.; Wang, Y.; Kong, X.; Bao, H.; Ni, Y.; Ma, L.; Jin, J.

2018-05-01

Mapping tree species is essential for sustainable planning as well as to improve our understanding of the role of different trees as different ecological service. However, crown-level tree species automatic classification is a challenging task due to the spectral similarity among diversified tree species, fine-scale spatial variation, shadow, and underlying objects within a crown. Advanced remote sensing data such as airborne Light Detection and Ranging (LiDAR) and hyperspectral imagery offer a great potential opportunity to derive crown spectral, structure and canopy physiological information at the individual crown scale, which can be useful for mapping tree species. In this paper, an innovative approach was developed for tree species classification at the crown level. The method utilized LiDAR data for individual tree crown delineation and morphological structure extraction, and Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery for pure crown-scale spectral extraction. Specifically, four steps were include: 1) A weighted mean filtering method was developed to improve the accuracy of the smoothed Canopy Height Model (CHM) derived from LiDAR data; 2) The marker-controlled watershed segmentation algorithm was, therefore, also employed to delineate the tree-level canopy from the CHM image in this study, and then individual tree height and tree crown were calculated according to the delineated crown; 3) Spectral features within 3 × 3 neighborhood regions centered on the treetops detected by the treetop detection algorithm were derived from the spectrally normalized CASI imagery; 4) The shape characteristics related to their crown diameters and heights were established, and different crown-level tree species were classified using the combination of spectral and shape characteristics. Analysis of results suggests that the developed classification strategy in this paper (OA = 85.12 %, Kc = 0.90) performed better than LiDAR-metrics method (OA = 79.86 %, Kc = 0.81) and spectral-metircs method (OA = 71.26, Kc = 0.69) in terms of classification accuracy, which indicated that the advanced method of data processing and sensitive feature selection are critical for improving the accuracy of crown-level tree species classification.

Satellite Based Cropland Carbon Monitoring System

NASA Astrophysics Data System (ADS)

Bandaru, V.; Jones, C. D.; Sedano, F.; Sahajpal, R.; Jin, H.; Skakun, S.; Pnvr, K.; Kommareddy, A.; Reddy, A.; Hurtt, G. C.; Izaurralde, R. C.

2017-12-01

Agricultural croplands act as both sources and sinks of atmospheric carbon dioxide (CO2); absorbing CO2 through photosynthesis, releasing CO2 through autotrophic and heterotrophic respiration, and sequestering CO2 in vegetation and soils. Part of the carbon captured in vegetation can be transported and utilized elsewhere through the activities of food, fiber, and energy production. As well, a portion of carbon in soils can be exported somewhere else by wind, water, and tillage erosion. Thus, it is important to quantify how land use and land management practices affect the net carbon balance of croplands. To monitor the impacts of various agricultural activities on carbon balance and to develop management strategies to make croplands to behave as net carbon sinks, it is of paramount importance to develop consistent and high resolution cropland carbon flux estimates. Croplands are typically characterized by fine scale heterogeneity; therefore, for accurate carbon flux estimates, it is necessary to account for the contribution of each crop type and their spatial distribution. As part of NASA CMS funded project, a satellite based Cropland Carbon Monitoring System (CCMS) was developed to estimate spatially resolved crop specific carbon fluxes over large regions. This modeling framework uses remote sensing version of Environmental Policy Integrated Climate Model and satellite derived crop parameters (e.g. leaf area index (LAI)) to determine vertical and lateral carbon fluxes. The crop type LAI product was developed based on the inversion of PRO-SAIL radiative transfer model and downscaled MODIS reflectance. The crop emergence and harvesting dates were estimated based on MODIS NDVI and crop growing degree days. To evaluate the performance of CCMS framework, it was implemented over croplands of Nebraska, and estimated carbon fluxes for major crops (i.e. corn, soybean, winter wheat, grain sorghum, alfalfa) grown in 2015. Key findings of the CCMS framework will be presented and discussed some of which include 1) comparison of remote sensing based crop type LAI and crop phenology estimates with observed field scale data 2) comparison of carbon flux estimates from CCMS framework with measured fluxes at flux tower sites 3) regional scale differences in carbon fluxes among various crops in Nebraska.

Development of a spatio-temporal disaggregation method (DisNDVI) for generating a time series of fine resolution NDVI images

NASA Astrophysics Data System (ADS)

Bindhu, V. M.; Narasimhan, B.

2015-03-01

Normalized Difference Vegetation Index (NDVI), a key parameter in understanding the vegetation dynamics, has high spatial and temporal variability. However, continuous monitoring of NDVI is not feasible at fine spatial resolution (<60 m) owing to the long revisit time needed by the satellites to acquire the fine spatial resolution data. Further, the study attains significance in the case of humid tropical regions of the earth, where the prevailing atmospheric conditions restrict availability of fine resolution cloud free images at a high temporal frequency. As an alternative to the lack of high resolution images, the current study demonstrates a novel disaggregation method (DisNDVI) which integrates the spatial information from a single fine resolution image and temporal information in terms of crop phenology from time series of coarse resolution images to generate estimates of NDVI at fine spatial and temporal resolution. The phenological variation of the pixels captured at the coarser scale provides the basis for relating the temporal variability of the pixel with the NDVI available at fine resolution. The proposed methodology was tested over a 30 km × 25 km spatially heterogeneous study area located in the south of Tamil Nadu, India. The robustness of the algorithm was assessed by an independent comparison of the disaggregated NDVI and observed NDVI obtained from concurrent Landsat ETM+ imagery. The results showed good spatial agreement across the study area dominated with agriculture and forest pixels, with a root mean square error of 0.05. The validation done at the coarser scale showed that disaggregated NDVI spatially averaged to 240 m compared well with concurrent MODIS NDVI at 240 m (R2 > 0.8). The validation results demonstrate the effectiveness of DisNDVI in improving the spatial and temporal resolution of NDVI images for utility in fine scale hydrological applications such as crop growth monitoring and estimation of evapotranspiration.

Nanoparticle inhalation augments particle-dependent systemic microvascular dysfunction

PubMed Central

Nurkiewicz, Timothy R; Porter, Dale W; Hubbs, Ann F; Cumpston, Jared L; Chen, Bean T; Frazer, David G; Castranova, Vincent

2008-01-01

Background We have shown that pulmonary exposure to fine particulate matter (PM) impairs endothelium dependent dilation in systemic arterioles. Ultrafine PM has been suggested to be inherently more toxic by virtue of its increased surface area. The purpose of this study was to determine if ultrafine PM (or nanoparticle) inhalation produces greater microvascular dysfunction than fine PM. Rats were exposed to fine or ultrafine TiO2 aerosols (primary particle diameters of ~1 μm and ~21 nm, respectively) at concentrations which do not alter bronchoalveolar lavage markers of pulmonary inflammation or lung damage. Results By histopathologic evaluation, no significant inflammatory changes were seen in the lung. However, particle-containing macrophages were frequently seen in intimate contact with the alveolar wall. The spinotrapezius muscle was prepared for in vivo microscopy 24 hours after inhalation exposures. Intraluminal infusion of the Ca2+ ionophore A23187 was used to evaluate endothelium-dependent arteriolar dilation. In control rats, A23187 infusion produced dose-dependent arteriolar dilations. In rats exposed to fine TiO2, A23187 infusion elicited vasodilations that were blunted in proportion to pulmonary particle deposition. In rats exposed to ultrafine TiO2, A23187 infusion produced arteriolar constrictions or significantly impaired vasodilator responses as compared to the responses observed in control rats or those exposed to a similar pulmonary load of fine particles. Conclusion These observations suggest that at equivalent pulmonary loads, as compared to fine TiO2, ultrafine TiO2 inhalation produces greater remote microvascular dysfunction. PMID:18269765

Remote sensing with unmanned aircraft systems for precision agriculture applications

USDA-ARS?s Scientific Manuscript database

The Federal Aviation Administration is revising regulations for using unmanned aircraft systems (UAS) in the national airspace. An important potential application of UAS may be as a remote-sensing platform for precision agriculture, but simply down-scaling remote sensing methodologies developed usi...

Remote Sensing as a Demonstration of Applied Physics.

ERIC Educational Resources Information Center

Colwell, Robert N.

1980-01-01

Provides information about the field of remote sensing, including discussions of geo-synchronous and sun-synchronous remote-sensing platforms, the actual physical processes and equipment involved in sensing, the analysis of images by humans and machines, and inexpensive, small scale methods, including aerial photography. (CS)

Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations

USGS Publications Warehouse

Cook, B.D.; Bolstad, P.V.; Naesset, E.; Anderson, R. Scott; Garrigues, S.; Morisette, J.T.; Nickeson, J.; Davis, K.J.

2009-01-01

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30??m to 1??km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600??ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400??m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine-resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire landscape. Failure to account for wetlands had little impact on landscape-scale estimates, because vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

Comet 81p/Wild 2: The Updated Stardust Coma Dust Fluence Measurement for Smaller (Sub 10-Micrometre) Particles

NASA Technical Reports Server (NTRS)

Price, M. C.; Kearsley, A. T.; Burchell, M. J.; Horz, Friedrich; Cole, M. J.

2009-01-01

Micrometre and smaller scale dust within cometary comae can be observed by telescopic remote sensing spectroscopy [1] and the particle size and abundance can be measured by in situ spacecraft impact detectors [2]. Initial interpretation of the samples returned from comet 81P/Wild 2 by the Stardust spacecraft [3] appears to show that very fine dust contributes not only a small fraction of the solid mass, but is also relatively sparse [4], with a low negative power function describing grain size distribution, contrasting with an apparent abundance indicated by the on-board Dust Flux Monitor Instrument (DFMI) [5] operational during the encounter. For particles above 10 m diameter there is good correspondence between results from the DFMI and the particle size inferred from experimental calibration [6] of measured aerogel track and aluminium foil crater dimensions (as seen in Figure 4 of [4]). However, divergence between data-sets becomes apparent at smaller sizes, especially submicrometre, where the returned sample data are based upon location and measurement of tiny craters found by electron microscopy of Al foils. Here effects of detection efficiency tail-off at each search magnification can be seen in the down-scale flattening of each scale component, but are reliably compensated by sensible extrapolation between segments. There is also no evidence of malfunction in the operation of DFMI during passage through the coma (S. Green, personal comm.), so can the two data sets be reconciled?

Co-Occurrence Patterns of Common and Rare Leaf-Litter Frogs, Epiphytic Ferns and Dung Beetles across a Gradient of Human Disturbance

PubMed Central

Oldekop, Johan A.; Bebbington, Anthony J.; Truelove, Nathan K.; Tysklind, Niklas; Villamarín, Santiago; Preziosi, Richard F.

2012-01-01

Indicator taxa are commonly used to identify priority areas for conservation or to measure biological responses to environmental change. Despite their widespread use, there is no general consensus about the ability of indicator taxa to predict wider trends in biodiversity. Many studies have focused on large-scale patterns of species co-occurrence to identify areas of high biodiversity, threat or endemism, but there is much less information about patterns of species co-occurrence at local scales. In this study, we assess fine-scale co-occurrence patterns of three indicator taxa (epiphytic ferns, leaf litter frogs and dung beetles) across a remotely sensed gradient of human disturbance in the Ecuadorian Amazon. We measure the relative contribution of rare and common species to patterns of total richness in each taxon and determine the ability of common and rare species to act as surrogate measures of human disturbance and each other. We find that the species richness of indicator taxa changed across the human disturbance gradient but that the response differed among taxa, and between rare and common species. Although we find several patterns of co-occurrence, these patterns differed between common and rare species. Despite showing complex patterns of species co-occurrence, our results suggest that species or taxa can act as reliable indicators of each other but that this relationship must be established and not assumed. PMID:22701730

[Stimulation at home and motor development among 36-month-old Mexican children].

PubMed

Osorio, Erika; Torres-Sánchez, Luisa; Hernández, María Del Carmen; López-Carrillo, Lizbeth; Schnaas, Lourdes

2010-01-01

To identify the relationship between stimulation at home and motor development among 36 month-old children. The development of gross and fine motor skills of 169 infants (50.9% boys and 49.1% girls) was assessed at the age of 36 months with the Peabody Developmental Motor Scale. The quality of home stimulation was determined during a prior evaluation (at 30 months) by means of the HOME Scale. Total stimulation at home was significantly associated with better performance in the gross and fine motor areas. Particular aspects of this home stimulation were related to better gross and fine motor functions. Static balance and locomotion (gross motor skills) and grasping and visual-motor integration (fine motor skills) are associated with particular aspects of home stimulation, such as parent-child interaction, verbal reinforcement of the child's positive actions and providing the child with clear boundaries.

