Adapting a Natura 2000 field guideline for a remote sensing-based assessment of heathland conservation status

NASA Astrophysics Data System (ADS)

Schmidt, Johannes; Fassnacht, Fabian Ewald; Neff, Christophe; Lausch, Angela; Kleinschmit, Birgit; Förster, Michael; Schmidtlein, Sebastian

2017-08-01

Remote sensing can be a valuable tool for supporting nature conservation monitoring systems. However, for many areas of conservation interest, there is still a considerable gap between field-based operational monitoring guidelines and the current remote sensing-based approaches. This hampers application in practice of the latter. Here, we propose a remote sensing approach for mapping the conservation status of Calluna-dominated Natura 2000 dwarf shrub habitats that is closely related to field mapping schemes. We transferred the evaluation criteria of the field guidelines to three related variables that can be captured by remote sensing: (1) coverage of the key species, (2) stand structural diversity, and (3) co-occurring species. Continuous information on these variables was obtained by regressing ground reference data from field surveys and UAV flights against airborne hyperspectral imagery. Merging the three resulting quality layers in an RGB representation allowed for illustrating the habitat quality in a continuous way. User-defined thresholds can be applied to this stack of quality layers to derive an overall assessment of habitat quality in terms of nature conservation, i.e. the conservation status. In our study, we found good accordance of the remotely sensed data with field-based information for the three variables key species, stand structural diversity and co-occurring vegetation (R2 of 0.79, 0.69, and 0.71, respectively) and it was possible to derive meaningful habitat quality maps. The conservation status could be derived with an accuracy of 65%. In interpreting these results it should be considered that the remote sensing based layers are independent estimates of habitat quality in their own right and not a mere replacement of the criteria used in the field guidelines. The approach is thought to be transferable to similar regions with minor adaptions. Our results refer to Calluna heathland which we consider a comparably easy target for remote sensing. Hence, the transfer of field guidelines to remote sensing indicators was rather successful in this case but needs further evaluation for other habitats.

Risk profiling of schistosomiasis using remote sensing: approaches, challenges and outlook.

PubMed

Walz, Yvonne; Wegmann, Martin; Dech, Stefan; Raso, Giovanna; Utzinger, Jürg

2015-03-17

Schistosomiasis is a water-based disease that affects an estimated 250 million people, mainly in sub-Saharan Africa. The transmission of schistosomiasis is spatially and temporally restricted to freshwater bodies that contain schistosome cercariae released from specific snails that act as intermediate hosts. Our objective was to assess the contribution of remote sensing applications and to identify remaining challenges in its optimal application for schistosomiasis risk profiling in order to support public health authorities to better target control interventions. We reviewed the literature (i) to deepen our understanding of the ecology and the epidemiology of schistosomiasis, placing particular emphasis on remote sensing; and (ii) to fill an identified gap, namely interdisciplinary research that bridges different strands of scientific inquiry to enhance spatially explicit risk profiling. As a first step, we reviewed key factors that govern schistosomiasis risk. Secondly, we examined remote sensing data and variables that have been used for risk profiling of schistosomiasis. Thirdly, the linkage between the ecological consequence of environmental conditions and the respective measure of remote sensing data were synthesised. We found that the potential of remote sensing data for spatial risk profiling of schistosomiasis is - in principle - far greater than explored thus far. Importantly though, the application of remote sensing data requires a tailored approach that must be optimised by selecting specific remote sensing variables, considering the appropriate scale of observation and modelling within ecozones. Interestingly, prior studies that linked prevalence of Schistosoma infection to remotely sensed data did not reflect that there is a spatial gap between the parasite and intermediate host snail habitats where disease transmission occurs, and the location (community or school) where prevalence measures are usually derived from. Our findings imply that the potential of remote sensing data for risk profiling of schistosomiasis and other neglected tropical diseases has yet to be fully exploited.

Water Quality Variable Estimation using Partial Least Squares Regression and Multi-Scale Remote Sensing.

NASA Astrophysics Data System (ADS)

Peterson, K. T.; Wulamu, A.

2017-12-01

Water, essential to all living organisms, is one of the Earth's most precious resources. Remote sensing offers an ideal approach to monitor water quality over traditional in-situ techniques that are highly time and resource consuming. Utilizing a multi-scale approach, incorporating data from handheld spectroscopy, UAS based hyperspectal, and satellite multispectral images were collected in coordination with in-situ water quality samples for the two midwestern watersheds. The remote sensing data was modeled and correlated to the in-situ water quality variables including chlorophyll content (Chl), turbidity, and total dissolved solids (TDS) using Normalized Difference Spectral Indices (NDSI) and Partial Least Squares Regression (PLSR). The results of the study supported the original hypothesis that correlating water quality variables with remotely sensed data benefits greatly from the use of more complex modeling and regression techniques such as PLSR. The final results generated from the PLSR analysis resulted in much higher R2 values for all variables when compared to NDSI. The combination of NDSI and PLSR analysis also identified key wavelengths for identification that aligned with previous study's findings. This research displays the advantages and future for complex modeling and machine learning techniques to improve water quality variable estimation from spectral data.

Remote sensing of rangeland biodiversity

USDA-ARS?s Scientific Manuscript database

Rangelands are managed based on state and transition models for an ecological site. Transitions to alternative ecological states are indicative of degrading rangelands. Three key variables may be remotely sensed to detect transitions between alternative states: amount of bare soil, presence of inva...

Modeling and Validation of Environmental Suitability for Schistosomiasis Transmission Using Remote Sensing

PubMed Central

Walz, Yvonne; Wegmann, Martin; Dech, Stefan; Vounatsou, Penelope; Poda, Jean-Noël; N'Goran, Eliézer K.; Utzinger, Jürg; Raso, Giovanna

2015-01-01

Background Schistosomiasis is the most widespread water-based disease in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and human water contact patterns. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. We investigated the potential of remote sensing to characterize habitat conditions of parasite and intermediate host snails and discuss the relevance for public health. Methodology We employed high-resolution remote sensing data, environmental field measurements, and ecological data to model environmental suitability for schistosomiasis-related parasite and snail species. The model was developed for Burkina Faso using a habitat suitability index (HSI). The plausibility of remote sensing habitat variables was validated using field measurements. The established model was transferred to different ecological settings in Côte d’Ivoire and validated against readily available survey data from school-aged children. Principal Findings Environmental suitability for schistosomiasis transmission was spatially delineated and quantified by seven habitat variables derived from remote sensing data. The strengths and weaknesses highlighted by the plausibility analysis showed that temporal dynamic water and vegetation measures were particularly useful to model parasite and snail habitat suitability, whereas the measurement of water surface temperature and topographic variables did not perform appropriately. The transferability of the model showed significant relations between the HSI and infection prevalence in study sites of Côte d’Ivoire. Conclusions/Significance A predictive map of environmental suitability for schistosomiasis transmission can support measures to gain and sustain control. This is particularly relevant as emphasis is shifting from morbidity control to interrupting transmission. Further validation of our mechanistic model needs to be complemented by field data of parasite- and snail-related fitness. Our model provides a useful tool to monitor the development of new hotspots of potential schistosomiasis transmission based on regularly updated remote sensing data. PMID:26587839

Modeling and Validation of Environmental Suitability for Schistosomiasis Transmission Using Remote Sensing.

PubMed

Walz, Yvonne; Wegmann, Martin; Dech, Stefan; Vounatsou, Penelope; Poda, Jean-Noël; N'Goran, Eliézer K; Utzinger, Jürg; Raso, Giovanna

2015-11-01

Schistosomiasis is the most widespread water-based disease in sub-Saharan Africa. Transmission is governed by the spatial distribution of specific freshwater snails that act as intermediate hosts and human water contact patterns. Remote sensing data have been utilized for spatially explicit risk profiling of schistosomiasis. We investigated the potential of remote sensing to characterize habitat conditions of parasite and intermediate host snails and discuss the relevance for public health. We employed high-resolution remote sensing data, environmental field measurements, and ecological data to model environmental suitability for schistosomiasis-related parasite and snail species. The model was developed for Burkina Faso using a habitat suitability index (HSI). The plausibility of remote sensing habitat variables was validated using field measurements. The established model was transferred to different ecological settings in Côte d'Ivoire and validated against readily available survey data from school-aged children. Environmental suitability for schistosomiasis transmission was spatially delineated and quantified by seven habitat variables derived from remote sensing data. The strengths and weaknesses highlighted by the plausibility analysis showed that temporal dynamic water and vegetation measures were particularly useful to model parasite and snail habitat suitability, whereas the measurement of water surface temperature and topographic variables did not perform appropriately. The transferability of the model showed significant relations between the HSI and infection prevalence in study sites of Côte d'Ivoire. A predictive map of environmental suitability for schistosomiasis transmission can support measures to gain and sustain control. This is particularly relevant as emphasis is shifting from morbidity control to interrupting transmission. Further validation of our mechanistic model needs to be complemented by field data of parasite- and snail-related fitness. Our model provides a useful tool to monitor the development of new hotspots of potential schistosomiasis transmission based on regularly updated remote sensing data.

Wind erosion in semiarid landscapes: Predictive models and remote sensing methods for the influence of vegetation

NASA Technical Reports Server (NTRS)

Musick, H. Brad

1993-01-01

The objectives of this research are: to develop and test predictive relations for the quantitative influence of vegetation canopy structure on wind erosion of semiarid rangeland soils, and to develop remote sensing methods for measuring the canopy structural parameters that determine sheltering against wind erosion. The influence of canopy structure on wind erosion will be investigated by means of wind-tunnel and field experiments using structural variables identified by the wind-tunnel and field experiments using model roughness elements to simulate plant canopies. The canopy structural variables identified by the wind-tunnel and field experiments as important in determining vegetative sheltering against wind erosion will then be measured at a number of naturally vegetated field sites and compared with estimates of these variables derived from analysis of remotely sensed data.

Linking remote sensing, land cover and disease.

PubMed

Curran, P J; Atkinson, P M; Foody, G M; Milton, E J

2000-01-01

Land cover is a critical variable in epidemiology and can be characterized remotely. A framework is used to describe both the links between land cover and radiation recorded in a remotely sensed image, and the links between land cover and the disease carried by vectors. The framework is then used to explore the issues involved when moving from remotely sensed imagery to land cover and then to vector density/disease risk. This exploration highlights the role of land cover; the need to develop a sound knowledge of each link in the predictive sequence; the problematic mismatch between the spatial units of the remotely sensed and epidemiological data and the challenges and opportunities posed by adding a temporal mismatch between the remotely sensed and epidemiological data. The paper concludes with a call for both greater understanding of the physical components of the proposed framework and the utilization of optimized statistical tools as prerequisites to progress in this field.

Factors affecting the remotely sensed response of coniferous forest plantations

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

Danson, F.M.; Curran, P.J.

1993-01-01

Remote sensing of forest biophysical properties has concentrated upon forest sites with a wide range of green vegetation amount and thereby leaf area index and canopy cover. However, coniferous forest plantations, an important forest type in Europe, are managed to maintain a large amount of green vegetation with little spatial variation. Therefore, the strength of the remotely sensed signal will, it is hypothesized, be determined more by the structure of this forest than by its cover. Airborne Thematic Mapper (ATM) and SPOT-1 HRV data were used to determine the effects of this structural variation on the remotely sensed response ofmore » a coniferous forest plantation in the United Kingdom. Red and near infrared radiance were strongly and negatively correlated with a range of structural properties and with the age of the stands but weakly correlated with canopy cover. A composite variable, related to the volume of the canopy, accounted for over 75% of the variation in near infrared radiance. A simple model that related forest structural variables to the remotely sensed response was used to understand and explain this response from a coniferous forest plantation.« less

Equivalent Sensor Radiance Generation and Remote Sensing from Model Parameters. Part 1; Equivalent Sensor Radiance Formulation

NASA Technical Reports Server (NTRS)

Wind, Galina; DaSilva, Arlindo M.; Norris, Peter M.; Platnick, Steven E.

2013-01-01

In this paper we describe a general procedure for calculating equivalent sensor radiances from variables output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint the algorithm takes explicit account of the model subgrid variability, in particular its description of the probably density function of total water (vapor and cloud condensate.) The equivalent sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies. We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products.) We focus on clouds and cloud/aerosol interactions, because they are very important to model development and improvement.

Multi-sensor Cloud Retrieval Simulator and Remote Sensing from Model Parameters . Pt. 1; Synthetic Sensor Radiance Formulation; [Synthetic Sensor Radiance Formulation

NASA Technical Reports Server (NTRS)

Wind, G.; DaSilva, A. M.; Norris, P. M.; Platnick, S.

2013-01-01

In this paper we describe a general procedure for calculating synthetic sensor radiances from variable output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint, the algorithm takes explicit account of the model subgrid variability, in particular its description of the probability density function of total water (vapor and cloud condensate.) The simulated sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies.We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products). We focus on clouds because they are very important to model development and improvement.

UAV low-altitude remote sensing for precision weed management

USDA-ARS?s Scientific Manuscript database

Precision weed management, an application of precision agriculture, accounts for within-field variability of weed infestation and herbicide damage. Unmanned aerial vehicles (UAVs) provide a unique platform for remote sensing of field crops. They are more efficient and flexible than manned agricultur...

Evaluating Remotely-Sensed Surface Soil Moisture Estimates Using Triple Collocation

USDA-ARS?s Scientific Manuscript database

Recent work has demonstrated the potential of enhancing remotely-sensed surface soil moisture validation activities through the application of triple collocation techniques which compare time series of three mutually independent geophysical variable estimates in order to acquire the root-mean-square...

Remote sensing of atmospheric particulates: Technological innovation and physical limitations in applications to short-range weather prediction

NASA Technical Reports Server (NTRS)

Curran, R. J.; Kropfil, R.; Hallett, J.

1984-01-01

Techniques for remote sensing of particles, from cloud droplet to hailstone size, using optical and microwave frequencies are reviewed. The inherent variability of atmospheric particulates is examined to delineate conditions when the signal can give information to be effectively utilized in a forecasting context. The physical limitations resulting from the phase, size, orientation and concentration variability of the particulates are assessed.

A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

NASA Astrophysics Data System (ADS)

Dorigo, W. A.; Zurita-Milla, R.; de Wit, A. J. W.; Brazile, J.; Singh, R.; Schaepman, M. E.

2007-05-01

During the last 50 years, the management of agroecosystems has been undergoing major changes to meet the growing demand for food, timber, fibre and fuel. As a result of this intensified use, the ecological status of many agroecosystems has been severely deteriorated. Modeling the behavior of agroecosystems is, therefore, of great help since it allows the definition of management strategies that maximize (crop) production while minimizing the environmental impacts. Remote sensing can support such modeling by offering information on the spatial and temporal variation of important canopy state variables which would be very difficult to obtain otherwise. In this paper, we present an overview of different methods that can be used to derive biophysical and biochemical canopy state variables from optical remote sensing data in the VNIR-SWIR regions. The overview is based on an extensive literature review where both statistical-empirical and physically based methods are discussed. Subsequently, the prevailing techniques of assimilating remote sensing data into agroecosystem models are outlined. The increasing complexity of data assimilation methods and of models describing agroecosystem functioning has significantly increased computational demands. For this reason, we include a short section on the potential of parallel processing to deal with the complex and computationally intensive algorithms described in the preceding sections. The studied literature reveals that many valuable techniques have been developed both for the retrieval of canopy state variables from reflective remote sensing data as for assimilating the retrieved variables in agroecosystem models. However, for agroecosystem modeling and remote sensing data assimilation to be commonly employed on a global operational basis, emphasis will have to be put on bridging the mismatch between data availability and accuracy on one hand, and model and user requirements on the other. This could be achieved by integrating imagery with different spatial, temporal, spectral, and angular resolutions, and the fusion of optical data with data of different origin, such as LIDAR and radar/microwave.

Regional Drought Monitoring Based on Multi-Sensor Remote Sensing

NASA Astrophysics Data System (ADS)

Rhee, Jinyoung; Im, Jungho; Park, Seonyoung

2014-05-01

Drought originates from the deficit of precipitation and impacts environment including agriculture and hydrological resources as it persists. The assessment and monitoring of drought has traditionally been performed using a variety of drought indices based on meteorological data, and recently the use of remote sensing data is gaining much attention due to its vast spatial coverage and cost-effectiveness. Drought information has been successfully derived from remotely sensed data related to some biophysical and meteorological variables and drought monitoring is advancing with the development of remote sensing-based indices such as the Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Normalized Difference Water Index (NDWI) to name a few. The Scaled Drought Condition Index (SDCI) has also been proposed to be used for humid regions proving the performance of multi-sensor data for agricultural drought monitoring. In this study, remote sensing-based hydro-meteorological variables related to drought including precipitation, temperature, evapotranspiration, and soil moisture were examined and the SDCI was improved by providing multiple blends of the multi-sensor indices for different types of drought. Multiple indices were examined together since the coupling and feedback between variables are intertwined and it is not appropriate to investigate only limited variables to monitor each type of drought. The purpose of this study is to verify the significance of each variable to monitor each type of drought and to examine the combination of multi-sensor indices for more accurate and timely drought monitoring. The weights for the blends of multiple indicators were obtained from the importance of variables calculated by non-linear optimization using a Machine Learning technique called Random Forest. The case study was performed in the Republic of Korea, which has four distinct seasons over the course of the year and contains complex topography with a variety of land cover types. Remote sensing data from the Tropical Rainfall Measuring Mission satellite (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) sensors were obtained for the period from 2000 to 2012, and observation data from 99 weather stations, 441 streamflow gauges, as well as the gridded observation data from Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation of the Water Resources (APHRODITE) were obtained for validation. The objective blends of multiple indicators helped better assessment of various types of drought, and can be useful for drought early warning system. Since the improved SDCI is based on remotely sensed data, it can be easily applied to regions with limited or no observation data for drought assessment and monitoring.

Crop traceability and remote sensing in tree fruit

NASA Astrophysics Data System (ADS)

Perry, Eileen M.; Rupp, Richard; Davenport, Joan; Leal, Juliano; Pierce, Francis J.; Schulthess, Urs

2004-01-01

Fresh market fruit crops such as apples have not employed precision agriculture tools, partially due to the labor intensive nature of the cropping systems. In this paper we describe new research in the development of precision agriculture tools for tree fruit, including the ability to track spatially variable orchard data before harvest through to the packing plant. Remote sensing is a key component of this system, and remote sensing products are being evaluated for their usefulness in guiding orchard management.

Proxies for soil organic carbon derived from remote sensing

NASA Astrophysics Data System (ADS)

Rasel, S. M. M.; Groen, T. A.; Hussin, Y. A.; Diti, I. J.

2017-07-01

The possibility of carbon storage in soils is of interest because compared to vegetation it contains more carbon. Estimation of soil carbon through remote sensing based techniques can be a cost effective approach, but is limited by available methods. This study aims to develop a model based on remotely sensed variables (elevation, forest type and above ground biomass) to estimate soil carbon stocks. Field observations on soil organic carbon, species composition, and above ground biomass were recorded in the subtropical forest of Chitwan, Nepal. These variables were also estimated using LiDAR data and a WorldView 2 image. Above ground biomass was estimated from the LiDAR image using a novel approach where the image was segmented to identify individual trees, and for these trees estimates of DBH and Height were made. Based on AIC (Akaike Information Criterion) a regression model with above ground biomass derived from LiDAR data, and forest type derived from WorldView 2 imagery was selected to estimate soil organic carbon (SOC) stocks. The selected model had a coefficient of determination (R2) of 0.69. This shows the scope of estimating SOC with remote sensing derived variables in sub-tropical forests.

Monitoring spatial variations in soil organic carbon using remote sensing and geographic information systems

NASA Astrophysics Data System (ADS)

Jaber, Salahuddin M.

Soil organic carbon (SOC) sequestration is a component of larger strategies to control the accumulation of greenhouse gases that may be causing global warming. To implement this approach, it is necessary to improve the methods of measuring SOC content. Among these methods are indirect remote sensing and geographic information systems (GIS) techniques that are required to provide non-intrusive, low cost, and spatially continuous information that cover large areas on a repetitive basis. The main goal of this study is to evaluate the effects of using Hyperion hyperspectral data on improving the existing remote sensing and GIS-based methodologies for rapidly, efficiently, and accurately measuring SOC content on farmland. The study area is Big Creek Watershed (BCW) in Southern Illinois. The methodology consists of compiling a GIS database (consisting of remote sensing and soil variables) for 303 composite soil samples collected from representative pixels along the Hyperion coverage area of the watershed. Stepwise procedures were used to calibrate and validate linear multiple regression models where SOC was regarded as the response and the other remote sensing and soil variables as the predictors. Two models were selected. The first was the best all variables model and the second was the best only raster variables model. Map algebra was implemented to extrapolate the best only raster variables model and produce a SOC map for the BGW. This study concluded that Hyperion data marginally improved the predictability of the existing SOC statistical models based on multispectral satellite remote sensing sensors with correlation coefficient of 0.37 and root mean square error of 3.19 metric tons/hectare to a 15-cm depth. The total SOC pool of the study area is about 225,232 metric tons to 15-cm depth. The nonforested wetlands contained the highest SOC density (34.3 metric tons/hectare/15cm) with total SOC content of about 2,003.5 metric tons to 15-cm depth, where croplands had the lowest SOC density (21.6 metric tons/hectare/15cm) with total SOC content of about 44,571.2 metric tons to 15-cm depth.

The application of remote sensing techniques to selected inter and intra urban data acquisition problems

NASA Technical Reports Server (NTRS)

Horton, F. E.

1970-01-01

The utility of remote sensing techniques to urban data acquisition problems in several distinct areas was identified. This endeavor included a comparison of remote sensing systems for urban data collection, the extraction of housing quality data from aerial photography, utilization of photographic sensors in urban transportation studies, urban change detection, space photography utilization, and an application of remote sensing techniques to the acquisition of data concerning intra-urban commercial centers. The systematic evaluation of variable extraction for urban modeling and planning at several different scales, and the model derivation for identifying and predicting economic growth and change within a regional system of cities are also studied.

Added-values of high spatiotemporal remote sensing data in crop yield estimation

NASA Astrophysics Data System (ADS)

Gao, F.; Anderson, M. C.

2017-12-01

Timely and accurate estimation of crop yield before harvest is critical for food market and administrative planning. Remote sensing derived parameters have been used for estimating crop yield by using either empirical or crop growth models. The uses of remote sensing vegetation index (VI) in crop yield modeling have been typically evaluated at regional and country scales using coarse spatial resolution (a few hundred to kilo-meters) data or assessed over a small region at field level using moderate resolution spatial resolution data (10-100m). Both data sources have shown great potential in capturing spatial and temporal variability in crop yield. However, the added value of data with both high spatial and temporal resolution data has not been evaluated due to the lack of such data source with routine, global coverage. In recent years, more moderate resolution data have become freely available and data fusion approaches that combine data acquired from different spatial and temporal resolutions have been developed. These make the monitoring crop condition and estimating crop yield at field scale become possible. Here we investigate the added value of the high spatial and temporal VI for describing variability of crop yield. The explanatory ability of crop yield based on high spatial and temporal resolution remote sensing data was evaluated in a rain-fed agricultural area in the U.S. Corn Belt. Results show that the fused Landsat-MODIS (high spatial and temporal) VI explains yield variability better than single data source (Landsat or MODIS alone), with EVI2 performing slightly better than NDVI. The maximum VI describes yield variability better than cumulative VI. Even though VI is effective in explaining yield variability within season, the inter-annual variability is more complex and need additional information (e.g. weather, water use and management). Our findings augment the importance of high spatiotemporal remote sensing data and supports new moderate resolution satellite missions for agricultural applications.

The U.S. Geological Survey Land Remote Sensing Program

USGS Publications Warehouse

,

2007-01-01

The fundamental goals of the U.S. Geological Survey's Land Remote Sens-ing (LRS) Program are to provide the Federal Government and the public with a primary source of remotely sensed data and applications and to be a leader in defining the future of land remote sensing, nationally and internationally. Remotely sensed data provide information that enhance the understand-ing of ecosystems and the capabilities for predicting ecosystem change. The data promote an understanding of the role of the environment and wildlife in human health issues, the requirements for disaster response, the effects of climate variability, and the availability of energy and mineral resources. Also, as land satellite systems acquire global coverage, the program coordinates a network of international receiving stations and users of the data. It is the responsibility of the program to assure that data from land imaging satellites, airborne photography, radar, and other technologies are available to the national and global science communities.

Optical Constituents Along a River Mouth and Inlet: Variability and Signature in Remotely Sensed Reflectance, and: Optical Constituents at the Mouth of the Columbia River: Variability and Signature in Remotely Sensed Reflectance

DTIC Science & Technology

2013-09-30

Vision Floc Camera (MVFC), a Sequoia Scientific LISST 100x Type B, an RBR CTD, and two pressure-actuated Niskin bottles. The Niskin bottles were...Eco bb2fl, that measures 3 backscattering at 532 and 650 nm and CDOM fluorescence, a WetLabs WetStar CDOM fluorometer, a Sequoia Scientific flow

Remote sensing of land surface phenology

USGS Publications Warehouse

Meier, G.A.; Brown, Jesslyn F.

2014-01-01

Remote sensing of land-surface phenology is an important method for studying the patterns of plant and animal growth cycles. Phenological events are sensitive to climate variation; therefore phenology data provide important baseline information documenting trends in ecology and detecting the impacts of climate change on multiple scales. The USGS Remote sensing of land surface phenology program produces annually, nine phenology indicator variables at 250 m and 1,000 m resolution for the contiguous U.S. The 12 year archive is available at http://phenology.cr.usgs.gov/index.php.

Ten ways remote sensing can contribute to conservation

USGS Publications Warehouse

Rose, Robert A.; Byler, Dirck; Eastman, J. Ron; Fleishman, Erica; Geller, Gary; Goetz, Scott; Guild, Liane; Hamilton, Healy; Hansen, Matt; Headley, Rachel; Hewson, Jennifer; Horning, Ned; Kaplin, Beth A.; Laporte, Nadine; Leidner, Allison K.; Leimgruber, Peter; Morisette, Jeffrey T.; Musinsky, John; Pintea, Lilian; Prados, Ana; Radeloff, Volker C.; Rowen, Mary; Saatchi, Sassan; Schill, Steve; Tabor, Karyn; Turner, Woody; Vodacek, Anthony; Vogelmann, James; Wegmann, Martin; Wilkie, David; Wilson, Cara

2014-01-01

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners’ use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?

Ten ways remote sensing can contribute to conservation.

PubMed

Rose, Robert A; Byler, Dirck; Eastman, J Ron; Fleishman, Erica; Geller, Gary; Goetz, Scott; Guild, Liane; Hamilton, Healy; Hansen, Matt; Headley, Rachel; Hewson, Jennifer; Horning, Ned; Kaplin, Beth A; Laporte, Nadine; Leidner, Allison; Leimgruber, Peter; Morisette, Jeffrey; Musinsky, John; Pintea, Lilian; Prados, Ana; Radeloff, Volker C; Rowen, Mary; Saatchi, Sassan; Schill, Steve; Tabor, Karyn; Turner, Woody; Vodacek, Anthony; Vogelmann, James; Wegmann, Martin; Wilkie, David; Wilson, Cara

2015-04-01

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners' use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions? © 2014 Society for Conservation Biology.

Hyper-temporal remote sensing for digital soil mapping: Characterizing soil-vegetation response to climatic variability

USDA-ARS?s Scientific Manuscript database

Indices derived from remotely-sensed imagery are commonly used to predict soil properties with digital soil mapping (DSM) techniques. The use of images from single dates or a small number of dates is most common for DSM; however, selection of the appropriate images is complicated by temporal variabi...

Estimating missing hourly climatic data using artificial neural network for energy balance based ET mapping applications

USDA-ARS?s Scientific Manuscript database

Remote sensing based evapotranspiration (ET) mapping is an important improvement for water resources management. Hourly climatic data and reference ET are crucial for implementing remote sensing based ET models such as METRIC and SEBAL. In Turkey, data on all climatic variables may not be available ...

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.

Validation of Ocean Color Remote Sensing Reflectance Using Autonomous Floats

NASA Technical Reports Server (NTRS)

Gerbi, Gregory P.; Boss, Emanuel; Werdell, P. Jeremy; Proctor, Christopher W.; Haentjens, Nils; Lewis, Marlon R.; Brown, Keith; Sorrentino, Diego; Zaneveld, J. Ronald V.; Barnard, Andrew H.;

Inroads of remote sensing into hydrologic science during the WRR era

NASA Astrophysics Data System (ADS)

Lettenmaier, Dennis P.; Alsdorf, Doug; Dozier, Jeff; Huffman, George J.; Pan, Ming; Wood, Eric F.

2015-09-01

The first issue of WRR appeared eight years after the launch of Sputnik, but by WRR's 25th anniversary, only seven papers that used remote sensing had appeared. Over the journal's second 25 years, that changed remarkably, and remote sensing is now widely used in hydrology and other geophysical sciences. We attribute this evolution to production of data sets that scientists not well versed in remote sensing can use, and to educational initiatives like NASA's Earth System Science Fellowship program that has supported over a thousand scientists, many in hydrology. We review progress in remote sensing in hydrology from a water balance perspective. We argue that progress is primarily attributable to a creative use of existing and past satellite sensors to estimate such variables as evapotranspiration rates or water storage in lakes and reservoirs and to new and planned missions. Recent transforming technologies include the Gravity Recovery and Climate Experiment (GRACE), the European Soil Moisture and Ocean Salinity (SMOS) and U.S. Soil Moisture Active Passive (SMAP) missions, and the Global Precipitation Measurement (GPM) mission. Future missions include Surface Water and Ocean Topography (SWOT) to measure river discharge and lake, reservoir, and wetland storage. Measurement of some important hydrologic variables remains problematic: retrieval of snow water equivalent (SWE) from space remains elusive especially in mountain areas, even though snow cover extent is well observed, and was the topic of 4 of the first 5 remote sensing papers published in WRR. We argue that this area deserves more strategic thinking from the hydrology community.

Some Insights of Spectral Optimization in Ocean Color Inversion

NASA Technical Reports Server (NTRS)

Lee, Zhongping; Franz, Bryan; Shang, Shaoling; Dong, Qiang; Arnone, Robert

2011-01-01

In the past decades various algorithms have been developed for the retrieval of water constituents from the measurement of ocean color radiometry, and one of the approaches is spectral optimization. This approach defines an error target (or error function) between the input remote sensing reflectance and the output remote sensing reflectance, with the latter modeled with a few variables that represent the optically active properties (such as the absorption coefficient of phytoplankton and the backscattering coefficient of particles). The values of the variables when the error reach a minimum (optimization is achieved) are considered the properties that form the input remote sensing reflectance; or in other words, the equations are solved numerically. The applications of this approach implicitly assume that the error is a monotonic function of the various variables. Here, with data from numerical simulation and field measurements, we show the shape of the error surface, in a way to justify the possibility of finding a solution of the various variables. In addition, because the spectral properties could be modeled differently, impacts of such differences on the error surface as well as on the retrievals are also presented.

Remote sensing of Essential Biodiversity Variables: new measurements linking ecosystem structure, function and composition

NASA Astrophysics Data System (ADS)

Schimel, D.; Pavlick, R.; Stavros, E. N.; Townsend, P. A.; Ustin, S.; Thompson, D. R.

2017-12-01

Remote sensing can inform a wide variety of essential biodiversity variables, including measurements that define primary productivity, forest structure, biome distribution, plant communities, land use-land cover change and climate drivers of change. Emerging remote sensing technologies can add significantly to remote sensing of EBVs, providing new, large scale insights on plant and habitat diversity itself, as well as causes and consequences of biodiversity change. All current biodiversity assessments identify major data gaps, with insufficient coverage in critical regions, limited observations to monitor change over time, with very limited revisit of sample locations, as well as taxon-specific biased biases. Remote sensing cannot fill many of the gaps in global biodiversity observations, but spectroscopic measurements in terrestrial and marine environments can aid in assessing plant/phytoplankton functional diversity and efficiently reveal patterns in space, as well as changes over time, and, by making use of chlorophyll fluorescence, reveal associated patterns in photosynthesis. LIDAR and RADAR measurements quantify ecosystem structure, and can precisely define changes due to growth, disturbance and land use. Current satellite-based EBVs have taken advantage of the extraordinary time series from LANDSAT and MODIS, but new measurements more directly reveal ecosystem structure, function and composition. We will present results from pre-space airborne studies showing the synergistic ability of a suite of new remote observation techniques to quantify biodiversity and ecosystem function and show how it changes during major disturbance events.

Advances in Remote Sensing for Vegetation Dynamics and Agricultural Management

NASA Technical Reports Server (NTRS)

Tucker, Compton; Puma, Michael

2015-01-01

Spaceborne remote sensing has led to great advances in the global monitoring of vegetation. For example, the NASA Global Inventory Modeling and Mapping Studies (GIMMS) group has developed widely used datasets from the Advanced Very High Resolution Radiometer (AVHRR) sensors as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) map imagery and normalized difference vegetation index datasets. These data are valuable for analyzing vegetation trends and variability at the regional and global levels. Numerous studies have investigated such trends and variability for both natural vegetation (e.g., re-greening of the Sahel, shifts in the Eurasian boreal forest, Amazonian drought sensitivity) and crops (e.g., impacts of extremes on agricultural production). Here, a critical overview is presented on recent developments and opportunities in the use of remote sensing for monitoring vegetation and crop dynamics.

A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States.

PubMed

Beckerman, Bernardo S; Jerrett, Michael; Serre, Marc; Martin, Randall V; Lee, Seung-Jae; van Donkelaar, Aaron; Ross, Zev; Su, Jason; Burnett, Richard T

2013-07-02

Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created a model to predict ambient particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM2.5 data set included 104,172 monthly observations at 1464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM2.5, land use and traffic indicators. Normalized cross-validated R(2) values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated R(2) were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM2.5 at multiple scales over the contiguous U.S.

Remote-sensing reflectance determinations in the coastal ocean environment: impact of instrumental characteristics and environmental variability.

PubMed

Toole, D A; Siegel, D A; Menzies, D W; Neumann, M J; Smith, R C

2000-01-20

Three independent ocean color sampling methodologies are compared to assess the potential impact of instrumental characteristics and environmental variability on shipboard remote-sensing reflectance observations from the Santa Barbara Channel, California. Results indicate that under typical field conditions, simultaneous determinations of incident irradiance can vary by 9-18%, upwelling radiance just above the sea surface by 8-18%, and remote-sensing reflectance by 12-24%. Variations in radiometric determinations can be attributed to a variety of environmental factors such as Sun angle, cloud cover, wind speed, and viewing geometry; however, wind speed is isolated as the major source of uncertainty. The above-water approach to estimating water-leaving radiance and remote-sensing reflectance is highly influenced by environmental factors. A model of the role of wind on the reflected sky radiance measured by an above-water sensor illustrates that, for clear-sky conditions and wind speeds greater than 5 m/s, determinations of water-leaving radiance at 490 nm are undercorrected by as much as 60%. A data merging procedure is presented to provide sky radiance correction parameters for above-water remote-sensing reflectance estimates. The merging results are consistent with statistical and model findings and highlight the importance of multiple field measurements in developing quality coastal oceanographic data sets for satellite ocean color algorithm development and validation.

Satellite Remote Sensing: Aerosol Measurements

NASA Technical Reports Server (NTRS)

Kahn, Ralph A.

2013-01-01

Aerosols are solid or liquid particles suspended in the air, and those observed by satellite remote sensing are typically between about 0.05 and 10 microns in size. (Note that in traditional aerosol science, the term "aerosol" refers to both the particles and the medium in which they reside, whereas for remote sensing, the term commonly refers to the particles only. In this article, we adopt the remote-sensing definition.) They originate from a great diversity of sources, such as wildfires, volcanoes, soils and desert sands, breaking waves, natural biological activity, agricultural burning, cement production, and fossil fuel combustion. They typically remain in the atmosphere from several days to a week or more, and some travel great distances before returning to Earth's surface via gravitational settling or washout by precipitation. Many aerosol sources exhibit strong seasonal variability, and most experience inter-annual fluctuations. As such, the frequent, global coverage that space-based aerosol remote-sensing instruments can provide is making increasingly important contributions to regional and larger-scale aerosol studies.

Remote sensing and human health: new sensors and new opportunities.

PubMed

Beck, L R; Lobitz, B M; Wood, B L

2000-01-01

Since the launch of Landsat-1 28 years ago, remotely sensed data have been used to map features on the earth's surface. An increasing number of health studies have used remotely sensed data for monitoring, surveillance, or risk mapping, particularly of vector-borne diseases. Nearly all studies used data from Landsat, the French Système Pour l'Observation de la Terre, and the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer. New sensor systems are in orbit, or soon to be launched, whose data may prove useful for characterizing and monitoring the spatial and temporal patterns of infectious diseases. Increased computing power and spatial modeling capabilities of geographic information systems could extend the use of remote sensing beyond the research community into operational disease surveillance and control. This article illustrates how remotely sensed data have been used in health applications and assesses earth-observing satellites that could detect and map environmental variables related to the distribution of vector-borne and other diseases.

Remote sensing and human health: new sensors and new opportunities

NASA Technical Reports Server (NTRS)

Beck, L. R.; Lobitz, B. M.; Wood, B. L.

2000-01-01

Since the launch of Landsat-1 28 years ago, remotely sensed data have been used to map features on the earth's surface. An increasing number of health studies have used remotely sensed data for monitoring, surveillance, or risk mapping, particularly of vector-borne diseases. Nearly all studies used data from Landsat, the French Systeme Pour l'Observation de la Terre, and the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer. New sensor systems are in orbit, or soon to be launched, whose data may prove useful for characterizing and monitoring the spatial and temporal patterns of infectious diseases. Increased computing power and spatial modeling capabilities of geographic information systems could extend the use of remote sensing beyond the research community into operational disease surveillance and control. This article illustrates how remotely sensed data have been used in health applications and assesses earth-observing satellites that could detect and map environmental variables related to the distribution of vector-borne and other diseases.

The role of satellite remote sensing in structured ecosystem risk assessments.

PubMed

Murray, Nicholas J; Keith, David A; Bland, Lucie M; Ferrari, Renata; Lyons, Mitchell B; Lucas, Richard; Pettorelli, Nathalie; Nicholson, Emily

2018-04-01

The current set of global conservation targets requires methods for monitoring the changing status of ecosystems. Protocols for ecosystem risk assessment are uniquely suited to this task, providing objective syntheses of a wide range of data to estimate the likelihood of ecosystem collapse. Satellite remote sensing can deliver ecologically relevant, long-term datasets suitable for analysing changes in ecosystem area, structure and function at temporal and spatial scales relevant to risk assessment protocols. However, there is considerable uncertainty about how to select and effectively utilise remotely sensed variables for risk assessment. Here, we review the use of satellite remote sensing for assessing spatial and functional changes of ecosystems, with the aim of providing guidance on the use of these data in ecosystem risk assessment. We suggest that decisions on the use of satellite remote sensing should be made a priori and deductively with the assistance of conceptual ecosystem models that identify the primary indicators representing the dynamics of a focal ecosystem. Copyright © 2017 Elsevier B.V. All rights reserved.

Restoration of color in a remote sensing image and its quality evaluation

NASA Astrophysics Data System (ADS)

Zhang, Zuxun; Li, Zhijiang; Zhang, Jianqing; Wang, Zhihe

2003-09-01

This paper is focused on the restoration of color remote sensing (including airborne photo). A complete approach is recommended. It propose that two main aspects should be concerned in restoring a remote sensing image, that are restoration of space information, restoration of photometric information. In this proposal, the restoration of space information can be performed by making the modulation transfer function (MTF) as degradation function, in which the MTF is obtained by measuring the edge curve of origin image. The restoration of photometric information can be performed by improved local maximum entropy algorithm. What's more, a valid approach in processing color remote sensing image is recommended. That is splits the color remote sensing image into three monochromatic images which corresponding three visible light bands and synthesizes the three images after being processed separately with psychological color vision restriction. Finally, three novel evaluation variables are obtained based on image restoration to evaluate the image restoration quality in space restoration quality and photometric restoration quality. An evaluation is provided at last.

Remote-sensing based approach to forecast habitat quality under climate change scenarios.

PubMed

Requena-Mullor, Juan M; López, Enrique; Castro, Antonio J; Alcaraz-Segura, Domingo; Castro, Hermelindo; Reyes, Andrés; Cabello, Javier

2017-01-01

As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071-2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological responses under climate change scenarios.

Remote-sensing based approach to forecast habitat quality under climate change scenarios

PubMed Central

Requena-Mullor, Juan M.; López, Enrique; Castro, Antonio J.; Alcaraz-Segura, Domingo; Castro, Hermelindo; Reyes, Andrés; Cabello, Javier

2017-01-01

As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071–2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological responses under climate change scenarios. PMID:28257501

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

EPA Science Inventory

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

Monitoring water quality by remote sensing

NASA Technical Reports Server (NTRS)

Brown, R. L. (Principal Investigator)

1977-01-01

The author has identified the following significant results. A limited study was conducted to determine the applicability of remote sensing for evaluating water quality conditions in the San Francisco Bay and delta. Considerable supporting data were available for the study area from other than overflight sources, but short-term temporal and spatial variability precluded their use. The study results were not sufficient to shed much light on the subject, but it did appear that, with the present state of the art in image analysis and the large amount of ground truth needed, remote sensing has only limited application in monitoring water quality.

Effects of 4D-Var data assimilation using remote sensing precipitation products in a WRF over the complex Heihe River Basin

NASA Astrophysics Data System (ADS)

Pan, Xiaoduo; Li, Xin; Cheng, Guodong

2017-04-01

Traditionally, ground-based, in situ observations, remote sensing, and regional climate modeling, individually, cannot provide the high-quality precipitation data required for hydrological prediction, especially over complex terrain. Data assimilation techniques are often used to assimilate ground observations and remote sensing products into models for dynamic downscaling. In this study, the Weather Research and Forecasting (WRF) model was used to assimilate two satellite precipitation products (TRMM 3B42 and FY-2D) using the 4D-Var data assimilation method. The results show that the assimilation of remote sensing precipitation products can improve the initial WRF fields of humidity and temperature, thereby improving precipitation forecasting and decreasing the spin-up time. Hence, assimilating TRMM and FY-2D remote sensing precipitation products using WRF 4D-Var can be viewed as a positive step toward improving the accuracy and lead time of numerical weather prediction models, particularly for short-term weather forecasting. Future work is proposed to assimilate a suite of remote sensing data, e.g., the combination of precipitation and soil moisture data, into a WRF model to improve 7-8 day forecasts of precipitation and other atmospheric variables.

Surveillance of Arthropod Vector-Borne Infectious Diseases Using Remote Sensing Techniques: A Review

PubMed Central

Kalluri, Satya; Gilruth, Peter; Rogers, David; Szczur, Martha

2007-01-01

Epidemiologists are adopting new remote sensing techniques to study a variety of vector-borne diseases. Associations between satellite-derived environmental variables such as temperature, humidity, and land cover type and vector density are used to identify and characterize vector habitats. The convergence of factors such as the availability of multi-temporal satellite data and georeferenced epidemiological data, collaboration between remote sensing scientists and biologists, and the availability of sophisticated, statistical geographic information system and image processing algorithms in a desktop environment creates a fertile research environment. The use of remote sensing techniques to map vector-borne diseases has evolved significantly over the past 25 years. In this paper, we review the status of remote sensing studies of arthropod vector-borne diseases due to mosquitoes, ticks, blackflies, tsetse flies, and sandflies, which are responsible for the majority of vector-borne diseases in the world. Examples of simple image classification techniques that associate land use and land cover types with vector habitats, as well as complex statistical models that link satellite-derived multi-temporal meteorological observations with vector biology and abundance, are discussed here. Future improvements in remote sensing applications in epidemiology are also discussed. PMID:17967056

A Multi-Temporal Remote Sensing Approach to Freshwater Turtle Conservation

NASA Astrophysics Data System (ADS)

Mui, Amy B.

Freshwater turtles are a globally declining taxa, and estimates of population status are not available for many species. Primary causes of decline stem from widespread habitat loss and degradation, and obtaining spatially-explicit information on remaining habitat across a relevant spatial scale has proven challenging. The discipline of remote sensing science has been employed widely in studies of biodiversity conservation, but it has not been utilized as frequently for cryptic, and less vagile species such as turtles, despite their vulnerable status. The work presented in this thesis investigates how multi-temporal remote sensing imagery can contribute key information for building spatially-explicit and temporally dynamic models of habitat and connectivity for the threatened, Blanding's turtle (Emydoidea blandingii) in southern Ontario, Canada. I began with outlining a methodological approach for delineating freshwater wetlands from high spatial resolution remote sensing imagery, using a geographic object-based image analysis (GEOBIA) approach. This method was applied to three different landscapes in southern Ontario, and across two biologically relevant seasons during the active (non-hibernating) period of Blanding's turtles. Next, relevant environmental variables associated with turtle presence were extracted from remote sensing imagery, and a boosted regression tree model was developed to predict the probability of occurrence of this species. Finally, I analysed the movement potential for Blanding's turtles in a disturbed landscape using a combination of approaches. Results indicate that (1) a parsimonious GEOBIA approach to land cover mapping, incorporating texture, spectral indices, and topographic information can map heterogeneous land cover with high accuracy, (2) remote-sensing derived environmental variables can be used to build habitat models with strong predictive power, and (3) connectivity potential is best estimated using a variety of approaches, though accurate estimates across human-altered landscapes is challenging. Overall, this body of work supports the use of remote sensing imagery in species distribution models to strengthen the precision, and power of predictive models, and also draws attention to the need to consider a multi-temporal examination of species habitat requirements.

Contrasting seasonality in optical-biogeochemical properties of the Baltic Sea.

PubMed

Simis, Stefan G H; Ylöstalo, Pasi; Kallio, Kari Y; Spilling, Kristian; Kutser, Tiit

2017-01-01

Optical-biogeochemical relationships of particulate and dissolved organic matter are presented in support of remote sensing of the Baltic Sea pelagic. This system exhibits strong seasonality in phytoplankton community composition and wide gradients of chromophoric dissolved organic matter (CDOM), properties which are poorly handled by existing remote sensing algorithms. Absorption and scattering properties of particulate matter reflected the seasonality in biological (phytoplankton succession) and physical (thermal stratification) processes. Inherent optical properties showed much wider variability when normalized to the chlorophyll-a concentration compared to normalization to either total suspended matter dry weight or particulate organic carbon. The particle population had the largest optical variability in summer and was dominated by organic matter in both seasons. The geographic variability of CDOM and relationships with dissolved organic carbon (DOC) are also presented. CDOM dominated light absorption at blue wavelengths, contributing 81% (median) of the absorption by all water constituents at 400 nm and 63% at 442 nm. Consequentially, 90% of water-leaving radiance at 412 nm originated from a layer (z90) no deeper than approximately 1.0 m. With water increasingly attenuating light at longer wavelengths, a green peak in light penetration and reflectance is always present in these waters, with z90 up to 3.0-3.5 m depth, whereas z90 only exceeds 5 m at biomass < 5 mg Chla m-3. High absorption combined with a weakly scattering particle population (despite median phytoplankton biomass of 14.1 and 4.3 mg Chla m-3 in spring and summer samples, respectively), characterize this sea as a dark water body for which dedicated or exceptionally robust remote sensing techniques are required. Seasonal and regional optical-biogeochemical models, data distributions, and an extensive set of simulated remote-sensing reflectance spectra for testing of remote sensing algorithms are provided as supplementary data.

Modeling Atmospheric CO2 Processes to Constrain the Missing Sink

NASA Technical Reports Server (NTRS)

Kawa, S. R.; Denning, A. S.; Erickson, D. J.; Collatz, J. C.; Pawson, S.

2005-01-01

We report on a NASA supported modeling effort to reduce uncertainty in carbon cycle processes that create the so-called missing sink of atmospheric CO2. Our overall objective is to improve characterization of CO2 source/sink processes globally with improved formulations for atmospheric transport, terrestrial uptake and release, biomass and fossil fuel burning, and observational data analysis. The motivation for this study follows from the perspective that progress in determining CO2 sources and sinks beyond the current state of the art will rely on utilization of more extensive and intensive CO2 and related observations including those from satellite remote sensing. The major components of this effort are: 1) Continued development of the chemistry and transport model using analyzed meteorological fields from the Goddard Global Modeling and Assimilation Office, with comparison to real time data in both forward and inverse modes; 2) An advanced biosphere model, constrained by remote sensing data, coupled to the global transport model to produce distributions of CO2 fluxes and concentrations that are consistent with actual meteorological variability; 3) Improved remote sensing estimates for biomass burning emission fluxes to better characterize interannual variability in the atmospheric CO2 budget and to better constrain the land use change source; 4) Evaluating the impact of temporally resolved fossil fuel emission distributions on atmospheric CO2 gradients and variability. 5) Testing the impact of existing and planned remote sensing data sources (e.g., AIRS, MODIS, OCO) on inference of CO2 sources and sinks, and use the model to help establish measurement requirements for future remote sensing instruments. The results will help to prepare for the use of OCO and other satellite data in a multi-disciplinary carbon data assimilation system for analysis and prediction of carbon cycle changes and carbodclimate interactions.

Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria

USGS Publications Warehouse

Stumpf, Rick P; Davis, Timothy W.; Wynne, Timothy T.; Graham, Jennifer L.; Loftin, Keith A.; Johengen, T.H.; Gossiaux, D.; Palladino, D.; Burtner, A.

2016-01-01

Using satellite imagery to quantify the spatial patterns of cyanobacterial toxins has several challenges. These challenges include the need for surrogate pigments – since cyanotoxins cannot be directly detected by remote sensing, the variability in the relationship between the pigments and cyanotoxins – especially microcystins (MC), and the lack of standardization of the various measurement methods. A dual-model strategy can provide an approach to address these challenges. One model uses either chlorophyll-a (Chl-a) or phycocyanin (PC) collected in situ as a surrogate to estimate the MC concentration. The other uses a remote sensing algorithm to estimate the concentration of the surrogate pigment. Where blooms are mixtures of cyanobacteria and eukaryotic algae, PC should be the preferred surrogate to Chl-a. Where cyanobacteria dominate, Chl-a is a better surrogate than PC for remote sensing. Phycocyanin is less sensitive to detection by optical remote sensing, it is less frequently measured, PC laboratory methods are still not standardized, and PC has greater intracellular variability. Either pigment should not be presumed to have a fixed relationship with MC for any water body. The MC-pigment relationship can be valid over weeks, but have considerable intra- and inter-annual variability due to changes in the amount of MC produced relative to cyanobacterial biomass. To detect pigments by satellite, three classes of algorithms (analytic, semi-analytic, and derivative) have been used. Analytical and semi-analytical algorithms are more sensitive but less robust than derivatives because they depend on accurate atmospheric correction; as a result derivatives are more commonly used. Derivatives can estimate Chl-a concentration, and research suggests they can detect and possibly quantify PC. Derivative algorithms, however, need to be standardized in order to evaluate the reproducibility of parameterizations between lakes. A strategy for producing useful estimates of microcystins from cyanobacterial biomass is described, provided cyanotoxin variability is addressed.

Assessing Wetland Hydroperiod and Soil Moisture with Remote Sensing: A Demonstration for the NASA Plum Brook Station Year 2

NASA Technical Reports Server (NTRS)

Brooks, Colin; Bourgeau-Chavez, Laura; Endres, Sarah; Battaglia, Michael; Shuchman, Robert

2015-01-01

Assist with the evaluation and measuring of wetlands hydroperiod at the Plum Brook Station using multi-source remote sensing data as part of a larger effort on projecting climate change-related impacts on the station's wetland ecosystems. MTRI expanded on the multi-source remote sensing capabilities to help estimate and measure hydroperiod and the relative soil moisture of wetlands at NASA's Plum Brook Station. Multi-source remote sensing capabilities are useful in estimating and measuring hydroperiod and relative soil moisture of wetlands. This is important as a changing regional climate has several potential risks for wetland ecosystem function. The year two analysis built on the first year of the project by acquiring and analyzing remote sensing data for additional dates and types of imagery, combined with focused field work. Five deliverables were planned and completed: (1) Show the relative length of hydroperiod using available remote sensing datasets, (2) Date linked table of wetlands extent over time for all feasible non-forested wetlands, (3) Utilize LIDAR data to measure topographic height above sea level of all wetlands, wetland to catchment area radio, slope of wetlands, and other useful variables (4), A demonstration of how analyzed results from multiple remote sensing data sources can help with wetlands vulnerability assessment; and (5) A MTRI style report summarizing year 2 results.

VARIABILITY IN NET PRIMARY PRODUCTION AND CARBON STORAGE IN BIOMASS ACROSS OREGON FORESTS - AN ASSESSMENT INTEGRATING DATA FROM FOREST INVENTORIES, INTENSIVE SITES, AND REMOTE SENSING. (R828309)

EPA Science Inventory

We used a combination of data from USDA Forest Service inventories, intensive
chronosequences, extensive sites, and satellite remote sensing, to estimate biomass
and net primary production (NPP) for the forested region of western Oregon. The
study area was divided int...

Boundary Layer Remote Sensing with Combined Active and Passive Techniques: GPS Radio Occultation and High-Resolution Stereo Imaging (WindCam) Small Satellite Concept

NASA Technical Reports Server (NTRS)

Mannucci, A.J.; Wu, D.L.; Teixeira, J.; Ao, C.O.; Xie, F.; Diner, D.J.; Wood, R.; Turk, Joe

2012-01-01

Objective: significant progress in understanding low-cloud boundary layer processes. This is the Single largest uncertainty in climate projections. Radio occultation has unique features suited to boundary layer remote sensing (1) Cloud penetrating (2) Very high vertical resolution (approximately 50m-100m) (3) Sensitivity to thermodynamic variables

Time Series Remote Sensing in Monitoring the Spatio-Temporal Dynamics of Plant Invasions: A Study of Invasive Saltcedar (Tamarix Spp.)

NASA Astrophysics Data System (ADS)

Diao, Chunyuan

In today's big data era, the increasing availability of satellite and airborne platforms at various spatial and temporal scales creates unprecedented opportunities to understand the complex and dynamic systems (e.g., plant invasion). Time series remote sensing is becoming more and more important to monitor the earth system dynamics and interactions. To date, most of the time series remote sensing studies have been conducted with the images acquired at coarse spatial scale, due to their relatively high temporal resolution. The construction of time series at fine spatial scale, however, is limited to few or discrete images acquired within or across years. The objective of this research is to advance the time series remote sensing at fine spatial scale, particularly to shift from discrete time series remote sensing to continuous time series remote sensing. The objective will be achieved through the following aims: 1) Advance intra-annual time series remote sensing under the pure-pixel assumption; 2) Advance intra-annual time series remote sensing under the mixed-pixel assumption; 3) Advance inter-annual time series remote sensing in monitoring the land surface dynamics; and 4) Advance the species distribution model with time series remote sensing. Taking invasive saltcedar as an example, four methods (i.e., phenological time series remote sensing model, temporal partial unmixing method, multiyear spectral angle clustering model, and time series remote sensing-based spatially explicit species distribution model) were developed to achieve the objectives. Results indicated that the phenological time series remote sensing model could effectively map saltcedar distributions through characterizing the seasonal phenological dynamics of plant species throughout the year. The proposed temporal partial unmixing method, compared to conventional unmixing methods, could more accurately estimate saltcedar abundance within a pixel by exploiting the adequate temporal signatures of saltcedar. The multiyear spectral angle clustering model could guide the selection of the most representative remotely sensed image for repetitive saltcedar mapping over space and time. Through incorporating spatial autocorrelation, the species distribution model developed in the study could identify the suitable habitats of saltcedar at a fine spatial scale and locate appropriate areas at high risk of saltcedar infestation. Among 10 environmental variables, the distance to the river and the phenological attributes summarized by the time series remote sensing were regarded as the most important. These methods developed in the study provide new perspectives on how the continuous time series can be leveraged under various conditions to investigate the plant invasion dynamics.

Spatial Irrigation Management Using Remote Sensing Water Balance Modeling and Soil Water Content Monitoring

NASA Astrophysics Data System (ADS)

Barker, J. Burdette

Spatially informed irrigation management may improve the optimal use of water resources. Sub-field scale water balance modeling and measurement were studied in the context of irrigation management. A spatial remote-sensing-based evapotranspiration and soil water balance model was modified and validated for use in real-time irrigation management. The modeled ET compared well with eddy covariance data from eastern Nebraska. Placement and quantity of sub-field scale soil water content measurement locations was also studied. Variance reduction factor and temporal stability were used to analyze soil water content data from an eastern Nebraska field. No consistent predictor of soil water temporal stability patterns was identified. At least three monitoring locations were needed per irrigation management zone to adequately quantify the mean soil water content. The remote-sensing-based water balance model was used to manage irrigation in a field experiment. The research included an eastern Nebraska field in 2015 and 2016 and a western Nebraska field in 2016 for a total of 210 plot-years. The response of maize and soybean to irrigation using variations of the model were compared with responses from treatments using soil water content measurement and a rainfed treatment. The remote-sensing-based treatment prescribed more irrigation than the other treatments in all cases. Excessive modeled soil evaporation and insufficient drainage times were suspected causes of the model drift. Modifying evaporation and drainage reduced modeled soil water depletion error. None of the included response variables were significantly different between treatments in western Nebraska. In eastern Nebraska, treatment differences for maize and soybean included evapotranspiration and a combined variable including evapotranspiration and deep percolation. Both variables were greatest for the remote-sensing model when differences were found to be statistically significant. Differences in maize yield in 2015 were attributed to random error. Soybean yield was lowest for the remote-sensing-based treatment and greatest for rainfed, possibly because of overwatering and lodging. The model performed well considering that it did not include soil water content measurements during the season. Future work should improve the soil evaporation and drainage formulations, because of excessive precipitation and include aerial remote sensing imagery and soil water content measurement as model inputs.

Dissolved organic carbon and its potential predictors in eutrophic lakes.

PubMed

Toming, Kaire; Kutser, Tiit; Tuvikene, Lea; Viik, Malle; Nõges, Tiina

2016-10-01

Understanding of the true role of lakes in the global carbon cycle requires reliable estimates of dissolved organic carbon (DOC) and there is a strong need to develop remote sensing methods for mapping lake carbon content at larger regional and global scales. Part of DOC is optically inactive. Therefore, lake DOC content cannot be mapped directly. The objectives of the current study were to estimate the relationships of DOC and other water and environmental variables in order to find the best proxy for remote sensing mapping of lake DOC. The Boosted Regression Trees approach was used to clarify in which relative proportions different water and environmental variables determine DOC. In a studied large and shallow eutrophic lake the concentrations of DOC and coloured dissolved organic matter (CDOM) were rather high while the seasonal and interannual variability of DOC concentrations was small. The relationships between DOC and other water and environmental variables varied seasonally and interannually and it was challenging to find proxies for describing seasonal cycle of DOC. Chlorophyll a (Chl a), total suspended matter and Secchi depth were correlated with DOC and therefore are possible proxies for remote sensing of seasonal changes of DOC in ice free period, while for long term interannual changes transparency-related variables are relevant as DOC proxies. CDOM did not appear to be a good predictor of the seasonality of DOC concentration in Lake Võrtsjärv since the CDOM-DOC coupling varied seasonally. However, combining the data from Võrtsjärv with the published data from six other eutrophic lakes in the world showed that CDOM was the most powerful predictor of DOC and can be used in remote sensing of DOC concentrations in eutrophic lakes. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

Modeling α- and β-diversity in a tropical forest from remotely sensed and spatial data

NASA Astrophysics Data System (ADS)

Hernández-Stefanoni, J. Luis; Gallardo-Cruz, J. Alberto; Meave, Jorge A.; Rocchini, Duccio; Bello-Pineda, Javier; López-Martínez, J. Omar

2012-10-01

Comprehensive information on species distribution and species composition patterns of plant communities is required for effective conservation and management of biodiversity. Remote sensing offers an inexpensive means of attaining complete spatial coverage for large areas, at regular time intervals, and can therefore be extremely useful for estimating both species richness and spatial variation of species composition (α- and β-diversity). An essential step to map such attributes is to identify and understand their main drivers. We used remotely sensed data as a surrogate of plant productivity and habitat structure variables for explaining α- and β-diversity, and evaluated the relative roles of productivity-habitat structure and spatial variables in explaining observed patterns of α- and β-diversity by using a Principal Coordinates of Neighbor Matrices analysis. We also examined the relationship between remotely sensed and field data, in order to map α- and β-diversity at the landscape-level in the Yucatan Peninsula, using a regression kriging procedure. These two procedures integrate the relationship of species richness and spatial species turnover both with remotely sensed data and spatial structure. The empirical models so obtained can be used to predict species richness and variation in species composition, and they can be regarded as valuable tools not only for identifying areas with high local species richness (α-diversity), but also areas with high species turnover (β-diversity). Ultimately, information obtained in this way can help maximize the number of species preserved in a landscape.

Hydrologic Remote Sensing and Land Surface Data Assimilation.

PubMed

Moradkhani, Hamid

2008-05-06

Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface-atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear updating rule and assumption of jointly normal distribution of errors in state variables and observation.

Influence of spatial variability of hydraulic characteristics of soils on surface parameters obtained from remote sensing data in infrared and microwaves

NASA Technical Reports Server (NTRS)

Brunet, Y.; Vauclin, M.

1985-01-01

The correct interpretation of thermal and hydraulic soil parameters infrared from remotely sensed data (thermal infrared, microwaves) implies a good understanding of the causes of their temporal and spatial variability. Given this necessity, the sensitivity of the surface variables (temperature, moisture) to the spatial variability of hydraulic soil properties is tested with a numerical model of heat and mass transfer between bare soil and atmosphere. The spatial variability of hydraulic soil properties is taken into account in terms of the scaling factor. For a given soil, the knowledge of its frequency distribution allows a stochastic use of the model. The results are treated statistically, and the part of the variability of soil surface parameters due to that of soil hydraulic properties is evaluated quantitatively.

Southern hemisphere low level wind circulation statistics from the Seasat scatterometer

NASA Technical Reports Server (NTRS)

Levy, Gad

1994-01-01

Analyses of remotely sensed low-level wind vector data over the Southern Ocean are performed. Five-day averages and monthly means are created and the month-to-month variability during the winter (July-September) of 1978 is investigated. The remotely sensed winds are compared to the Australian Bureau of Meteorology (ABM) and the National Meteorological Center (NMC) surface analyses. In southern latitudes the remotely sensed winds are stronger than what the weather services' analyses suggest, indicating under-estimation by ABM and NMC in these regions. The evolution of the low-level jet and the major stormtracks during the season are studied and different flow regimes are identified. The large-scale variability of the meridional flow is studied with the aid of empirical orthogonal function (EOF) analysis. The dominance of quasi-stationary wave numbers 3,4, and 5 in the winter flows is evident in both the EOF analysis and the mean flow. The signature of an exceptionally strong blocking situation is evident in July and the special conditions leading to it are discussed. A very large intraseasonal variability with different flow regimes at different months is documented.

[An operational remote sensing algorithm of land surface evapotranspiration based on NOAA PAL dataset].

PubMed

Hou, Ying-Yu; He, Yan-Bo; Wang, Jian-Lin; Tian, Guo-Liang

2009-10-01

Based on the time series 10-day composite NOAA Pathfinder AVHRR Land (PAL) dataset (8 km x 8 km), and by using land surface energy balance equation and "VI-Ts" (vegetation index-land surface temperature) method, a new algorithm of land surface evapotranspiration (ET) was constructed. This new algorithm did not need the support from meteorological observation data, and all of its parameters and variables were directly inversed or derived from remote sensing data. A widely accepted ET model of remote sensing, i. e., SEBS model, was chosen to validate the new algorithm. The validation test showed that both the ET and its seasonal variation trend estimated by SEBS model and our new algorithm accorded well, suggesting that the ET estimated from the new algorithm was reliable, being able to reflect the actual land surface ET. The new ET algorithm of remote sensing was practical and operational, which offered a new approach to study the spatiotemporal variation of ET in continental scale and global scale based on the long-term time series satellite remote sensing images.