Quantum-gravity predictions for the fine-structure constant

NASA Astrophysics Data System (ADS)

Eichhorn, Astrid; Held, Aaron; Wetterich, Christof

2018-07-01

Asymptotically safe quantum fluctuations of gravity can uniquely determine the value of the gauge coupling for a large class of grand unified models. In turn, this makes the electromagnetic fine-structure constant calculable. The balance of gravity and matter fluctuations results in a fixed point for the running of the gauge coupling. It is approached as the momentum scale is lowered in the transplanckian regime, leading to a uniquely predicted value of the gauge coupling at the Planck scale. The precise value of the predicted fine-structure constant depends on the matter content of the grand unified model. It is proportional to the gravitational fluctuation effects for which computational uncertainties remain to be settled.

Partially natural two Higgs doublet models

DOE PAGES

Draper, Patrick; Haber, Howard E.; Ruderman, Joshua T.

2016-06-21

It is possible that the electroweak scale is low due to the fine-tuning of microscopic parameters, which can result from selection effects. The experimental discovery of new light fundamental scalars other than the Standard Model Higgs boson would seem to disfavor this possibility, since generically such states imply parametrically worse fine-tuning with no compelling connection to selection effects. We discuss counterexamples where the Higgs boson is light because of fine-tuning, and a second scalar doublet is light because a discrete symmetry relates its mass to the mass of the Standard Model Higgs boson. Our examples require new vectorlike fermions atmore » the electroweak scale, and the models possess a rich electroweak vacuum structure. Furthermore, the mechanism that we discuss does not protect a small CP-odd Higgs mass in split or high-scale supersymmetry-breaking scenarios of the MSSM due to an incompatibility between the discrete symmetries and holomorphy.« less

The Fine-Scale Functional Correlation of Striate Cortex in Sighted and Blind People

PubMed Central

Butt, Omar H.; Benson, Noah C.; Datta, Ritobrato

2013-01-01

To what extent are spontaneous neural signals within striate cortex organized by vision? We examined the fine-scale pattern of striate cortex correlations within and between hemispheres in rest-state BOLD fMRI data from sighted and blind people. In the sighted, we find that corticocortico correlation is well modeled as a Gaussian point-spread function across millimeters of striate cortical surface, rather than degrees of visual angle. Blindness produces a subtle change in the pattern of fine-scale striate correlations between hemispheres. Across participants blind before the age of 18, the degree of pattern alteration covaries with the strength of long-range correlation between left striate cortex and Broca's area. This suggests that early blindness exchanges local, vision-driven pattern synchrony of the striate cortices for long-range functional correlations potentially related to cross-modal representation. PMID:24107953

Predation risk, elk, and aspen: tests of a behaviorally mediated trophic cascade in the Greater Yellowstone Ecosystem.

PubMed

Winnie, John A

2012-12-01

Aspen in the Greater Yellowstone Ecosystem are hypothesized to be recovering from decades of heavy browsing by elk due to a behaviorally mediated trophic cascade (BMTC). Several authors have suggested that wolves interact with certain terrain features, creating places of high predation risk at fine spatial scales, and that elk avoid these places, which creates refugia for plants. This hypothesized BMTC could release aspen from elk browsing pressure, leading to a patchy recovery in places of high risk. I tested whether four specific, hypothesized fine-scale risk factors are correlated with changes in current elk browsing pressure on aspen, or with aspen recruitment since wolf reintroduction, in the Daly Creek drainage in Yellowstone National Park, and near two aspen enclosures outside of the park boundary. Aspen were not responding to hypothesized fine-scale risk factors in ways consistent with the current BMTC hypothesis.

Airframe-Jet Engine Integration Noise

NASA Technical Reports Server (NTRS)

Tam, Christopher; Antcliff, Richard R. (Technical Monitor)

2003-01-01

It has been found experimentally that the noise radiated by a jet mounted under the wing of an aircraft exceeds that of the same jet in a stand-alone environment. The increase in noise is referred to as jet engine airframe integration noise. The objectives of the present investigation are, (1) To obtain a better understanding of the physical mechanisms responsible for jet engine airframe integration noise or installation noise. (2) To develop a prediction model for jet engine airframe integration noise. It is known that jet mixing noise consists of two principal components. They are the noise from the large turbulence structures of the jet flow and the noise from the fine scale turbulence. In this investigation, only the effect of jet engine airframe interaction on the fine scale turbulence noise of a jet is studied. The fine scale turbulence noise is the dominant noise component in the sideline direction. Thus we limit out consideration primarily to the sideline.

Distribution of fine-scale mantle heterogeneity from observations of Pdiff coda

USGS Publications Warehouse

Earle, P.S.; Shearer, P.M.

2001-01-01

We present stacked record sections of Global Seismic Network data that image the average amplitude and polarization of the high-frequency Pdiff coda and investigate their implications on the depth extent of fine-scale (~10 km) mantle heterogeneity. The extended 1-Hz coda lasts for at least 150 sec and is observed to a distance of 130??. The coda's polarization angle is about the same as the main Pdiff arrival (4.4 sec/deg) and is nearly constant with time. Previous studies show that multiple scattering from heterogeneity restricted to the lowermost mantle generates an extended Pdiff coda with a constant polarization. Here we present an alternative model that satisfies our Pdiff observations. The model consists of single scattering from weak (~1%) fine-scale (~2 km) structures distributed throughout the mantle. Although this model is nonunique, it demonstrates that Pdiff coda observations do not preclude the existence of scattering contributions from the entire mantle.

Application of pinch-and-swell structure rheology gauge to determine rock paleo-rheological parameters in Taili, western Liaoning, NE China

NASA Astrophysics Data System (ADS)

Sun, Zhengquan; Zeng, Zuoxun; Wu, Linbo; Xu, Shaopeng; Yang, Shuang; Chen, Deli; Wang, Jianxiu

2017-05-01

New results, in combination with previously published ones, reveal that when the Stress Exponent of the Competent layer (SEC) ranges from 1 to 10 (1 < n < 10), Pinch-and-Swell structure Rheology Gauge (PSRG) can only be available under the condition that the Viscosity ratio between the Competent layer and its corresponding Matrix layer (VCM) is larger than 10. Therefore, we made the attempt to calculate the viscosity ratio of pinch-and-swell structure of competent layer to the related matrix and stress exponent. Based on this knowledge, we applied this gauge to calculate SECs and VCMs of eight types of pinch-and-swell structures, which are widely developed in the Taili area of the west Liaoning Province in China. Statistical analysis of the SEC resulted in intervals of four types of competent layers, that is, Medium-scale Granitic coarse-to-pegmatitic Veins, Small-scale Augen Granite aplite Veins, Small-scale Granite aplite Veins, and Small-scale Augen Quartz-K-feldspar veins, with intervals of [3.50, 4.63], [2.64, 4.29], [2.70, 3.51], and [2.50, 3.36] respectively. The preferred intervals of VCM of the five types of pinch-and-swell structures, Small-scale Augen Granite aplite Veins + Fine-grained Biotite-Hornblende-plagioclase Gneiss, Medium-scale Granitic coarse-to-pegmatitic Veins + Fine-grained Biotite-Hornblende-plagioclase Gneiss, Small-scale Augen Granite aplite Veins + medium-to-fine-grained granitic gneiss, Medium-scale Granitic coarse-to-pegmatitic Veins + medium-to-fine-grained granitic gneiss, and Small-scale Augen Granite aplite Veins + fine-grained biotite-plagioclase gneiss, are [19.98, 62.51], [15.90, 61.17], [26.72, 93.27], [22.21, 107.26], and [76.33, 309.39] respectively. The similarities between these calculated SEC statistical preferred intervals and the physical experimental results verify the validity of the PSRG. The competent layers of the pinch-and-swell structures were presented in this study as power-law flow with SEC values that increased with the thickness of the layer. Grain-size plays an important role in the rheology of pinch-and-swell structures. The results offer a case for the application of PSRG and determine the key rock rheological parameters of North China Craton for future related studies.

REMOTE RAMAN SPECTROSCOPY OF VARIOUS MIXED AND COMPOSITE MINERAL PHASES AT 7.2 m DISTANCE

NASA Technical Reports Server (NTRS)

Sharma, S. K.; Misra, A. K.; Ismail, Syed; Singh, U. N.

2006-01-01

Remote Raman [e.g.,1-5] and micro-Raman spectroscopy [e.g., 6-10] are being evaluated on geological samples for their potential applications on Mars rover or lander. The Raman lines of minerals are sharp and distinct. The Raman finger-prints of minerals do not shift appreciably but remain distinct even in sub-micron grains and, therefore, can be used for mineral identification in fine-grained rocks [e.g., 4,7]. In this work we have evaluated the capability of a directly coupled remote Raman system (co-axial configuration) for distinguishing the mineralogy of multiple crystals in the exciting laser beam. We have measured the Raman spectra of minerals in the near vicinity of each other and excited with a laser beam (e.g. -quartz (Qz) and K-feldspar (Feld) plates, each 5 mm thick). The spectra of composite transparent mineral plates of 5 mm thickness of -quartz and gypsum over calcite crystal were measured with the composite samples perpendicular to the exciting laser beam. The measurements of remote Raman spectra of various bulk minerals, and mixed and composite minerals with our portable UH remote Raman system were carried out at the Langley Research Center in a fully illuminated laboratory.

Development and psychometric evaluation of the Primary Health Care Engagement (PHCE) Scale: a pilot survey of rural and remote nurses.

PubMed

Kosteniuk, Julie G; Wilson, Erin C; Penz, Kelly L; MacLeod, Martha L P; Stewart, Norma J; Kulig, Judith C; Karunanayake, Chandima P; Kilpatrick, Kelley

2016-01-01

To report the development and psychometric evaluation of a scale to measure rural and remote (rural/remote) nurses' perceptions of the engagement of their workplaces in key dimensions of primary health care (PHC). Amidst ongoing PHC reforms, a comprehensive instrument is needed to evaluate the degree to which rural/remote health care settings are involved in the key dimensions that characterize PHC delivery, particularly from the perspective of professionals delivering care. This study followed a three-phase process of instrument development and psychometric evaluation. A literature review and expert consultation informed instrument development in the first phase, followed by an iterative process of content evaluation in the second phase. In the final phase, a pilot survey was undertaken and item discrimination analysis employed to evaluate the internal consistency reliability of each subscale in the preliminary 60-item Primary Health Care Engagement (PHCE) Scale. The 60-item scale was subsequently refined to a 40-item instrument. The pilot survey sample included 89 nurses in current practice who had experience in rural/remote practice settings. Participants completed either a web-based or paper survey from September to December, 2013. Following item discrimination analysis, the 60-item instrument was refined to a 40-item PHCE Scale consisting of 10 subscales, each including three to five items. Alpha estimates of the 10 refined subscales ranged from 0.61 to 0.83, with seven of the subscales demonstrating acceptable reliability (α ⩾ 0.70). The refined 40-item instrument exhibited good internal consistency reliability (α=0.91). The 40-item PHCE Scale may be considered for use in future studies regardless of locale, to measure the extent to which health care professionals perceive their workplaces to be engaged in key dimensions of PHC.

Observing and modeling dynamics in terrestrial gross primary productivity and phenology from remote sensing: An assessment using in-situ measurements

NASA Astrophysics Data System (ADS)

Verma, Manish K.

Terrestrial gross primary productivity (GPP) is the largest and most variable component of the carbon cycle and is strongly influenced by phenology. Realistic characterization of spatio-temporal variation in GPP and phenology is therefore crucial for understanding dynamics in the global carbon cycle. In the last two decades, remote sensing has become a widely-used tool for this purpose. However, no study has comprehensively examined how well remote sensing models capture spatiotemporal patterns in GPP, and validation of remote sensing-based phenology models is limited. Using in-situ data from 144 eddy covariance towers located in all major biomes, I assessed the ability of 10 remote sensing-based methods to capture spatio-temporal variation in GPP at annual and seasonal scales. The models are based on different hypotheses regarding ecophysiological controls on GPP and span a range of structural and computational complexity. The results lead to four main conclusions: (i) at annual time scale, models were more successful capturing spatial variability than temporal variability; (ii) at seasonal scale, models were more successful in capturing average seasonal variability than interannual variability; (iii) simpler models performed as well or better than complex models; and (iv) models that were best at explaining seasonal variability in GPP were different from those that were best able to explain variability in annual scale GPP. Seasonal phenology of vegetation follows bounded growth and decay, and is widely modeled using growth functions. However, the specific form of the growth function affects how phenological dynamics are represented in ecosystem and remote sensing-base models. To examine this, four different growth functions (the logistic, Gompertz, Mirror-Gompertz and Richards function) were assessed using remotely sensed and in-situ data collected at several deciduous forest sites. All of the growth functions provided good statistical representation of in-situ and remote sensing time series. However, the Richards function captured observed asymmetric dynamics that were not captured by the other functions. The timing of key phenophase transitions derived using the Richards function therefore agreed best with observations. This suggests that ecosystem models and remote-sensing algorithms would benefit from using the Richards function to represent phenological dynamics.

Lessons learned from the implementation of remote control for the interoperability standard ISO/IEEE11073-20601 in a standard weighing scale.

PubMed

Barrón-González, Héctor Gilberto; Martínez-Espronceda, Miguel; Trigo, Jesús Daniel; Led, Santiago; Serrano, Luis

2016-01-01

The Point of Care (PoC) version of the interoperability standard ISO/IEEE11073 (X73) provided a mechanism to control remotely agents through documents X73-10201 and X73-20301. The newer version of X73 oriented to Personal Health Devices (PHD) has no mechanisms to do such a thing. The authors are working toward a common proposal with the PHD Working Group (PHD-WG) in order to adapt the remote control capabilities from X73PoC to X73PHD. However, this theoretical adaptation has to be implemented and tested to evaluate whether or not its inclusion entails an acceptable overhead and extra cost. Such proof-of-concept assessment is the main objective of this paper. For the sake of simplicity, a weighing scale with a configurable operation was chosen as use case. First, in a previous stage of the research - the model was defined. Second, the implementation methodology - both in terms of hardware and software - was defined and executed. Third, an evaluation methodology to test the remote control features was defined. Then, a thorough comparison between a weighing scale with and without remote control was performed. The results obtained indicate that, when implementing remote control in a weighing scale, the relative weight of such feature represents an overhead of as much as 53%, whereas the number of Implementation Conformance Statements (ICSs) to be satisfied by the manufacturer represent as much as 34% regarding the implementation without remote control. The new feature facilitates remote control of PHDs but, at the same time, increases overhead and costs, and, therefore, manufacturers need to weigh this trade-off. As a conclusion, this proof-of-concept helps in fostering the evolution of the remote control proposal to extend X73PHD and promotes its inclusion as part of the standard, as well as it illustrates the methodological steps for its extrapolation to other specializations. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Virtualization of Fuelbeds: Building the Next Generation of Fuels Data for Multiple-Scale Fire Modeling and Ecological Analysis

NASA Astrophysics Data System (ADS)

Rowell, Eric Martin

The primary goal of this research is to advance methods for deriving fine-grained, scalable, wildland fuels attributes in 3-dimensions using terrestrial and airborne laser scanning technology. It is fundamentally a remote sensing research endeavor applied to the problem of fuels characterization. Advancements in laser scanning are beginning to have significant impacts on a range of modeling frameworks in fire research, especially those utilizing 3-dimensional data and benefiting from efficient data scaling. The pairing of laser scanning and fire modeling is enabling advances in understanding how fuels variability modulates fire behavior and effects. This dissertation details the development of methods and techniques to characterize and quantify surface fuelbeds using both terrestrial and airborne laser scanning. The primary study site is Eglin Airforce Base, Florida, USA, which provides a range of fuel types and conditions in a fire-adapted landscape along with the multi-disciplinary expertise, logistical support, and prescribed fire necessary for detailed characterization of fire as a physical process. Chapter 1 provides a research overview and discusses the state of fuels science and the related needs for highly resolved fuels data in the southeastern United States. Chapter 2, describes the use of terrestrial laser scanning for sampling fuels at multiple scales and provides analysis of the spatial accuracy of fuelbed models in 3-D. Chapter 3 describes the development of a voxel-based occupied volume method for predicting fuel mass. Results are used to inform prediction of landscape-scale fuel load using airborne laser scanning metrics as well as to predict post-fire fuel consumption. Chapter 4 introduces a novel fuel simulation approach which produces spatially explicit, statistically-defensible estimates of fuel properties and demonstrates a pathway for resampling observed data. This method also can be directly compared to terrestrial laser scanning data to assess how energy interception of the laser pulse affects characterization of the fuelbed. Chapter 5 discusses the contribution of this work to fire science and describes ongoing and future research derived from this work. Chapters 2 and 4 have been published in International Journal of Wildland Fire and Canadian Journal of Remote Sensing, respectively, and Chapter 3 is in preparation for publication.

Prospects for mirage mediation

NASA Astrophysics Data System (ADS)

Pierce, Aaron; Thaler, Jesse

2006-09-01

Mirage mediation reduces the fine-tuning in the minimal supersymmetric standard model by dynamically arranging a cancellation between anomaly-mediated and modulus-mediated supersymmetry breaking. We explore the conditions under which a mirage ``messenger scale'' is generated near the weak scale and the little hierarchy problem is solved. We do this by explicitly including the dynamics of the SUSY-breaking sector needed to cancel the cosmological constant. The most plausible scenario for generating a low mirage scale does not readily admit an extra-dimensional interpretation. We also review the possibilities for solving the μ/Bμ problem in such theories, a potential hidden source of fine-tuning.

Sea-land segmentation for infrared remote sensing images based on superpixels and multi-scale features

NASA Astrophysics Data System (ADS)

Lei, Sen; Zou, Zhengxia; Liu, Dunge; Xia, Zhenghuan; Shi, Zhenwei

2018-06-01

Sea-land segmentation is a key step for the information processing of ocean remote sensing images. Traditional sea-land segmentation algorithms ignore the local similarity prior of sea and land, and thus fail in complex scenarios. In this paper, we propose a new sea-land segmentation method for infrared remote sensing images to tackle the problem based on superpixels and multi-scale features. Considering the connectivity and local similarity of sea or land, we interpret the sea-land segmentation task in view of superpixels rather than pixels, where similar pixels are clustered and the local similarity are explored. Moreover, the multi-scale features are elaborately designed, comprising of gray histogram and multi-scale total variation. Experimental results on infrared bands of Landsat-8 satellite images demonstrate that the proposed method can obtain more accurate and more robust sea-land segmentation results than the traditional algorithms.

Gross and fine motor skills of children with Hurler syndrome (MPS-IH) post umbilical cord blood transplantation: a case series report.

PubMed

Dusing, Stacey C; Rosenberg, Angela; Hiemenz, Jennifer R; Piner, Shelley; Escolar, Maria

2005-01-01

Recent advancements in medical treatment of Hurler syndrome have resulted in longer life expectancies and a greater need for therapeutic services. The purpose of this case series is to provide recommendations for assessing children with Hurler syndrome after umbilical cord blood transplant (UCBT). CLINICAL DESCRIPTIONS: Two children with Hurler syndrome were seen for longitudinal assessments following an UCBT for Hurler syndrome. The raw scores and percentage of fine and gross motor items each child completed on the Motor Scale of the Bayley Scales of Infant Development II (BSID-II) were reviewed. Both children gained new motor skills with each successive motor assessment. Both children were able to complete a higher percentage of fine motor skills than gross motor skills in the most advanced item set assessed. The children presented in these two case reports both had better fine motor skills than gross motor skills, which inflated their standard scores on the BSID-II. Clinicians assessing children with Hurler syndrome should use standardized assessments that allow for differentiation of fine and gross motor skills to prevent this situation.

Towards a Fine-Resolution Global Coupled Climate System for Prediction on Decadal/Centennial Scales

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

McClean, Julie L.

The over-arching goal of this project was to contribute to the realization of a fully coupled fine resolution Earth System Model simulation in which a weather-scale atmosphere is coupled to an ocean in which mesoscale eddies are largely resolved. Both a prototype fine-resolution fully coupled ESM simulation and a first-ever multi-decadal forced fine-resolution global coupled ocean/ice simulation were configured, tested, run, and analyzed as part of this grant. Science questions focused on the gains from the use of high horizontal resolution, particularly in the ocean and sea-ice, with respect to climatically important processes. Both these fine resolution coupled ocean/sea icemore » and fully-coupled simulations and precedent stand-alone eddy-resolving ocean and eddy-permitting coupled ocean/ice simulations were used to explore the high resolution regime. Overall, these studies showed that the presence of mesoscale eddies significantly impacted mixing processes and the global meridional overturning circulation in the ocean simulations. Fourteen refereed publications and a Ph.D. dissertation resulted from this grant.« less

The moment of recognition. Rabbinic discourse, infancy, and psychoanalysis.

PubMed

Flashman, A J

1992-01-01

Professionals and educated laymen agree that the past 30 years have brought about a revolution in our understanding of infant development during the very first months of life. The inchoate "blooming buzzing confusion" once felt to characterize the neonate has given way to a well-documented realm of finely tuned perceptions and highly complex interactions. These shifts in our thinking are generally assumed to imply that periods chronologically more remote from our own are conceptually more remote from our modern achievements. But in fact, they are not. I here examine ancient and medieval rabbinic texts and find these "modern" issues discussed. The formulations of these texts, I suggest, sharpen the psychoanalytic focus on the role of the integrative function in very early development.

Effects of large-scale deforestation on precipitation in the monsoon regions: Remote versus local effects

PubMed Central

Devaraju, N.; Bala, Govindasamy; Modak, Angshuman

2015-01-01

In this paper, using idealized climate model simulations, we investigate the biogeophysical effects of large-scale deforestation on monsoon regions. We find that the remote forcing from large-scale deforestation in the northern middle and high latitudes shifts the Intertropical Convergence Zone southward. This results in a significant decrease in precipitation in the Northern Hemisphere monsoon regions (East Asia, North America, North Africa, and South Asia) and moderate precipitation increases in the Southern Hemisphere monsoon regions (South Africa, South America, and Australia). The magnitude of the monsoonal precipitation changes depends on the location of deforestation, with remote effects showing a larger influence than local effects. The South Asian Monsoon region is affected the most, with 18% decline in precipitation over India. Our results indicate that any comprehensive assessment of afforestation/reforestation as climate change mitigation strategies should carefully evaluate the remote effects on monsoonal precipitation alongside the large local impacts on temperatures. PMID:25733889

The value of remote sensing techniques in supporting effective extrapolation across multiple marine spatial scales.

PubMed

Strong, James Asa; Elliott, Michael

2017-03-15

The reporting of ecological phenomena and environmental status routinely required point observations, collected with traditional sampling approaches to be extrapolated to larger reporting scales. This process encompasses difficulties that can quickly entrain significant errors. Remote sensing techniques offer insights and exceptional spatial coverage for observing the marine environment. This review provides guidance on (i) the structures and discontinuities inherent within the extrapolative process, (ii) how to extrapolate effectively across multiple spatial scales, and (iii) remote sensing techniques and data sets that can facilitate this process. This evaluation illustrates that remote sensing techniques are a critical component in extrapolation and likely to underpin the production of high-quality assessments of ecological phenomena and the regional reporting of environmental status. Ultimately, is it hoped that this guidance will aid the production of robust and consistent extrapolations that also make full use of the techniques and data sets that expedite this process. Copyright © 2017 Elsevier Ltd. All rights reserved.

Monitoring Crop Phenology and Growth Stages from Space: Opportunities and Challenges

NASA Astrophysics Data System (ADS)

Gao, F.; Anderson, M. C.; Mladenova, I. E.; Kustas, W. P.; Alfieri, J. G.

2014-12-01

Crop growth stages in concert with weather and soil moisture conditions can have a significant impact on crop yields. In the U.S., crop growth stages and conditions are reported by farmers at the county level. These reports are somewhat subjective and fluctuate between different reporters, locations and times. Remote sensing data provide an alternative approach to monitoring crop growth over large areas in a more consistent and quantitative way. In the recent years, remote sensing data have been used to detect vegetation phenology at 1-km spatial resolution globally. However, agricultural applications at field scale require finer spatial resolution remote sensing data. Landsat (30-m) data have been successfully used for agricultural applications. There are many medium resolution sensors available today or in near future. These include Landsat, SPOT, RapidEye, ASTER and future Sentinel-2 etc. Approaches have been developed in the past several years to integrate remote sensing data from different sensors which may have different sensor characteristics, and spatial and temporal resolutions. This allows us opportunities today to map crop growth stages and conditions using dense time-series remote sensing at field scales. However, remotely sensed phenology (or phenological metrics) is normally derived based on the mathematical functions of the time-series data. The phenological metrics are determined by either identifying inflection (curvature) points or some pre-defined thresholds in the remote sensing phenology algorithms. Furthermore, physiological crop growth stages may not be directly correlated to the remotely sensed phenology. The relationship between remotely sensed phenology and crop growth stages is likely to vary for specific crop types and varieties, growing stages, conditions and even locations. In this presentation, we will examine the relationship between remotely sensed phenology and crop growth stages using in-situ measurements from Fluxnet sites and crop progress reports from USDA NASS. We will present remote sensing approaches and focus on: 1) integrating multiple sources of remote sensing data; and 2) extracting crop phenology at field scales. An example in the U.S. Corn Belt area will be presented and analyzed. Future directions for mapping crop growth stages will be discussed.