The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): A comparison of photo-interpreted, Landsat-based, and lidar-based habitat maps

Treesearch

Steven H. Ackers; Raymond J. Davis; Keith A. Olsen; Katie M. Dugger

2015-01-01

Wildlife habitat mapping has evolved at a rapid pace over the last fewdecades. Beginning with simple, often subjective, hand-drawn maps, habitat mapping now involves complex species distribution models (SDMs) using mapped predictor variables derived from remotely sensed data. For species that inhabit large geographic areas, remote sensing technology is often...

Methods for georectification and spectral scaling of remote imagery using ArcView, ArcGIS, and ENVI

USDA-ARS?s Scientific Manuscript database

Remote sensing images can be used to support variable-rate (VR) application of material from aircraft. Geographic coordinates must be assigned to an image (georeferenced) so that the variable-rate system can determine where in the field to apply these inputs and adjust the system when a zone has bee...

Methods for Georeferencing and Spectral Scaling of Remote Imagery using ArcView, ArcGIS, and ENVI

USDA-ARS?s Scientific Manuscript database

Remote sensing images can be used to support variable-rate (VR) application of material from aircraft. Geographic coordinates must be assigned to an image (georeferenced) so that the variable-rate system can determine where in the field to apply these inputs and adjust the system when a zone has bee...

Airborne multispectral remote sensing data to estimate several oenological parameters in vineyard production. A case study of application of remote sensing data to precision viticulture in central Italy.

NASA Astrophysics Data System (ADS)

Tramontana, Gianluca; Girard, Filippo; Belli, Claudio; Comandini, Maria Cristina; Pietromarchi, Paolo; Tiberi, Domenico; Papale, Dario

2010-05-01

It is widely recognized that environmental differences within the vineyard, with respect to soils, microclimate, and topography, can influence grape characteristics and crop yields. Besides, the central Italy landscape is characterized by a high level of fragmentation and heterogeneity It requires stringent Remote sensing technical features in terms of spectral, geometric and temporal resolution to aimed at supporting applications for precision viticulture. In response to the needs of the Italian grape and wine industry for an evaluation of precision viticulture technologies, the DISAFRI (University of Tuscia) and the Agricultural Research Council - Oenological research unit (ENC-CRA) jointly carried out an experimental study during the year 2008. The study was carried out on 2 areas located in the town of Velletri, near Rome; for each area, two varieties (red and white grape) were studied: Nero d'Avola and Sauvignon blanc in first area , Merlot and Sauvignon blanc in second. Remote sensing data were acquired in different periods using a low cost multisensor Airborne remote sensing platform developed by DISAFRI (ASPIS-2 Advanced Spectroscopic Imager System). ASPIS-2, an evolution of the ASPIS sensor (Papale et al 2008, Sensors), is a multispectral sensor based on 4 CCD and 3 interferential filters per CCD. The filters are user selectable during the flight and in this way Aspis is able to acquire data in 12 bands in the visible and near infrared regions with a bandwidth of 10 or 20 nm. To the purposes of this study 7 spectral band were acquired and 15 vegetation indices calculated. During the ripeness period several vegetative and oenochemical parameters were monitored. Anova test shown that several oenochemical variables, such as sugars, total acidity, polyphenols and anthocyanins differ according to the variety taken into consideration. In order to evaluate the time autocorrelation of several oenological parameters value, a simple linear regression between oenological variables monitored during the season and have been carried out. This statistical analysis shown a significant time autocorrelation of series in particular for sugar content and total acidity. In order to estimate the empirical relationships between the oenochemical parameters acquired during the ripeness period and the remote sensing variables, a simple regression analysis has been carried out. The remotely sensed data were significantly correlated with the following oenochemical parameters: Leaf Surface Exposed (SFE) (correlation coefficient R2 ~ 0.8), wood pruning (R2 ~ 0.8), reducing sugars (R2 ~ 0.6 and Root Mean Square Error ~ 5g/l), acidity holder (R2 ~ 0.6 and RMSE ~ 0.5g/l), polyphenols content (R2 ~ 0.9) and Anthocyanins (R2 ~ 0.89). Vegetation index that showed better relationship with oenological variables was MCARI1 (1.2*[2.5*(R800- R670)-1.3*(R800- R550)]. This study demonstrates that the low cost airborne multispectral remote sensing systems like ASPIS can support the precision viticulture. The empirical relationships between oenological parameters and remote sensing data can be applied to obtain thematic and predictive maps. These maps will be simple and effective tools to guide producers in differentiating harvest and winemaking and to improve the quality of wine production.

Integration of Remote Sensing Data In Operational Flood Forecast In Southwest Germany

NASA Astrophysics Data System (ADS)

Bach, H.; Appel, F.; Schulz, W.; Merkel, U.; Ludwig, R.; Mauser, W.

Methods to accurately assess and forecast flood discharge are mandatory to minimise the impact of hydrological hazards. However, existing rainfall-runoff models rarely accurately consider the spatial characteristics of the watershed, which is essential for a suitable and physics-based description of processes relevant for runoff formation. Spatial information with low temporal variability like elevation, slopes and land use can be mapped or extracted from remote sensing data. However, land surface param- eters of high temporal variability, like soil moisture and snow properties are hardly available and used in operational forecasts. Remote sensing methods can improve flood forecast by providing information on the actual water retention capacities in the watershed and facilitate the regionalisation of hydrological models. To prove and demonstrate this, the project 'InFerno' (Integration of remote sensing data in opera- tional water balance and flood forecast modelling) has been set up, funded by DLR (50EE0053). Within InFerno remote sensing data (optical and microwave) are thor- oughly processed to deliver spatially distributed parameters of snow properties and soil moisture. Especially during the onset of a flood this information is essential to estimate the initial conditions of the model. At the flood forecast centres of 'Baden- Württemberg' and 'Rheinland-Pfalz' (Southwest Germany) the remote sensing based maps on soil moisture and snow properties will be integrated in the continuously op- erated water balance and flood forecast model LARSIM. The concept is to transfer the developed methodology from the Neckar to the Mosel basin. The major challenges lie on the one hand in the implementation of algorithms developed for a multisensoral synergy and the creation of robust, operationally applicable remote sensing products. On the other hand, the operational flood forecast must be adapted to make full use of the new data sources. In the operational phase of the project ESA's ENVISAT satellite, which will be launched in 2002, will serve as remote sensing data source. Until EN- VISAT data is available, algorithm retrieval, software development and product gener- ation is performed using existing sensors with ENVISAT-like specifications. Based on these data sets test cases and demonstration runs are conducted and will be presented to prove the advantages of the approach.

Controlling Malaria and Other Diseases Using Remote Sensing

NASA Technical Reports Server (NTRS)

Kiang, Richard K.; Wharton, Stephen W. (Technical Monitor)

2001-01-01

Remote sensing offers the vantage of monitoring a vast area of the Earth continuously. Once developed and launched, a satellite gives years of service in collecting data from the land, the oceans, and the atmosphere. Since the 1980s, attempts have been made to relate disease occurrence with remotely sensed environmental and geophysical parameters, using data from Landsat, SPOT, AVHRR, and other satellites. With higher spatial resolution, the recent satellite sensors provide a new outlook for disease control. At sub-meter to I 10m resolution, surface types associated with disease carriers can be identified more accurately. The Ikonos panchromatic sensor with I m resolution, and the Advanced Land Imager with 1 Om resolution on the newly launched Earth Observing-1, both have displayed remarkable mapping capabilities. In addition, an entire array of geophysical parameters can now be measured or inferred from various satellites. Airborne remote sensing, with less concerns on instrument weight, size, and power consumption, also offers a low-cost alternative for regional applications. NASA/GSFC began to collaborate with the Mahidol University on malaria and filariasis control using remote sensing in late 2000. The objectives are: (1) To map the breeding sites for the major vector species; (2) To identify the potential sites for larvicide and insecticide applications; (3) To explore the linkage of vector population and transmission intensity to environmental variables; (4) To monitor the impact of climate change and human activities on vector population and transmission; and (5) To develop a predictive model for disease distribution. Field studies are being conducted in several provinces in Thailand. Data analyses will soon begin. Malaria data in South Korea are being used as surrogates for developing classification techniques. GIS has been shown to be invaluable in making the voluminous remote sensing data more readily understandable. It will be used throughout this study to clearly demonstrate the spatial relationship between the disease intensities, geophysical variables, and socioeconomic parameters. Asides from malaria and filariasis, application of remote sensing to the control of other diseases have been vigorously pursued by NASA's Environment and Health Initiative. The current program includes projects on Rift Valley fever, St. Louis encephalitis, dengue fever, ebola, African dust and diseases, meningitis, asthma, bartonellosis, cholera, and urban health concerns. Results from these projects indicate that remote sensing will play an increasingly important role in disease control in the future.

A Prototype Hydrologic Observatory for the Neuse River Basin Using Remote Sensing Data as a Part of the CUAHSI-HIS Effort

NASA Astrophysics Data System (ADS)

Kanwar, R.; Narayan, U.; Lakshmi, V.

2005-12-01

Remote sensing has the potential to immensely advance the science and application of hydrology as it provides multi-scale and multi-temporal measurements of several hydrologic parameters. There is a wide variety of remote sensing data sources available to a hydrologist with a myriad of data formats, access techniques, data quality issues and temporal and spatial extents. It is very important to make data availability and its usage as convenient as possible for potential users. The CUAHSI Hydrologic Information System (HIS) initiative addresses this issue of better data access and management for hydrologists with a focus on in-situ data, that is point measurements of water and energy fluxes which make up the 'more conventional' sources of hydrologic data. This paper explores various sources of remotely sensed hydrologic data available, their data formats and volumes, current modes of data acquisition by end users, metadata associated with data itself, and requirements from potential data models that would allow a seamless integration of remotely sensed hydrologic observations into the Hydrologic Information System. Further, a prototype hydrologic observatory (HO) for the Neuse River Basin is developed using surface temperature, vegetation indices and soil moisture estimates available from remote sensing. The prototype (HO) uses the CUAHSI digital library system (DLS) on the back (server) end. On the front (client) end, a rich visual environment has been developed in order to provide better decision making tools in order to make an optimal choice in the selection of remote sensing data for a particular application. An easy point and click interface to the remote sensing data is also implemented for common users who are just interested in location based query of hydrologic variable values.

Integrated Approach to Inform the New York City Water Supply System Coupling SAR Remote Sensing Observations and the SWAT Watershed Model

NASA Astrophysics Data System (ADS)

Tesser, D.; Hoang, L.; McDonald, K. C.

2017-12-01

Efforts to improve municipal water supply systems increasingly rely on an ability to elucidate variables that drive hydrologic dynamics within large watersheds. However, fundamental model variables such as precipitation, soil moisture, evapotranspiration, and soil freeze/thaw state remain difficult to measure empirically across large, heterogeneous watersheds. Satellite remote sensing presents a method to validate these spatially and temporally dynamic variables as well as better inform the watershed models that monitor the water supply for many of the planet's most populous urban centers. PALSAR 2 L-band, Sentinel 1 C-band, and SMAP L-band scenes covering the Cannonsville branch of the New York City (NYC) water supply watershed were obtained for the period of March 2015 - October 2017. The SAR data provides information on soil moisture, free/thaw state, seasonal surface inundation, and variable source areas within the study site. Integrating the remote sensing products with watershed model outputs and ground survey data improves the representation of related processes in the Soil and Water Assessment Tool (SWAT) utilized to monitor the NYC water supply. PALSAR 2 supports accurate mapping of the extent of variable source areas while Sentinel 1 presents a method to model the timing and magnitude of snowmelt runoff events. SMAP Active Radar soil moisture product directly validates SWAT outputs at the subbasin level. This blended approach verifies the distribution of soil wetness classes within the watershed that delineate Hydrologic Response Units (HRUs) in the modified SWAT-Hillslope. The research expands the ability to model the NYC water supply source beyond a subset of the watershed while also providing high resolution information across a larger spatial scale. The global availability of these remote sensing products provides a method to capture fundamental hydrology variables in regions where current modeling efforts and in situ data remain limited.

Combining Remotely Sensed Environmental Characteristics with Social and Behavioral Conditions that Affect Surface Water Use in Spatiotemporal Modelling of Schistosomiasis in Ghana

NASA Astrophysics Data System (ADS)

Kulinkina, A. V.; Walz, Y.; Liss, A.; Kosinski, K. C.; Biritwum, N. K.; Naumova, E. N.

2016-06-01

Schistosoma haematobium transmission is influenced by environmental conditions that determine the suitability of the parasite and intermediate host snail habitats, as well as by socioeconomic conditions, access to water and sanitation infrastructure, and human behaviors. Remote sensing is a demonstrated valuable tool to characterize environmental conditions that support schistosomiasis transmission. Socioeconomic and behavioral conditions that propagate repeated domestic and recreational surface water contact are more difficult to quantify at large spatial scales. We present a mixed-methods approach that builds on the remotely sensed ecological variables by exploring water and sanitation related community characteristics as independent risk factors of schistosomiasis transmission.

Forest inventory with LiDAR and stereo DSM on Washington department of natural resources lands

Treesearch

Jacob L. Strunk; Peter J. Gould

2015-01-01

DNR’s forest inventory group has completed its first version of a new remote-sensing based forest inventory system covering 1.4 million acres of DNR forest lands. We use a combination of field plots, lidar, NAIP, and a NAIP-derived canopy surface DSM. Given that height drives many key inventory variables (e.g. height, volume, biomass, carbon), remote-sensing derived...

Remote sensing and implications for variable-rate application using agricultural aircraft

NASA Astrophysics Data System (ADS)

Thomson, Steven J.; Smith, Lowrey A.; Ray, Jeffrey D.; Zimba, Paul V.

2004-01-01

Aircraft routinely used for agricultural spray application are finding utility for remote sensing. Data obtained from remote sensing can be used for prescription application of pesticides, fertilizers, cotton growth regulators, and water (the latter with the assistance of hyperspectral indices and thermal imaging). Digital video was used to detect weeds in early cotton, and preliminary data were obtained to see if nitrogen status could be detected in early soybeans. Weeds were differentiable from early cotton at very low altitudes (65-m), with the aid of supervised classification algorithms in the ENVI image analysis software. The camera was flown at very low altitude for acceptable pixel resolution. Nitrogen status was not detectable by statistical analysis of digital numbers (DNs) obtained from images, but soybean cultivar differences were statistically discernable (F=26, p=0.01). Spectroradiometer data are being analyzed to identify narrow spectral bands that might aid in selecting camera filters for determination of plant nitrogen status. Multiple camera configurations are proposed to allow vegetative indices to be developed more readily. Both remotely sensed field images and ground data are to be used for decision-making in a proposed variable-rate application system for agricultural aircraft. For this system, prescriptions generated from digital imagery and data will be coupled with GPS-based swath guidance and programmable flow control.

Essential Biodiversity Variables: A framework for communication between the biodiversity community and space agencies

NASA Astrophysics Data System (ADS)

Leidner, A. K.; Skidmore, A. K.; Turner, W. W.; Geller, G. N.

2017-12-01

The biodiversity community is working towards developing a consensus on a set of Essential Biodiversity Variables (EBVs) that can be used to measure and monitor biodiversity change over time. These EBVs will inform research, modeling, policy, and assessment efforts. The synoptic coverage provided by satellite data make remote sensing a particularly important observation tool to inform many EBVs. Biodiversity is a relatively new subject matter for space agencies, and thus the definition, description, and requirements of EBVs with a significant remote sensing component can foster ways for the biodiversity community to clearly and concisely communicate observational needs to space agencies and the Committee on Earth Observing Satellites (CEOS, the international coordinating body for civilian space agencies). Here, we present an overview of EBVs with a particular emphasis on those for which remote sensing will play a significant role and also report on the results of recent workshops to prioritize and refine EBVs. Our goal is to provide a framework for the biodiversity community to coalesce around a set of observational needs to convey to space agencies. Compared to many physical science disciplines, the biodiversity community represents a wide range of sub-disciplines and organizations (academia, non-governmental organizations, research institutes, national and local natural resource management agencies, etc.), which creates additional challenges when communicating needs to space agencies unfamiliar with the topic. EBVs thus offer a communication pathway that could increase awareness within space agencies of the uses of remote sensing for biodiversity research and applications, which in turn could foster greater use of remote sensing in the broader biodiversity community.

A Macroecological Analysis of SERA Derived Forest Heights and Implications for Forest Volume Remote Sensing

PubMed Central

Brolly, Matthew; Woodhouse, Iain H.; Niklas, Karl J.; Hammond, Sean T.

2012-01-01

Individual trees have been shown to exhibit strong relationships between DBH, height and volume. Often such studies are cited as justification for forest volume or standing biomass estimation through remote sensing. With resolution of common satellite remote sensing systems generally too low to resolve individuals, and a need for larger coverage, these systems rely on descriptive heights, which account for tree collections in forests. For remote sensing and allometric applications, this height is not entirely understood in terms of its location. Here, a forest growth model (SERA) analyzes forest canopy height relationships with forest wood volume. Maximum height, mean, H100, and Lorey's height are examined for variability under plant number density, resource and species. Our findings, shown to be allometrically consistent with empirical measurements for forested communities world-wide, are analyzed for implications to forest remote sensing techniques such as LiDAR and RADAR. Traditional forestry measures of maximum height, and to a lesser extent H100 and Lorey's, exhibit little consistent correlation with forest volume across modeled conditions. The implication is that using forest height to infer volume or biomass from remote sensing requires species and community behavioral information to infer accurate estimates using height alone. SERA predicts mean height to provide the most consistent relationship with volume of the height classifications studied and overall across forest variations. This prediction agrees with empirical data collected from conifer and angiosperm forests with plant densities ranging between 102–106 plants/hectare and heights 6–49 m. Height classifications investigated are potentially linked to radar scattering centers with implications for allometry. These findings may be used to advance forest biomass estimation accuracy through remote sensing. Furthermore, Lorey's height with its specific relationship to remote sensing physics is recommended as a more universal indicator of volume when using remote sensing than achieved using either maximum height or H100. PMID:22457800

A macroecological analysis of SERA derived forest heights and implications for forest volume remote sensing.

PubMed

Brolly, Matthew; Woodhouse, Iain H; Niklas, Karl J; Hammond, Sean T

2012-01-01

Individual trees have been shown to exhibit strong relationships between DBH, height and volume. Often such studies are cited as justification for forest volume or standing biomass estimation through remote sensing. With resolution of common satellite remote sensing systems generally too low to resolve individuals, and a need for larger coverage, these systems rely on descriptive heights, which account for tree collections in forests. For remote sensing and allometric applications, this height is not entirely understood in terms of its location. Here, a forest growth model (SERA) analyzes forest canopy height relationships with forest wood volume. Maximum height, mean, H₁₀₀, and Lorey's height are examined for variability under plant number density, resource and species. Our findings, shown to be allometrically consistent with empirical measurements for forested communities world-wide, are analyzed for implications to forest remote sensing techniques such as LiDAR and RADAR. Traditional forestry measures of maximum height, and to a lesser extent H₁₀₀ and Lorey's, exhibit little consistent correlation with forest volume across modeled conditions. The implication is that using forest height to infer volume or biomass from remote sensing requires species and community behavioral information to infer accurate estimates using height alone. SERA predicts mean height to provide the most consistent relationship with volume of the height classifications studied and overall across forest variations. This prediction agrees with empirical data collected from conifer and angiosperm forests with plant densities ranging between 10²-10⁶ plants/hectare and heights 6-49 m. Height classifications investigated are potentially linked to radar scattering centers with implications for allometry. These findings may be used to advance forest biomass estimation accuracy through remote sensing. Furthermore, Lorey's height with its specific relationship to remote sensing physics is recommended as a more universal indicator of volume when using remote sensing than achieved using either maximum height or H₁₀₀.

Spatialized Application of Remotely Sensed Data Assimilation Methods for Farmland Drought Monitoring Using Two Different Crop Models

NASA Astrophysics Data System (ADS)

Silvestro, Paolo Cosmo; Casa, Raffaele; Pignatti, Stefano; Castaldi, Fabio; Yang, Hao; Guijun, Yang

2016-08-01

The aim of this work was to develop a tool to evaluate the effect of water stress on yield losses at the farmland and regional scale, by assimilating remotely sensed biophysical variables into crop growth models. Biophysical variables were retrieved from HJ1A, HJ1B and Landsat 8 images, using an algorithm based on the training of artificial neural networks on PROSAIL.For the assimilation, two crop models of differing degree of complexity were used: Aquacrop and SAFY. For Aquacrop, an optimization procedure to reduce the difference between the remotely sensed and simulated CC was developed. For the modified version of SAFY, the assimilation procedure was based on the Ensemble Kalman Filter.These procedures were tested in a spatialized application, by using data collected in the rural area of Yangling (Shaanxi Province) between 2013 and 2015Results were validated by utilizing yield data both from ground measurements and statistical survey.

Remote sensing estimation of the total phosphorus concentration in a large lake using band combinations and regional multivariate statistical modeling techniques.

PubMed

Gao, Yongnian; Gao, Junfeng; Yin, Hongbin; Liu, Chuansheng; Xia, Ting; Wang, Jing; Huang, Qi

2015-03-15

Remote sensing has been widely used for ater quality monitoring, but most of these monitoring studies have only focused on a few water quality variables, such as chlorophyll-a, turbidity, and total suspended solids, which have typically been considered optically active variables. Remote sensing presents a challenge in estimating the phosphorus concentration in water. The total phosphorus (TP) in lakes has been estimated from remotely sensed observations, primarily using the simple individual band ratio or their natural logarithm and the statistical regression method based on the field TP data and the spectral reflectance. In this study, we investigated the possibility of establishing a spatial modeling scheme to estimate the TP concentration of a large lake from multi-spectral satellite imagery using band combinations and regional multivariate statistical modeling techniques, and we tested the applicability of the spatial modeling scheme. The results showed that HJ-1A CCD multi-spectral satellite imagery can be used to estimate the TP concentration in a lake. The correlation and regression analysis showed a highly significant positive relationship between the TP concentration and certain remotely sensed combination variables. The proposed modeling scheme had a higher accuracy for the TP concentration estimation in the large lake compared with the traditional individual band ratio method and the whole-lake scale regression-modeling scheme. The TP concentration values showed a clear spatial variability and were high in western Lake Chaohu and relatively low in eastern Lake Chaohu. The northernmost portion, the northeastern coastal zone and the southeastern portion of western Lake Chaohu had the highest TP concentrations, and the other regions had the lowest TP concentration values, except for the coastal zone of eastern Lake Chaohu. These results strongly suggested that the proposed modeling scheme, i.e., the band combinations and the regional multivariate statistical modeling techniques, demonstrated advantages for estimating the TP concentration in a large lake and had a strong potential for universal application for the TP concentration estimation in large lake waters worldwide. Copyright © 2014 Elsevier Ltd. All rights reserved.

Snowpack spatial variability: Towards understanding its effect on remote sensing measurements and snow slope stability

NASA Astrophysics Data System (ADS)

Marshall, Hans-Peter

The distribution of water in the snow-covered areas of the world is an important climate change indicator, and it is a vital component of the water cycle. At local and regional scales, the snow water equivalent (SWE), the amount of liquid water a given area of the snowpack represents, is very important for water resource management, flood forecasting, and prediction of available hydropower energy. Measurements from only a few automatic weather stations, such as the SNOTEL network, or sparse manual snowpack measurements are typically extrapolated for estimating SWE over an entire basin. Widespread spatial variability in the distribution of SWE and snowpack stratigraphy at local scales causes large errors in these basin estimates. Remote sensing measurements offer a promising alternative, due to their large spatial coverage and high temporal resolution. Although snow cover extent can currently be estimated from remote sensing data, accurately quantifying SWE from remote sensing measurements has remained difficult, due to a high sensitivity to variations in grain size and stratigraphy. In alpine snowpacks, the large degree of spatial variability of snowpack properties and geometry, caused by topographic, vegetative, and microclimatic effects, also makes prediction of snow avalanches very difficult. Ground-based radar and penetrometer measurements can quickly and accurately characterize snowpack properties and SWE in the field. A portable lightweight radar was developed, and allows a real-time estimate of SWE to within 10%, as well as measurements of depths of all major density transitions within the snowpack. New analysis techniques developed in this thesis allow accurate estimates of mechanical properties and an index of grain size to be retrieved from the SnowMicroPenetrometer. These two tools together allow rapid characterization of the snowpack's geometry, mechanical properties, and SWE, and are used to guide a finite element model to study the stress distribution on a slope. The ability to accurately characterize snowpack properties at much higher resolutions and spatial extent than previously possible will hopefully help lead to a more complete understanding of spatial variability, its effect on remote sensing measurements and snow slope stability, and result in improvements in avalanche prediction and accuracy of SWE estimates from space.

Contrasting seasonality in optical-biogeochemical properties of the Baltic Sea

PubMed Central

Ylöstalo, Pasi; Kallio, Kari Y.; Spilling, Kristian; Kutser, Tiit

2017-01-01

Optical-biogeochemical relationships of particulate and dissolved organic matter are presented in support of remote sensing of the Baltic Sea pelagic. This system exhibits strong seasonality in phytoplankton community composition and wide gradients of chromophoric dissolved organic matter (CDOM), properties which are poorly handled by existing remote sensing algorithms. Absorption and scattering properties of particulate matter reflected the seasonality in biological (phytoplankton succession) and physical (thermal stratification) processes. Inherent optical properties showed much wider variability when normalized to the chlorophyll-a concentration compared to normalization to either total suspended matter dry weight or particulate organic carbon. The particle population had the largest optical variability in summer and was dominated by organic matter in both seasons. The geographic variability of CDOM and relationships with dissolved organic carbon (DOC) are also presented. CDOM dominated light absorption at blue wavelengths, contributing 81% (median) of the absorption by all water constituents at 400 nm and 63% at 442 nm. Consequentially, 90% of water-leaving radiance at 412 nm originated from a layer (z90) no deeper than approximately 1.0 m. With water increasingly attenuating light at longer wavelengths, a green peak in light penetration and reflectance is always present in these waters, with z90 up to 3.0–3.5 m depth, whereas z90 only exceeds 5 m at biomass < 5 mg Chla m-3. High absorption combined with a weakly scattering particle population (despite median phytoplankton biomass of 14.1 and 4.3 mg Chla m-3 in spring and summer samples, respectively), characterize this sea as a dark water body for which dedicated or exceptionally robust remote sensing techniques are required. Seasonal and regional optical-biogeochemical models, data distributions, and an extensive set of simulated remote-sensing reflectance spectra for testing of remote sensing algorithms are provided as supplementary data. PMID:28384157

Utilization of combined remote sensing techniques to detect environmental variables influencing malaria vector densities in rural West Africa

PubMed Central

2012-01-01

Introduction The use of remote sensing has found its way into the field of epidemiology within the last decades. With the increased sensor resolution of recent and future satellites new possibilities emerge for high resolution risk modeling and risk mapping. Methods A SPOT 5 satellite image, taken during the rainy season 2009 was used for calculating indices by combining the image's spectral bands. Besides the widely used Normalized Difference Vegetation Index (NDVI) other indices were tested for significant correlation against field observations. Multiple steps, including the detection of surface water, its breeding appropriateness for Anopheles and modeling of vector imagines abundance, were performed. Data collection on larvae, adult vectors and geographic parameters in the field, was amended by using remote sensing techniques to gather data on altitude (Digital Elevation Model = DEM), precipitation (Tropical Rainfall Measurement Mission = TRMM), land surface temperatures (LST). Results The DEM derived altitude as well as indices calculations combining the satellite's spectral bands (NDTI = Normalized Difference Turbidity Index, NDWI Mac Feeters = Normalized Difference Water Index) turned out to be reliable indicators for surface water in the local geographic setting. While Anopheles larvae abundance in habitats is driven by multiple, interconnected factors - amongst which the NDVI - and precipitation events, the presence of vector imagines was found to be correlated negatively to remotely sensed LST and positively to the cumulated amount of rainfall in the preceding 15 days and to the Normalized Difference Pond Index (NDPI) within the 500 m buffer zone around capture points. Conclusions Remotely sensed geographical and meteorological factors, including precipitations, temperature, as well as vegetation, humidity and land cover indicators could be used as explanatory variables for surface water presence, larval development and imagines densities. This modeling approach based on remotely sensed information is potentially useful for counter measures that are putting on at the environmental side, namely vector larvae control via larviciding and water body reforming. PMID:22443452

Utilization of combined remote sensing techniques to detect environmental variables influencing malaria vector densities in rural West Africa.

PubMed

Dambach, Peter; Machault, Vanessa; Lacaux, Jean-Pierre; Vignolles, Cécile; Sié, Ali; Sauerborn, Rainer

2012-03-23

The use of remote sensing has found its way into the field of epidemiology within the last decades. With the increased sensor resolution of recent and future satellites new possibilities emerge for high resolution risk modeling and risk mapping. A SPOT 5 satellite image, taken during the rainy season 2009 was used for calculating indices by combining the image's spectral bands. Besides the widely used Normalized Difference Vegetation Index (NDVI) other indices were tested for significant correlation against field observations. Multiple steps, including the detection of surface water, its breeding appropriateness for Anopheles and modeling of vector imagines abundance, were performed. Data collection on larvae, adult vectors and geographic parameters in the field, was amended by using remote sensing techniques to gather data on altitude (Digital Elevation Model = DEM), precipitation (Tropical Rainfall Measurement Mission = TRMM), land surface temperatures (LST). The DEM derived altitude as well as indices calculations combining the satellite's spectral bands (NDTI = Normalized Difference Turbidity Index, NDWI Mac Feeters = Normalized Difference Water Index) turned out to be reliable indicators for surface water in the local geographic setting. While Anopheles larvae abundance in habitats is driven by multiple, interconnected factors - amongst which the NDVI - and precipitation events, the presence of vector imagines was found to be correlated negatively to remotely sensed LST and positively to the cumulated amount of rainfall in the preceding 15 days and to the Normalized Difference Pond Index (NDPI) within the 500 m buffer zone around capture points. Remotely sensed geographical and meteorological factors, including precipitations, temperature, as well as vegetation, humidity and land cover indicators could be used as explanatory variables for surface water presence, larval development and imagines densities. This modeling approach based on remotely sensed information is potentially useful for counter measures that are putting on at the environmental side, namely vector larvae control via larviciding and water body reforming. © 2012 Dambach et al; licensee BioMed Central Ltd.

Estimation of Snow Parameters Based on Passive Microwave Remote Sensing and Meteorological Information

NASA Technical Reports Server (NTRS)

Tsang, Leung; Hwang, Jenq-Neng

1996-01-01

A method to incorporate passive microwave remote sensing measurements within a spatially distributed snow hydrology model to provide estimates of the spatial distribution of Snow Water Equivalent (SWE) as a function of time is implemented. The passive microwave remote sensing measurements are at 25 km resolution. However, in mountain regions the spatial variability of SWE over a 25 km footprint is large due to topographic influences. On the other hand, the snow hydrology model has built-in topographic information and the capability to estimate SWE at a 1 km resolution. In our work, the snow hydrology SWE estimates are updated and corrected using SSM/I passive microwave remote sensing measurements. The method is applied to the Upper Rio Grande River Basin in the mountains of Colorado. The change in prediction of SWE from hydrology modeling with and without updating is compared with measurements from two SNOTEL sites in and near the basin. The results indicate that the method incorporating the remote sensing measurements into the hydrology model is able to more closely estimate the temporal evolution of the measured values of SWE as a function of time.

Earth Observations from the International Space Station: Benefits for Humanity

NASA Technical Reports Server (NTRS)

Stefanov, William L.