Numerical modelling of hydro-morphological processes dominated by fine suspended sediment in a stormwater pond

NASA Astrophysics Data System (ADS)

Guan, Mingfu; Ahilan, Sangaralingam; Yu, Dapeng; Peng, Yong; Wright, Nigel

2018-01-01

Fine sediment plays crucial and multiple roles in the hydrological, ecological and geomorphological functioning of river systems. This study employs a two-dimensional (2D) numerical model to track the hydro-morphological processes dominated by fine suspended sediment, including the prediction of sediment concentration in flow bodies, and erosion and deposition caused by sediment transport. The model is governed by 2D full shallow water equations with which an advection-diffusion equation for fine sediment is coupled. Bed erosion and sedimentation are updated by a bed deformation model based on local sediment entrainment and settling flux in flow bodies. The model is initially validated with the three laboratory-scale experimental events where suspended load plays a dominant role. Satisfactory simulation results confirm the model's capability in capturing hydro-morphodynamic processes dominated by fine suspended sediment at laboratory-scale. Applications to sedimentation in a stormwater pond are conducted to develop the process-based understanding of fine sediment dynamics over a variety of flow conditions. Urban flows with 5-year, 30-year and 100-year return period and the extreme flood event in 2012 are simulated. The modelled results deliver a step change in understanding fine sediment dynamics in stormwater ponds. The model is capable of quantitatively simulating and qualitatively assessing the performance of a stormwater pond in managing urban water quantity and quality.

Investigating the Impact of Surface Heterogeneity on the Convective Boundary Layer Over Urban Areas Through Coupled Large-Eddy Simulation and Remote Sensing

NASA Technical Reports Server (NTRS)

Dominguez, Anthony; Kleissl, Jan P.; Luvall, Jeffrey C.

2011-01-01

Large-eddy Simulation (LES) was used to study convective boundary layer (CBL) flow through suburban regions with both large and small scale heterogeneities in surface temperature. Constant remotely sensed surface temperatures were applied at the surface boundary at resolutions of 10 m, 90 m, 200 m, and 1 km. Increasing the surface resolution from 1 km to 200 m had the most significant impact on the mean and turbulent flow characteristics as the larger scale heterogeneities became resolved. While previous studies concluded that scales of heterogeneity much smaller than the CBL inversion height have little impact on the CBL characteristics, we found that further increasing the surface resolution (resolving smaller scale heterogeneities) results in an increase in mean surface heat flux, thermal blending height, and potential temperature profile. The results of this study will help to better inform sub-grid parameterization for meso-scale meteorological models. The simulation tool developed through this study (combining LES and high resolution remotely sensed surface conditions) is a significant step towards future studies on the micro-scale meteorology in urban areas.

Remote sensing of freeze-thaw transitions in Arctic soils using the complex resistivity method

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

Wu, Yuxin; Hubbard, Susan S; Ulrich, Craig

2013-01-01

Our ability to monitor freeze - thaw transitions is critical to developing a predictive understanding of biogeochemical transitions and carbon dynamics in high latitude environments. In this study, we conducted laboratory column experiments to explore the potential of the complex resistivity method for monitoring the freeze - thaw transitions of the arctic permafrost soils. Samples for the experiment were collected from the upper active layer of Gelisol soils at the Barrow Environmental Observatory, Barrow Alaska. Freeze - thaw transitions were induced through exposing the soil column to controlled temperature environments at 4 C and -20 C. Complex resistivity and temperaturemore » measurements were collected regularly during the freeze - thaw transitions using electrodes and temperature sensors installed along the column. During the experiments, over two orders of magnitude of resistivity variations were observed when the temperature was increased or decreased between -20 C and 0 C. Smaller resistivity variations were also observed during the isothermal thawing or freezing processes that occurred near 0 C. Single frequency electrical phase response and imaginary conductivity at 1 Hz were found to be exclusively related to the unfrozen water in the soil matrix, suggesting that these geophysical 24 attributes can be used as a proxy for the monitoring of the onset and progression of the freeze - thaw transitions. Spectral electrical responses and fitted Cole Cole parameters contained additional information about the freeze - thaw transition affected by the soil grain size distribution. Specifically, a shift of the observed spectral response to lower frequency was observed during isothermal thawing process, which we interpret to be due to sequential thawing, first from fine then to coarse particles within the soil matrix. Our study demonstrates the potential of the complex resistivity method for remote monitoring of freeze - thaw transitions in arctic soils. Although conducted at the laboratory scale, this study provides the foundation for exploring the potential of the complex resistivity signals for monitoring spatiotemporal variations of freeze - thaw transitions over field-relevant scales.« less

Detecting Precontact Anthropogenic Microtopographic Features in a Forested Landscape with Lidar: A Case Study from the Upper Great Lakes Region, AD 1000-1600

PubMed Central

Howey, Meghan C. L.; Sullivan, Franklin B.; Tallant, Jason; Kopple, Robert Vande; Palace, Michael W.

2016-01-01

Forested settings present challenges for understanding the full extent of past human landscape modifications. Field-based archaeological reconnaissance in forests is low-efficiency and most remote sensing techniques are of limited utility, and together, this means many past sites and features in forests are unknown. Archaeologists have increasingly used light detection and ranging (lidar), a remote sensing tool that uses pulses of light to measure reflecting surfaces at high spatial resolution, to address these limitations. Archaeology studies using lidar have made significant progress identifying permanent structures built by large-scale complex agriculturalist societies. Largely unaccounted for, however, are numerous small and more practical modifications of landscapes by smaller-scale societies. Here we show these may also be detectable with lidar by identifying remnants of food storage pits (cache pits) created by mobile hunter-gatherers in the upper Great Lakes during Late Precontact (ca. AD 1000–1600) that now only exist as subtle microtopographic features. Years of intensive field survey identified 69 cache pit groups between two inland lakes in northern Michigan, almost all of which were located within ~500 m of a lakeshore. Applying a novel series of image processing techniques and statistical analyses to a high spatial resolution DTM we created from commercial-grade lidar, our detection routine identified 139 high potential cache pit clusters. These included most of the previously known clusters as well as several unknown clusters located >1500 m from either lakeshore, much further from lakeshores than all previously identified cultural sites. Food storage is understood to have emerged regionally as a risk-buffering strategy after AD 1000 but our results indicate the current record of hunter-gatherer cache pit food storage is markedly incomplete and this practice and its associated impact on the landscape may be greater than anticipated. Our study also demonstrates the potential of harnessing commercial-grade lidar for other fine-grained archaeology applications. PMID:27584031

Key landscape and biotic indicators of watersheds sensitivity to forest disturbance identified using remote sensing and historical hydrography data

NASA Astrophysics Data System (ADS)

Buma, Brian; Livneh, Ben

2017-07-01

Water is one of the most critical resources derived from natural systems. While it has long been recognized that forest disturbances like fire influence watershed streamflow characteristics, individual studies have reported conflicting results with some showing streamflow increases post-disturbance and others decreases, while other watersheds are insensitive to even large disturbance events. Characterizing the differences between sensitive (e.g. where streamflow does change post-disturbance) and insensitive watersheds is crucial to anticipating response to future disturbance events. Here, we report on an analysis of a national-scale, gaged watershed database together with high-resolution forest mortality imagery. A simple watershed response model was developed based on the runoff ratio for watersheds (n = 73) prior to a major disturbance, detrended for variation in precipitation inputs. Post-disturbance deviations from the expected water yield and streamflow timing from expected (based on observed precipitation) were then analyzed relative to the abiotic and biotic characteristics of the individual watershed and observed extent of forest mortality. The extent of the disturbance was significantly related to change in post-disturbance water yield (p < 0.05), and there were several distinctive differences between watersheds exhibiting post-disturbance increases, decreases, and those showing no change in water yield. Highly disturbed, arid watersheds with low soil: water contact time are the most likely to see increases, with the magnitude positively correlated with the extent of disturbance. Watersheds dominated by deciduous forest with low bulk density soils typically show reduced yield post-disturbance. Post-disturbance streamflow timing change was associated with climate, forest type, and soil. Snowy coniferous watersheds were generally insensitive to disturbance, whereas finely textured soils with rapid runoff were sensitive. This is the first national scale investigation of streamflow post-disturbance using fused gage and remotely sensed data at high resolution, and gives important insights that can be used to anticipate changes in streamflow resulting from future disturbances.

Measuring landscape-scale spread and persistence of an invaded submerged plant community from airborne remote sensing.

PubMed

Santos, Maria J; Khanna, Shruti; Hestir, Erin L; Greenberg, Jonathan A; Ustin, Susan L

2016-09-01

Processes of spread and patterns of persistence of invasive species affect species and communities in the new environment. Predicting future rates of spread is of great interest for timely management decisions, but this depends on models that rely on understanding the processes of invasion and historic observations of spread and persistence. Unfortunately, the rates of spread and patterns of persistence are difficult to model or directly observe, especially when multiple rates of spread and diverse persistence patterns may be co-occurring over the geographic distribution of the invaded ecosystem. Remote sensing systematically acquires data over large areas at fine spatial and spectral resolutions over multiple time periods that can be used to quantify spread processes and persistence patterns. We used airborne imaging spectroscopy data acquired once a year for 5 years from 2004 to 2008 to map an invaded submerged aquatic vegetation (SAV) community across 2220 km 2 of waterways in the Sacramento-San Joaquin River Delta, California, USA, and measured its spread rate and its persistence. Submerged aquatic vegetation covered 13-23 km 2 of the waterways (6-11%) every year. Yearly new growth accounted for 40-60% of the SAV area, ~50% of which survived to following year. Spread rates were overall negative and persistence decreased with time. From this dataset, we were able to identify both radial and saltatorial spread of the invaded SAV in the entire extent of the Delta over time. With both decreasing spread rate and persistence, it is possible that over time the invasion of this SAV community could decrease its ecological impact. A landscape-scale approach allows measurements of all invasion fronts and the spatial anisotropies associated with spread processes and persistence patterns, without spatial interpolation, at locations both proximate and distant to the focus of invasion at multiple points in time. © 2016 by the Ecological Society of America.

Detecting Precontact Anthropogenic Microtopographic Features in a Forested Landscape with Lidar: A Case Study from the Upper Great Lakes Region, AD 1000-1600.

PubMed

Howey, Meghan C L; Sullivan, Franklin B; Tallant, Jason; Kopple, Robert Vande; Palace, Michael W

2016-01-01

Forested settings present challenges for understanding the full extent of past human landscape modifications. Field-based archaeological reconnaissance in forests is low-efficiency and most remote sensing techniques are of limited utility, and together, this means many past sites and features in forests are unknown. Archaeologists have increasingly used light detection and ranging (lidar), a remote sensing tool that uses pulses of light to measure reflecting surfaces at high spatial resolution, to address these limitations. Archaeology studies using lidar have made significant progress identifying permanent structures built by large-scale complex agriculturalist societies. Largely unaccounted for, however, are numerous small and more practical modifications of landscapes by smaller-scale societies. Here we show these may also be detectable with lidar by identifying remnants of food storage pits (cache pits) created by mobile hunter-gatherers in the upper Great Lakes during Late Precontact (ca. AD 1000-1600) that now only exist as subtle microtopographic features. Years of intensive field survey identified 69 cache pit groups between two inland lakes in northern Michigan, almost all of which were located within ~500 m of a lakeshore. Applying a novel series of image processing techniques and statistical analyses to a high spatial resolution DTM we created from commercial-grade lidar, our detection routine identified 139 high potential cache pit clusters. These included most of the previously known clusters as well as several unknown clusters located >1500 m from either lakeshore, much further from lakeshores than all previously identified cultural sites. Food storage is understood to have emerged regionally as a risk-buffering strategy after AD 1000 but our results indicate the current record of hunter-gatherer cache pit food storage is markedly incomplete and this practice and its associated impact on the landscape may be greater than anticipated. Our study also demonstrates the potential of harnessing commercial-grade lidar for other fine-grained archaeology applications.