2015-01-01

The International Space Station (ISS) is a unique terrestrial remote sensing platform for observation of the Earth's land surface, oceans, and atmosphere. Unlike automated remote-sensing platforms it has a human crew; is equipped with both internal and externally-mounted active and passive remote sensing instruments; and has an inclined, low-Earth orbit that provides variable views and lighting (day and night) over 95 percent of the inhabited surface of the Earth. As such, it provides a useful complement to autonomous, sun-synchronous sensor systems in higher altitude polar orbits. Beginning in May 2012, NASA ISS sensor systems have been available to respond to requests for data through the International Charter, Space and Major Disasters, also known as the "International Disaster Charter" or IDC. Data from digital handheld cameras, multispectral, and hyperspectral imaging systems has been acquired in response to IDC activations and delivered to requesting agencies through the United States Geological Survey. The characteristics of the ISS for Earth observation will be presented, including past, current, and planned NASA, International Partner, and commercial remote sensing systems. The role and capabilities of the ISS for humanitarian benefit, specifically collection of remotely sensed disaster response data, will be discussed.

Novel Kalman Filter Algorithm for Statistical Monitoring of Extensive Landscapes with Synoptic Sensor Data

PubMed Central

Czaplewski, Raymond L.

2015-01-01

Wall-to-wall remotely sensed data are increasingly available to monitor landscape dynamics over large geographic areas. However, statistical monitoring programs that use post-stratification cannot fully utilize those sensor data. The Kalman filter (KF) is an alternative statistical estimator. I develop a new KF algorithm that is numerically robust with large numbers of study variables and auxiliary sensor variables. A National Forest Inventory (NFI) illustrates application within an official statistics program. Practical recommendations regarding remote sensing and statistical issues are offered. This algorithm has the potential to increase the value of synoptic sensor data for statistical monitoring of large geographic areas. PMID:26393588

Assimilation of remote sensing observations into a continuous distributed hydrological model: impacts on the hydrologic cycle

NASA Astrophysics Data System (ADS)

Laiolo, Paola; Gabellani, Simone; Campo, Lorenzo; Cenci, Luca; Silvestro, Francesco; Delogu, Fabio; Boni, Giorgio; Rudari, Roberto

2015-04-01

The reliable estimation of hydrological variables (e.g. soil moisture, evapotranspiration, surface temperature) in space and time is of fundamental importance in operational hydrology to improve the forecast of the rainfall-runoff response of catchments and, consequently, flood predictions. Nowadays remote sensing can offer a chance to provide good space-time estimates of several hydrological variables and then improve hydrological model performances especially in environments with scarce in-situ data. This work investigates the impact of the assimilation of different remote sensing products on the hydrological cycle by using a continuous physically based distributed hydrological model. Three soil moisture products derived by ASCAT (Advanced SCATterometer) are used to update the model state variables. The satellite-derived products are assimilated into the hydrological model using different assimilation techniques: a simple nudging and the Ensemble Kalman Filter. Moreover two assimilation strategies are evaluated to assess the impact of assimilating the satellite products at model spatial resolution or at the satellite scale. The experiments are carried out for three Italian catchments on multi year period. The benefits on the model predictions of discharge, LST, evapotranspiration and soil moisture dynamics are tested and discussed.

Relating remotely sensed optical variability to marine benthic biodiversity.

PubMed

Herkül, Kristjan; Kotta, Jonne; Kutser, Tiit; Vahtmäe, Ele

2013-01-01

Biodiversity is important in maintaining ecosystem viability, and the availability of adequate biodiversity data is a prerequisite for the sustainable management of natural resources. As such, there is a clear need to map biodiversity at high spatial resolutions across large areas. Airborne and spaceborne optical remote sensing is a potential tool to provide such biodiversity data. The spectral variation hypothesis (SVH) predicts a positive correlation between spectral variability (SV) of a remotely sensed image and biodiversity. The SVH has only been tested on a few terrestrial plant communities. Our study is the first attempt to apply the SVH in the marine environment using hyperspectral imagery recorded by Compact Airborne Spectrographic Imager (CASI). All coverage-based diversity measures of benthic macrophytes and invertebrates showed low but statistically significant positive correlations with SV whereas the relationship between biomass-based diversity measures and SV were weak or lacking. The observed relationships did not vary with spatial scale. SV had the highest independent effect among predictor variables in the statistical models of coverage-derived total benthic species richness and Shannon index. Thus, the relevance of SVH in marine benthic habitats was proved and this forms a prerequisite for the future use of SV in benthic biodiversity assessments.

Variability of the volume and thickness of sea ice in the Bay of Bothnia

NASA Astrophysics Data System (ADS)

Ronkainen, Iina; Lehtiranta, Jonni; Lensu, Mikko; Rinne, Eero; Hordoir, Robinson; Haapala, Jari

2017-04-01

Variability of the volume and thickness of sea ice in the Bay of Bothnia In our study, we want to quantify the variability of sea ice volume and thickness in the Bay of Bothnia and to introduce the drivers of the observed variability. There has been similar studies, but only for fast ice. We use various different data sets: in-situ ice thickness data, remote sensing data, model data and ice charts. In-situ data is from the regular monitoring stations in the coastal fast ice zone and from field campaigns. The remote sensing data is helicopter-borne and ship-borne electromagnetic data. The models we use are HELMI and NEMO-Nordic. We analyze the different data sets and compare them to each other to solve the inter-annual variability and to discuss the ratio of level and deformed ice.

Remote sensing and avian influenza: A review of image processing methods for extracting key variables affecting avian influenza virus survival in water from Earth Observation satellites

NASA Astrophysics Data System (ADS)

Tran, Annelise; Goutard, Flavie; Chamaillé, Lise; Baghdadi, Nicolas; Lo Seen, Danny

2010-02-01

Recent studies have highlighted the potential role of water in the transmission of avian influenza (AI) viruses and the existence of often interacting variables that determine the survival rate of these viruses in water; the two main variables are temperature and salinity. Remote sensing has been used to map and monitor water bodies for several decades. In this paper, we review satellite image analysis methods used for water detection and characterization, focusing on the main variables that influence AI virus survival in water. Optical and radar imagery are useful for detecting water bodies at different spatial and temporal scales. Methods to monitor the temperature of large water surfaces are also available. Current methods for estimating other relevant water variables such as salinity, pH, turbidity and water depth are not presently considered to be effective.

Classification of permafrost active layer depth from remotely sensed and topographic evidence

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

Peddle, D.R.; Franklin, S.E.

1993-04-01

The remote detection of permafrost (perennially frozen ground) has important implications to environmental resource development, engineering studies, natural hazard prediction, and climate change research. In this study, the authors present results from two experiments into the classification of permafrost active layer depth within the zone of discontinuous permafrost in northern Canada. A new software system based on evidential reasoning was implemented to permit the integrated classification of multisource data consisting of landcover, terrain aspect, and equivalent latitude, each of which possessed different formats, data types, or statistical properties that could not be handled by conventional classification algorithms available to thismore » study. In the first experiment, four active layer depth classes were classified using ground based measurements of the three variables with an accuracy of 83% compared to in situ soil probe determination of permafrost active layer depth at over 500 field sites. This confirmed the environmental significance of the variables selected, and provided a baseline result to which a remote sensing classification could be compared. In the second experiment, evidence for each input variable was obtained from image processing of digital SPOT imagery and a photogrammetric digital elevation model, and used to classify active layer depth with an accuracy of 79%. These results suggest the classification of evidence from remotely sensed measures of spectral response and topography may provide suitable indicators of permafrost active layer depth.« less

A Comparative Distributed Evaluation of the NWS-RDHM using Shape Matching and Traditional Measures with In Situ and Remotely Sensed Information

NASA Astrophysics Data System (ADS)

KIM, J.; Bastidas, L. A.

2011-12-01

We evaluate, calibrate and diagnose the performance of National Weather Service RDHM distributed model over the Durango River Basin in Colorado using simultaneously in situ and remotely sensed information from different discharge gaging stations (USGS), information about snow cover (SCV) and snow water equivalent (SWE) in situ from several SNOTEL sites and snow information distributed over the catchment from remotely sensed information (NOAA-NASA). In the process of evaluation we attempt to establish the optimal degree of parameter distribution over the catchment by calibration. A multi-criteria approach based on traditional measures (RMSE) and similarity based pattern comparisons using the Hausdorff and Earth Movers Distance approaches is used for the overall evaluation of the model performance. These pattern based approaches (shape matching) are found to be extremely relevant to account for the relatively large degree of inaccuracy in the remotely sensed SWE (judged inaccurate in terms of the value but reliable in terms of the distribution pattern) and the high reliability of the SCV (yes/no situation) while at the same time allow for an evaluation that quantifies the accuracy of the model over the entire catchment considering the different types of observations. The Hausdorff norm, due to its intrinsically multi-dimensional nature, allows for the incorporation of variables such as the terrain elevation as one of the variables for evaluation. The EMD, because of its extremely high computational overburden, requires the mapping of the set of evaluation variables into a two dimensional matrix for computation.

Application of Thermal Infrared Remote Sensing for Quantitative Evaluation of Crop Characteristics

NASA Technical Reports Server (NTRS)

Shaw, J.; Luvall, J.; Rickman, D.; Mask, P.; Wersinger, J.; Sullivan, D.; Arnold, James E. (Technical Monitor)

2002-01-01

Evidence suggests that thermal infrared emittance (TIR) at the field-scale is largely a function of the integrated crop/soil moisture continuum. Because soil moisture dynamics largely determine crop yields in non-irrigated farming (85 % of Alabama farms are non-irrigated), TIR may be an effective method of mapping within field crop yield variability, and possibly, absolute yields. The ability to map yield variability at juvenile growth stages can lead to improved soil fertility and pest management, as well as facilitating the development of economic forecasting. Researchers at GHCC/MSFC/NASA and Auburn University are currently investigating the role of TIR in site-specific agriculture. Site-specific agriculture (SSA), or precision farming, is a method of crop production in which zones and soils within a field are delineated and managed according to their unique properties. The goal of SSA is to improve farm profits and reduce environmental impacts through targeted agrochemical applications. The foundation of SSA depends upon the spatial and temporal characterization of soil and crop properties through the creation of management zones. Management zones can be delineated using: 1) remote sensing (RS) data, 2) conventional soil testing and soil mapping, and 3) yield mapping. Portions of this research have concentrated on using remote sensing data to map yield variability in corn (Zea mays L.) and soybean (Glycine max L.) crops. Remote sensing data have been collected for several fields in the Tennessee Valley region at various crop growth stages during the last four growing seasons. Preliminary results of this study will be presented.

Quantitative mapping of particulate iron in an ocean dump using remotely sensed data

NASA Technical Reports Server (NTRS)

Ohlhorst, C. W.; Bahn, G. S.

1978-01-01

A remote sensing experiment was conducted at the industrial acid waste ocean dump site located approximately 38 n mi SE of Cape Henlopen, Delaware, to see if there was a relationship between aircraft remotely sensed spectral signatures and the iron concentration measured in the plume. Results are presented which show that aircraft remotely sensed spectral data can be used to quantify and map an acid waste dump in terms of its particulate iron concentration. A single variable equation using the ratio of band 2 (440-490 nm) radiance to band 4 (540-580 nm) radiance was used to quantify the acid plume and the surrounding water. The acid waste varied in age from freshly dumped to 3 1/2 hours old. Particulate iron concentrations in the acid waste were estimated to range up to 1.1 mg/liter at the 0.46 meter depth. A classification technique was developed to remove sunglitter-affected pixels from the data set.

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...

Implications of sampling design and sample size for national carbon accounting systems.

PubMed

Köhl, Michael; Lister, Andrew; Scott, Charles T; Baldauf, Thomas; Plugge, Daniel

2011-11-08

Countries willing to adopt a REDD regime need to establish a national Measurement, Reporting and Verification (MRV) system that provides information on forest carbon stocks and carbon stock changes. Due to the extensive areas covered by forests the information is generally obtained by sample based surveys. Most operational sampling approaches utilize a combination of earth-observation data and in-situ field assessments as data sources. We compared the cost-efficiency of four different sampling design alternatives (simple random sampling, regression estimators, stratified sampling, 2-phase sampling with regression estimators) that have been proposed in the scope of REDD. Three of the design alternatives provide for a combination of in-situ and earth-observation data. Under different settings of remote sensing coverage, cost per field plot, cost of remote sensing imagery, correlation between attributes quantified in remote sensing and field data, as well as population variability and the percent standard error over total survey cost was calculated. The cost-efficiency of forest carbon stock assessments is driven by the sampling design chosen. Our results indicate that the cost of remote sensing imagery is decisive for the cost-efficiency of a sampling design. The variability of the sample population impairs cost-efficiency, but does not reverse the pattern of cost-efficiency of the individual design alternatives. Our results clearly indicate that it is important to consider cost-efficiency in the development of forest carbon stock assessments and the selection of remote sensing techniques. The development of MRV-systems for REDD need to be based on a sound optimization process that compares different data sources and sampling designs with respect to their cost-efficiency. This helps to reduce the uncertainties related with the quantification of carbon stocks and to increase the financial benefits from adopting a REDD regime.

The role of remote sensing and GIS for spatial prediction of vector-borne diseases transmission: a systematic review.

PubMed

Palaniyandi, M

2012-12-01

There have been several attempts made to the appreciation of remote sensing and GIS for the study of vectors, biodiversity, vector presence, vector abundance and the vector-borne diseases with respect to space and time. This study was made for reviewing and appraising the potential use of remote sensing and GIS applications for spatial prediction of vector-borne diseases transmission. The nature of the presence and the abundance of vectors and vector-borne diseases, disease infection and the disease transmission are not ubiquitous and are confined with geographical, environmental and climatic factors, and are localized. The presence of vectors and vector-borne diseases is most complex in nature, however, it is confined and fueled by the geographical, climatic and environmental factors including man-made factors. The usefulness of the present day availability of the information derived from the satellite data including vegetation indices of canopy cover and its density, soil types, soil moisture, soil texture, soil depth, etc. is integrating the information in the expert GIS engine for the spatial analysis of other geoclimatic and geoenvironmental variables. The present study gives the detailed information on the classical studies of the past and present, and the future role of remote sensing and GIS for the vector-borne diseases control. The ecological modeling directly gives us the relevant information to understand the spatial variation of the vector biodiversity, vector presence, vector abundance and the vector-borne diseases in association with geoclimatic and the environmental variables. The probability map of the geographical distribution and seasonal variations of horizontal and vertical distribution of vector abundance and its association with vector -borne diseases can be obtained with low cost remote sensing and GIS tool with reliable data and speed.

Mapping and modeling the urban landscape in Bangkok, Thailand: Physical-spectral-spatial relations of population-environmental interactions

NASA Astrophysics Data System (ADS)

Shao, Yang

This research focuses on the application of remote sensing, geographic information systems, statistical modeling, and spatial analysis to examine the dynamics of urban land cover, urban structure, and population-environment interactions in Bangkok, Thailand, with an emphasis on rural-to-urban migration from rural Nang Rong District, Northeast Thailand to the primate city of Bangkok. The dissertation consists of four main sections: (1) development of remote sensing image classification and change-detection methods for characterizing imperviousness for Bangkok, Thailand from 1993-2002; (2) development of 3-D urban mapping methods, using high spatial resolution IKONOS satellite images, to assess high-rises and other urban structures; (3) assessment of urban spatial structure from 2-D and 3-D perspectives; and (4) an analysis of the spatial clustering of migrants from Nang Rong District in Bangkok and the neighborhood environments of migrants' locations. Techniques are developed to improve the accuracy of the neural network classification approach for the analysis of remote sensing data, with an emphasis on the spectral unmixing problem. The 3-D building heights are derived using the shadow information on the high-resolution IKONOS image. The results from the 2-D and 3-D mapping are further examined to assess urban structure and urban feature identification. This research contributes to image processing of remotely-sensed images and urban studies. The rural-urban migration process and migrants' settlement patterns are examined using spatial statistics, GIS, and remote sensing perspectives. The results show that migrants' spatial clustering in urban space is associated with the source village and a number of socio-demographic variables. In addition, the migrants' neighborhood environments in urban setting are modeled using a set of geographic and socio-demographic variables, and the results are scale-dependent.

Use of remote sensing and a geographical information system in a national helminth control programme in Chad.

PubMed Central

Brooker, Simon; Beasley, Michael; Ndinaromtan, Montanan; Madjiouroum, Ester Mobele; Baboguel, Marie; Djenguinabe, Elie; Hay, Simon I.; Bundy, Don A. P.

2002-01-01

OBJECTIVE: To design and implement a rapid and valid epidemiological assessment of helminths among schoolchildren in Chad using ecological zones defined by remote sensing satellite sensor data and to investigate the environmental limits of helminth distribution. METHODS: Remote sensing proxy environmental data were used to define seven ecological zones in Chad. These were combined with population data in a geographical information system (GIS) in order to define a sampling protocol. On this basis, 20 schools were surveyed. Multilevel analysis, by means of generalized estimating equations to account for clustering at the school level, was used to investigate the relationship between infection patterns and key environmental variables. FINDINGS: In a sample of 1023 schoolchildren, 22.5% were infected with Schistosoma haematobium and 32.7% with hookworm. None were infected with Ascaris lumbricoides or Trichuris trichiura. The prevalence of S. haematobium and hookworm showed marked geographical heterogeneity and the observed patterns showed a close association with the defined ecological zones and significant relationships with environmental variables. These results contribute towards defining the thermal limits of geohelminth species. Predictions of infection prevalence were made for each school surveyed with the aid of models previously developed for Cameroon. These models correctly predicted that A. lumbricoides and T. trichiura would not occur in Chad but the predictions for S. haematobium were less reliable at the school level. CONCLUSION: GIS and remote sensing can play an important part in the rapid planning of helminth control programmes where little information on disease burden is available. Remote sensing prediction models can indicate patterns of geohelminth infection but can only identify potential areas of high risk for S. haematobium. PMID:12471398

Improving simulations of snow water equivalent and total water storage changes over the Upper Yangtze River basin using multi-source remote sensing data

NASA Astrophysics Data System (ADS)

Han, P.; Long, D.

2017-12-01

Snow water equivalent (SWE) and total water storage (TWS) changes are important hydrological state variables over cryospheric regions, such as China's Upper Yangtze River (UYR) basin. Accurate simulation of these two state variables plays a critical role in understanding hydrological processes over this region and, in turn, benefits water resource management, hydropower development, and ecological integrity over the lower reaches of the Yangtze River, one of the largest rivers globally. In this study, an improved CREST model coupled with a snow and glacier melting module was used to simulate SWE and TWS changes over the UYR, and to quantify contributions of snow and glacier meltwater to the total runoff. Forcing, calibration, and validation data are mainly from multi-source remote sensing observations, including satellite-based precipitation estimates, passive microwave remote sensing-based SWE, and GRACE-derived TWS changes, along with streamflow measurements at the Zhimenda gauging station. Results show that multi-source remote sensing information can be extremely valuable in model forcing, calibration, and validation over the poorly gauged region. The simulated SWE and TWS changes and the observed counterparts are highly consistent, showing NSE coefficients higher than 0.8. The results also show that the contributions of snow and glacier meltwater to the total runoff are 8% and 6%, respectively, during the period 2003‒2014, which is an important source of runoff. Moreover, from this study, the TWS is found to increase at a rate of 5 mm/a ( 0.72 Gt/a) for the period 2003‒2014. The snow melting module may overestimate SWE for high precipitation events and was improved in this study. Key words: CREST model; Remote Sensing; Melting model; Source Region of the Yangtze River

Explaining the variability of Photochemical Reflectance Index (PRI): deconvolution of variability related to Light Use Efficiency and Canopy attributes.

NASA Astrophysics Data System (ADS)

Merlier, Elodie; Hmimina, Gabriel; Dufrêne, Eric; Soudani, Kamel

2014-05-01

The Photochemical Reflectance Index (PRI) was designed as a proxy of the state of xanthophyll cycle which is used as a response of plants to excess of light (Gamon et al., 1990; 1992). Strong relationships between PRI and LUE were shown at leaf and canopy scales and over a wide range of species (Garbulsky et al., 2011). However, its use at canopy scale was shown to be significantly hampered by effects of confounding factors such as the PRI sensitivity to leaf pigment content (Gamon et al. 2001; Nakaji et al. 2006) and to canopy structure (Hilker et al. 2008). Several approaches aimed at correcting such effects and recent works focused on the deconvolution of LUE related and LUE unrelated PRI variability (Rahimzadeh-Bajgiran et al. 2012).In this study, the PRI variability at canopy scale is investigated over two years on three species (Fagus sylvatica, Quercus robur and Pinus sylvestris) growing under two water regimes. At daily scale, PRI variability is mainly explained by radiation conditions. As already reported at leaf scale in Hmimina et al. (2014), analysis of PRI responses to incoming photosynthetically active radiation over seasonal scale allowed to separate two sources of variability : a constitutive variability mainly related to canopy structure and leaf chlorophyll content and a facultative variability mainly related to LUE and soil moisture content. These results highlight the composite nature of PRI signal measured at canopy scale and the importance of disentangling its sources of variability in order to accurately assess ecosystem light use efficiency. Gamon JA, Field CB, Bilger W, Björkman O, Fredeen AL, Peñuelas J. 1990. Remote sensing of the xanthophyll cycle and chlorophyll fluorescence in sunflower leaves and canopies. Oecologia 85, 1-7. Gamon JA, Field CB, Fredeen A AL, Thayer S. 2001. Assessing photosynthetic downregulation in sunflower stands with an optically-based model. Photosynthesis Research 67, 113-125. Gamon JA, Peñuelas J, Field CB. 1992. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment 41, 35-44. Garbulsky MF, Peñuelas J, Gamon J, Inoue Y, Filella I. 2011. The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: A review and meta-analysis. Remote Sensing of Environment 115, 281-297. Hilker T, Coops NC, Hall FG, Black TA, Wulder MA, Nesic Z, Krishnan P. 2008. Separating physiologically and directionally induced changes in PRI using BRDF models. Remote Sensing of Environment 112, 2777-2788. Hmimina G, Dufrêne E, Soudani K. 2014. Relationship between PRI and leaf ecophysiological and biochemical parameters under two different water statuses: toward a rapid and efficient correction method using real-time measurements. Plant, Cell & Environment 37, 2, 473-487. Nakaji T, Oguma H, Fujinuma Y. 2006. Seasonal changes in the relationship between photochemical reflectance index and photosynthetic light use efficiency of Japanese larch needles. International Journal of Remote Sensing 27, 493-509. Rahimzadeh-Bajgiran P, Munehiro M, Omasa K. 2012. Relationships between the photochemical reflectance index (PRI) and chlorophyll fluorescence parameters and plant pigment indices at different leaf growth stages. Photosynthesis Research 113, 261-271.

An evaluation of the use of remotely sensed parameters for prediction of incidence and risk associated with Vibrio parahaemolyticus in Gulf Coast oysters (Crassostrea virginica).

PubMed

Phillips, A M B; Depaola, A; Bowers, J; Ladner, S; Grimes, D J

2007-04-01

The U.S. Food and Drug Administration recently published a Vibrio parahaemolyticus risk assessment for consumption of raw oysters that predicts V. parahaemolyticus densities at harvest based on water temperature. We retrospectively compared archived remotely sensed measurements (sea surface temperature, chlorophyll, and turbidity) with previously published data from an environmental study of V. parahaemolyticus in Alabama oysters to assess the utility of the former data for predicting V. parahaemolyticus densities in oysters. Remotely sensed sea surface temperature correlated well with previous in situ measurements (R(2) = 0.86) of bottom water temperature, supporting the notion that remotely sensed sea surface temperature data are a sufficiently accurate substitute for direct measurement. Turbidity and chlorophyll levels were not determined in the previous study, but in comparison with the V. parahaemolyticus data, remotely sensed values for these parameters may explain some of the variation in V. parahaemolyticus levels. More accurate determination of these effects and the temporal and spatial variability of these parameters may further improve the accuracy of prediction models. To illustrate the utility of remotely sensed data as a basis for risk management, predictions based on the U.S. Food and Drug Administration V. parahaemolyticus risk assessment model were integrated with remotely sensed sea surface temperature data to display graphically variations in V. parahaemolyticus density in oysters associated with spatial variations in water temperature. We believe images such as these could be posted in near real time, and that the availability of such information in a user-friendly format could be the basis for timely and informed risk management decisions.

Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

2018-04-01

Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure and increase the robustness of the proposed algorithm. The proposed algorithm is validated with a publicly available 10-class object detection dataset.

Compensating for environmental variability in the thermal inertia approach to remote sensing of soil moisture

NASA Technical Reports Server (NTRS)

Idso, S. B.; Jackson, R. D.; Reginato, R. J.

1976-01-01

A procedure is developed for removing data scatter in the thermal-inertia approach to remote sensing of soil moisture which arises from environmental variability in time and space. It entails the utilization of nearby National Weather Service air temperature measurements to normalize measured diurnal surface temperature variations to what they would have been for a day of standard diurnal air temperature variation, arbitrarily assigned to be 18 C. Tests of the procedure's basic premise on a bare loam soil and a crop of alfalfa indicate it to be conceptually sound. It is possible that the technique could also be useful in other thermal-inertia applications, such as lithographic mapping.

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters

PubMed Central

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme. PMID:27362762

Measuring grassland structure for recovery of grassland species at risk

NASA Astrophysics Data System (ADS)

Guo, Xulin; Gao, Wei; Wilmshurst, John

2005-09-01

An action plan for recovering species at risk (SAR) depends on an understanding of the plant community distribution, vegetation structure, quality of the food source and the impact of environmental factors such as climate change at large scale and disturbance at small scale, as these are fundamental factors for SAR habitat. Therefore, it is essential to advance our knowledge of understanding the SAR habitat distribution, habitat quality and dynamics, as well as developing an effective tool for measuring and monitoring SAR habitat changes. Using the advantages of non-destructive, low cost, and high efficient land surface vegetation biophysical parameter characterization, remote sensing is a potential tool for helping SAR recovery action. The main objective of this paper is to assess the most suitable techniques for using hyperspectral remote sensing to quantify grassland biophysical characteristics. The challenge of applying remote sensing in semi-arid and arid regions exists simply due to the lower biomass vegetation and high soil exposure. In conservation grasslands, this problem is enhanced because of the presence of senescent vegetation. Results from this study demonstrated that hyperspectral remote sensing could be the solution for semi-arid grassland remote sensing applications. Narrow band raw data and derived spectral vegetation indices showed stronger relationships with biophysical variables compared to the simulated broad band vegetation indices.

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters.

PubMed

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme.

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.

Remote sensing of atmosphere and oceans; Proceedings of Symposium 1 and of the Topical Meeting of the 27th COSPAR Plenary Meeting, Espoo, Finland, July 18-29, 1988

NASA Technical Reports Server (NTRS)

Raschke, E. (Editor); Ghazi, A. (Editor); Gower, J. F. R. (Editor); Mccormick, P. (Editor); Gruber, A. (Editor); Hasler, A. F. (Editor)

1989-01-01

Papers are presented on the contribution of space remote sensing observations to the World Climate Research Program and the Global Change Program, covering topics such as space observations for global environmental monitoring, experiments related to land surface fluxes, studies of atmospheric composition, structure, motions, and precipitation, and remote sensing for oceanography, observational studies of the atmosphere, clouds, and the earth radiation budget. Also, papers are given on results from space observations for meteorology, oceanography, and mesoscale atmospheric and ocean processes. The topics include vertical atmospheric soundings, surface water temperature determination, sea level variability, data on the prehurricane atmosphere, linear and circular mesoscale convective systems, Karman vortex clouds, and temporal patterns of phytoplankton abundance.

Spatiotemporal Variability of Soil Hydraulic Properties from Field Data and Remote Sensing in the Walnut Gulch Experimental Watershed

NASA Astrophysics Data System (ADS)

Becker, R.; Gebremichael, M.; Marker, M.

2015-12-01

Soil moisture is one of the main input variables for hydrological models. However due to the high spatial and temporal variability of soil properties it is often difficult to obtain accurate soil information at the required resolution. The new satellite SMAP promises to deliver soil moisture information at higher resolutions and could therefore improve the results of hydrological models. Nevertheless it still has to be investigated how precisely the SMAP soil moisture data can be used to delineate rainfall-runoff generation processes and if SMAP imagery can significantly improve the results of surface runoff models. Important parameters to understand the spatiotemporal distribution of soil humidity are infiltration and hydraulic conductivities apart from soil texture and macrostructure. During the SMAPVEX15-field campaign data on hydraulic conductivity and infiltration rates is collected in the Walnut Gulch Experimental Watershed (WGEW) in Southeastern Arizona in order to analyze the spatiotemporal variability of soil hydraulic properties. A Compact Constant Head Permeameter is used for in situ measurements of saturated hydraulic conductivity within the soil layers and a Hood Infiltrometer is used to determine infiltration rates at the undisturbed soil surface. Sampling sites were adjacent to the USDA-ARS meteorological and soil moisture measuring sites in the WGEW to take advantage of the long-term database of soil and climate data. Furthermore a sample plot of 3x3km was selected, where the spatial variability of soil hydraulic properties within a SMAP footprint was investigated. The results of the ground measurement based analysis are then compared with the remote sensing data derived from SMAP and aircraft-based microwave data to determine how well these spatiotemporal variations are captured by the remotely sensed data with the final goal of evaluating the use of future satellite soil moisture products for the improvement of rainfall runoff models. The results reveal several interesting features on the spatiotemporal variability of soil moisture at multiple scales, and the capabilities and limitations of remote sensing derived products in reproducing them.

Driving terrestrial ecosystem models from space

NASA Technical Reports Server (NTRS)

Waring, R. H.

1993-01-01

Regional air pollution, land-use conversion, and projected climate change all affect ecosystem processes at large scales. Changes in vegetation cover and growth dynamics can impact the functioning of ecosystems, carbon fluxes, and climate. As a result, there is a need to assess and monitor vegetation structure and function comprehensively at regional to global scales. To provide a test of our present understanding of how ecosystems operate at large scales we can compare model predictions of CO2, O2, and methane exchange with the atmosphere against regional measurements of interannual variation in the atmospheric concentration of these gases. Recent advances in remote sensing of the Earth's surface are beginning to provide methods for estimating important ecosystem variables at large scales. Ecologists attempting to generalize across landscapes have made extensive use of models and remote sensing technology. The success of such ventures is dependent on merging insights and expertise from two distinct fields. Ecologists must provide the understanding of how well models emulate important biological variables and their interactions; experts in remote sensing must provide the biophysical interpretation of complex optical reflectance and radar backscatter data.

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

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

Hyperspectral remote sensing of the responses of vegetation ecosystems to physical and biological changes of the environment

NASA Astrophysics Data System (ADS)

Krezhova, Dora; Krezhov, Kiril; Maneva, Svetla; Moskova, Irina; Petrov, Nikolay

2016-07-01

Hyperspectral remote sensing technique, based on reflectance measurements acquired in a high number of contiguous spectral bands in the visible and near infrared spectral ranges, was used to detect the influence of some environmental changes to vegetation ecosystems. Adverse physical and biological conditions give rise to morphological, physiological, and biochemical changes in the plants that affect the manner in which they interact with the light. All green vegetation species have unique spectral features, mainly because of the chlorophyll and carotenoid, and other pigments, and water content. Because spectral reflectance is a function of the illumination conditions, tissue optical properties and biochemical content of the plants it may be used to collect information on several important biophysical parameters such as color and the spectral signature of features, vegetation chlorophyll absorption characteristics, vegetation moisture content, etc. Remotely sensed data collected by means of a portable fiber-optics spectrometer in the spectral range 350-1100 nm were used to extract information on the influence of some environmental changes. Stress factors such as enhanced UV-radiation, salinity, viral infections, were applied to some young plants species (potato, tomato, plums). The test data were subjected to different digital image processing techniques. This included statistical (Student's t-criterion), first derivative and cluster analyses and some vegetation indices. Statistical analyses were carried out in four most informative for the investigated species regions: green (520-580 nm), red (640-680 nm), red edge (680-720 nm) and near infrared (720-780 nm). The strong relationship, which was found between the results from the remote sensing technique and some biochemical and serological analyses (stress markers, DAS-ELISA), indicates the importance of hyperspectral reflectance data for conducting, easily and without damage, rapid assessments of plant biophysical variables. Emphasis is put on current capability and future potential of remote sensing for assessment of the plant health and on the optimum spectral regions and vegetation indices for sensing these biophysical variables.

Assessing the performance of remotely-sensed flooding indicators and their potential contribution to early warning for leptospirosis in Cambodia.

PubMed

Ledien, Julia; Sorn, Sopheak; Hem, Sopheak; Huy, Rekol; Buchy, Philippe; Tarantola, Arnaud; Cappelle, Julien

2017-01-01

Remote sensing can contribute to early warning for diseases with environmental drivers, such as flooding for leptospirosis. In this study we assessed whether and which remotely-sensed flooding indicator could be used in Cambodia to study any disease for which flooding has already been identified as an important driver, using leptospirosis as a case study. The performance of six potential flooding indicators was assessed by ground truthing. The Modified Normalized Difference Water Index (MNDWI) was used to estimate the Risk Ratio (RR) of being infected by leptospirosis when exposed to floods it detected, in particular during the rainy season. Chi-square tests were also calculated. Another variable-the time elapsed since the first flooding of the year-was created using MNDWI values and was also included as explanatory variable in a generalized linear model (GLM) and in a boosted regression tree model (BRT) of leptospirosis infections, along with other explanatory variables. Interestingly, MNDWI thresholds for both detecting water and predicting the risk of leptospirosis seroconversion were independently evaluated at -0.3. Value of MNDWI greater than -0.3 was significantly related to leptospirosis infection (RR = 1.61 [1.10-1.52]; χ2 = 5.64, p-value = 0.02, especially during the rainy season (RR = 2.03 [1.25-3.28]; χ2 = 8.15, p-value = 0.004). Time since the first flooding of the year was a significant risk factor in our GLM model (p-value = 0.042). These results suggest that MNDWI may be useful as a risk indicator in an early warning remote sensing tool for flood-driven diseases like leptospirosis in South East Asia.

Essential climatic variables estimation with satellite imagery

NASA Astrophysics Data System (ADS)

Kolotii, A.; Kussul, N.; Shelestov, A.; Lavreniuk, M. S.

2016-12-01

According to Sendai Framework for Disaster Risk Reduction 2015 - 2030 Leaf Area Index (LAI) is considered as one of essential climatic variables. This variable represents the amount of leaf material in ecosystems and controls the links between biosphere and atmosphere through various processes and enables monitoring and quantitative assessment of vegetation state. LAI has added value for such important global resources monitoring tasks as drought mapping and crop yield forecasting with use of data from different sources [1-2]. Remote sensing data from space can be used to estimate such biophysical parameter at regional and national scale. High temporal satellite imagery is usually required to capture main parameters of crop growth [3]. Sentinel-2 mission launched in 2015 be ESA is a source of high spatial and temporal resolution satellite imagery for mapping biophysical parameters. Products created with use of automated Sen2-Agri system deployed during Sen2-Agri country level demonstration project for Ukraine will be compared with our independent results of biophysical parameters mapping. References Shelestov, A., Kolotii, A., Camacho, F., Skakun, S., Kussul, O., Lavreniuk, M., & Kostetsky, O. (2015, July). Mapping of biophysical parameters based on high resolution EO imagery for JECAM test site in Ukraine. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1733-1736 Kolotii, A., Kussul, N., Shelestov, A., Skakun, S., Yailymov, B., Basarab, R., ... & Ostapenko, V. (2015). Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 39-44. Kussul, N., Lemoine, G., Gallego, F. J., Skakun, S. V., Lavreniuk, M., & Shelestov, A. Y. Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 9 (6), 2500-2508.

Remote Sensing of Terrestrial Water Storage and Application to Drought Monitoring

NASA Technical Reports Server (NTRS)

Rodell, Matt

2007-01-01

Terrestrial water storage (TWS) consists of groundwater, soil moisture and permafrost, surface water, snow and ice, and wet biomass. TWS variability tends to be dominated by snow and ice in polar and alpine regions, by soil moisture in mid-latitudes, and by surface water in wet, tropical regions such as the Amazon (Rodell and Famiglietti, 2001; Bates et al., 2007). Drought may be defined as a period of abnormally dry weather long enough to cause significant deficits in one or more of the TWS components. Thus, along with observations of the agricultural and socioeconomic impacts, measurements of TWS and its components enable quantification of drought severity. Each of the TWS components exhibits significant spatial variability, while installation and maintenance of sufficiently dense monitoring networks is costly and labor-intensive. Thus satellite remote sensing is an appealing alternative to traditional measurement techniques. Several current remote sensing instruments are able to detect variations in one or more TWS variables, including the Advanced Microwave Scanning Radiometer (AMSR) on NASA's Aqua satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra and Aqua. Future satellite missions have been proposed to improve this capability, including the European Space Agency's Soil Moisture Ocean Salinity mission (SMOS) and the Soil Moisture Active Passive (SMAP), Surface Water Ocean Topography (SWOT), and Snow and Cold Land Processes (SCLP) missions recommended by the US National Academy of Science's Decadal Survey for Earth Science (NRC, 2007). However, only one remote sensing technology is able to monitor changes in TWS from the land surface to the base of the deepest aquifer: satellite gravimetry. This paper focuses on NASA's Gravity Recovery and Climate Experiment mission (GRACE; http://www.csr.utexas.edu/grace/) and its potential as a tool for drought monitoring.

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.

A new multi-sensor integrated index for drought monitoring

NASA Astrophysics Data System (ADS)

Jiao, W.; Wang, L.; Tian, C.

2017-12-01

Drought is perceived as one of the most expensive and least understood natural disasters. The remote-sensing-based integrated drought indices, which integrate multiple variables, could reflect the drought conditions more comprehensively than single drought indices. However, most of current remote-sensing-based integrated drought indices focus on agricultural drought (i.e., deficit in soil moisture), their application in monitoring meteorological drought (i.e., deficit in precipitation) was limited. More importantly, most of the remote-sensing-based integrated drought indices did not take into consideration of the spatially non-stationary nature of the related variables, so such indices may lose essential local details when integrating multiple variables. In this regard, we proposed a new mathematical framework for generating integrated drought index for meteorological drought monitoring. The geographically weighted regression (GWR) model and principal component analysis were used to composite Moderate-resolution Imaging Spectroradiometer (MODIS) based temperature condition index (TCI), the Vegetation Index based on the Universal Pattern Decomposition method (VIUPD) based vegetation condition index (VCI), tropical rainfall measuring mission (TRMM) based Precipitation Condition Index (PCI) and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) based soil moisture condition index (SMCI). We called the new remote-sensing-based integrated drought index geographical-location-based integrated drought index (GLIDI). We examined the utility of the GLIDI for drought monitoring in various climate divisions across the continental United States (CONUS). GLIDI showed high correlations with in-situ drought indices and outperformed most other existing drought indices. The results also indicate that the performance of GLIDI is not affected by environmental factors such as land cover, precipitation, temperature and soil conditions. As such, the GLIDI has considerable potential for drought monitoring across various environmental conditions.

Calibration of a distributed hydrologic model for six European catchments using remote sensing data

NASA Astrophysics Data System (ADS)

Stisen, S.; Demirel, M. C.; Mendiguren González, G.; Kumar, R.; Rakovec, O.; Samaniego, L. E.

2017-12-01

While observed streamflow has been the single reference for most conventional hydrologic model calibration exercises, the availability of spatially distributed remote sensing observations provide new possibilities for multi-variable calibration assessing both spatial and temporal variability of different hydrologic processes. In this study, we first identify the key transfer parameters of the mesoscale Hydrologic Model (mHM) controlling both the discharge and the spatial distribution of actual evapotranspiration (AET) across six central European catchments (Elbe, Main, Meuse, Moselle, Neckar and Vienne). These catchments are selected based on their limited topographical and climatic variability which enables to evaluate the effect of spatial parameterization on the simulated evapotranspiration patterns. We develop a European scale remote sensing based actual evapotranspiration dataset at a 1 km grid scale driven primarily by land surface temperature observations from MODIS using the TSEB approach. Using the observed AET maps we analyze the potential benefits of incorporating spatial patterns from MODIS data to calibrate the mHM model. This model allows calibrating one-basin-at-a-time or all-basins-together using its unique structure and multi-parameter regionalization approach. Results will indicate any tradeoffs between spatial pattern and discharge simulation during model calibration and through validation against independent internal discharge locations. Moreover, added value on internal water balances will be analyzed.

An Extended Kriging Method to Interpolate Near-Surface Soil Moisture Data Measured by Wireless Sensor Networks

PubMed Central

Zhang, Jialin; Li, Xiuhong; Yang, Rongjin; Liu, Qiang; Zhao, Long; Dou, Baocheng

2017-01-01

In the practice of interpolating near-surface soil moisture measured by a wireless sensor network (WSN) grid, traditional Kriging methods with auxiliary variables, such as Co-kriging and Kriging with external drift (KED), cannot achieve satisfactory results because of the heterogeneity of soil moisture and its low correlation with the auxiliary variables. This study developed an Extended Kriging method to interpolate with the aid of remote sensing images. The underlying idea is to extend the traditional Kriging by introducing spectral variables, and operating on spatial and spectral combined space. The algorithm has been applied to WSN-measured soil moisture data in HiWATER campaign to generate daily maps from 10 June to 15 July 2012. For comparison, three traditional Kriging methods are applied: Ordinary Kriging (OK), which used WSN data only, Co-kriging and KED, both of which integrated remote sensing data as covariate. Visual inspections indicate that the result from Extended Kriging shows more spatial details than that of OK, Co-kriging, and KED. The Root Mean Square Error (RMSE) of Extended Kriging was found to be the smallest among the four interpolation results. This indicates that the proposed method has advantages in combining remote sensing information and ground measurements in soil moisture interpolation. PMID:28617351

Urban heat island impacts on plant phenology: intra-urban variability and response to land cover

NASA Astrophysics Data System (ADS)

Zipper, Samuel C.; Schatz, Jason; Singh, Aditya; Kucharik, Christopher J.; Townsend, Philip A.; Loheide, Steven P., II

2016-05-01

Despite documented intra-urban heterogeneity in the urban heat island (UHI) effect, little is known about spatial or temporal variability in plant response to the UHI. Using an automated temperature sensor network in conjunction with Landsat-derived remotely sensed estimates of start/end of the growing season, we investigate the impacts of the UHI on plant phenology in the city of Madison WI (USA) for the 2012-2014 growing seasons. Median urban growing season length (GSL) estimated from temperature sensors is ˜5 d longer than surrounding rural areas, and UHI impacts on GSL are relatively consistent from year-to-year. Parks within urban areas experience a subdued expression of GSL lengthening resulting from interactions between the UHI and a park cool island effect. Across all growing seasons, impervious cover in the area surrounding each temperature sensor explains >50% of observed variability in phenology. Comparisons between long-term estimates of annual mean phenological timing, derived from remote sensing, and temperature-based estimates of individual growing seasons show no relationship at the individual sensor level. The magnitude of disagreement between temperature-based and remotely sensed phenology is a function of impervious and grass cover surrounding the sensor, suggesting that realized GSL is controlled by both local land cover and micrometeorological conditions.

Object-Based Random Forest Classification of Land Cover from Remotely Sensed Imagery for Industrial and Mining Reclamation

NASA Astrophysics Data System (ADS)

Chen, Y.; Luo, M.; Xu, L.; Zhou, X.; Ren, J.; Zhou, J.

2018-04-01

The RF method based on grid-search parameter optimization could achieve a classification accuracy of 88.16 % in the classification of images with multiple feature variables. This classification accuracy was higher than that of SVM and ANN under the same feature variables. In terms of efficiency, the RF classification method performs better than SVM and ANN, it is more capable of handling multidimensional feature variables. The RF method combined with object-based analysis approach could highlight the classification accuracy further. The multiresolution segmentation approach on the basis of ESP scale parameter optimization was used for obtaining six scales to execute image segmentation, when the segmentation scale was 49, the classification accuracy reached the highest value of 89.58 %. The classification accuracy of object-based RF classification was 1.42 % higher than that of pixel-based classification (88.16 %), and the classification accuracy was further improved. Therefore, the RF classification method combined with object-based analysis approach could achieve relatively high accuracy in the classification and extraction of land use information for industrial and mining reclamation areas. Moreover, the interpretation of remotely sensed imagery using the proposed method could provide technical support and theoretical reference for remotely sensed monitoring land reclamation.

Using hyperspectral data in precision farming applications

USDA-ARS?s Scientific Manuscript database

Precision farming practices such as variable rate applications of fertilizer and agricultural chemicals require accurate field variability mapping. This chapter investigated the value of hyperspectral remote sensing in providing useful information for five applications of precision farming: (a) Soil...

Assessing spatio-temporal variability and trends in modelled and measured Greenland Ice Sheet albedo (2000-2013)

NASA Astrophysics Data System (ADS)

Alexander, P. M.; Tedesco, M.; Fettweis, X.; van de Wal, R. S. W.; Smeets, C. J. P. P.; van den Broeke, M. R.

2014-12-01

Accurate measurements and simulations of Greenland Ice Sheet (GrIS) surface albedo are essential, given the role of surface albedo in modulating the amount of absorbed solar radiation and meltwater production. In this study, we assess the spatio-temporal variability of GrIS albedo during June, July, and August (JJA) for the period 2000-2013. We use two remote sensing products derived from data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS), as well as outputs from the Modèle Atmosphérique Régionale (MAR) regional climate model (RCM) and data from in situ automatic weather stations. Our results point to an overall consistency in spatio-temporal variability between remote sensing and RCM albedo, but reveal a difference in mean albedo of up to ~0.08 between the two remote sensing products north of 70° N. At low elevations, albedo values simulated by the RCM are positively biased with respect to remote sensing products by up to ~0.1 and exhibit low variability compared with observations. We infer that these differences are the result of a positive bias in simulated bare ice albedo. MODIS albedo, RCM outputs, and in situ observations consistently indicate a decrease in albedo of -0.03 to -0.06 per decade over the period 2003-2013 for the GrIS ablation area. Nevertheless, satellite products show a decline in JJA albedo of -0.03 to -0.04 per decade for regions within the accumulation area that is not confirmed by either the model or in situ observations. These findings appear to contradict a previous study that found an agreement between in situ and MODIS trends for individual months. The results indicate a need for further evaluation of high elevation albedo trends, a reconciliation of MODIS mean albedo at high latitudes, and the importance of accurately simulating bare ice albedo in RCMs.

Variable acuity remote viewing system flight demonstration

NASA Technical Reports Server (NTRS)

Fisher, R. W.

1983-01-01

The Variable Acuity Remote Viewing System (VARVS), originally developed under contract to the Navy (ONR) as a laboratory brassboard, was modified for flight demonstration. The VARVS system was originally conceived as a technique which could circumvent the acuity/field of view/bandwidth tradeoffs that exists in remote viewing to provide a nearly eye limited display in both field of view (160 deg) and resolution (2 min arc) while utilizing conventional TV sensing, transmission, and display equipment. The modifications for flight demonstration consisted of modifying the sensor so it could be installed and flow in a Piper PA20 aircraft, equipped for remote control and modifying the display equipment so it could be integrated with the NASA Research RPB (RPRV) remote control cockpit.

Decision Support Systems To Manage Water Resources At Irrigation District Level In Southern Italy Using Remote Sensing Information. An Integrated Project (AQUATER)

NASA Astrophysics Data System (ADS)

Rinaldi, M.; Castrignanò, A.; Mastrorilli, M.; Rana, G.; Ventrella, D.; Acutis, M.; D'Urso, G.; Mattia, F.

2006-08-01

An efficient management of water resources is crucial point for Italy and in particular for southern areas characterized by Mediterranean climate in order to improve the economical and environmental sustainability of the agricultural activity. A three-year Project (2005-2008) has been funded by the Italian Ministry of Agriculture and Forestry Policies; it involves four Italian research institutions: the Agricultural Research Council (ISA, Bari), the National Research Council (ISSIA, Bari) and two Universities (Federico II-Naples and Milan). It is focused on the remote sensing, the plant and the climate and, for interdisciplinary relationships, the project working group consists of agronomists, engineers and physicists. The aims of the Project are: a) to produce a Decision Support System (DSS) combining remote sensing information, spatial data and simulation models to manage water resources in irrigation districts; b) to simulate irrigation scenarios to evaluate the effects of water stress on crop yield using agro-ecological indicators; c) to identify the most sensitive areas to drought risk in Southern Italy. The tools used in this Project will be: 1. Remote sensing images, topographic maps, soil and land use maps; 2. Geographic Information Systems; 3. Geostatistic methodologies; 4. Ground truth measurements (land use, canopy and soil temperatures, soil and plant water status, Normalized Difference Vegetation Index, Crop Water Stress Index, Leaf Area Index, actual evapotranspiration, crop coefficients, crop yield, agro-ecological indicators); 5. Crop simulation models. The Project is structured in four work packages with specific objectives, high degree of interaction and information exchange: 1) Remote Sensing and Image Analysis; 2) Cropping Systems; 3) Modelling and Softwares Development; 4) Stakeholders. The final product will be a DSS with the purpose of integrating remote sensing images, to estimate crop and soil variables related to drought, to assimilate these variables into a simulation model at district scale and, finally, to estimate evapotranspiration, plant water status and drought indicators. A project Web home page, a technical course about DSS for the employers of irrigation authorities and dissemination of results (meetings, publications, reports), are also planned.

Developing a Dynamic SPARROW Water Quality Decision Support System Using NASA Remotely-Sensed Products

NASA Astrophysics Data System (ADS)

Al-Hamdan, M. Z.; Smith, R. A.; Hoos, A.; Schwarz, G. E.; Alexander, R. B.; Crosson, W. L.; Srikishen, J.; Estes, M., Jr.; Cruise, J.; Al-Hamdan, A.; Ellenburg, W. L., II; Flores, A.; Sanford, W. E.; Zell, W.; Reitz, M.; Miller, M. P.; Journey, C. A.; Befus, K. M.; Swann, R.; Herder, T.; Sherwood, E.; Leverone, J.; Shelton, M.; Smith, E. T.; Anastasiou, C. J.; Seachrist, J.; Hughes, A.; Graves, D.

2017-12-01

The USGS Spatially Referenced Regression on Watershed Attributes (SPARROW) surface water quality modeling system has been widely used for long term, steady state water quality analysis. However, users have increasingly requested a dynamic version of SPARROW that can provide seasonal estimates of nutrients and suspended sediment to receiving waters. The goal of this NASA-funded project is to develop a dynamic decision support system to enhance the southeast SPARROW water quality model and finer-scale dynamic models for selected coastal watersheds through the use of remotely-sensed data and other NASA Land Information System (LIS) products. The spatial and temporal scale of satellite remote sensing products and LIS modeling data make these sources ideal for the purposes of development and operation of the dynamic SPARROW model. Remote sensing products including MODIS vegetation indices, SMAP surface soil moisture, and OMI atmospheric chemistry along with LIS-derived evapotranspiration (ET) and soil temperature and moisture products will be included in model development and operation. MODIS data will also be used to map annual land cover/land use in the study areas and in conjunction with Landsat and Sentinel to identify disturbed areas that might be sources of sediment and increased phosphorus loading through exposure of the bare soil. These data and others constitute the independent variables in a regression analysis whose dependent variables are the water quality constituents total nitrogen, total phosphorus, and suspended sediment. Remotely-sensed variables such as vegetation indices and ET can be proxies for nutrient uptake by vegetation; MODIS Leaf Area Index can indicate sources of phosphorus from vegetation; soil moisture and temperature are known to control rates of denitrification; and bare soil areas serve as sources of enhanced nutrient and sediment production. The enhanced SPARROW dynamic models will provide improved tools for end users to manage water quality in near real time and for the formulation of future scenarios to inform strategic planning. Time-varying SPARROW outputs will aid water managers in decision making regarding allocation of resources in protecting aquatic habitats, planning for harmful algal blooms, and restoration of degraded habitats, stream segments, or lakes.

Model for the Interpretation of Hyperspectral Remote-Sensing Reflectance

NASA Technical Reports Server (NTRS)

Lee, Zhongping; Carder, Kendall L.; Hawes, Steve K.; Steward, Robert G.; Peacock, Thomas G.; Davis, Curtiss O.

1994-01-01

Remote-sensing reflectance is easier to interpret for the open ocean than for coastal regions because the optical signals are highly coupled to the phytoplankton (e.g., chlorophyll) concentrations. For estuarine or coastal waters, variable terrigenous colored dissolved organic matter (CDOM), suspended sediments, and bottom reflectance, all factors that do not covary with the pigment concentration, confound data interpretation. In this research, remote-sensing reflectance models are suggested for coastal waters, to which contributions that are due to bottom reflectance, CDOM fluorescence, and water Raman scattering are included. Through the use of two parameters to model the combination of the backscattering coefficient and the Q factor, excellent agreement was achieved between the measured and modeled remote-sensing reflectance for waters from the West Florida Shelf to the Mississippi River plume. These waters cover a range of chlorophyll of 0.2-40 mg/cu m and gelbstoff absorption at 440 nm from 0.02-0.4/m. Data with a spectral resolution of 10 nm or better, which is consistent with that provided by the airborne visible and infrared imaging spectrometer (AVIRIS) and spacecraft spectrometers, were used in the model evaluation.

Estimation of Monthly Near Surface Air Temperature Using Geographically Weighted Regression in China

NASA Astrophysics Data System (ADS)

Wang, M. M.; He, G. J.; Zhang, Z. M.; Zhang, Z. J.; Liu, X. G.

2018-04-01

Near surface air temperature (NSAT) is a primary descriptor of terrestrial environment conditions. The availability of NSAT with high spatial resolution is deemed necessary for several applications such as hydrology, meteorology and ecology. In this study, a regression-based NSAT mapping method is proposed. This method is combined remote sensing variables with geographical variables, and uses geographically weighted regression to estimate NSAT. The altitude was selected as geographical variable; and the remote sensing variables include land surface temperature (LST) and Normalized Difference vegetation index (NDVI). The performance of the proposed method was assessed by predict monthly minimum, mean, and maximum NSAT from point station measurements in China, a domain with a large area, complex topography, and highly variable station density, and the NSAT maps were validated against the meteorology observations. Validation results with meteorological data show the proposed method achieved an accuracy of 1.58 °C. It is concluded that the proposed method for mapping NSAT is very operational and has good precision.

Using Remote Sensing to Estimate Crop Water Use to Improve Irrigation Water Management

NASA Astrophysics Data System (ADS)

Reyes-Gonzalez, Arturo

Irrigation water is scarce. Hence, accurate estimation of crop water use is necessary for proper irrigation managements and water conservation. Satellite-based remote sensing is a tool that can estimate crop water use efficiently. Several models have been developed to estimate crop water requirement or actual evapotranspiration (ETa) using remote sensing. One of them is the Mapping EvapoTranspiration at High Resolution using Internalized Calibration (METRIC) model. This model has been compared with other methods for ET estimations including weighing lysimeters, pan evaporation, Bowen Ratio Energy Balance System (BREBS), Eddy Covariance (EC), and sap flow. However, comparison of METRIC model outputs to an atmometer for ETa estimation has not yet been attempted in eastern South Dakota. The results showed a good relationship between ETa estimated by the METRIC model and estimated with atmometer (r2 = 0.87 and RMSE = 0.65 mm day-1). However, ETa values from atmometer were consistently lower than ET a values from METRIC. The verification of remotely sensed estimates of surface variables is essential for any remote-sensing study. The relationships between LAI, Ts, and ETa estimated using the remote sensing-based METRIC model and in-situ measurements were established. The results showed good agreement between the variables measured in situ and estimated by the METRIC model. LAI showed r2 = 0.76, and RMSE = 0.59 m2 m -2, Ts had r2 = 0.87 and RMSE 1.24 °C and ETa presented r2= 0.89 and RMSE = 0.71 mm day -1. Estimation of ETa using energy balance method can be challenging and time consuming. Thus, there is a need to develop a simple and fast method to estimate ETa using minimum input parameters. Two methods were used, namely 1) an energy balance method (EB method) that used input parameters of the Landsat image, weather data, a digital elevation map, and a land cover map and 2) a Kc-NDVI method that use two input parameters: the Landsat image and weather data. A strong relationship was found between the two methods with r2 of 0.97 and RMSE of 0.37 mm day -1. Hence, the Kc-NDVI method performed well for ET a estimations, indicating that Kc-NDVI method can be a robust and reliable method to estimate ETa in a short period of time. Estimation of crop evapotranspiration (ETc) using satellite remote sensing-based vegetation index such as the Normalized Difference Vegetation Index (NDVI). The NDVI was calculated using near-infrared and red wavebands. The relationship between NDVI and tabulated Kc's was used to generate Kc maps. ETc maps were developed as an output of Kc maps multiplied by reference evapotranspiration (ETr). Daily ETc maps helped to explain the variability of crop water use during the growing season. Based on the results we can conclude that ETc maps developed from remotely sensed multispectral vegetation indices are a useful tool for quantifying crop water use at regional and field scales.

AN IMPROVED STRATEGY FOR REGRESSION OF BIOPHYSICAL VARIABLES AND LANDSAT ETM+ DATA. (R828309)

EPA Science Inventory

Empirical models are important tools for relating field-measured biophysical variables to remote sensing data. Regression analysis has been a popular empirical method of linking these two types of data to provide continuous estimates for variables such as biomass, percent wood...

Remote Sensing of In-Flight Icing Conditions: Operational, Meteorological, and Technological Considerations

NASA Technical Reports Server (NTRS)

Ryerson, Charles C.

2000-01-01

Remote-sensing systems that map aircraft icing conditions in the flight path from airports or aircraft would allow icing to be avoided and exited. Icing remote-sensing system development requires consideration of the operational environment, the meteorological environment, and the technology available. Operationally, pilots need unambiguous cockpit icing displays for risk management decision-making. Human factors, aircraft integration, integration of remotely sensed icing information into the weather system infrastructures, and avoid-and-exit issues need resolution. Cost, maintenance, power, weight, and space concern manufacturers, operators, and regulators. An icing remote-sensing system detects cloud and precipitation liquid water, drop size, and temperature. An algorithm is needed to convert these conditions into icing potential estimates for cockpit display. Specification development requires that magnitudes of cloud microphysical conditions and their spatial and temporal variability be understood at multiple scales. The core of an icing remote-sensing system is the technology that senses icing microphysical conditions. Radar and microwave radiometers penetrate clouds and can estimate liquid water and drop size. Retrieval development is needed; differential attenuation and neural network assessment of multiple-band radar returns are most promising to date. Airport-based radar or radiometers are the most viable near-term technologies. A radiometer that profiles cloud liquid water, and experimental techniques to use radiometers horizontally, are promising. The most critical operational research needs are to assess cockpit and aircraft system integration, develop avoid-and-exit protocols, assess human factors, and integrate remote-sensing information into weather and air traffic control infrastructures. Improved spatial characterization of cloud and precipitation liquid-water content, drop-size spectra, and temperature are needed, as well as an algorithm to convert sensed conditions into a measure of icing potential. Technology development also requires refinement of inversion techniques. These goals can be accomplished with collaboration among federal agencies including NASA, the FAA, the National Center for Atmospheric Research, NOAA, and the Department of Defense. This report reviews operational, meteorological, and technological considerations in developing the capability to remotely map in-flight icing conditions from the ground and from the air.

Two Optical Atmospheric Remote Sensing Techniques and AN Associated Analytic Solution to a Class of Integral Equations

NASA Astrophysics Data System (ADS)

Manning, Robert Michael

This work concerns itself with the analysis of two optical remote sensing methods to be used to obtain parameters of the turbulent atmosphere pertinent to stochastic electromagnetic wave propagation studies, and the well -posed solution to a class of integral equations that are central to the development of these remote sensing methods. A remote sensing technique is theoretically developed whereby the temporal frequency spectrum of the scintillations of a stellar source or a point source within the atmosphere, observed through a variable radius aperture, is related to the space-time spectrum of atmospheric scintillation. The key to this spectral remote sensing method is the spatial filtering performed by a finite aperture. The entire method is developed without resorting to a priori information such as results from stochastic wave propagation theory. Once the space-time spectrum of the scintillations is obtained, an application of known results of atmospheric wave propagation theory and simple geometric considerations are shown to yield such important information such as the spectrum of atmospheric turbulence, the cross-wind velocity, and the path profile of the atmospheric refractive index structure parameter. A method is also developed to independently verify the Taylor frozen flow hypothesis. The success of the spectral remote sensing method relies on the solution to a Fredholm integral equation of the first kind. An entire class of such equations, that are peculiar to inverse diffraction problems, is studied and a well-posed solution (in the sense of Hadamard) is obtained and probed. Conditions of applicability are derived and shown not to limit the useful operating range of the spectral remote sensing method. The general integral equation solution obtained is then applied to another remote sensing problem having to do with the characterization of the particle size distribution to atmospheric aerosols and hydrometeors. By measuring the diffraction pattern in the focal plane of a lens created by the passage of a laser beam through a distribution of particles, it is shown that the particle-size distribution of the particles can be obtained. An intermediate result of the analysis also gives the total volume concentration of the particles.

Using mm-scale seafloor roughness to improve monitoring of macrobenthos by remote sensing

NASA Astrophysics Data System (ADS)

Feldens, Peter; Schönke, Mischa; Wilken, Dennis; Papenmeier, Svenja

2017-04-01

In this study, we determine seafloor roughness at mm-scales by laser line-scanning to improve the remote marine habitat monitoring of macrobenthic organisms. Towards this purpose, a new autonomous lander system has been developed. Remote sensing of the seafloor is required to obtain a comprehensive view of the marine environment. It allows for analyzing spatiotemporal dynamics, monitoring of natural seabed variations, and evaluating possible anthropogenic impacts, all being crucial in regard to marine spatial planning as well as the sustainable and economic use of the sea. One aspect of ongoing remote sensing research is the identification of marine life, including both fauna and flora. The monitoring of seafloor fauna - including benthic communities - is mainly done using optical imaging systems and sample retrieval. The identification of new remote sensing indicator variables characteristic for the physical nature of the respective habitat would allow an improved spatial monitoring. A poorly investigated indicator variable is mm-scale seafloor microtopography and -roughness, which can be measured by laser line scanning and in turn strongly affects acoustic scatter. Two field campaigns have been conducted offshore Sylt Island in 2015 and 2016 to measure the microtopography of seafloor covered by sand masons, blue mussels, and oysters and to collect multi-frequency acoustic data. The acoustic data and topography of the blue mussel and oyster fields are currently being analyzed. The mm-scale microtopography of sand mason covered seafloor were transformed into the frequency domain and the average of the magnitude at different spatial wavelengths was used as a measure of roughness. The presence of sand masons causes a measurable difference in roughness magnitude at spatial wavelengths between 0.02 m and 0.0036 m, with the magnitude depending on sand mason abundance. This effect was not detected by commonly used 1D roughness profiles but required consideration of the complete spectrum. The influenced spatial wavelengths correspond to acoustic frequencies of 75 kHz and 400 kHz that are common for acoustic monitoring purposes. The available results indicate that the development of habitat-specific indicator variables, e.g. related to the abundance of sand masons or mussels, is possible and that remote sensing may assist the monitoring of benthic habitats in the future.

Hyperspectral remote sensing of coral reefs: Deriving bathymetry, aquatic optical properties and a benthic spectral unmixing classification using AVIRIS data in the Hawaiian Islands

NASA Astrophysics Data System (ADS)

Goodman, James Ansell

My research focuses on the development and application of hyperspectral remote sensing as a valuable component in the assessment and management of coral ecosystems. Remote sensing provides an important quantitative ability to investigate the spatial dynamics of coral health and evaluate the impacts of local, regional and global change on this important natural resource. Furthermore, advances in detector capabilities and analysis methods, particularly with respect to hyperspectral remote sensing, are also increasing the accuracy and level of effectiveness of the resulting data products. Using imagery of Kaneohe Bay and French Frigate Shoals in the Hawaiian Islands, acquired in 2000 by NASA's Airborne Visible InfraRed Imaging Spectrometer (AVIRIS), I developed, applied and evaluated algorithms for analyzing coral reefs using hyperspectral remote sensing data. Research included developing methods for acquiring in situ underwater reflectance, collecting spectral measurements of the dominant bottom components in Kaneohe Bay, applying atmospheric correction and sunglint removal algorithms, employing a semianalytical optimization model to derive bathymetry and aquatic optical properties, and developing a linear unmixing approach for deriving bottom composition. Additionally, algorithm development focused on using fundamental scientific principles to facilitate the portability of methods to diverse geographic locations and across variable environmental conditions. Assessments of this methodology compared favorably with available field measurements and habitat information, and the overall analysis demonstrated the capacity to derive information on water properties, bathymetry and habitat composition. Thus, results illustrated a successful approach for extracting environmental information and habitat composition from a coral reef environment using hyperspectral remote sensing.