Where the wild things are: Predicting hotspots of seabird aggregations in the California Current System

USGS Publications Warehouse

Nur, N.; Jahncke, J.; Herzog, M.P.; Howar, J.; Hyrenbach, K.D.; Zamon, J.E.; Ainley, D.G.; Wiens, J.A.; Morgan, K.; Balance, L.T.; Stralberg, D.

2011-01-01

Marine Protected Areas (MPAs) provide an important tool for conservation of marine ecosystems. To be most effective, these areas should be strategically located in a manner that supports ecosystem function. To inform marine spatial planning and support strategic establishment of MPAs within the California Current System, we identified areas predicted to support multispecies aggregations of seabirds ("hotspot????). We developed habitat-association models for 16 species using information from at-sea observations collected over an 11-year period (1997-2008), bathymetric data, and remotely sensed oceanographic data for an area from north of Vancouver Island, Canada, to the USA/Mexico border and seaward 600 km from the coast. This approach enabled us to predict distribution and abundance of seabirds even in areas of few or no surveys. We developed single-species predictive models using a machine-learning algorithm: bagged decision trees. Single-species predictions were then combined to identify potential hotspots of seabird aggregation, using three criteria: (1) overall abundance among species, (2) importance of specific areas ("core area????) to individual species, and (3) predicted persistence of hotspots across years. Model predictions were applied to the entire California Current for four seasons (represented by February, May, July, and October) in each of 11 years. Overall, bathymetric variables were often important predictive variables, whereas oceanographic variables derived from remotely sensed data were generally less important. Predicted hotspots often aligned with currently protected areas (e.g., National Marine Sanctuaries), but we also identified potential hotspots in Northern California/Southern Oregon (from Cape Mendocino to Heceta Bank), Southern California (adjacent to the Channel Islands), and adjacent to Vancouver Island, British Columbia, that are not currently included in protected areas. Prioritization and identification of multispecies hotspots will depend on which group of species is of highest management priority. Modeling hotspots at a broad spatial scale can contribute to MPA site selection, particularly if complemented by fine-scale information for focal areas. ?? 2011 by the Ecological Society of America.

Evapotranspiration estimates derived using multi-platform remote sensing in a semiarid region

USDA-ARS?s Scientific Manuscript database

Evapotranspiration (ET) is a key component of the water balance, especially in arid and semiarid regions. The current study takes advantage of spatially-distributed, near real-time information provided by satellite remote sensing to develop a regional scale ET product derived from remotely-sensed ob...

Uniform competency-based local feature extraction for remote sensing images

NASA Astrophysics Data System (ADS)

Sedaghat, Amin; Mohammadi, Nazila

2018-01-01

Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

Fine-scale genetic structure in populations of the Chagas' disease vector Triatoma infestans (Hemiptera, Reduvidae).

PubMed

Pérez de Rosas, Alicia R; Segura, Elsa L; Fusco, Octavio; Guiñazú, Adolfo L Bareiro; García, Beatriz A

2013-03-01

Fine scale patterns of genetic structure and dispersal in Triatoma infestans populations from Argentina was analysed. A total of 314 insects from 22 domestic and peridomestic sites from the locality of San Martín (Capayán department, Catamarca province) were typed for 10 polymorphic microsatellite loci. The results confirm subdivision of T. infestans populations with restricted dispersal among sampling sites and suggest inbreeding and/or stratification within the different domestic and peridomestic structures. Spatial correlation analysis showed that the scale of structuring is approximately of 400 m, indicating that active dispersal would occur within this distance range. It was detected difference in scale of structuring among sexes, with females dispersing over greater distances than males. This study suggests that insecticide treatment and surveillance should be extended within a radius of 400 m around the infested area, which would help to reduce the probability of reinfestation by covering an area of active dispersal. The inferences made from fine-scale spatial genetic structure analyses of T. infestans populations has demonstrated to be important for community-wide control programs, providing a complementary approach to help improve vector control strategies.

A coarse-to-fine approach for medical hyperspectral image classification with sparse representation

NASA Astrophysics Data System (ADS)

Chang, Lan; Zhang, Mengmeng; Li, Wei

2017-10-01

A coarse-to-fine approach with sparse representation is proposed for medical hyperspectral image classification in this work. Segmentation technique with different scales is employed to exploit edges of the input image, where coarse super-pixel patches provide global classification information while fine ones further provide detail information. Different from common RGB image, hyperspectral image has multi bands to adjust the cluster center with more high precision. After segmentation, each super pixel is classified by recently-developed sparse representation-based classification (SRC), which assigns label for testing samples in one local patch by means of sparse linear combination of all the training samples. Furthermore, segmentation with multiple scales is employed because single scale is not suitable for complicate distribution of medical hyperspectral imagery. Finally, classification results for different sizes of super pixel are fused by some fusion strategy, offering at least two benefits: (1) the final result is obviously superior to that of segmentation with single scale, and (2) the fusion process significantly simplifies the choice of scales. Experimental results using real medical hyperspectral images demonstrate that the proposed method outperforms the state-of-the-art SRC.

The TRMM Multi-satellite Precipitation Analysis (TMPA): Quasi-Global Precipitation Estimates at Fine Scales

NASA Technical Reports Server (NTRS)

Huffman, George J.; Adler, Robert F.; Bolvin, David T.; Gu, Guojun; Nelkin, Eric J.; Bowman, Kenneth P.; Stocker, Erich; Wolff, David B.

2006-01-01

The TRMM Multi-satellite Precipitation Analysis (TMPA) provides a calibration-based sequential scheme for combining multiple precipitation estimates from satellites, as well as gauge analyses where feasible, at fine scales (0.25 degrees x 0.25 degrees and 3-hourly). It is available both after and in real time, based on calibration by the TRMM Combined Instrument and TRMM Microwave Imager precipitation products, respectively. Only the after-real-time product incorporates gauge data at the present. The data set covers the latitude band 50 degrees N-S for the period 1998 to the delayed present. Early validation results are as follows: The TMPA provides reasonable performance at monthly scales, although it is shown to have precipitation rate dependent low bias due to lack of sensitivity to low precipitation rates in one of the input products (based on AMSU-B). At finer scales the TMPA is successful at approximately reproducing the surface-observation-based histogram of precipitation, as well as reasonably detecting large daily events. The TMPA, however, has lower skill in correctly specifying moderate and light event amounts on short time intervals, in common with other fine-scale estimators. Examples are provided of a flood event and diurnal cycle determination.

Fine-tuned Remote Laser Welding of Aluminum to Copper with Local Beam Oscillation

NASA Astrophysics Data System (ADS)

Fetzer, Florian; Jarwitz, Michael; Stritt, Peter; Weber, Rudolf; Graf, Thomas

Local beam oscillation in remote laser welding of aluminum to copper was investigated. Sheets of 1 mm thickness were welded in overlap configuration with aluminum as top material. The laser beam was scanned in a sinusoidal mode perpendicular to the direction of feed and the influence of the oscillation parameters frequency and amplitude on the weld geometry was investigated. Scanning frequencies up to 1 kHz and oscillation amplitudes in the range from 0.25 mm to 1 mm were examined. Throughout the experiments the laser power and the feed rate were kept constant. A decrease of welding depth with amplitude and frequency is found. The scanning amplitude had a strong influence and allowed coarse setting of the welding depth into the lower material, while the frequency allowed fine tuning in the order of 10% of the obtained depth. The oscillation parameters were found to act differently on the aluminum sheet compared to copper sheet regarding the amount of fused material. It is possible to influence the geometry of the fused zones separately for both sheets. Therefore the average composition in the weld can be set with high precision via the oscillation parameters. A setting of the generated intermetallics in the weld zone is possible without adjustment of laser power and feed rate.

A super-cusp divertor configuration for tokamaks

NASA Astrophysics Data System (ADS)

Ryutov, D. D.

2015-10-01

> This study demonstrates a remarkable flexibility of advanced divertor configurations created with the remote poloidal field coils. The emphasis here is on the configurations with three poloidal field nulls in the divertor area. We are seeking the structures where all three nulls lie on the same separatrix, thereby creating two zones of a very strong flux expansion, as envisaged in the concept of Takase's cusp divertor. It turns out that the set of remote coils can indeed produce a cusp divertor, with additional advantages of: (i) a large stand-off distance between the divertor and the coils and (ii) a thorough control that these coils exert over the fine features of the configuration. In reference to these additional favourable properties acquired by the cusp divertor, the resulting configuration could be called `a super-cusp'. General geometrical features of the three-null configurations produced by remote coils are described. Issues on the way to practical applications include the need for a more sophisticated control system and possible constraints related to excessively high currents in the divertor coils.

Profiling of Saharan dust from the Caribbean to western Africa - Part 2: Shipborne lidar measurements versus forecasts

NASA Astrophysics Data System (ADS)

Ansmann, Albert; Rittmeister, Franziska; Engelmann, Ronny; Basart, Sara; Jorba, Oriol; Spyrou, Christos; Remy, Samuel; Skupin, Annett; Baars, Holger; Seifert, Patric; Senf, Fabian; Kanitz, Thomas

2017-12-01

A unique 4-week ship cruise from Guadeloupe to Cabo Verde in April-May 2013 see part 1, Rittmeister et al. (2017) is used for an in-depth comparison of dust profiles observed with a polarization/Raman lidar aboard the German research vessel Meteor over the remote tropical Atlantic and respective dust forecasts of a regional (SKIRON) and two global atmospheric (dust) transport models (NMMB/BSC-Dust, MACC/CAMS). New options of model-observation comparisons are presented. We analyze how well the modeled fine dust (submicrometer particles) and coarse dust contributions to light extinction and mass concentration match respective lidar observations, and to what extent models, adjusted to aerosol optical thickness observations, are able to reproduce the observed layering and mixing of dust and non-dust (mostly marine) aerosol components over the remote tropical Atlantic. Based on the coherent set of dust profiles at well-defined distances from Africa (without any disturbance by anthropogenic aerosol sources over the ocean), we investigate how accurately the models handle dust removal at distances of 1500 km to more than 5000 km west of the Saharan dust source regions. It was found that (a) dust predictions are of acceptable quality for the first several days after dust emission up to 2000 km west of the African continent, (b) the removal of dust from the atmosphere is too strong for large transport paths in the global models, and (c) the simulated fine-to-coarse dust ratio (in terms of mass concentration and light extinction) is too high in the models compared to the observations. This deviation occurs initially close to the dust sources and then increases with distance from Africa and thus points to an overestimation of fine dust emission in the models.

Western Lake Erie Basin: Soft-data-constrained, NHDPlus resolution watershed modeling and exploration of applicable conservation scenarios.

PubMed

Yen, Haw; White, Michael J; Arnold, Jeffrey G; Keitzer, S Conor; Johnson, Mari-Vaughn V; Atwood, Jay D; Daggupati, Prasad; Herbert, Matthew E; Sowa, Scott P; Ludsin, Stuart A; Robertson, Dale M; Srinivasan, Raghavan; Rewa, Charles A

2016-11-01

Complex watershed simulation models are powerful tools that can help scientists and policy-makers address challenging topics, such as land use management and water security. In the Western Lake Erie Basin (WLEB), complex hydrological models have been applied at various scales to help describe relationships between land use and water, nutrient, and sediment dynamics. This manuscript evaluated the capacity of the current Soil and Water Assessment Tool (SWAT) to predict hydrological and water quality processes within WLEB at the finest resolution watershed boundary unit (NHDPlus) along with the current conditions and conservation scenarios. The process based SWAT model was capable of the fine-scale computation and complex routing used in this project, as indicated by measured data at five gaging stations. The level of detail required for fine-scale spatial simulation made the use of both hard and soft data necessary in model calibration, alongside other model adaptations. Limitations to the model's predictive capacity were due to a paucity of data in the region at the NHDPlus scale rather than due to SWAT functionality. Results of treatment scenarios demonstrate variable effects of structural practices and nutrient management on sediment and nutrient loss dynamics. Targeting treatment to acres with critical outstanding conservation needs provides the largest return on investment in terms of nutrient loss reduction per dollar spent, relative to treating acres with lower inherent nutrient loss vulnerabilities. Importantly, this research raises considerations about use of models to guide land management decisions at very fine spatial scales. Decision makers using these results should be aware of data limitations that hinder fine-scale model interpretation. Copyright © 2016 Elsevier B.V. All rights reserved.