Extracting Forest Canopy Characteristics from Remote Sensing Imagery: Implications for Sentinel-2 Mission

NASA Astrophysics Data System (ADS)

Gholizadeh, Asa; Kopaekova, Veronika; Rogass, Christian; Mielke, Christian; Misurec, Jan

2016-08-01

Systematic quantification and monitoring of forest biophysical and biochemical variables is required to assess the response of ecosystems to climate change. Remote sensing has been introduced as a time and cost- efficient way to carry out large scale monitoring of vegetation parameters. Red-Edge Position (REP) is a hyperspectrally detectable parameter which is sensitive to vegetation Chl. In the current study, REP was modelled for the Norway spruce forest canopy resampled to HyMap and Sentinel-2 spectral resolution as well as calculated from the real HyMap and Sentinel-2 simulated data. Different REP extraction methods (4PLI, PF, LE, 4PLIH and 4PLIS) were assessed. The study showed the way for effective utilization of the forthcoming hyper and superspectral remote sensing sensors from orbit to monitor vegetation attributes.

The investigation of advanced remote sensing, radiative transfer and inversion techniques for the measurement of atmospheric constituents

NASA Technical Reports Server (NTRS)

Deepak, Adarsh; Wang, Pi-Huan

1985-01-01

The research program is documented for developing space and ground-based remote sensing techniques performed during the period from December 15, 1977 to March 15, 1985. The program involved the application of sophisticated radiative transfer codes and inversion methods to various advanced remote sensing concepts for determining atmospheric constituents, particularly aerosols. It covers detailed discussions of the solar aureole technique for monitoring columnar aerosol size distribution, and the multispectral limb scattered radiance and limb attenuated radiance (solar occultation) techniques, as well as the upwelling scattered solar radiance method for determining the aerosol and gaseous characteristics. In addition, analytical models of aerosol size distribution and simulation studies of the limb solar aureole radiance technique and the variability of ozone at high altitudes during satellite sunrise/sunset events are also described in detail.

Using Remotely Sensed Information for Near Real-Time Landslide Hazard Assessment

NASA Technical Reports Server (NTRS)

Kirschbaum, Dalia; Adler, Robert; Peters-Lidard, Christa

2013-01-01

The increasing availability of remotely sensed precipitation and surface products provides a unique opportunity to explore how landslide susceptibility and hazard assessment may be approached at larger spatial scales with higher resolution remote sensing products. A prototype global landslide hazard assessment framework has been developed to evaluate how landslide susceptibility and satellite-derived precipitation estimates can be used to identify potential landslide conditions in near-real time. Preliminary analysis of this algorithm suggests that forecasting errors are geographically variable due to the resolution and accuracy of the current susceptibility map and the application of satellite-based rainfall estimates. This research is currently working to improve the algorithm through considering higher spatial and temporal resolution landslide susceptibility information and testing different rainfall triggering thresholds, antecedent rainfall scenarios, and various surface products at regional and global scales.

Remote Sensing Proxies for Vector-borne Disease Risk Assessment (Invited)

NASA Astrophysics Data System (ADS)

Anyamba, A.

2010-12-01

The spread of re-emerging vector-borne diseases such Rift Valley fever (RVF) and Chikungunya (CHIK) is a major issue of global public health concern. This combined with a variable climate regime has opened an avenue for satellite remote sensing to contribute towards a comprehensive understanding of some of the drivers influencing such vector-borne disease outbreaks. Satellite derived measurements such as vegetation indices, rainfall estimates, and land-surface temperature; can be used to infer the complex mosaic of factors that influence ecology and habitat suitability, emergence and population dynamics of disease vectors. However, there are still some gaps in application including appropriate temporal resolution of remote sensing measurements, the complexity of the virus-vector-disease-ecology system and human components that contribute to disease risk that need to be addressed. Geographic Distribution of Recent Rift Valley fever oubreaks

Remote Sensing of Terrestrial Snow and Ice for Global Change Studies

NASA Technical Reports Server (NTRS)

Kelly, Richard; Hall, Dorothy K.

2007-01-01

Snow and ice play a significant role in the Earth's water cycle and are sensitive and informative indicators climate change. Significant changes in terrestrial snow and ice water storage are forecast, and while evidence of large-scale changes is emerging, in situ measurements alone are insufficient to help us understand and explain these changes. Imaging remote sensing systems are capable of successfully observing snow and ice in the cryosphere. This chapter examines how those remote sensing sensors, that now have more than 35 years of observation records, are capable of providing information about snow cover, snow water equivalent, snow melt, ice sheet temperature and ice sheet albedo. While significant progress has been made, especially in the last five years, a better understanding is required of the records of satellite observations of these cryospheric variables.

Verification of retail food outlet location data from a local health department using ground-truthing and remote-sensing technology: assessing differences by neighborhood characteristics.

PubMed

Rossen, Lauren M; Pollack, Keshia M; Curriero, Frank C

2012-09-01

Obtaining valid and accurate data on community food environments is critical for research evaluating associations between the food environment and health outcomes. This study utilized ground-truthing and remote-sensing technology to validate a food outlet retail list obtained from an urban local health department in Baltimore, Maryland in 2009. Ten percent of outlets (n=169) were assessed, and differences in accuracy were explored by neighborhood characteristics (96 census tracts) to determine if discrepancies were differential or non-differential. Inaccuracies were largely unrelated to a variety of neighborhood-level variables, with the exception of number of vacant housing units. Although remote-sensing technologies are a promising low-cost alternative to direct observation, this study demonstrated only moderate levels of agreement with ground-truthing. Published by Elsevier Ltd.

Efficient Approaches for Propagating Hydrologic Forcing Uncertainty: High-Resolution Applications Over the Western United States

NASA Astrophysics Data System (ADS)

Hobbs, J.; Turmon, M.; David, C. H.; Reager, J. T., II; Famiglietti, J. S.

2017-12-01

NASA's Western States Water Mission (WSWM) combines remote sensing of the terrestrial water cycle with hydrological models to provide high-resolution state estimates for multiple variables. The effort includes both land surface and river routing models that are subject to several sources of uncertainty, including errors in the model forcing and model structural uncertainty. Computational and storage constraints prohibit extensive ensemble simulations, so this work outlines efficient but flexible approaches for estimating and reporting uncertainty. Calibrated by remote sensing and in situ data where available, we illustrate the application of these techniques in producing state estimates with associated uncertainties at kilometer-scale resolution for key variables such as soil moisture, groundwater, and streamflow.

Biological and remote sensing perspectives of pigmentation in coral reef organisms.

PubMed

Hedley, John D; Mumby, Peter J

2002-01-01

Coral reef communities face unprecedented pressures on local, regional and global scales as a consequence of climate change and anthropogenic disturbance. Optical remote sensing, from satellites or aircraft, is possibly the only means of measuring the effects of such stresses at appropriately large spatial scales (many thousands of square kilometres). To map key variables such as coral community structure, percentages of living coral or percentages of dead coral, a remote sensing instrument must be able to distinguish the reflectance spectra (i.e. "spectral signature", reflected light as a function of wavelength) of each category. For biotic classes, reflectance is a complex function of pigmentation, structure and morphology. Studies of coral "colour" fall into two disparate but potentially complementary types. Firstly, biological studies tend to investigate the structure and significance of pigmentation in reef organisms. These studies often lack details that would be useful from a remote sensing perspective such as intraspecific variation in pigment concentration or the contribution of fluorescence to reflectance. Secondly, remote sensing studies take empirical measurements of spectra and seek wavelengths that discriminate benthic categories. Benthic categories used in remote sensing sometimes consist of species groupings that are biologically or spectrally inappropriate (e.g. merging of algal phyla with distinct pigments). Here, we attempt to bridge the gap between biological and remote sensing perspectives of pigmentation in reef taxa. The aim is to assess the extent to which spectral discrimination can be given a biological foundation, to reduce the ad hoc nature of discriminatory criteria, and to understand the fundamental (biological) limitations in the spectral separability of biotic classes. Sources of pigmentation in reef biota are reviewed together with remote sensing studies where spectral discrimination has been effectively demonstrated between benthic categories. The basis of reflectance is considered as the sum of pigmented components, such as zooxanthellae, host tissues and skeletons of corals. Problems in the empirical in situ measurement of reflectance are identified, such as the differing types of reflectance which can be measured, the interaction of the light field with morphology, and depth-dependent variability of measured reflectance due to fluorescence. The latter is estimated in some cases to introduce an error of up to 20% when depth differs by 8 m. Spectral features useful in discriminating reef benthos are identified and related to pigmentation. The slope in the reflectance spectra between 650 and 690 nm is dependent on chlorophyll-a concentration and can be used to discriminate bare sand with no algal component from chlorophyll-a containing benthos (algae, corals). The slope in reflectance at various locations between 500 and 560 nm can be useful in discriminating bleached and unbleached corals, possibly due to reduced peridinin concentration. Rhodophyta may be discernible by the presence of a dip in reflectance at 570 nm, due to a phycoerythrin absorption peak. However, the utility of some discriminatory criteria in deeper waters is mitigated by the relatively poor transmission of light through water at longer wavelengths (especially > 600 nm). Contrary to suggested categorizations of fluorescent pigments in coral host tissues, it is shown that these pigments form an almost continuous distribution with respect to their excitation and emission peaks. Remote sensing by induced fluorescence is a promising approach, but further details about the variation and distribution of these pigments are required. It is hoped that this review will promote cross-disciplinary collaboration between pigment biologists and the reef remote sensing community. Where possible, the discriminative criteria adopted in remote sensing should be related to biological phenomena, thus lending an intuitive, process-orientated basis for interpreting spectral data. Similarly, remote sensing may provide a novel scaling perspective to biological studies of pigmentation in reef organisms.

Remotely-sensed, nocturnal, dew point correlates with malaria transmission in Southern Province, Zambia: a time-series study

PubMed Central

2014-01-01

Background Plasmodium falciparum transmission has decreased significantly in Zambia in the last decade. The malaria transmission is influenced by environmental variables. Incorporation of environmental variables in models of malaria transmission likely improves model fit and predicts probable trends in malaria disease. This work is based on the hypothesis that remotely-sensed environmental factors, including nocturnal dew point, are associated with malaria transmission and sustain foci of transmission during the low transmission season in the Southern Province of Zambia. Methods Thirty-eight rural health centres in Southern Province, Zambia were divided into three zones based on transmission patterns. Correlations between weekly malaria cases and remotely-sensed nocturnal dew point, nocturnal land surface temperature as well as vegetation indices and rainfall were evaluated in time-series analyses from 2012 week 19 to 2013 week 36. Zonal as well as clinic-based, multivariate, autoregressive, integrated, moving average (ARIMAX) models implementing environmental variables were developed to model transmission in 2011 week 19 to 2012 week 18 and forecast transmission in 2013 week 37 to week 41. Results During the dry, low transmission season significantly higher vegetation indices, nocturnal land surface temperature and nocturnal dew point were associated with the areas of higher transmission. Environmental variables improved ARIMAX models. Dew point and normalized differentiated vegetation index were significant predictors and improved all zonal transmission models. In the high-transmission zone, this was also seen for land surface temperature. Clinic models were improved by adding dew point and land surface temperature as well as normalized differentiated vegetation index. The mean average error of prediction for ARIMAX models ranged from 0.7 to 33.5%. Forecasts of malaria incidence were valid for three out of five rural health centres; however, with poor results at the zonal level. Conclusions In this study, the fit of ARIMAX models improves when environmental variables are included. There is a significant association of remotely-sensed nocturnal dew point with malaria transmission. Interestingly, dew point might be one of the factors sustaining malaria transmission in areas of general aridity during the dry season. PMID:24927747

Remotely-sensed, nocturnal, dew point correlates with malaria transmission in Southern Province, Zambia: a time-series study.

PubMed

Nygren, David; Stoyanov, Cristina; Lewold, Clemens; Månsson, Fredrik; Miller, John; Kamanga, Aniset; Shiff, Clive J

2014-06-13

Plasmodium falciparum transmission has decreased significantly in Zambia in the last decade. The malaria transmission is influenced by environmental variables. Incorporation of environmental variables in models of malaria transmission likely improves model fit and predicts probable trends in malaria disease. This work is based on the hypothesis that remotely-sensed environmental factors, including nocturnal dew point, are associated with malaria transmission and sustain foci of transmission during the low transmission season in the Southern Province of Zambia. Thirty-eight rural health centres in Southern Province, Zambia were divided into three zones based on transmission patterns. Correlations between weekly malaria cases and remotely-sensed nocturnal dew point, nocturnal land surface temperature as well as vegetation indices and rainfall were evaluated in time-series analyses from 2012 week 19 to 2013 week 36. Zonal as well as clinic-based, multivariate, autoregressive, integrated, moving average (ARIMAX) models implementing environmental variables were developed to model transmission in 2011 week 19 to 2012 week 18 and forecast transmission in 2013 week 37 to week 41. During the dry, low transmission season significantly higher vegetation indices, nocturnal land surface temperature and nocturnal dew point were associated with the areas of higher transmission. Environmental variables improved ARIMAX models. Dew point and normalized differentiated vegetation index were significant predictors and improved all zonal transmission models. In the high-transmission zone, this was also seen for land surface temperature. Clinic models were improved by adding dew point and land surface temperature as well as normalized differentiated vegetation index. The mean average error of prediction for ARIMAX models ranged from 0.7 to 33.5%. Forecasts of malaria incidence were valid for three out of five rural health centres; however, with poor results at the zonal level. In this study, the fit of ARIMAX models improves when environmental variables are included. There is a significant association of remotely-sensed nocturnal dew point with malaria transmission. Interestingly, dew point might be one of the factors sustaining malaria transmission in areas of general aridity during the dry season.

Photosynthetic parameters in the Beaufort Sea in relation to the phytoplankton community structure

NASA Astrophysics Data System (ADS)

Huot, Y.; Babin, M.; Bruyant, F.

2013-05-01

To model phytoplankton primary production from remotely sensed data, a method to estimate photosynthetic parameters describing the photosynthetic rates per unit biomass is required. Variability in these parameters must be related to environmental variables that are measurable remotely. In the Arctic, a limited number of measurements of photosynthetic parameters have been carried out with the concurrent environmental variables needed. Such measurements and their relationship to environmental variables will be required to improve the accuracy of remotely sensed estimates of phytoplankton primary production and our ability to predict future changes. During the MALINA cruise, a large dataset of these parameters was obtained. Together with previously published datasets, we use environmental and trophic variables to provide functional relationships for these parameters. In particular, we describe several specific aspects: the maximum rate of photosynthesis (Pmaxchl) normalized to chlorophyll decreases with depth and is higher for communities composed of large cells; the saturation parameter (Ek) decreases with depth but is independent of the community structure; and the initial slope of the photosynthesis versus irradiance curve (αchl) normalized to chlorophyll is independent of depth but is higher for communities composed of larger cells. The photosynthetic parameters were not influenced by temperature over the range encountered during the cruise (-2 to 8 °C).

Thermal and Hydrologic Signatures of Soil Controls on Evaporation: A Combined Energy and Water Balance Approach with Implications for Remote Sensing of Evaporation

NASA Technical Reports Server (NTRS)

Salvucci, Guido D.

2000-01-01

The overall goal of this research is to examine the feasibility of applying a newly developed diagnostic model of soil water evaporation to large land areas using remotely sensed input parameters. The model estimates the rate of soil evaporation during periods when it is limited by the net transport resulting from competing effects of capillary rise and drainage. The critical soil hydraulic properties are implicitly estimated via the intensity and duration of the first stage (energy limited) evaporation, removing a major obstacle in the remote estimation of evaporation over large areas. This duration, or 'time to drying' (t(sub d)) is revealed through three signatures detectable in time series of remote sensing variables. The first is a break in soil albedo that occurs as a small vapor transmission zone develops near the surface. The second is a break in either surface to air temperature differences or in the diurnal surface temperature range, both of which indicate increased sensible heat flux (and/or storage) required to balance the decrease in latent heat flux. The third is a break in the temporal pattern of near surface soil moisture. Soil moisture tends to decrease rapidly during stage I drying (as water is removed from storage), and then become more or less constant during soil limited, or 'stage II' drying (as water is merely transmitted from deeper soil storage). The research tasks address: (1) improvements in model structure, including extensions to transpiration and aggregation over spatially variable soil and topographic landscape attributes; and (2) applications of the model using remotely sensed input parameters.

Thermal and Hydrologic Signatures of Soil Controls on Evaporation: A Combined Energy and Water Balance Approach with Implications for Remote Sensing of Evaporation

NASA Technical Reports Server (NTRS)

Salvucci, Guido D.

1997-01-01

The overall goal of this research is to examine the feasibility of applying a newly developed diagnostic model of soil water evaporation to large land areas using remotely sensed input parameters. The model estimates the rate of soil evaporation during periods when it is limited by the net transport resulting from competing effects of capillary rise and drainage. The critical soil hydraulic properties are implicitly estimated via the intensity and duration of the first stage (energy limited) evaporation, removing a major obstacle in the remote estimation of evaporation over large areas. This duration, or "time to drying" (t(sub d)), is revealed through three signatures detectable in time series of remote sensing variables. The first is a break in soil albedo that occurs as a small vapor transmission zone develops near the surface. The second is a break in either surface to air temperature differences or in the diurnal surface temperature range, both of which indicate increased sensible heat flux (and/or storage) required to balance the decrease in latent heat flux. The third is a break in the temporal pattern of near surface soil moisture. Soil moisture tends to decrease rapidly during stage 1 drying (as water is removed from storage), and then become more or less constant during soil limited, or "stage 2" drying (as water is merely transmitted from deeper soil storage). The research tasks address: (1) improvements in model structure, including extensions to transpiration and aggregation over spatially variable soil and topographic landscape attributes; and (2) applications of the model using remotely sensed input parameters.

Data Collection for Disaster Response from the International Space Station

NASA Astrophysics Data System (ADS)

Stefanov, W. L.; Evans, C. A.

2015-04-01

Remotely sensed data acquired by orbital sensor systems has emerged as a vital tool to identify the extent of damage resulting from a natural disaster, as well as providing near-real time mapping support to response efforts on the ground and humanitarian aid efforts. The International Space Station (ISS) is a unique terrestrial remote sensing platform for acquiring disaster response imagery. Unlike automated remote-sensing platforms it has a human crew; is equipped with both internal and externally-mounted remote sensing instruments; and has an inclined, low-Earth orbit that provides variable views and lighting (day and night) over 90 percent of the inhabited surface of the Earth. As such, it provides a useful complement to autonomous sensor systems in higher altitude polar orbits. NASA remote sensing assets on the station began collecting International Charter, Space and Major Disasters, also known informally as the International Disaster Charter (IDC) response data in May 2012. Since the start of IDC response in 2012, and as of late March 2015, there have been 123 IDC activations; NASA sensor systems have collected data for thirty-four of these events. Of the successful data collections, eight involved two or more ISS sensor systems responding to the same event. Data has also been collected by International Partners in response to natural disasters, most notably JAXA and Roscosmos/Energia through the Urugan program.

Spectral slopes of the absorption coefficient of colored dissolved and detrital material inverted from UV-visible remote sensing reflectance

PubMed Central

Wei, Jianwei; Lee, Zhongping; Ondrusek, Michael; Mannino, Antonio; Tzortziou, Maria; Armstrong, Roy

2017-01-01

The spectral slope of the absorption coefficient of colored dissolved and detrital material (CDM), Scdm (units: nm−1), is an important optical parameter for characterizing the absorption spectral shape of CDM. Although highly variable in natural waters, in most remote sensing algorithms, this slope is either kept as a constant or empirically modeled with multiband ocean color in the visible domain. In this study, we explore the potential of semianalytically retrieving Scdm with added ocean color information in the ultraviolet (UV) range between 360 and 400 nm. Unique features of hyperspectral remote sensing reflectance in the UV-visible wavelengths (360–500 nm) have been observed in various waters across a range of coastal and open ocean environments. Our data and analyses indicate that ocean color in the UV domain is particularly sensitive to the variation of the CDM spectral slope. Here, we used a synthesized data set to show that adding UV wavelengths to the ocean color measurements will improve the retrieval of Scdm from remote sensing reflectance considerably, while the spectral band settings of past and current satellite ocean color sensors cannot fully account for the spectral variation of remote sensing reflectance. Results of this effort support the concept to include UV wavelengths in the next generation of satellite ocean color sensors. PMID:29201583

Spectral slopes of the absorption coefficient of colored dissolved and detrital material inverted from UV-visible remote sensing reflectance.

PubMed

Wei, Jianwei; Lee, Zhongping; Ondrusek, Michael; Mannino, Antonio; Tzortziou, Maria; Armstrong, Roy

2016-03-01

The spectral slope of the absorption coefficient of colored dissolved and detrital material (CDM), S cdm (units: nm -1 ), is an important optical parameter for characterizing the absorption spectral shape of CDM. Although highly variable in natural waters, in most remote sensing algorithms, this slope is either kept as a constant or empirically modeled with multiband ocean color in the visible domain. In this study, we explore the potential of semianalytically retrieving S cdm with added ocean color information in the ultraviolet (UV) range between 360 and 400 nm. Unique features of hyperspectral remote sensing reflectance in the UV-visible wavelengths (360-500 nm) have been observed in various waters across a range of coastal and open ocean environments. Our data and analyses indicate that ocean color in the UV domain is particularly sensitive to the variation of the CDM spectral slope. Here, we used a synthesized data set to show that adding UV wavelengths to the ocean color measurements will improve the retrieval of S cdm from remote sensing reflectance considerably, while the spectral band settings of past and current satellite ocean color sensors cannot fully account for the spectral variation of remote sensing reflectance. Results of this effort support the concept to include UV wavelengths in the next generation of satellite ocean color sensors.

Predictor variable resolution governs modeled soil types

USDA-ARS?s Scientific Manuscript database

Soil mapping identifies different soil types by compressing a unique suite of spatial patterns and processes across multiple spatial scales. It can be quite difficult to quantify spatial patterns of soil properties with remotely sensed predictor variables. More specifically, matching the right scale...

USE OF REMOTELY SENSED DATA FOR PARAMETERIZING AND VALIDATING LAND-USE HYDROLOGIC MODELS

EPA Science Inventory

Variability in vegetation greenness was determined for the Galveston Bay watershed using biweekly Normalized Difference Vegetation Index (NDVI) data derived from the Advanced Very High Resolution Radiometer (AVHRR) flown on NOAA satellites. NDVI variability was compared with regi...

Propagation Limitations in Remote Sensing.

DTIC Science & Technology

Contents: Multi-sensors and systems in remote sensing ; Radar sensing systems over land; Remote sensing techniques in oceanography; Influence of...propagation media and background; Infrared techniques in remote sensing ; Photography in remote sensing ; Analytical studies in remote sensing .

The variability of winds over the ocean

NASA Technical Reports Server (NTRS)

Pierson, W. J.

1981-01-01

The present state of knowledge of the synoptic scale, the mesoscale, and the microscale in describing the winds, especially over the ocean, is summarized both in terms of conventional data and remotely sensed properties and effects of the winds. A description is then given of some of the areas posing problems in modeling each scale and interpreting the various kinds of measurements that are made. It is noted that not much is known about the wind, especially in the mesoscale, that affects the ability to use remotely sensed data in an optimum way.

Advancing Technologies for Climate Observation

NASA Technical Reports Server (NTRS)

Wu, D.; Esper, J.; Ehsan, N.; Johnson, T.; Mast, W.; Piepmeier, J.; Racette, P.

2014-01-01

Climate research needs Accurate global cloud ice measurements Cloud ice properties are fundamental controlling variables of radiative transfer and precipitation Cost-effective, sensitive instruments for diurnal and wide-swath coverage Mature technology for space remote sensing IceCube objectivesDevelop and validate a flight-qualified 883 GHz receiver for future use in ice cloud radiometer missions Raise TRL (57) of 883 GHz receiver technology Reduce instrument cost and risk by developing path to space for COTS sub-mm-wave receiver systems Enable remote sensing of global cloud ice with advanced technologies and techniques

Using Remote Sensing Mapping and Growth Response to Environmental Variability to Aide Aquatic Invasive Plant Management

NASA Technical Reports Server (NTRS)

Bubenheim, David L.; Schlick, Greg; Genovese, Vanessa; Wilson, Kenneth D.

2018-01-01

Management of aquatic weeds in complex watersheds and river systems present many challenges to assessment, planning and implementation of management practices for floating and submerged aquatic invasive plants. The Delta Region Areawide Aquatic Weed Project (DRAAWP), a USDA sponsored area-wide project, is working to enhance planning, decision-making and operational efficiency in the California Sacramento-San Joaquin Delta. Satellite and airborne remote sensing are used map (area coverage and biomass density), direct operations, and assess management impacts on plant communities. Archived satellite records enable review of results following previous climate and management events and aide in developing long-term strategies. Examples of remote sensing aiding effectiveness of aquatic weed management will be discussed as well as areas for potential technological improvement. Modeling at local and watershed scales using the SWAT modeling tool provides insight into land-use effects on water quality (described by Zhang in same Symposium). Controlled environment growth studies have been conducted to quantify the growth response of invasive aquatic plants to water quality and other environmental factors. Environmental variability occurs across a range of time scales from long-term climate and seasonal trends to short-term water flow mediated variations. Response time for invasive species response are examined at time scales of weeks, day, and hours using a combination of study duration and growth assessment techniques to assess water quality, temperature (air and water), nitrogen, phosphorus, and light effects. These provide response parameters for plant growth models in response to the variation and interact with management and economic models associated with aquatic weed management. Plant growth models are to be informed by remote sensing and applied spatially across the Delta to balance location and type of aquatic plant, growth response to altered environments and phenology. Initial utilization of remote sensing tools developed for mapping of aquatic invasive plants improved operational efficiency in management practices. These assessment methods provide a comprehensive and quantitative view of aquatic invasive plants communities in the California Delta.

Modeling the Hydrological Regime of Turkana Lake (Kenya, Ethiopia) by Combining Spatially Distributed Hydrological Modeling and Remote Sensing Datasets

NASA Astrophysics Data System (ADS)

Anghileri, D.; Kaelin, A.; Peleg, N.; Fatichi, S.; Molnar, P.; Roques, C.; Longuevergne, L.; Burlando, P.

2017-12-01

Hydrological modeling in poorly gauged basins can benefit from the use of remote sensing datasets although there are challenges associated with the mismatch in spatial and temporal scales between catchment scale hydrological models and remote sensing products. We model the hydrological processes and long-term water budget of the Lake Turkana catchment, a transboundary basin between Kenya and Ethiopia, by integrating several remote sensing products into a spatially distributed and physically explicit model, Topkapi-ETH. Lake Turkana is the world largest desert lake draining a catchment of 145'500 km2. It has three main contributing rivers: the Omo river, which contributes most of the annual lake inflow, the Turkwel river, and the Kerio rivers, which contribute the remaining part. The lake levels have shown great variations in the last decades due to long-term climate fluctuations and the regulation of three reservoirs, Gibe I, II, and III, which significantly alter the hydrological seasonality. Another large reservoir is planned and may be built in the next decade, generating concerns about the fate of Lake Turkana in the long run because of this additional anthropogenic pressure and increasing evaporation driven by climate change. We consider different remote sensing datasets, i.e., TRMM-V7 for precipitation, MERRA-2 for temperature, as inputs to the spatially distributed hydrological model. We validate the simulation results with other remote sensing datasets, i.e., GRACE for total water storage anomalies, GLDAS-NOAH for soil moisture, ERA-Interim/Land for surface runoff, and TOPEX/Poseidon for satellite altimetry data. Results highlight how different remote sensing products can be integrated into a hydrological modeling framework accounting for their relative uncertainties. We also carried out simulations with the artificial reservoirs planned in the north part of the catchment and without any reservoirs, to assess their impacts on the catchment hydrological regime and the Lake Turkana level variability.

A multi-sensor remote sensing approach for measuring primary production from space

NASA Technical Reports Server (NTRS)

Gautier, Catherine

1989-01-01

It is proposed to develop a multi-sensor remote sensing method for computing marine primary productivity from space, based on the capability to measure the primary ocean variables which regulate photosynthesis. The three variables and the sensors which measure them are: (1) downwelling photosynthetically available irradiance, measured by the VISSR sensor on the GOES satellite, (2) sea-surface temperature from AVHRR on NOAA series satellites, and (3) chlorophyll-like pigment concentration from the Nimbus-7/CZCS sensor. These and other measured variables would be combined within empirical or analytical models to compute primary productivity. With this proposed capability of mapping primary productivity on a regional scale, we could begin realizing a more precise and accurate global assessment of its magnitude and variability. Applications would include supplementation and expansion on the horizontal scale of ship-acquired biological data, which is more accurate and which supplies the vertical components of the field, monitoring oceanic response to increased atmospheric carbon dioxide levels, correlation with observed sedimentation patterns and processes, and fisheries management.

Quantifying biological integrity of California sage scrub communities using plant life-form cover.

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

Hamada, Y.; Stow, D. A.; Franklin, J.

2010-01-01

The California sage scrub (CSS) community type in California's Mediterranean-type ecosystems supports a large number of rare, threatened, and endangered species, and is critically degraded and endangered. Monitoring ecological variables that provide information about community integrity is vital to conserving these biologically diverse communities. Fractional cover of true shrub, subshrub, herbaceous vegetation, and bare ground should fill information gaps between generalized vegetation type maps and detailed field-based plot measurements of species composition and provide an effective means for quantifying CSS community integrity. Remote sensing is the only tool available for estimating spatially comprehensive fractional cover over large extent, and fractionalmore » cover of plant life-form types is one of the measures of vegetation state that is most amenable to remote sensing. The use of remote sensing does not eliminate the need for either field surveying or vegetation type mapping; rather it will likely require a combination of approaches to reliably estimate life-form cover and to provide comprehensive information for communities. According to our review and synthesis, life-form fractional cover has strong potential for providing ecologically meaningful intermediate-scale information, which is unattainable from vegetation type maps and species-level field measurements. Thus, we strongly recommend incorporating fractional cover of true shrub, subshrub, herb, and bare ground in CSS community monitoring methods. Estimating life-form cover at a 25 m x 25 m spatial scale using remote sensing would be an appropriate approach for initial implementation. Investigation of remote sensing techniques and an appropriate spatial scale; collaboration of resource managers, biologists, and remote sensing specialists, and refinement of protocols are essential for integrating life-form fractional cover mapping into strategies for sustainable long-term CSS community management.« less

Integrating Remote Sensing Information Into A Distributed Hydrological Model for Improving Water Budget Predictions in Large-scale Basins through Data Assimilation.

PubMed

Qin, Changbo; Jia, Yangwen; Su, Z; Zhou, Zuhao; Qiu, Yaqin; Suhui, Shen

2008-07-29

This paper investigates whether remote sensing evapotranspiration estimates can be integrated by means of data assimilation into a distributed hydrological model for improving the predictions of spatial water distribution over a large river basin with an area of 317,800 km2. A series of available MODIS satellite images over the Haihe River basin in China are used for the year 2005. Evapotranspiration is retrieved from these 1×1 km resolution images using the SEBS (Surface Energy Balance System) algorithm. The physically-based distributed model WEP-L (Water and Energy transfer Process in Large river basins) is used to compute the water balance of the Haihe River basin in the same year. Comparison between model-derived and remote sensing retrieval basin-averaged evapotranspiration estimates shows a good piecewise linear relationship, but their spatial distribution within the Haihe basin is different. The remote sensing derived evapotranspiration shows variability at finer scales. An extended Kalman filter (EKF) data assimilation algorithm, suitable for non-linear problems, is used. Assimilation results indicate that remote sensing observations have a potentially important role in providing spatial information to the assimilation system for the spatially optical hydrological parameterization of the model. This is especially important for large basins, such as the Haihe River basin in this study. Combining and integrating the capabilities of and information from model simulation and remote sensing techniques may provide the best spatial and temporal characteristics for hydrological states/fluxes, and would be both appealing and necessary for improving our knowledge of fundamental hydrological processes and for addressing important water resource management problems.

Predicting Near-Term Water Quality from Satellite Observations of Watershed Conditions

NASA Astrophysics Data System (ADS)

Weiss, W. J.; Wang, L.; Hoffman, K.; West, D.; Mehta, A. V.; Lee, C.

2017-12-01

Despite the strong influence of watershed conditions on source water quality, most water utilities and water resource agencies do not currently have the capability to monitor watershed sources of contamination with great temporal or spatial detail. Typically, knowledge of source water quality is limited to periodic grab sampling; automated monitoring of a limited number of parameters at a few select locations; and/or monitoring relevant constituents at a treatment plant intake. While important, such observations are not sufficient to inform proactive watershed or source water management at a monthly or seasonal scale. Satellite remote sensing data on the other hand can provide a snapshot of an entire watershed at regular, sub-monthly intervals, helping analysts characterize watershed conditions and identify trends that could signal changes in source water quality. Accordingly, the authors are investigating correlations between satellite remote sensing observations of watersheds and source water quality, at a variety of spatial and temporal scales and lags. While correlations between remote sensing observations and direct in situ measurements of water quality have been well described in the literature, there are few studies that link remote sensing observations across a watershed with near-term predictions of water quality. In this presentation, the authors will describe results of statistical analyses and discuss how these results are being used to inform development of a desktop decision support tool to support predictive application of remote sensing data. Predictor variables under evaluation include parameters that describe vegetative conditions; parameters that describe climate/weather conditions; and non-remote sensing, in situ measurements. Water quality parameters under investigation include nitrogen, phosphorus, organic carbon, chlorophyll-a, and turbidity.

Integrating Remote Sensing Information Into A Distributed Hydrological Model for Improving Water Budget Predictions in Large-scale Basins through Data Assimilation

PubMed Central

Qin, Changbo; Jia, Yangwen; Su, Z.(Bob); Zhou, Zuhao; Qiu, Yaqin; Suhui, Shen

2008-01-01

This paper investigates whether remote sensing evapotranspiration estimates can be integrated by means of data assimilation into a distributed hydrological model for improving the predictions of spatial water distribution over a large river basin with an area of 317,800 km2. A series of available MODIS satellite images over the Haihe River basin in China are used for the year 2005. Evapotranspiration is retrieved from these 1×1 km resolution images using the SEBS (Surface Energy Balance System) algorithm. The physically-based distributed model WEP-L (Water and Energy transfer Process in Large river basins) is used to compute the water balance of the Haihe River basin in the same year. Comparison between model-derived and remote sensing retrieval basin-averaged evapotranspiration estimates shows a good piecewise linear relationship, but their spatial distribution within the Haihe basin is different. The remote sensing derived evapotranspiration shows variability at finer scales. An extended Kalman filter (EKF) data assimilation algorithm, suitable for non-linear problems, is used. Assimilation results indicate that remote sensing observations have a potentially important role in providing spatial information to the assimilation system for the spatially optical hydrological parameterization of the model. This is especially important for large basins, such as the Haihe River basin in this study. Combining and integrating the capabilities of and information from model simulation and remote sensing techniques may provide the best spatial and temporal characteristics for hydrological states/fluxes, and would be both appealing and necessary for improving our knowledge of fundamental hydrological processes and for addressing important water resource management problems. PMID:27879946

Earth view: A business guide to orbital remote sensing

NASA Technical Reports Server (NTRS)

Bishop, Peter C.

1990-01-01

The following subject areas are covered: Earth view - a guide to orbital remote sensing; current orbital remote sensing systems (LANDSAT, SPOT image, MOS-1, Soviet remote sensing systems); remote sensing satellite; and remote sensing organizations.

Use of remote sensing in agriculture

NASA Technical Reports Server (NTRS)

Pettry, D. E.; Powell, N. L.

1975-01-01

The remote sensing studies of (a) cultivated peanut areas in Southeastern Virginia; (b) studies at the Virginia Truck and Ornamentals Research Station near Painter, Virginia, the Eastern Virginia Research Station near Warsaw, Virginia, the Tidewater Research and Continuing Education Center near Suffolk, Virginia, and the Southern Piedmont Research and Continuing Education Center Blackstone, Virginia; and (c) land use classification studies at Virginia Beach, Virginia are presented. The practical feasibility of using false color infrared imagery to detect and determine the areal extent of peanut disease infestation of Cylindrocladium black rot and Sclerotinia blight is demonstrated. These diseases pose a severe hazard to this major agricultural food commodity. The value of remote sensing technology in terrain analyses and land use classification of diverse land areas is also investigated. Continued refinement of spectral signatures of major agronomic crops and documentation of pertinent environmental variables have provided a data base for the generation of an agricultural-environmental prediction model.

Initial Scientific Assessment of the EOS Data and Information System (EOSDIS)

NASA Technical Reports Server (NTRS)

1989-01-01

Crucial to the success of the Earth Observing System (Eos) is the Eos Data and Information System (EosDIS). The goals of Eos depend not only on its instruments and science investigations, but also on how well EosDlS helps scientists integrate reliable, large-scale data sets of geophysical and biological measurements made from Eos data, and on how successfully Eos scientists interact with other investigations in Earth System Science. Current progress in the use of remote sensing for science is hampered by requirements that the scientist understand in detail the instrument, the electromagnetic properties of the surface, and a suite of arcane tape formats, and by the immaturity of some of the techniques for estimating geophysical and biological variables from remote sensing data. These shortcomings must be transcended if remote sensing data are to be used by a much wider population of scientists who study environmental change at regional and global scales.

Mapping wildland fuels for fire management across multiple scales: integrating remote sensing, GIS, and biophysical modeling

USGS Publications Warehouse

Keane, Robert E.; Burgan, Robert E.; Van Wagtendonk, Jan W.

2001-01-01

Fuel maps are essential for computing spatial fire hazard and risk and simulating fire growth and intensity across a landscape. However, fuel mapping is an extremely difficult and complex process requiring expertise in remotely sensed image classification, fire behavior, fuels modeling, ecology, and geographical information systems (GIS). This paper first presents the challenges of mapping fuels: canopy concealment, fuelbed complexity, fuel type diversity, fuel variability, and fuel model generalization. Then, four approaches to mapping fuels are discussed with examples provided from the literature: (1) field reconnaissance; (2) direct mapping methods; (3) indirect mapping methods; and (4) gradient modeling. A fuel mapping method is proposed that uses current remote sensing and image processing technology. Future fuel mapping needs are also discussed which include better field data and fuel models, accurate GIS reference layers, improved satellite imagery, and comprehensive ecosystem models.

Modeling the impacts of phenological and inter-annual changes in landscape metrics on local biodiversity of agricultural lands of Eastern Ontario using multi-spatial and multi-temporal remote sensing data

NASA Astrophysics Data System (ADS)

Alavi-Shoushtari, N.; King, D.

2017-12-01

Agricultural landscapes are highly variable ecosystems and are home to many local farmland species. Seasonal, phenological and inter-annual agricultural landscape dynamics have potential to affect the richness and abundance of farmland species. Remote sensing provides data and techniques which enable monitoring landscape changes in multiple temporal and spatial scales. MODIS high temporal resolution remote sensing images enable detection of seasonal and phenological trends, while Landsat higher spatial resolution images, with its long term archive enables inter-annual trend analysis over several decades. The objective of this study to use multi-spatial and multi-temporal remote sensing data to model the response of farmland species to landscape metrics. The study area is the predominantly agricultural region of eastern Ontario. 92 sample landscapes were selected within this region using a protocol designed to maximize variance in composition and configuration heterogeneity while controlling for amount of forest and spatial autocorrelation. Two sample landscape extents (1×1km and 3×3km) were selected to analyze the impacts of spatial scale on biodiversity response. Gamma diversity index data for four taxa groups (birds, butterflies, plants, and beetles) were collected during the summers of 2011 and 2012 within the cropped area of each landscape. To extract the seasonal and phenological metrics a 2000-2012 MODIS NDVI time-series was used, while a 1985-2012 Landsat time-series was used to model the inter-annual trends of change in the sample landscapes. The results of statistical modeling showed significant relationships between farmland biodiversity for several taxa and the phenological and inter-annual variables. The following general results were obtained: 1) Among the taxa groups, plant and beetles diversity was most significantly correlated with the phenological variables; 2) Those phenological variables which are associated with the variability in the start of season date across the sample landscapes and the variability in the corresponding NDVI values at that date showed the strongest correlation with the biodiversity indices; 3) The significance of the models improved when using 3×3km site extent both for MODIS and Landsat based models due most likely to the larger sample size over 3x3km.

An overview of mesoscale aerosol processes, comparisons, and validation studies from DRAGON networks

NASA Astrophysics Data System (ADS)

Holben, Brent N.; Kim, Jhoon; Sano, Itaru; Mukai, Sonoyo; Eck, Thomas F.; Giles, David M.; Schafer, Joel S.; Sinyuk, Aliaksandr; Slutsker, Ilya; Smirnov, Alexander; Sorokin, Mikhail; Anderson, Bruce E.; Che, Huizheng; Choi, Myungje; Crawford, James H.; Ferrare, Richard A.; Garay, Michael J.; Jeong, Ukkyo; Kim, Mijin; Kim, Woogyung; Knox, Nichola; Li, Zhengqiang; Lim, Hwee S.; Liu, Yang; Maring, Hal; Nakata, Makiko; Pickering, Kenneth E.; Piketh, Stuart; Redemann, Jens; Reid, Jeffrey S.; Salinas, Santo; Seo, Sora; Tan, Fuyi; Tripathi, Sachchida N.; Toon, Owen B.; Xiao, Qingyang

2018-01-01

Over the past 24 years, the AErosol RObotic NETwork (AERONET) program has provided highly accurate remote-sensing characterization of aerosol optical and physical properties for an increasingly extensive geographic distribution including all continents and many oceanic island and coastal sites. The measurements and retrievals from the AERONET global network have addressed satellite and model validation needs very well, but there have been challenges in making comparisons to similar parameters from in situ surface and airborne measurements. Additionally, with improved spatial and temporal satellite remote sensing of aerosols, there is a need for higher spatial-resolution ground-based remote-sensing networks. An effort to address these needs resulted in a number of field campaign networks called Distributed Regional Aerosol Gridded Observation Networks (DRAGONs) that were designed to provide a database for in situ and remote-sensing comparison and analysis of local to mesoscale variability in aerosol properties. This paper describes the DRAGON deployments that will continue to contribute to the growing body of research related to meso- and microscale aerosol features and processes. The research presented in this special issue illustrates the diversity of topics that has resulted from the application of data from these networks.

Airborne Particles: What We Have Learned About Their Role in Climate from Remote Sensing, and Prospects for Future Advances

NASA Technical Reports Server (NTRS)

Kahn, Ralph A.

2013-01-01

Desert dust, wildfire smoke, volcanic ash, biogenic and urban pollution particles, all affect the regional-scale climate of Earth in places and at times; some have global-scale impacts on the column radiation balance, cloud properties, atmospheric stability structure, and circulation patterns. Remote sensing has played a central role in identifying the sources and transports of airborne particles, mapping their three-dimensional distribution and variability, quantifying their amount, and constraining aerosol air mass type. The measurements obtained from remote sensing have strengths and limitations, and their value for characterizing Earths environment is enhanced immensely when they are combined with direct, in situ observations, and used to constrain aerosol transport and climate models. A similar approach has been taken to study the role particles play in determining the climate of Mars, though based on far fewer observations. This presentation will focus what we have learned from remote sensing about the impacts aerosol have on Earths climate; a few points about how aerosols affect the climate of Mars will also be introduced, in the context of how we might assess aerosol-climate impacts more generally on other worlds.

Review of FEWS NET Biophysical Monitoring Requirements

NASA Technical Reports Server (NTRS)

Ross, K. W.; Brown, Molly E.; Verdin, J.; Underwood, L. W.

2009-01-01

The Famine Early Warning System Network (FEWS NET) provides monitoring and early warning support to decision makers responsible for responding to famine and food insecurity. FEWS NET transforms satellite remote sensing data into rainfall and vegetation information that can be used by these decision makers. The National Aeronautics and Space Administration has recently funded activities to enhance remote sensing inputs to FEWS NET. To elicit Earth observation requirements, a professional review questionnaire was disseminated to FEWS NET expert end-users: it focused upon operational requirements to determine additional useful remote sensing data and; subsequently, beneficial FEWS NET biophysical supplementary inputs. The review was completed by over 40 experts from around the world, enabling a robust set of professional perspectives to be gathered and analyzed rapidly. Reviewers were asked to evaluate the relative importance of environmental variables and spatio-temporal requirements for Earth science data products, in particular for rainfall and vegetation products. The results showed that spatio-temporal resolution requirements are complex and need to vary according to place, time, and hazard: that high resolution remote sensing products continue to be in demand, and that rainfall and vegetation products were valued as data that provide actionable food security information.

Method and system for spatially variable rate application of agricultural chemicals based on remotely sensed vegetation data

NASA Technical Reports Server (NTRS)

Lewis, Mark David (Inventor); Seal, Michael R. (Inventor); Hood, Kenneth Brown (Inventor); Johnson, James William (Inventor)

2007-01-01

Remotely sensed spectral image data are used to develop a Vegetation Index file which represents spatial variations of actual crop vigor throughout a field that is under cultivation. The latter information is processed to place it in a format that can be used by farm personnel to correlate and calibrate it with actually observed crop conditions existing at control points within the field. Based on the results, farm personnel formulate a prescription request, which is forwarded via email or FTP to a central processing site, where the prescription is prepared. The latter is returned via email or FTP to on-side farm personnel, who can load it into a controller on a spray rig that directly applies inputs to the field at a spatially variable rate.

Method and apparatus for spatially variable rate application of agricultural chemicals based on remotely sensed vegetation data

NASA Technical Reports Server (NTRS)

Hood, Kenneth Brown (Inventor); Johnson, James William (Inventor); Seal, Michael R. (Inventor); Lewis, Mark David (Inventor)

2004-01-01

Remotely sensed spectral image data are used to develop a Vegetation Index file which represents spatial variations of actual crop vigor throughout a field that is under cultivation. The latter information is processed to place it in a format that can be used by farm personnel to correlate and calibrate it with actually observed crop conditions existing at control points within the field. Based on the results, farm personnel formulate a prescription request, which is forwarded via email or FTP to a central processing site, where the prescription is prepared. The latter is returned via email or FTP to on-side farm personnel, who can load it into a controller on a spray rig that directly applies inputs to the field at a spatially variable rate.

The influence of grazing on surface climatological variables of tallgrass prairie

NASA Technical Reports Server (NTRS)

Seastedt, T. R.; Dyer, M. I.; Turner, Clarence L.

1992-01-01

Mass and energy exchange between most grassland canopies and the atmosphere are mediated by grazing activities. Ambient temperatures can be increased or decreased by grazers. Data have been assembled from simulated grazing experiments on Konza Prairie Research Natural Area and observations on adjacent pastures grazed by cattle show significant changes in primary production, nutrient content, and bidirectional reflectance characteristics as a function of grazing intensity. The purpose of this research was to provide algorithms that would allow incorporation of grazing effects into models of energy budgets using remote sensing procedures. The approach involved: (1) linking empirical measurements of plant biomass and grazing intensities to remotely sensed canopy reflectance, and (2) using a higher resolution, mechanistic grazing model to derive plant ecophysiological parameters that influence reflectance and other surface climatological variables.

Technology study of quantum remote sensing imaging

NASA Astrophysics Data System (ADS)

Bi, Siwen; Lin, Xuling; Yang, Song; Wu, Zhiqiang

2016-02-01

According to remote sensing science and technology development and application requirements, quantum remote sensing is proposed. First on the background of quantum remote sensing, quantum remote sensing theory, information mechanism, imaging experiments and prototype principle prototype research situation, related research at home and abroad are briefly introduced. Then we expounds compress operator of the quantum remote sensing radiation field and the basic principles of single-mode compression operator, quantum quantum light field of remote sensing image compression experiment preparation and optical imaging, the quantum remote sensing imaging principle prototype, Quantum remote sensing spaceborne active imaging technology is brought forward, mainly including quantum remote sensing spaceborne active imaging system composition and working principle, preparation and injection compression light active imaging device and quantum noise amplification device. Finally, the summary of quantum remote sensing research in the past 15 years work and future development are introduced.

An improved strategy for regression of biophysical variables and Landsat ETM+ data.

Treesearch

Warren B. Cohen; Thomas K. Maiersperger; Stith T. Gower; David P. Turner

2003-01-01

Empirical models are important tools for relating field-measured biophysical variables to remote sensing data. Regression analysis has been a popular empirical method of linking these two types of data to provide continuous estimates for variables such as biomass, percent woody canopy cover, and leaf area index (LAI). Traditional methods of regression are not...

Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa.

PubMed

Ebhuoma, Osadolor; Gebreslasie, Michael

2016-06-14

Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of Knowledge(SM) databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably.

Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa

PubMed Central

Ebhuoma, Osadolor; Gebreslasie, Michael

2016-01-01

Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of KnowledgeSM databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably. PMID:27314369

Meteorological, environmental remote sensing and neural network analysis of the epidemiology of malaria transmission in Thailand.

PubMed

Kiang, Richard; Adimi, Farida; Soika, Valerii; Nigro, Joseph; Singhasivanon, Pratap; Sirichaisinthop, Jeeraphat; Leemingsawat, Somjai; Apiwathnasorn, Chamnarn; Looareesuwan, Sornchai

2006-11-01

In many malarious regions malaria transmission roughly coincides with rainy seasons, which provide for more abundant larval habitats. In addition to precipitation, other meteorological and environmental factors may also influence malaria transmission. These factors can be remotely sensed using earth observing environmental satellites and estimated with seasonal climate forecasts. The use of remote sensing usage as an early warning tool for malaria epidemics have been broadly studied in recent years, especially for Africa, where the majority of the world's malaria occurs. Although the Greater Mekong Subregion (GMS), which includes Thailand and the surrounding countries, is an epicenter of multidrug resistant falciparum malaria, the meteorological and environmental factors affecting malaria transmissions in the GMS have not been examined in detail. In this study, the parasitological data used consisted of the monthly malaria epidemiology data at the provincial level compiled by the Thai Ministry of Public Health. Precipitation, temperature, relative humidity, and vegetation index obtained from both climate time series and satellite measurements were used as independent variables to model malaria. We used neural network methods, an artificial-intelligence technique, to model the dependency of malaria transmission on these variables. The average training accuracy of the neural network analysis for three provinces (Kanchanaburi, Mae Hong Son, and Tak) which are among the provinces most endemic for malaria, is 72.8% and the average testing accuracy is 62.9% based on the 1994-1999 data. A more complex neural network architecture resulted in higher training accuracy but also lower testing accuracy. Taking into account of the uncertainty regarding reported malaria cases, we divided the malaria cases into bands (classes) to compute training accuracy. Using the same neural network architecture on the 19 most endemic provinces for years 1994 to 2000, the mean training accuracy weighted by provincial malaria cases was 73%. Prediction of malaria cases for 2001 using neural networks trained for 1994-2000 gave a weighted accuracy of 53%. Because there was a significant decrease (31%) in the number of malaria cases in the 19 provinces from 2000 to 2001, the networks overestimated malaria transmissions. The decrease in transmission was not due to climatic or environmental changes. Thailand is a country with long borders. Migrant populations from the neighboring countries enlarge the human malaria reservoir because these populations have more limited access to health care. This issue also confounds the complexity of modeling malaria based on meteorological and environmental variables alone. In spite of the relatively low resolution of the data and the impact of migrant populations, we have uncovered a reasonably clear dependency of malaria on meteorological and environmental remote sensing variables. When other contextual determinants do not vary significantly, using neural network analysis along with remote sensing variables to predict malaria endemicity should be feasible.

Accounting for disturbance history in models: using remote sensing to constrain carbon and nitrogen pool spin-up.

PubMed

Hanan, Erin J; Tague, Christina; Choate, Janet; Liu, Mingliang; Kolden, Crystal; Adam, Jennifer

2018-03-24

Disturbances such as wildfire, insect outbreaks, and forest clearing, play an important role in regulating carbon, nitrogen, and hydrologic fluxes in terrestrial watersheds. Evaluating how watersheds respond to disturbance requires understanding mechanisms that interact over multiple spatial and temporal scales. Simulation modeling is a powerful tool for bridging these scales; however, model projections are limited by uncertainties in the initial state of plant carbon and nitrogen stores. Watershed models typically use one of two methods to initialize these stores: spin-up to steady state or remote sensing with allometric relationships. Spin-up involves running a model until vegetation reaches equilibrium based on climate. This approach assumes that vegetation across the watershed has reached maturity and is of uniform age, which fails to account for landscape heterogeneity and non-steady-state conditions. By contrast, remote sensing, can provide data for initializing such conditions. However, methods for assimilating remote sensing into model simulations can also be problematic. They often rely on empirical allometric relationships between a single vegetation variable and modeled carbon and nitrogen stores. Because allometric relationships are species- and region-specific, they do not account for the effects of local resource limitation, which can influence carbon allocation (to leaves, stems, roots, etc.). To address this problem, we developed a new initialization approach using the catchment-scale ecohydrologic model RHESSys. The new approach merges the mechanistic stability of spin-up with the spatial fidelity of remote sensing. It uses remote sensing to define spatially explicit targets for one or several vegetation state variables, such as leaf area index, across a watershed. The model then simulates the growth of carbon and nitrogen stores until the defined targets are met for all locations. We evaluated this approach in a mixed pine-dominated watershed in central Idaho, and a chaparral-dominated watershed in southern California. In the pine-dominated watershed, model estimates of carbon, nitrogen, and water fluxes varied among methods, while the target-driven method increased correspondence between observed and modeled streamflow. In the chaparral watershed, where vegetation was more homogeneously aged, there were no major differences among methods. Thus, in heterogeneous, disturbance-prone watersheds, the target-driven approach shows potential for improving biogeochemical projections. © 2018 by the Ecological Society of America.

What Models and Satellites Tell Us (and Don't Tell Us) About Arctic Sea Ice Melt Season Length

NASA Astrophysics Data System (ADS)

Ahlert, A.; Jahn, A.

2017-12-01

Melt season length—the difference between the sea ice melt onset date and the sea ice freeze onset date—plays an important role in the radiation balance of the Arctic and the predictability of the sea ice cover. However, there are multiple possible definitions for sea ice melt and freeze onset in climate models, and none of them exactly correspond to the remote sensing definition. Using the CESM Large Ensemble model simulations, we show how this mismatch between model and remote sensing definitions of melt and freeze onset limits the utility of melt season remote sensing data for bias detection in models. It also opens up new questions about the precise physical meaning of the melt season remote sensing data. Despite these challenges, we find that the increase in melt season length in the CESM is not as large as that derived from remote sensing data, even when we account for internal variability and different definitions. At the same time, we find that the CESM ensemble members that have the largest trend in sea ice extent over the period 1979-2014 also have the largest melt season trend, driven primarily by the trend towards later freeze onsets. This might be an indication that an underestimation of the melt season length trend is one factor contributing to the generally underestimated sea ice loss within the CESM, and potentially climate models in general.

Use of Satellite-based Remote Sensing to inform Evapotranspiration parameters in Cropping System Models

NASA Astrophysics Data System (ADS)

Dhungel, S.; Barber, M. E.

2016-12-01

The objectives of this paper are to use an automated satellite-based remote sensing evapotranspiration (ET) model to assist in parameterization of a cropping system model (CropSyst) and to examine the variability of consumptive water use of various crops across the watershed. The remote sensing model is a modified version of the Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC™) energy balance model. We present the application of an automated python-based implementation of METRIC to estimate ET as consumptive water use for agricultural areas in three watersheds in Eastern Washington - Walla Walla, Lower Yakima and Okanogan. We used these ET maps with USDA crop data to identify the variability of crop growth and water use for the major crops in these three watersheds. Some crops, such as grapes and alfalfa, showed high variability in water use in the watershed while others, such as corn, had comparatively less variability. The results helped us to estimate the range and variability of various crop parameters that are used in CropSyst. The paper also presents a systematic approach to estimate parameters of CropSyst for a crop in a watershed using METRIC results. Our initial application of this approach was used to estimate irrigation application rate for CropSyst for a selected farm in Walla Walla and was validated by comparing crop growth (as Leaf Area Index - LAI) and consumptive water use (ET) from METRIC and CropSyst. This coupling of METRIC with CropSyst will allow for more robust parameters in CropSyst and will enable accurate predictions of changes in irrigation practices and crop rotation, which are a challenge in many cropping system models.

Assessing Greater Sage-Grouse Selection of Brood-Rearing Habitat Using Remotely-Sensed Imagery: Can Readily Available High-Resolution Imagery Be Used to Identify Brood-Rearing Habitat Across a Broad Landscape?

PubMed

Westover, Matthew; Baxter, Jared; Baxter, Rick; Day, Casey; Jensen, Ryan; Petersen, Steve; Larsen, Randy

2016-01-01

Greater sage-grouse populations have decreased steadily since European settlement in western North America. Reduced availability of brood-rearing habitat has been identified as a limiting factor for many populations. We used radio-telemetry to acquire locations of sage-grouse broods from 1998 to 2012 in Strawberry Valley, Utah. Using these locations and remotely-sensed NAIP (National Agricultural Imagery Program) imagery, we 1) determined which characteristics of brood-rearing habitat could be used in widely available, high resolution imagery 2) assessed the spatial extent at which sage-grouse selected brood-rearing habitat, and 3) created a predictive habitat model to identify areas of preferred brood-rearing habitat. We used AIC model selection to evaluate support for a list of variables derived from remotely-sensed imagery. We examined the relationship of these explanatory variables at three spatial extents (45, 200, and 795 meter radii). Our top model included 10 variables (percent shrub, percent grass, percent tree, percent paved road, percent riparian, meters of sage/tree edge, meters of riparian/tree edge, distance to tree, distance to transmission lines, and distance to permanent structures). Variables from each spatial extent were represented in our top model with the majority being associated with the larger (795 meter) spatial extent. When applied to our study area, our top model predicted 75% of naïve brood locations suggesting reasonable success using this method and widely available NAIP imagery. We encourage application of our methodology to other sage-grouse populations and species of conservation concern.

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

NASA Astrophysics Data System (ADS)

Hadley, Brian Christopher

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

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.

Analysis of the spatial and temporal variability of terrestrial water storage and snowpack in the Pacific Northwestern United States

EPA Science Inventory

The spatial and temporal variability of terrestrial water storage and snowpack in the Pacific Northwest (PNW) was analyzed for water years 2001–2010 using measurements from the Gravity Recovery and Climate Experiment (GRACE) instrument. GRACE provides remotely-sensed measurements...

Integration of aerial imaging and variable-rate technology for site-specific aerial herbicide application

USDA-ARS?s Scientific Manuscript database

As remote sensing and variable rate technology are becoming more available for aerial applicators, practical methodologies on effective integration of these technologies are needed for site-specific aerial applications of crop production and protection materials. The objectives of this study were to...

Great Basin vegetation response to groundwater fluctuation, climate variability, and previous land cultivation: The application of multitemporal measurements from remote sensing data to regional vegetation dynamics

NASA Astrophysics Data System (ADS)

Elmore, Andrew James

The conversion of large natural basins to managed watersheds for the purpose of providing water to urban centers has had a negative impact on semiarid ecosystems, worldwide. We view semiarid plant communities as being adapted to short, regular periods of drought. However, human induced changes in the water balance often remove these systems from the range of natural variability that has been historically established. This thesis explores vegetation changes over a 13-yr period for Owens Valley, in eastern California. Using remotely sensed measurements of vegetation cover, an extensive vegetation survey, field data and observations, precipitation records, and data on water table depth, I identify the key modes of response of xeric, phreatophytic, and exotic Great Basin plant communities. Three specific advancements were reached as a result of this work. (1) A change classification technique was developed that was used to separate regions of land-cover that were dependent on precipitation from regions dependent on groundwater. This technique utilized Spectral Mixture Analysis of annually acquired Landsat Thematic Mapper remote sensing data, to retrieve regional estimates of percent vegetation cover. (2) A threshold response related to depth-to-water dependence was identified for phreatophytic Alkali Meadow communities. Plant communities that were subject to groundwater depths below this threshold exhibited greater invasion by precipitation sensitive plants. (3) The floristic differences between previously cultivated and uncultivated land were found to account for an increased sensitivity of plant communities to precipitation variability. Through (2) and (3), two human influences (groundwater decline and previous land cultivation) were shown to alter land cover such that the land became more sensitive to precipitation change. Climate change predictions include a component of increased climate variability for the western United States; therefore, these results place serious doubt on the sustainability of human activities in this region. The results from this work broadly cover topics from remote sensing techniques to the ecology of Great Basin plant communities and are applicable wherever large regions of land are being managed in an era of changing environmental conditions.

Using Remote Sensing, Weather, and Demographic Data to Create Risk Maps for Zika, Dengue, and Chikungunya in Brazil

NASA Astrophysics Data System (ADS)

Manore, C.; Conrad, J.; Del Valle, S.; Ziemann, A.; Fairchild, G.; Generous, E. N.

2017-12-01

Mosquito-borne diseases such as Zika, dengue, and chikungunya viruses have dynamics coupled to weather, ecology, human infrastructure, socio-economic demographics, and behavior. We use time-varying remote sensing and weather data, along with demographics and ecozones to predict risk through time for Zika, dengue, and chikungunya outbreaks in Brazil. We use distributed lag methods to quantify the lag between outbreaks and weather. Our statistical model indicates that the relationships between the variables are complex, but that quantifying risk is possible with the right data at appropriate spatio-temporal scales.

A hyper-temporal remote sensing protocol for high-resolution mapping of ecological sites

PubMed Central

Karl, Jason W.

2017-01-01

Ecological site classification has emerged as a highly effective land management framework, but its utility at a regional scale has been limited due to the spatial ambiguity of ecological site locations in the U.S. or the absence of ecological site maps in other regions of the world. In response to these shortcomings, this study evaluated the use of hyper-temporal remote sensing (i.e., hundreds of images) for high spatial resolution mapping of ecological sites. We posit that hyper-temporal remote sensing can provide novel insights into the spatial variability of ecological sites by quantifying the temporal response of land surface spectral properties. This temporal response provides a spectral ‘fingerprint’ of the soil-vegetation-climate relationship which is central to the concept of ecological sites. Consequently, the main objective of this study was to predict the spatial distribution of ecological sites in a semi-arid rangeland using a 28-year time series of normalized difference vegetation index from Landsat TM 5 data and modeled using support vector machine classification. Results from this study show that support vector machine classification using hyper-temporal remote sensing imagery was effective in modeling ecological site classes, with a 62% correct classification. These results were compared to Gridded Soil Survey Geographic database and expert delineated maps of ecological sites which had a 51 and 89% correct classification, respectively. An analysis of the effects of ecological state on ecological site misclassifications revealed that sites in degraded states (e.g., shrub-dominated/shrubland and bare/annuals) had a higher rate of misclassification due to their close spectral similarity with other ecological sites. This study identified three important factors that need to be addressed to improve future model predictions: 1) sampling designs need to fully represent the range of both within class (i.e., states) and between class (i.e., ecological sites) spectral variability through time, 2) field sampling protocols that accurately characterize key soil properties (e.g., texture, depth) need to be adopted, and 3) additional environmental covariates (e.g. terrain attributes) need to be evaluated that may help further differentiate sites with similar spectral signals. Finally, the proposed hyper-temporal remote sensing framework may provide a standardized approach to evaluate and test our ecological site concepts through examining differences in vegetation dynamics in response to climatic variability and other drivers of land-use change. Results from this study demonstrate the efficacy of the hyper-temporal remote sensing approach for high resolution mapping of ecological sites, and highlights its utility in terms of reduced cost and time investment relative to traditional manual mapping approaches. PMID:28414731

A hyper-temporal remote sensing protocol for high-resolution mapping of ecological sites.