Western Lake Erie Basin: Soft-data-constrained, NHDPlus resolution watershed modeling and exploration of applicable conservation scenarios

USGS Publications Warehouse

Yen, Haw; White, Michael J.; Arnold, Jeffrey G.; Keitzer, S. Conor; Johnson, Mari-Vaughn V; Atwood, Jay D.; Daggupati, Prasad; Herbert, Matthew E.; Sowa, Scott P.; Ludsin, Stuart A.; Robertson, Dale M.; Srinivasan, Raghavan; Rewa, Charles A.

2016-01-01

Complex watershed simulation models are powerful tools that can help scientists and policy-makers address challenging topics, such as land use management and water security. In the Western Lake Erie Basin (WLEB), complex hydrological models have been applied at various scales to help describe relationships between land use and water, nutrient, and sediment dynamics. This manuscript evaluated the capacity of the current Soil and Water Assessment Tool (SWAT2012) to predict hydrological and water quality processes within WLEB at the finest resolution watershed boundary unit (NHDPlus) along with the current conditions and conservation scenarios. The process based SWAT model was capable of the fine-scale computation and complex routing used in this project, as indicated by measured data at five gaging stations. The level of detail required for fine-scale spatial simulation made the use of both hard and soft data necessary in model calibration, alongside other model adaptations. Limitations to the model's predictive capacity were due to a paucity of data in the region at the NHDPlus scale rather than due to SWAT functionality. Results of treatment scenarios demonstrate variable effects of structural practices and nutrient management on sediment and nutrient loss dynamics. Targeting treatment to acres with critical outstanding conservation needs provides the largest return on investment in terms of nutrient loss reduction per dollar spent, relative to treating acres with lower inherent nutrient loss vulnerabilities. Importantly, this research raises considerations about use of models to guide land management decisions at very fine spatial scales. Decision makers using these results should be aware of data limitations that hinder fine-scale model interpretation.

Linking Belowground Plant Traits With Ecosystem Processes: A Multi-Biome Perspective

NASA Astrophysics Data System (ADS)

Iversen, C. M.; Norby, R. J.; Childs, J.; McCormack, M. L.; Walker, A. P.; Hanson, P. J.; Warren, J.; Sloan, V. L.; Sullivan, P. F.; Wullschleger, S.; Powell, A. S.

2015-12-01

Fine plant roots are short-lived, narrow-diameter roots that play an important role in ecosystem carbon, water, and nutrient cycling in biomes ranging from the tundra to the tropics. Root ecologists make measurements at a millimeter scale to answer a question with global implications: In response to a changing climate, how do fine roots modulate the exchange of carbon between soils and the atmosphere and how will this response affect our future climate? In a Free-Air CO2 Enrichment experiment in Oak Ridge, TN, elevated [CO2] caused fine roots to dive deeper into the soil profile in search of limiting nitrogen, which led to increased soil C storage in deep soils. In contrast, the fine roots of trees and shrubs in an ombrotrophic bog are constrained to nutrient-poor, oxic soils above the average summer water table depth, though this may change with warmer, drier conditions. Tundra plant species are similarly constrained to surface organic soils by permafrost or waterlogged soils, but have many adaptations that alter ecosystem C fluxes, including aerenchyma that oxygenate the rhizosphere but also allow direct methane flux to the atmosphere. FRED, a global root trait database, will allow terrestrial biosphere models to represent the complexity of root traits across the globe, informing both model representation of ecosystem C and nutrient fluxes, but also the gaps where measurements are needed on plant-soil interactions (for example, in the tropical biome). While the complexity of mm-scale measurements may never have a place in large-scale global models, close collaboration between empiricists and modelers can help to guide the scaling of important, yet small-scale, processes to quantify their important roles in larger-scale ecosystem fluxes.

Remote sensing, geographical information systems, and spatial modeling for analyzing public transit services

NASA Astrophysics Data System (ADS)

Wu, Changshan

Public transit service is a promising transportation mode because of its potential to address urban sustainability. Current ridership of public transit, however, is very low in most urban regions, particularly those in the United States. This woeful transit ridership can be attributed to many factors, among which poor service quality is key. Given this, there is a need for transit planning and analysis to improve service quality. Traditionally, spatially aggregate data are utilized in transit analysis and planning. Examples include data associated with the census, zip codes, states, etc. Few studies, however, address the influences of spatially aggregate data on transit planning results. In this research, previous studies in transit planning that use spatially aggregate data are reviewed. Next, problems associated with the utilization of aggregate data, the so-called modifiable areal unit problem (MAUP), are detailed and the need for fine resolution data to support public transit planning is argued. Fine resolution data is generated using intelligent interpolation techniques with the help of remote sensing imagery. In particular, impervious surface fraction, an important socio-economic indicator, is estimated through a fully constrained linear spectral mixture model using Landsat Enhanced Thematic Mapper Plus (ETM+) data within the metropolitan area of Columbus, Ohio in the United States. Four endmembers, low albedo, high albedo, vegetation, and soil are selected to model heterogeneous urban land cover. Impervious surface fraction is estimated by analyzing low and high albedo endmembers. With the derived impervious surface fraction, three spatial interpolation methods, spatial regression, dasymetric mapping, and cokriging, are developed to interpolate detailed population density. Results suggest that cokriging applied to impervious surface is a better alternative for estimating fine resolution population density. With the derived fine resolution data, a multiple route maximal covering/shortest path (MRMCSP) model is proposed to address the tradeoff between public transit service quality and access coverage in an established bus-based transit system. Results show that it is possible to improve current transit service quality by eliminating redundant or underutilized service stops. This research illustrates that fine resolution data can be efficiently generated to support urban planning, management and analysis. Further, this detailed data may necessitate the development of new spatial optimization models for use in analysis.

Rooting strategies in a subtropical savanna: a landscape-scale three-dimensional assessment.

PubMed

Zhou, Yong; Boutton, Thomas W; Wu, X Ben; Wright, Cynthia L; Dion, Anais L

2018-04-01

In resource-limited savannas, the distribution and abundance of fine roots play an important role in acquiring essential resources and structuring vegetation patterns and dynamics. However, little is known regarding the three-dimensional distribution of fine roots in savanna ecosystems at the landscape scale. We quantified spatial patterns of fine root density to a depth of 1.2 m in a subtropical savanna landscape using spatially specific sampling. Kriged maps revealed that fine root density was highest at the centers of woody patches, decreased towards the canopy edges, and reached lowest values within the grassland matrix throughout the entire soil profile. Lacunarity analyses indicated that spatial heterogeneities of fine root density decreased continuously to a depth of 50 cm and then increased in deeper portions of the soil profile across this landscape. This vertical pattern might be related to inherent differences in root distribution between trees/shrubs and herbaceous species, and the presence/absence of an argillic horizon across this landscape. The greater density of fine roots beneath woody patches in both upper and lower portions of the soil profile suggests an ability to acquire disproportionately more resources than herbaceous species, which may facilitate the development and persistence of woody patches across this landscape.

Improvement of storage, handling, and transportability of fine coal. Quarterly technical progress report number 8, October 1--December 31, 1995

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

NONE

The Mulled Coal process was developed as a means of overcoming the adverse handling characteristics of wet fine coal without thermal drying. The process involves the addition of a low cost, harmless reagent to wet fine coal using off-the-shelf mixing equipment. Based on laboratory- and bench-scale testing, Mulled coal can be stored, shipped, and burned without causing any of the plugging, pasting, carryback and freezing problems normally associated with wet coal. On the other hand, Mulled Coal does not cause the fugitive and airborne dust problems normally associated with thermally dried coal. The objectives of this project are to demonstratemore » that: the Mulled Coal process, which has been proved to work on a wide range of wet fine coals at bench scale, will work equally well on a continuous basis, producing consistent quality, and at a convincing rate of production in a commercial coal preparation plant; the wet product from a fine coal cleaning circuit can be converted to a solid fuel form for ease of handling and cost savings in storage and rail car transportation; and a wet fine coal product thus converted to a solid fuel form, can be stored, shipped, and burned with conventional fuel handling, transportation, and combustion systems.« less

Population Turnover in Remote Oceania Shortly after Initial Settlement.

PubMed

Lipson, Mark; Skoglund, Pontus; Spriggs, Matthew; Valentin, Frederique; Bedford, Stuart; Shing, Richard; Buckley, Hallie; Phillip, Iarawai; Ward, Graeme K; Mallick, Swapan; Rohland, Nadin; Broomandkhoshbacht, Nasreen; Cheronet, Olivia; Ferry, Matthew; Harper, Thomas K; Michel, Megan; Oppenheimer, Jonas; Sirak, Kendra; Stewardson, Kristin; Auckland, Kathryn; Hill, Adrian V S; Maitland, Kathryn; Oppenheimer, Stephen J; Parks, Tom; Robson, Kathryn; Williams, Thomas N; Kennett, Douglas J; Mentzer, Alexander J; Pinhasi, Ron; Reich, David

2018-04-02

Ancient DNA from Vanuatu and Tonga dating to about 2,900-2,600 years ago (before present, BP) has revealed that the "First Remote Oceanians" associated with the Lapita archaeological culture were directly descended from the population that, beginning around 5000 BP, spread Austronesian languages from Taiwan to the Philippines, western Melanesia, and eventually Remote Oceania. Thus, ancestors of the First Remote Oceanians must have passed by the Papuan-ancestry populations they encountered in New Guinea, the Bismarck Archipelago, and the Solomon Islands with minimal admixture [1]. However, all present-day populations in Near and Remote Oceania harbor >25% Papuan ancestry, implying that additional eastward migration must have occurred. We generated genome-wide data for 14 ancient individuals from Efate and Epi Islands in Vanuatu from 2900-150 BP, as well as 185 present-day individuals from 18 islands. We find that people of almost entirely Papuan ancestry arrived in Vanuatu by around 2300 BP, most likely reflecting migrations a few hundred years earlier at the end of the Lapita period, when there is also evidence of changes in skeletal morphology and cessation of long-distance trade between Near and Remote Oceania [2, 3]. Papuan ancestry was subsequently diluted through admixture but remains at least 80%-90% in most islands. Through a fine-grained analysis of ancestry profiles, we show that the Papuan ancestry in Vanuatu derives from the Bismarck Archipelago rather than the geographically closer Solomon Islands. However, the Papuan ancestry in Polynesia-the most remote Pacific islands-derives from different sources, documenting a third stream of migration from Near to Remote Oceania. Copyright © 2018 Elsevier Ltd. All rights reserved.

Multiscale observations of CO2, 13CO2, and pollutants at Four Corners for emission verification and attribution

PubMed Central

Lindenmaier, Rodica; Dubey, Manvendra K.; Henderson, Bradley G.; Butterfield, Zachary T.; Herman, Jay R.; Rahn, Thom; Lee, Sang-Hyun

2014-01-01

There is a pressing need to verify air pollutant and greenhouse gas emissions from anthropogenic fossil energy sources to enforce current and future regulations. We demonstrate the feasibility of using simultaneous remote sensing observations of column abundances of CO2, CO, and NO2 to inform and verify emission inventories. We report, to our knowledge, the first ever simultaneous column enhancements in CO2 (3–10 ppm) and NO2 (1–3 Dobson Units), and evidence of δ13CO2 depletion in an urban region with two large coal-fired power plants with distinct scrubbing technologies that have resulted in ∆NOx/∆CO2 emission ratios that differ by a factor of two. Ground-based total atmospheric column trace gas abundances change synchronously and correlate well with simultaneous in situ point measurements during plume interceptions. Emission ratios of ∆NOx/∆CO2 and ∆SO2/∆CO2 derived from in situ atmospheric observations agree with those reported by in-stack monitors. Forward simulations using in-stack emissions agree with remote column CO2 and NO2 plume observations after fine scale adjustments. Both observed and simulated column ∆NO2/∆CO2 ratios indicate that a large fraction (70–75%) of the region is polluted. We demonstrate that the column emission ratios of ∆NO2/∆CO2 can resolve changes from day-to-day variation in sources with distinct emission factors (clean and dirty power plants, urban, and fires). We apportion these sources by using NO2, SO2, and CO as signatures. Our high-frequency remote sensing observations of CO2 and coemitted pollutants offer promise for the verification of power plant emission factors and abatement technologies from ground and space. PMID:24843169

A 10-Year Assessment of Hemlock Decline in the Catskill Mountain Region of New York State Using Hyperspectral Remote Sensing Techniques.

PubMed

Hanavan, Ryan P; Pontius, Jennifer; Hallett, Richard

2015-02-01

The hemlock woolly adelgid is a serious pest of Eastern and Carolina hemlock in the eastern United States. Successfully managing the hemlock resource in the region depends on careful monitoring of the spread of this invasive pest and the targeted application of management options such as biological control, chemical, or silvicultural treatments. To inform these management activities and test the applicability of a landscape-scale remote sensing effort to monitor hemlock condition, hyperspectral collections, and concurrent ground-truthing in 2001 and 2012 of hemlock condition were compared with field metrics spanning a 10-yr survey in the Catskills region of New York. Fine twig dieback significantly increased from 9 to 15% and live crown ratio significantly decreased from 67 to 56% in 2001 and 2012, respectively. We found a significant shift from 59% "healthy" hemlock in 2001 to only 16% in 2012. However, this shift from healthy to declining classifications was mostly a shift to decline class 2 "early decline". These results indicate that while there has been significant increase in decline symptoms as measured in both field and remote sensing assessments, a majority of the declining areas identified in the resulting spatial coverages remain in the "early decline" category and widespread mortality has not yet occurred. While this slow decline across the region stands in contrast to many reports of mortality within 10 yr, the results from this work are in line with other long-term monitoring studies and indicate that armed with the spatial information provided here, continued management strategies can be focused on particular areas to help control the further decline of hemlock in the region. Published by Oxford University Press on behalf of Entomological Society of America 2015. This work is written by US Government employees and is in the public domain in the US.

Lead isotopic studies of lunar soils - Their bearing on the time scale of agglutinate formation

NASA Technical Reports Server (NTRS)

Church, S. E.; Tilton, G. R.; Chen, J. H.

1976-01-01

Fines (smaller than 75 microns) and bulk soil were studied to analyze loss of volatile lead; losses of the order of 10% to 30% radiogenic lead during the production of agglutinates are assessed. Lead isotope data from fine-agglutinate pairs are analyzed for information on the time scale of micrometeorite bombardment, from the chords generated by the data in concordia diagrams. Resulting mean lead loss ages were compared to spallogenic gas exposure ages for all samples. Labile parentless radiogenic Pb residing preferentially on or in the fines is viewed as possibly responsible for aberrant lead loss ages. Bulk soils plot above the concordia curve (in a field of excess radiogenic Pb) for all samples with anomalous ages.

A Bicycle-Based Field Measurement System for the Study of Thermal Exposure in Cuyahoga County, Ohio, USA.

PubMed

Rajkovich, Nicholas B; Larsen, Larissa

2016-01-25

Collecting a fine scale of microclimate data can help to determine how physical characteristics (e.g., solar radiation, albedo, sky view factor, vegetation) contribute to human exposure to ground and air temperatures. These data also suggest how urban design strategies can reduce the negative impacts of the urban heat island effect. However, urban microclimate measurement poses substantial challenges. For example, data taken at local airports are not representative of the conditions at the neighborhood or district level because of variation in impervious surfaces, vegetation, and waste heat from vehicles and buildings. In addition, fixed weather stations cannot be deployed quickly to capture data from a heat wave. While remote sensing can provide data on land cover and ground surface temperatures, resolution and cost remain significant limitations. This paper describes the design and validation of a mobile measurement bicycle. This bicycle permits movement from space to space within a city to assess the physical and thermal properties of microclimates. The construction of the vehicle builds on investigations of the indoor thermal environment of buildings using thermal comfort carts.

A Bicycle-Based Field Measurement System for the Study of Thermal Exposure in Cuyahoga County, Ohio, USA

PubMed Central

Rajkovich, Nicholas B.; Larsen, Larissa

2016-01-01

Collecting a fine scale of microclimate data can help to determine how physical characteristics (e.g., solar radiation, albedo, sky view factor, vegetation) contribute to human exposure to ground and air temperatures. These data also suggest how urban design strategies can reduce the negative impacts of the urban heat island effect. However, urban microclimate measurement poses substantial challenges. For example, data taken at local airports are not representative of the conditions at the neighborhood or district level because of variation in impervious surfaces, vegetation, and waste heat from vehicles and buildings. In addition, fixed weather stations cannot be deployed quickly to capture data from a heat wave. While remote sensing can provide data on land cover and ground surface temperatures, resolution and cost remain significant limitations. This paper describes the design and validation of a mobile measurement bicycle. This bicycle permits movement from space to space within a city to assess the physical and thermal properties of microclimates. The construction of the vehicle builds on investigations of the indoor thermal environment of buildings using thermal comfort carts. PMID:26821037

A sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image

NASA Astrophysics Data System (ADS)

Li, Jing; Xie, Weixin; Pei, Jihong

2018-03-01

Sea-land segmentation is one of the key technologies of sea target detection in remote sensing images. At present, the existing algorithms have the problems of low accuracy, low universality and poor automatic performance. This paper puts forward a sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image removing island. Firstly, the coastline data is extracted and all of land area is labeled by using the geographic information in large-field remote sensing image. Secondly, three features (local entropy, local texture and local gradient mean) is extracted in the sea-land border area, and the three features combine a 3D feature vector. And then the MultiGaussian model is adopted to describe 3D feature vectors of sea background in the edge of the coastline. Based on this multi-gaussian sea background model, the sea pixels and land pixels near coastline are classified more precise. Finally, the coarse segmentation result and the fine segmentation result are fused to obtain the accurate sea-land segmentation. Comparing and analyzing the experimental results by subjective vision, it shows that the proposed method has high segmentation accuracy, wide applicability and strong anti-disturbance ability.

Subsonic stability and control derivatives for an unpowered, remotely piloted 3/8-scale F-15 airplane model obtained from flight test

NASA Technical Reports Server (NTRS)

Iliff, K. W.; Maine, R. E.; Shafer, M. F.

1976-01-01

In response to the interest in airplane configuration characteristics at high angles of attack, an unpowered remotely piloted 3/8-scale F-15 airplane model was flight tested. The subsonic stability and control characteristics of this airplane model over an angle of attack range of -20 to 53 deg are documented. The remotely piloted technique for obtaining flight test data was found to provide adequate stability and control derivatives. The remotely piloted technique provided an opportunity to test the aircraft mathematical model in an angle of attack regime not previously examined in flight test. The variation of most of the derivative estimates with angle of attack was found to be consistent, particularly when the data were supplemented by uncertainty levels.

Evaluation of soil and vegetation response to drought using SMOS soil moisture satellite observations

NASA Astrophysics Data System (ADS)

Piles, Maria; Sánchez, Nilda; Vall-llossera, Mercè; Ballabrera, Joaquim; Martínez, Justino; Martínez-Fernández, José; Camps, Adriano; Font, Jordi

2014-05-01

Soil moisture plays an important role in determining the likelihood of droughts and floods that may affect an area. Knowledge of soil moisture distribution as a function of time and space is highly relevant for hydrological, ecological and agricultural applications, especially in water-limited or drought-prone regions. However, measuring soil moisture is challenging because of its high variability; point-scale in-situ measurements are scarce being remote sensing the only practical means to obtain regional- and global-scale soil moisture estimates. The ESA's Soil Moisture and Ocean Salinity (SMOS) is the first satellite mission ever designed to measuring the Earth's surface soil moisture at near daily time scales with levels of accuracy previously not attained. Since its launch in November 2009, significant efforts have been dedicated to validate and fine-tune the retrieval algorithms so that SMOS-derived soil moisture estimates meet the standards required for a wide variety of applications. In this line, the SMOS Barcelona Expert Center (BEC) is distributing daily, monthly, and annual temporal averages of 0.25-deg global soil moisture maps, which have proved useful for assessing drought and water-stress conditions. In addition, a downscaling algorithm has been developed to combine SMOS and NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) data into fine-scale (< 1km) soil moisture estimates, which permits extending the applicability of the data to regional and local studies. Fine-scale soil moisture maps are currently limited to the Iberian Peninsula but the algorithm is dynamic and can be transported to any region. Soil moisture maps are generated in a near real-time fashion at BEC facilities and are used by Barcelona's fire prevention services to detect extremely dry soil and vegetation conditions posing a risk of fire. Recently, they have been used to explain drought-induced tree mortality episodes and forest decline in the Catalonia region. These soil moisture products can also be a useful tool to monitor the effectiveness of land restoration management practices. The aim of this work is to demonstrate the feasibility of using SMOS soil moisture maps for monitoring drought and water-stress conditions. In previous research, SMOS-derived Soil Moisture Anomalies (SSMA), calculated in a ten-day basis, were shown to be in close relationship with well-known drought indices (the Standardized Precipitation Index and the Standardized Precipitation Evapotranspiration Index). In this work, SSMA have been calculated for the period 2010-2013 in representative arid, semi-arid, sub-humid and humid areas across global land biomes. The SSMA reflect the cumulative precipitation anomalies and is known to provide 'memory' in the climate and hydrological system; the water retained in the soil after a rainfall event is temporally more persistent than the rainfall event itself, and has a greater persistence during periods of low precipitation. Besides, the Normalized Difference Vegetation Index (NDVI) from MODIS is used as an indicator of vegetation activity and growth. The NDVI time series are expected to reflect the changes in surface vegetation density and status induced by water-deficit conditions. Understanding the relationships between SSMA and NDVI concurrent time series should provide new insight about the sensitivity of land biomes to drought.

Climate, soil and plant functional types as drivers of global fine-root trait variation

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

Freschet, Grégoire T.; Valverde-Barrantes, Oscar J.; Tucker, Caroline M.

Ecosystem functioning relies heavily on below-ground processes, which are largely regulated by plant fine-roots and their functional traits. However, our knowledge of fine-root trait distribution relies to date on local- and regional-scale studies with limited numbers of species, growth forms and environmental variation. We compiled a world-wide fine-root trait dataset, featuring 1115 species from contrasting climatic areas, phylogeny and growth forms to test a series of hypotheses pertaining to the influence of plant functional types, soil and climate variables, and the degree of manipulation of plant growing conditions on species fine-root trait variation. Most particularly, we tested the competing hypothesesmore » that fine-root traits typical of faster return on investment would be most strongly associated with conditions of limiting versus favourable soil resource availability. We accounted for both data source and species phylogenetic relatedness. We demonstrate that: (i) Climate conditions promoting soil fertility relate negatively to fine-root traits favouring fast soil resource acquisition, with a particularly strong positive effect of temperature on fine-root diameter and negative effect on specific root length (SRL), and a negative effect of rainfall on root nitrogen concentration; (ii) Soil bulk density strongly influences species fine-root morphology, by favouring thicker, denser fine-roots; (iii) Fine-roots from herbaceous species are on average finer and have higher SRL than those of woody species, and N 2-fixing capacity positively relates to root nitrogen; and (iv) Plants growing in pots have higher SRL than those grown in the field. Synthesis. This study reveals both the large variation in fine-root traits encountered globally and the relevance of several key plant functional types and soil and climate variables for explaining a substantial part of this variation. Climate, particularly temperature, and plant functional types were the two strongest predictors of fine-root trait variation. High trait variation occurred at local scales, suggesting that wide-ranging below-ground resource economics strategies are viable within most climatic areas and soil conditions.« less

Climate, soil and plant functional types as drivers of global fine-root trait variation

DOE PAGES

Freschet, Grégoire T.; Valverde-Barrantes, Oscar J.; Tucker, Caroline M.; ...

2017-03-08

Ecosystem functioning relies heavily on below-ground processes, which are largely regulated by plant fine-roots and their functional traits. However, our knowledge of fine-root trait distribution relies to date on local- and regional-scale studies with limited numbers of species, growth forms and environmental variation. We compiled a world-wide fine-root trait dataset, featuring 1115 species from contrasting climatic areas, phylogeny and growth forms to test a series of hypotheses pertaining to the influence of plant functional types, soil and climate variables, and the degree of manipulation of plant growing conditions on species fine-root trait variation. Most particularly, we tested the competing hypothesesmore » that fine-root traits typical of faster return on investment would be most strongly associated with conditions of limiting versus favourable soil resource availability. We accounted for both data source and species phylogenetic relatedness. We demonstrate that: (i) Climate conditions promoting soil fertility relate negatively to fine-root traits favouring fast soil resource acquisition, with a particularly strong positive effect of temperature on fine-root diameter and negative effect on specific root length (SRL), and a negative effect of rainfall on root nitrogen concentration; (ii) Soil bulk density strongly influences species fine-root morphology, by favouring thicker, denser fine-roots; (iii) Fine-roots from herbaceous species are on average finer and have higher SRL than those of woody species, and N 2-fixing capacity positively relates to root nitrogen; and (iv) Plants growing in pots have higher SRL than those grown in the field. Synthesis. This study reveals both the large variation in fine-root traits encountered globally and the relevance of several key plant functional types and soil and climate variables for explaining a substantial part of this variation. Climate, particularly temperature, and plant functional types were the two strongest predictors of fine-root trait variation. High trait variation occurred at local scales, suggesting that wide-ranging below-ground resource economics strategies are viable within most climatic areas and soil conditions.« less

Virtual Computing Laboratories: A Case Study with Comparisons to Physical Computing Laboratories

ERIC Educational Resources Information Center

Burd, Stephen D.; Seazzu, Alessandro F.; Conway, Christopher

2009-01-01

Current technology enables schools to provide remote or virtual computing labs that can be implemented in multiple ways ranging from remote access to banks of dedicated workstations to sophisticated access to large-scale servers hosting virtualized workstations. This paper reports on the implementation of a specific lab using remote access to…

Landsat's role in ecological applications of remote sensing.

Treesearch

Warren B. Cohen; Samuel N. Goward

2004-01-01

Remote sensing, geographic information systems, and modeling have combined to produce a virtual explosion of growth in ecological investigations and applications that are explicitly spatial and temporal. Of all remotely sensed data, those acquired by landsat sensors have played the most pivotal role in spatial and temporal scaling. Modern terrestrial ecology relies on...

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

PubMed Central

Zheng, Guang; Moskal, L. Monika

2009-01-01

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

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

PubMed

Zheng, Guang; Moskal, L Monika

2009-01-01

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

Portable air cleaners should be at the forefront of the public health response to landscape fire smoke.

PubMed

Barn, Prabjit K; Elliott, Catherine T; Allen, Ryan W; Kosatsky, Tom; Rideout, Karen; Henderson, Sarah B

2016-11-25

Landscape fires can produce large quantities of smoke that degrade air quality in both remote and urban communities. Smoke from these fires is a complex mixture of fine particulate matter and gases, exposure to which is associated with increased respiratory and cardiovascular morbidity and mortality. The public health response to short-lived smoke events typically advises people to remain indoors with windows and doors closed, but does not emphasize the use of portable air cleaners (PAC) to create private or public clean air shelters. High efficiency particulate air filters and electrostatic precipitators can lower indoor concentrations of fine particulate matter and improve respiratory and cardiovascular outcomes. We argue that PACs should be at the forefront of the public health response to landscape fire smoke events.

From broadscale patterns to fine-scale processes: habitat structure influences genetic differentiation in the pitcher plant midge across multiple spatial scales.

PubMed

Rasic, Gordana; Keyghobadi, Nusha

2012-01-01

The spatial scale at which samples are collected and analysed influences the inferences that can be drawn from landscape genetic studies. We examined genetic structure and its landscape correlates in the pitcher plant midge, Metriocnemus knabi, an inhabitant of the purple pitcher plant, Sarracenia purpurea, across several spatial scales that are naturally delimited by the midge's habitat (leaf, plant, cluster of plants, bog and system of bogs). We analysed 11 microsatellite loci in 710 M. knabi larvae from two systems of bogs in Algonquin Provincial Park (Canada) and tested the hypotheses that variables related to habitat structure are associated with genetic differentiation in this midge. Up to 54% of variation in individual-based genetic distances at several scales was explained by broadscale landscape variables of bog size, pitcher plant density within bogs and connectivity of pitcher plant clusters. Our results indicate that oviposition behaviour of females at fine scales, as inferred from the spatial locations of full-sib larvae, and spatially limited gene flow at broad scales represent the important processes underlying observed genetic patterns in M. knabi. Broadscale landscape features (bog size and plant density) appear to influence oviposition behaviour of midges, which in turn influences the patterns of genetic differentiation observed at both fine and broad scales. Thus, we inferred linkages among genetic patterns, landscape patterns and ecological processes across spatial scales in M. knabi. Our results reinforce the value of exploring such links simultaneously across multiple spatial scales and landscapes when investigating genetic diversity within a species. © 2011 Blackwell Publishing Ltd.

Remote Sensing in Agriculture: An Introductory Review.

ERIC Educational Resources Information Center

Curran, Paul J.

1987-01-01

Discusses the use of remote sensing techniques to obtain locational, estimated, and mapped information at the scales varying from individual fields and farms, to entire continents and the world. (AEM)

Tracking and Predicting Fine Scale Sea Ice Motion by Constructing Super-Resolution Images and Fusing Multiple Satellite Sensors

DTIC Science & Technology

2013-09-30

COVERED 00-00-2013 to 00-00-2013 4. TITLE AND SUBTITLE Tracking and Predicting Fine Scale Sea Ice Motion by Constructing Super-Resolution Images...limited, but potentially provide more detailed data. Initial assessments have been made on MODIS data in terms of its suitability. While clouds obscure...estimates. 2 Data from Aqua, Terra, and Suomi NPP satellites were investigated. Aqua and Terra are older satellites that fly the MODIS instrument

A Minimum-Residual Finite Element Method for the Convection-Diffusion Equation

DTIC Science & Technology

2013-05-01

4p . We note that these two choices of discretization for V are not mutually exclusive, and that novel choices for Vh are likely the key to yielding...the inside with the positive- definite operator A, which is precisely the discrete system that arises under the optimal test function framework of DPG...converts the fine-scale problem into a symmetric-positive definite one, allowing for a well-behaved subgrid model of fine scale behavior. We begin again

A nanobuffer reporter library for fine-scale imaging and perturbation of endocytic organelles | Office of Cancer Genomics

Cancer.gov

Endosomes, lysosomes and related catabolic organelles are a dynamic continuum of vacuolar structures that impact a number of cell physiological processes such as protein/lipid metabolism, nutrient sensing and cell survival. Here we develop a library of ultra-pH-sensitive fluorescent nanoparticles with chemical properties that allow fine-scale, multiplexed, spatio-temporal perturbation and quantification of catabolic organelle maturation at single organelle resolution to support quantitative investigation of these processes in living cells.

Variations in Grain-Scale Sediment Structure in a Gravel-Bed Channel as a Function of Fine Sediment Content and Morphological Location

NASA Astrophysics Data System (ADS)

Voepel, H.; Ahmed, S. I.; Hodge, R. A.; Leyland, J.; Sear, D. A.

2016-12-01

One of the major causes of uncertainty in estimates of bedload transport rates in gravel bed rivers is a lack of understanding of grain-scale sediment structure, and the impact that this structure has on bed stability. Furthermore, grain-scale structure varies throughout a channel and over time in ways that have not been fully quantified. Our research aims to quantify variations in sediment structure caused by two key variables; morphological location within a riffle-pool sequence (reflecting variation in hydraulic conditions), and the fine sediment content of the gravel bed (sand and clay). We report results from a series of flume experiments in which we water-worked a gravel bed with a riffle-pool morphology. The fine sediment content of the bed was incrementally increased over a series of runs from gravel only, to coarse sand, fine sand and two concentrations of clay. After each experimental run intact samples of the bed at different locations were extracted and the internal structure of the bed was measured using non-destructive, micro-focus X-ray computed tomography (CT) imaging. The CT images were processed to measure the properties of individual grains, including volume, center of mass, dimension, and contact points. From these data we were able to quantify the sediment structure through metrics including measurement of grain pivot angles, grain exposure and protrusion, and vertical variation in bed porosity and fine sediment content. Metrics derived from the CT data were verified using data from grain counts and tilt-table measurements on co-located samples. Comparison of the metrics across different morphological locations and fine sediment content demonstrates how these factors affect the bed structure. These results have implications for the development of sediment entrainment models for gravel bed rivers.

Physical and Optical Characteristics of the October 2010 Haze Event Over Singapore: A Photometric and Lidar Analysis

NASA Technical Reports Server (NTRS)

Salinas, Santo V.; Chew, Boon Ning; Miettinen, Jukka; Campbell, James R.; Welton, Ellsworth J.; Reid, Jeffrey S.; Yu, Liya E.; Liew, Soo Chin

2013-01-01

Trans-boundary biomass burning smoke episodes have increased dramatically during the past 20-30 years and have become an annual phenomenon in the South-East-Asia region. On 15th October 2010, elevated levels of fire activity were detected by remote sensing satellites (e.g. MODIS). On the same date, measurements of fine particulate matter (PM2.5) at Singapore and Malaysia found high levels of fine mode particles in the local environment. All these observations were indicative of the initial onset of a smoke episode that lasted for several days. In this work, we investigate the temporal evolution of this smoke episode by analyzing the physical and optical properties of smoke particles with the aid of an AERONET Sun photometer, an MPLNet micropulse lidar, and surface PM2.5 measurements. Elevated levels of fire activity coupled with high aerosol optical depth and PM2.5 were observed over a period of nine days. Increased variability of parameters such as aerosol optical depth, Angstrom exponent number and its fine mode equivalents all indicated high levels of fine particulate presence in the atmosphere. Smoke particle growth due to aging, coagulation and condensation mechanisms was detected during the afternoons and over several days. Retrieved lidar ratios were compatible with the presence of fine particulate within the boundary/aerosol layer. Moreover, retrieved particle size distribution as well as single scattering albedo indicated the prevalence of the fine mode particulate regime as well as particles showing enhanced levels of absorption respectively.

Deep learning-based fine-grained car make/model classification for visual surveillance

NASA Astrophysics Data System (ADS)

Gundogdu, Erhan; Parıldı, Enes Sinan; Solmaz, Berkan; Yücesoy, Veysel; Koç, Aykut

2017-10-01

Fine-grained object recognition is a potential computer vision problem that has been recently addressed by utilizing deep Convolutional Neural Networks (CNNs). Nevertheless, the main disadvantage of classification methods relying on deep CNN models is the need for considerably large amount of data. In addition, there exists relatively less amount of annotated data for a real world application, such as the recognition of car models in a traffic surveillance system. To this end, we mainly concentrate on the classification of fine-grained car make and/or models for visual scenarios by the help of two different domains. First, a large-scale dataset including approximately 900K images is constructed from a website which includes fine-grained car models. According to their labels, a state-of-the-art CNN model is trained on the constructed dataset. The second domain that is dealt with is the set of images collected from a camera integrated to a traffic surveillance system. These images, which are over 260K, are gathered by a special license plate detection method on top of a motion detection algorithm. An appropriately selected size of the image is cropped from the region of interest provided by the detected license plate location. These sets of images and their provided labels for more than 30 classes are employed to fine-tune the CNN model which is already trained on the large scale dataset described above. To fine-tune the network, the last two fully-connected layers are randomly initialized and the remaining layers are fine-tuned in the second dataset. In this work, the transfer of a learned model on a large dataset to a smaller one has been successfully performed by utilizing both the limited annotated data of the traffic field and a large scale dataset with available annotations. Our experimental results both in the validation dataset and the real field show that the proposed methodology performs favorably against the training of the CNN model from scratch.

Measure Twice, Build Once: Bench-Scale Testing to Evaluate Bioretention Media Design (Presentation)

EPA Science Inventory

Rain garden design manuals and guidelines typically recommend using native soils or engineered media that meet specifications for low content of clay, silt, fine and very fine sands, and organic matter. These characteristics promote stormwater infiltration and sorption of heavy ...

Geochemical and mineralogical interpretation of the Viking inorganic chemical results. [for Martian surface materials

NASA Technical Reports Server (NTRS)

Toulmin, P., III; Rose, H. J., Jr.; Christian, R. P.; Baird, A. K.; Evans, P. H.; Clark, B. C.; Keil, K.; Kelliher, W. C.

1977-01-01

The current status of geochemical, mineralogical, petrological interpretation of refined Viking Lander data is reviewed, and inferences that can be drawn from data on the composition of Martian surface materials are presented. The materials are dominantly fine silicate particles admixed with, or including, iron oxide particles. Both major element and trace element abundances in all samples are indicative of mafic source rocks (rather than more highly differentiated salic materials). The surface fines are nearly identical in composition at the two widely separated Lander sites, except for some lithologic diversity at the 100-m scale. This implies that some agency (presumably aeolian processes) has thoroughly homogenized them on a planetary scale. The most plausible model for the mineralogical constitution of the fine-grained surface materials at the two Lander sites is a fine-grained mixture dominated by iron-rich smectites, or their degradation products, with ferric oxides, probably including maghemite and carbonates (such as calcite), but not such less stable phases as magnesite or siderite.