PubMed

Maynard, Jonathan J; Karl, Jason W

2017-01-01

Ecological site classification has emerged as a highly effective land management framework, but its utility at a regional scale has been limited due to the spatial ambiguity of ecological site locations in the U.S. or the absence of ecological site maps in other regions of the world. In response to these shortcomings, this study evaluated the use of hyper-temporal remote sensing (i.e., hundreds of images) for high spatial resolution mapping of ecological sites. We posit that hyper-temporal remote sensing can provide novel insights into the spatial variability of ecological sites by quantifying the temporal response of land surface spectral properties. This temporal response provides a spectral 'fingerprint' of the soil-vegetation-climate relationship which is central to the concept of ecological sites. Consequently, the main objective of this study was to predict the spatial distribution of ecological sites in a semi-arid rangeland using a 28-year time series of normalized difference vegetation index from Landsat TM 5 data and modeled using support vector machine classification. Results from this study show that support vector machine classification using hyper-temporal remote sensing imagery was effective in modeling ecological site classes, with a 62% correct classification. These results were compared to Gridded Soil Survey Geographic database and expert delineated maps of ecological sites which had a 51 and 89% correct classification, respectively. An analysis of the effects of ecological state on ecological site misclassifications revealed that sites in degraded states (e.g., shrub-dominated/shrubland and bare/annuals) had a higher rate of misclassification due to their close spectral similarity with other ecological sites. This study identified three important factors that need to be addressed to improve future model predictions: 1) sampling designs need to fully represent the range of both within class (i.e., states) and between class (i.e., ecological sites) spectral variability through time, 2) field sampling protocols that accurately characterize key soil properties (e.g., texture, depth) need to be adopted, and 3) additional environmental covariates (e.g. terrain attributes) need to be evaluated that may help further differentiate sites with similar spectral signals. Finally, the proposed hyper-temporal remote sensing framework may provide a standardized approach to evaluate and test our ecological site concepts through examining differences in vegetation dynamics in response to climatic variability and other drivers of land-use change. Results from this study demonstrate the efficacy of the hyper-temporal remote sensing approach for high resolution mapping of ecological sites, and highlights its utility in terms of reduced cost and time investment relative to traditional manual mapping approaches.

Applications of Remote Sensing to Emergency Management.

DTIC Science & Technology

1980-02-15

Contents: Foundations of Remote Sensing : Data Acquisition and Interpretation; Availability of Remote Sensing Technology for Disaster Response...Imaging Systems, Current and Near Future Satellite and Aircraft Remote Sensing Systems; Utilization of Remote Sensing in Disaster Response: Categories of...Disasters, Phases of Monitoring Activities; Recommendations for Utilization of Remote Sensing Technology in Disaster Response; Selected Reading List.

Troubleshooting RSIG

EPA Pesticide Factsheets

Remote Sensing Information Gateway, a tool that allows scientists, researchers and decision makers to access a variety of multi-terabyte, environmental datasets and to subset the data and obtain only needed variables, greatly improving the download time.

The remote sensing image segmentation mean shift algorithm parallel processing based on MapReduce

NASA Astrophysics Data System (ADS)

Chen, Xi; Zhou, Liqing

2015-12-01

With the development of satellite remote sensing technology and the remote sensing image data, traditional remote sensing image segmentation technology cannot meet the massive remote sensing image processing and storage requirements. This article put cloud computing and parallel computing technology in remote sensing image segmentation process, and build a cheap and efficient computer cluster system that uses parallel processing to achieve MeanShift algorithm of remote sensing image segmentation based on the MapReduce model, not only to ensure the quality of remote sensing image segmentation, improved split speed, and better meet the real-time requirements. The remote sensing image segmentation MeanShift algorithm parallel processing algorithm based on MapReduce shows certain significance and a realization of value.

RSIG Data Inventory

EPA Pesticide Factsheets

Remote Sensing Information Gateway, a tool that allows scientists, researchers and decision makers to access a variety of multi-terabyte, environmental datasets and to subset the data and obtain only needed variables, greatly improving the download time.

RSIG Video Demonstrations

EPA Pesticide Factsheets

Remote Sensing Information Gateway, a tool that allows scientists, researchers and decision makers to access a variety of multi-terabyte, environmental datasets and to subset the data and obtain only needed variables, greatly improving the download time.

How RSIG Regrids Data

EPA Pesticide Factsheets

Remote Sensing Information Gateway, a tool that allows scientists, researchers and decision makers to access a variety of multi-terabyte, environmental datasets and to subset the data and obtain only needed variables, greatly improving the download time.

Combining remotely sensed and other measurements for hydrologic areal averages

NASA Technical Reports Server (NTRS)

Johnson, E. R.; Peck, E. L.; Keefer, T. N.

1982-01-01

A method is described for combining measurements of hydrologic variables of various sampling geometries and measurement accuracies to produce an estimated mean areal value over a watershed and a measure of the accuracy of the mean areal value. The method provides a means to integrate measurements from conventional hydrological networks and remote sensing. The resulting areal averages can be used to enhance a wide variety of hydrological applications including basin modeling. The correlation area method assigns weights to each available measurement (point, line, or areal) based on the area of the basin most accurately represented by the measurement. The statistical characteristics of the accuracy of the various measurement technologies and of the random fields of the hydrologic variables used in the study (water equivalent of the snow cover and soil moisture) required to implement the method are discussed.

REMOTE SENSING TECHNOLOGIES APPLICATIONS RESEARCH

EPA Science Inventory

Remote sensing technologies applications research supports the ORD Landscape Sciences Program (LSP) in two separate areas: operational remote sensing, and remote sensing research and development. Operational remote sensing is provided to the LSP through the use of current and t...

Analysis of spatiotemporal variability of C-factor derived from remote sensing data

NASA Astrophysics Data System (ADS)

Pechanec, Vilém; Mráz, Alexander; Benc, Antonín; Cudlín, Pavel

2018-01-01

Soil erosion is an important phenomenon that contributes to the degradation of agricultural land. Even though it is a natural process, human activities can significantly increase its impact on land degradation and present serious limitation on sustainable agricultural land use. Nowadays, the risk of soil erosion is assessed either qualitatively by expert assessment or quantitatively using model-based approach. One of the primary factors affecting the soil erosion assessment is a cover-management factor, C-factor. In the Czech Republic, several models are used to assess the C-factor on a long-term basis based on data collected using traditional tabular methods. This paper presents work to investigate the estimation of both long-term and short-term cover-management factors using remote sensing data. The results demonstrate a successful development of C-factor maps for each month of 2014, growing season average, and annual average for the Czech Republic. C-factor values calculated from remote sensing data confirmed expected trend in their temporal variability for selected crops. The results presented in this paper can be used for enhancing existing methods for estimating C-factor, planning future agricultural activities, and designing technical remediations and improvement activities of land use in the Czech Republic, which are also financially supported by the European Union funds.

Estimating impacts of plantation forestry on plant biodiversity in southern Chile-a spatially explicit modelling approach.

PubMed

Braun, Andreas Christian; Koch, Barbara

2016-10-01

Monitoring the impacts of land-use practices is of particular importance with regard to biodiversity hotspots in developing countries. Here, conserving the high level of unique biodiversity is challenged by limited possibilities for data collection on site. Especially for such scenarios, assisting biodiversity assessments by remote sensing has proven useful. Remote sensing techniques can be applied to interpolate between biodiversity assessments taken in situ. Through this approach, estimates of biodiversity for entire landscapes can be produced, relating land-use intensity to biodiversity conditions. Such maps are a valuable basis for developing biodiversity conservation plans. Several approaches have been published so far to interpolate local biodiversity assessments in remote sensing data. In the following, a new approach is proposed. Instead of inferring biodiversity using environmental variables or the variability of spectral values, a hypothesis-based approach is applied. Empirical knowledge about biodiversity in relation to land-use is formalized and applied as ascription rules for image data. The method is exemplified for a large study site (over 67,000 km(2)) in central Chile, where forest industry heavily impacts plant diversity. The proposed approach yields a coefficient of correlation of 0.73 and produces a convincing estimate of regional biodiversity. The framework is broad enough to be applied to other study sites.

Estimating actual evapotranspiration from remote sensing imagery using R: the package 'TriangleMethod'.

NASA Astrophysics Data System (ADS)

Gampe, David; Huber García, Verena; Marzahn, Philip; Ludwig, Ralf

2017-04-01

Actual evaporation (Eta) is an essential variable to assess water availability, drought risk and food security, among others. Measurements of Eta are however limited to a small footprint, hampering a spatially explicit analysis and application and are very often not available at all. To overcome the problem of data scarcity, Eta can be assessed by various remote sensing approaches such as the Triangle Method (Jiang & Islam, 1999). Here, Eta is estimated by using the Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST). In this study, the R-package 'TriangleMethod' was compiled to efficiently perform the calculations of NDVI and processing LST to finally derive Eta from the applied data set. The package contains all necessary calculation steps and allows easy processing of a large data base of remote sensing images. By default, the parameterization for the Landsat TM and ETM+ sensors are implemented, however, the algorithms can be easily extended to additional sensors. The auxiliary variables required to estimate Eta with this method, such as elevation, solar radiation and air temperature at the overpassing time, can be processed as gridded information to allow for a better representation of the study area. The package was successfully applied in various studies in Spain, Palestine, Costa Rica and Canada.

Using NOAA/AVHRR based remote sensing data and PCR method for estimation of Aus rice yield in Bangladesh

NASA Astrophysics Data System (ADS)

Nizamuddin, Mohammad; Akhand, Kawsar; Roytman, Leonid; Kogan, Felix; Goldberg, Mitch

2015-06-01

Rice is a dominant food crop of Bangladesh accounting about 75 percent of agricultural land use for rice cultivation and currently Bangladesh is the world's fourth largest rice producing country. Rice provides about two-third of total calorie supply and about one-half of the agricultural GDP and one-sixth of the national income in Bangladesh. Aus is one of the main rice varieties in Bangladesh. Crop production, especially rice, the main food staple, is the most susceptible to climate change and variability. Any change in climate will, thus, increase uncertainty regarding rice production as climate is major cause year-to-year variability in rice productivity. This paper shows the application of remote sensing data for estimating Aus rice yield in Bangladesh using official statistics of rice yield with real time acquired satellite data from Advanced Very High Resolution Radiometer (AVHRR) sensor and Principal Component Regression (PCR) method was used to construct a model. The simulated result was compared with official agricultural statistics showing that the error of estimation of Aus rice yield was less than 10%. Remote sensing, therefore, is a valuable tool for estimating crop yields well in advance of harvest, and at a low cost.

Utility of Satellite Remote Sensing for Land-Atmosphere Coupling and Drought Metrics

NASA Technical Reports Server (NTRS)

Roundy, Joshua K.; Santanello, Joseph A.

2017-01-01

Feedbacks between the land and the atmosphere can play an important role in the water cycle and a number of studies have quantified Land-Atmosphere (L-A) interactions and feedbacks through observations and prediction models. Due to the complex nature of L-A interactions, the observed variables are not always available at the needed temporal and spatial scales. This work derives the Coupling Drought Index (CDI) solely from satellite data and evaluates the input variables and the resultant CDI against in-situ data and reanalysis products. NASA's AQUA satellite and retrievals of soil moisture and lower tropospheric temperature and humidity properties are used as input. Overall, the AQUA-based CDI and its inputs perform well at a point, spatially, and in time (trends) compared to in-situ and reanalysis products. In addition, this work represents the first time that in-situ observations were utilized for the coupling classification and CDI. The combination of in-situ and satellite remote sensing CDI is unique and provides an observational tool for evaluating models at local and large scales. Overall, results indicate that there is sufficient information in the signal from simultaneous measurements of the land and atmosphere from satellite remote sensing to provide useful information for applications of drought monitoring and coupling metrics.

Utility of Satellite Remote Sensing for Land-Atmosphere Coupling and Drought Metrics

PubMed Central

Roundy, Joshua K.; Santanello, Joseph A.

2018-01-01

Feedbacks between the land and the atmosphere can play an important role in the water cycle and a number of studies have quantified Land-Atmosphere (L-A) interactions and feedbacks through observations and prediction models. Due to the complex nature of L-A interactions, the observed variables are not always available at the needed temporal and spatial scales. This work derives the Coupling Drought Index (CDI) solely from satellite data and evaluates the input variables and the resultant CDI against in-situ data and reanalysis products. NASA’s AQUA satellite and retrievals of soil moisture and lower tropospheric temperature and humidity properties are used as input. Overall, the AQUA-based CDI and its inputs perform well at a point, spatially, and in time (trends) compared to in-situ and reanalysis products. In addition, this work represents the first time that in-situ observations were utilized for the coupling classification and CDI. The combination of in-situ and satellite remote sensing CDI is unique and provides an observational tool for evaluating models at local and large scales. Overall, results indicate that there is sufficient information in the signal from simultaneous measurements of the land and atmosphere from satellite remote sensing to provide useful information for applications of drought monitoring and coupling metrics. PMID:29645012

System and method for cancelling the effects of stray magnetic fields from the output of a variable reluctance sensor

DOEpatents

Chen, Chingchi; Degner, Michael W.

2002-11-19

A sensor system for sensing a rotation of a sensing wheel is disclosed. The sensor system has a sensing coil in juxtaposition with the sensing wheel. Moreover, the sensing coil has a sensing coil output signal indicative of the rotational speed of the sensing wheel. Further, a cancellation coil is located remotely from the sensing coil and connected in series therewith. Additionally, the cancellation coil has a cancellation coil output signal indicative of an environmental disturbance which is effecting the sensing coil output signal. The cancellation coil output signal operates to cancel the effects of the environmental disturbance on the sensing coil output signal.

Assessing the performance of remotely-sensed flooding indicators and their potential contribution to early warning for leptospirosis in Cambodia

PubMed Central

Ledien, Julia; Sorn, Sopheak; Hem, Sopheak; Huy, Rekol; Buchy, Philippe

2017-01-01

Remote sensing can contribute to early warning for diseases with environmental drivers, such as flooding for leptospirosis. In this study we assessed whether and which remotely-sensed flooding indicator could be used in Cambodia to study any disease for which flooding has already been identified as an important driver, using leptospirosis as a case study. The performance of six potential flooding indicators was assessed by ground truthing. The Modified Normalized Difference Water Index (MNDWI) was used to estimate the Risk Ratio (RR) of being infected by leptospirosis when exposed to floods it detected, in particular during the rainy season. Chi-square tests were also calculated. Another variable—the time elapsed since the first flooding of the year—was created using MNDWI values and was also included as explanatory variable in a generalized linear model (GLM) and in a boosted regression tree model (BRT) of leptospirosis infections, along with other explanatory variables. Interestingly, MNDWI thresholds for both detecting water and predicting the risk of leptospirosis seroconversion were independently evaluated at -0.3. Value of MNDWI greater than -0.3 was significantly related to leptospirosis infection (RR = 1.61 [1.10–1.52]; χ2 = 5.64, p-value = 0.02, especially during the rainy season (RR = 2.03 [1.25–3.28]; χ2 = 8.15, p-value = 0.004). Time since the first flooding of the year was a significant risk factor in our GLM model (p-value = 0.042). These results suggest that MNDWI may be useful as a risk indicator in an early warning remote sensing tool for flood-driven diseases like leptospirosis in South East Asia. PMID:28704461

Ground-Based Passive Microwave Remote Sensing Observations of Soil Moisture at S and L Band with Insight into Measurement Accuracy

NASA Technical Reports Server (NTRS)

Laymon, Charles A.; Crosson, William L.; Jackson, Thomas J.; Manu, Andrew; Tsegaye, Teferi D.; Soman, V.; Arnold, James E. (Technical Monitor)

2001-01-01

Accurate estimates of spatially heterogeneous algorithm variables and parameters are required in determining the spatial distribution of soil moisture using radiometer data from aircraft and satellites. A ground-based experiment in passive microwave remote sensing of soil moisture was conducted in Huntsville, Alabama from July 1-14, 1996 to study retrieval algorithms and their sensitivity to variable and parameter specification. With high temporal frequency observations at S and L band, we were able to observe large scale moisture changes following irrigation and rainfall events, as well as diurnal behavior of surface moisture among three plots, one bare, one covered with short grass and another covered with alfalfa. The L band emitting depth was determined to be on the order of 0-3 or 0-5 cm below 0.30 cubic centimeter/cubic centimeter with an indication of a shallower emitting depth at higher moisture values. Surface moisture behavior was less apparent on the vegetated plots than it was on the bare plot because there was less moisture gradient and because of difficulty in determining vegetation water content and estimating the vegetation b parameter. Discrepancies between remotely sensed and gravimetric, soil moisture estimates on the vegetated plots point to an incomplete understanding of the requirements needed to correct for the effects of vegetation attenuation. Quantifying the uncertainty in moisture estimates is vital if applications are to utilize remotely-sensed soil moisture data. Computations based only on the real part of the complex dielectric constant and/or an alternative dielectric mixing model contribute a relatively insignificant amount of uncertainty to estimates of soil moisture. Rather, the retrieval algorithm is much more sensitive to soil properties, surface roughness and biomass.

Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota

USGS Publications Warehouse

Corcoran, Jennifer M.; Knight, Joseph F.; Gallant, Alisa L.

2013-01-01

Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.

Elucidating the impact of temperature variability and extremes on cereal croplands through remote sensing.

PubMed

Duncan, John M A; Dash, Jadunandan; Atkinson, Peter M

2015-04-01

Remote sensing-derived wheat crop yield-climate models were developed to highlight the impact of temperature variation during thermo-sensitive periods (anthesis and grain-filling; TSP) of wheat crop development. Specific questions addressed are: can the impact of temperature variation occurring during the TSP on wheat crop yield be detected using remote sensing data and what is the impact? Do crop critical temperature thresholds during TSP exist in real world cropping landscapes? These questions are tested in one of the world's major wheat breadbaskets of Punjab and Haryana, north-west India. Warming average minimum temperatures during the TSP had a greater negative impact on wheat crop yield than warming maximum temperatures. Warming minimum and maximum temperatures during the TSP explain a greater amount of variation in wheat crop yield than average growing season temperature. In complex real world cereal croplands there was a variable yield response to critical temperature threshold exceedance, specifically a more pronounced negative impact on wheat yield with increased warming events above 35 °C. The negative impact of warming increases with a later start-of-season suggesting earlier sowing can reduce wheat crop exposure harmful temperatures. However, even earlier sown wheat experienced temperature-induced yield losses, which, when viewed in the context of projected warming up to 2100 indicates adaptive responses should focus on increasing wheat tolerance to heat. This study shows it is possible to capture the impacts of temperature variation during the TSP on wheat crop yield in real world cropping landscapes using remote sensing data; this has important implications for monitoring the impact of climate change, variation and heat extremes on wheat croplands. © 2014 John Wiley & Sons Ltd.

[Maximum entropy model versus remote sensing-based methods for extracting Oncomelania hupensis snail habitats].

PubMed

Cong-Cong, Xia; Cheng-Fang, Lu; Si, Li; Tie-Jun, Zhang; Sui-Heng, Lin; Yi, Hu; Ying, Liu; Zhi-Jie, Zhang

2016-12-02

To explore the technique of maximum entropy model for extracting Oncomelania hupensis snail habitats in Poyang Lake zone. The information of snail habitats and related environment factors collected in Poyang Lake zone were integrated to set up the maximum entropy based species model and generate snail habitats distribution map. Two Landsat 7 ETM+ remote sensing images of both wet and drought seasons in Poyang Lake zone were obtained, where the two indices of modified normalized difference water index (MNDWI) and normalized difference vegetation index (NDVI) were applied to extract snail habitats. The ROC curve, sensitivities and specificities were applied to assess their results. Furthermore, the importance of the variables for snail habitats was analyzed by using Jackknife approach. The evaluation results showed that the area under receiver operating characteristic curve (AUC) of testing data by the remote sensing-based method was only 0.56, and the sensitivity and specificity were 0.23 and 0.89 respectively. Nevertheless, those indices above-mentioned of maximum entropy model were 0.876, 0.89 and 0.74 respectively. The main concentration of snail habitats in Poyang Lake zone covered the northeast part of Yongxiu County, northwest of Yugan County, southwest of Poyang County and middle of Xinjian County, and the elevation was the most important environment variable affecting the distribution of snails, and the next was land surface temperature (LST). The maximum entropy model is more reliable and accurate than the remote sensing-based method for the sake of extracting snail habitats, which has certain guiding significance for the relevant departments to carry out measures to prevent and control high-risk snail habitats.

The Estrada Real project and endemic diseases: the case of schistosomiasis, geoprocessing and tourism.

PubMed

Carvalho, Omar S; Scholte, Ronaldo G C; Guimarães, Ricardo J P S; Freitas, Corina C; Drummond, Sandra C; Amaral, Ronaldo S; Dutra, Luciano V; Oliveira, Guilherme; Massara, Cristiano L; Enk, Martin J

2010-07-01

Geographical Information System (GIS) is a tool that has recently been applied to better understand spatial disease distributions. Using meteorological, social, sanitation, mollusc distribution data and remote sensing variables, this study aimed to further develop the GIS technology by creating a model for the spatial distribution of schistosomiasis and to apply this model to an area with rural tourism in the Brazilian state of Minas Gerais (MG). The Estrada Real, covering about 1,400 km, is the largest and most important Brazilian tourism project, involving 163 cities in MG with different schistosomiasis prevalence rates. The model with three variables showed a R(2) = 0.34, with a standard deviation of risk estimated adequate for public health needs. The main variables selected for modelling were summer vegetation, summer minimal temperature and winter minimal temperature. The results confirmed the importance of Remote Sensing data and the valuable contribution of GIS in identifying priority areas for intervention in tourism regions which are endemic to schistosomiasis.

Mapping vegetation communities using statistical data fusion in the Ozark National Scenic Riverways, Missouri, USA

USGS Publications Warehouse

Chastain, R.A.; Struckhoff, M.A.; He, H.S.; Larsen, D.R.

2008-01-01

A vegetation community map was produced for the Ozark National Scenic Riverways consistent with the association level of the National Vegetation Classification System. Vegetation communities were differentiated using a large array of variables derived from remote sensing and topographic data, which were fused into independent mathematical functions using a discriminant analysis classification approach. Remote sensing data provided variables that discriminated vegetation communities based on differences in color, spectral reflectance, greenness, brightness, and texture. Topographic data facilitated differentiation of vegetation communities based on indirect gradients (e.g., landform position, slope, aspect), which relate to variations in resource and disturbance gradients. Variables derived from these data sources represent both actual and potential vegetation community patterns on the landscape. A hybrid combination of statistical and photointerpretation methods was used to obtain an overall accuracy of 63 percent for a map with 49 vegetation community and land-cover classes, and 78 percent for a 33-class map of the study area.

Advances in satellite remote sensing of environmental variables for epidemiological applications.

PubMed

Goetz, S J; Prince, S D; Small, J

2000-01-01

Earth-observing satellites have provided an unprecedented view of the land surface but have been exploited relatively little for the measurement of environmental variables of particular relevance to epidemiology. Recent advances in techniques to recover continuous fields of air temperature, humidity, and vapour pressure deficit from remotely sensed observations have significant potential for disease vector monitoring and related epidemiological applications. We report on the development of techniques to map environmental variables with relevance to the prediction of the relative abundance of disease vectors and intermediate hosts. Improvements to current methods of obtaining information on vegetation properties, canopy and surface temperature and soil moisture over large areas are also discussed. Algorithms used to measure these variables incorporate visible, near-infrared and thermal infrared radiation observations derived from time series of satellite-based sensors, focused here primarily but not exclusively on the Advanced Very High Resolution Radiometer (AVHRR) instruments. The variables compare favourably with surface measurements over a broad array of conditions at several study sites, and maps of retrieved variables captured patterns of spatial variability comparable to, and locally more accurate than, spatially interpolated meteorological observations. Application of multi-temporal maps of these variables are discussed in relation to current epidemiological research on the distribution and abundance of some common disease vectors.

[Biooptical properties of marine phytoplankton as they apply to satellite remote sensing

NASA Technical Reports Server (NTRS)

Yentsch, Charles S.

1992-01-01

This final report covers research performed over a period of 10 years from 1982 to 1992. During this time, Grant #NAGW410 was funded under three titles through a series of Supplements. The original proposal was entitled 'Photoecology, optical properties and remote sensing of warm core rings'; the second and major portion was entitled 'Continuation of studies of biooptical properties of phytoplankton and the study of mesoscale and submesoscale features using fluorescence and colorimetry'; with the final portion named 'Studies of biooptical properties of phytoplankton, with reference to identification of spectral types associated with meso- and submesoscale features in the ocean'. The focus of these projects was to try to expand our knowledge of the biooptical properties of marine phytoplankton as they apply to satellite remote sensing. We used a variety of techniques, new and old, to better measure these optical properties at appropriate scales, in some cases at the level of individual cells. We also exploited the specialized oceanic conditions that occur within certain regions and features of the ocean around the world in order to explain the tremendous variability one sees in a single remote sensing image. This document strives to provide as complete a summary as possible for this large body of work, including the pertinent publications supported by this funding.

Spatio-Temporal Analysis of Surface Soil Moisture in Evaluating Ground Truth Monitoring Sites for Remotely Sensed Observations

USDA-ARS?s Scientific Manuscript database

Soil moisture is an intrinsic state variable that varies considerably in space and time. Although soil moisture is highly variable, repeated measurements of soil moisture at the field or small watershed scale can often reveal certain locations as being temporally stable and representative of the are...

Tunnel-Site Selection by Remote Sensing Techniques

DTIC Science & Technology

A study of the role of remote sensing for geologic reconnaissance for tunnel-site selection was commenced. For this study, remote sensing was defined...conventional remote sensing . Future research directions are suggested, and the extension of remote sensing to include airborne passive microwave

Investigating the relationship between tree heights derived from SIBBORK forest model and remote sensing measurements

NASA Astrophysics Data System (ADS)

Osmanoglu, B.; Feliciano, E. A.; Armstrong, A. H.; Sun, G.; Montesano, P.; Ranson, K.

2017-12-01

Tree heights are one of the most commonly used remote sensing parameters to measure biomass of a forest. In this project, we investigate the relationship between remotely sensed tree heights (e.g. G-LiHT lidar and commercially available high resolution satellite imagery, HRSI) and the SIBBORK modeled tree heights. G-LiHT is a portable, airborne imaging system that simultaneously maps the composition, structure, and function of terrestrial ecosystems using lidar, imaging spectroscopy and thermal mapping. Ground elevation and canopy height models were generated using the lidar data acquired in 2012. A digital surface model was also generated using the HRSI technique from the commercially available WorldView data in 2016. The HRSI derived height and biomass products are available at the plot (10x10m) level. For this study, we parameterized the SIBBORK individual-based gap model for Howland forest, Maine. The parameterization was calibrated using field data for the study site and results show that the simulated forest reproduces the structural complexity of Howland old growth forest, based on comparisons of key variables including, aboveground biomass, forest height and basal area. Furthermore carbon cycle and ecosystem observational capabilities will be enhanced over the next 6 years via the launch of two LiDAR (NASA's GEDI and ICESAT 2) and two SAR (NASA's ISRO NiSAR and ESA's Biomass) systems. Our aim is to present the comparison of canopy height models obtained with SIBBORK forest model and remote sensing techniques, highlighting the synergy between individual-based forest modeling and high-resolution remote sensing.

PolarBRDF: A general purpose Python package for visualization and quantitative analysis of multi-angular remote sensing measurements

NASA Astrophysics Data System (ADS)

Singh, Manoj K.; Gautam, Ritesh; Gatebe, Charles K.; Poudyal, Rajesh

2016-11-01

The Bidirectional Reflectance Distribution Function (BRDF) is a fundamental concept for characterizing the reflectance property of a surface, and helps in the analysis of remote sensing data from satellite, airborne and surface platforms. Multi-angular remote sensing measurements are required for the development and evaluation of BRDF models for improved characterization of surface properties. However, multi-angular data and the associated BRDF models are typically multidimensional involving multi-angular and multi-wavelength information. Effective visualization of such complex multidimensional measurements for different wavelength combinations is presently somewhat lacking in the literature, and could serve as a potentially useful research and teaching tool in aiding both interpretation and analysis of BRDF measurements. This article describes a newly developed software package in Python (PolarBRDF) to help visualize and analyze multi-angular data in polar and False Color Composite (FCC) forms. PolarBRDF also includes functionalities for computing important multi-angular reflectance/albedo parameters including spectral albedo, principal plane reflectance and spectral reflectance slope. Application of PolarBRDF is demonstrated using various case studies obtained from airborne multi-angular remote sensing measurements using NASA's Cloud Absorption Radiometer (CAR). Our visualization program also provides functionalities for untangling complex surface/atmosphere features embedded in pixel-based remote sensing measurements, such as the FCC imagery generation of BRDF measurements of grasslands in the presence of wildfire smoke and clouds. Furthermore, PolarBRDF also provides quantitative information of the angular distribution of scattered surface/atmosphere radiation, in the form of relevant BRDF variables such as sunglint, hotspot and scattering statistics.

PolarBRDF: A general purpose Python package for visualization and quantitative analysis of multi-angular remote sensing measurements

NASA Astrophysics Data System (ADS)

Poudyal, R.; Singh, M.; Gautam, R.; Gatebe, C. K.

2016-12-01

The Bidirectional Reflectance Distribution Function (BRDF) is a fundamental concept for characterizing the reflectance property of a surface, and helps in the analysis of remote sensing data from satellite, airborne and surface platforms. Multi-angular remote sensing measurements are required for the development and evaluation of BRDF models for improved characterization of surface properties. However, multi-angular data and the associated BRDF models are typically multidimensional involving multi-angular and multi-wavelength information. Effective visualization of such complex multidimensional measurements for different wavelength combinations is presently somewhat lacking in the literature, and could serve as a potentially useful research and teaching tool in aiding both interpretation and analysis of BRDF measurements. This article describes a newly developed software package in Python (PolarBRDF) to help visualize and analyze multi-angular data in polar and False Color Composite (FCC) forms. PolarBRDF also includes functionalities for computing important multi-angular reflectance/albedo parameters including spectral albedo, principal plane reflectance and spectral reflectance slope. Application of PolarBRDF is demonstrated using various case studies obtained from airborne multi-angular remote sensing measurements using NASA's Cloud Absorption Radiometer (CAR)- http://car.gsfc.nasa.gov/. Our visualization program also provides functionalities for untangling complex surface/atmosphere features embedded in pixel-based remote sensing measurements, such as the FCC imagery generation of BRDF measurements of grasslands in the presence of wildfire smoke and clouds. Furthermore, PolarBRDF also provides quantitative information of the angular distribution of scattered surface/atmosphere radiation, in the form of relevant BRDF variables such as sunglint, hotspot and scattering statistics.

Polarbrdf: A General Purpose Python Package for Visualization Quantitative Analysis of Multi-Angular Remote Sensing Measurements

NASA Technical Reports Server (NTRS)

Singh, Manoj K.; Gautam, Ritesh; Gatebe, Charles K.; Poudyal, Rajesh

2016-01-01

The Bidirectional Reflectance Distribution Function (BRDF) is a fundamental concept for characterizing the reflectance property of a surface, and helps in the analysis of remote sensing data from satellite, airborne and surface platforms. Multi-angular remote sensing measurements are required for the development and evaluation of BRDF models for improved characterization of surface properties. However, multi-angular data and the associated BRDF models are typically multidimensional involving multi-angular and multi-wavelength information. Effective visualization of such complex multidimensional measurements for different wavelength combinations is presently somewhat lacking in the literature, and could serve as a potentially useful research and teaching tool in aiding both interpretation and analysis of BRDF measurements. This article describes a newly developed software package in Python (PolarBRDF) to help visualize and analyze multi-angular data in polar and False Color Composite (FCC) forms. PolarBRDF also includes functionalities for computing important multi-angular reflectance/albedo parameters including spectral albedo, principal plane reflectance and spectral reflectance slope. Application of PolarBRDF is demonstrated using various case studies obtained from airborne multi-angular remote sensing measurements using NASA's Cloud Absorption Radiometer (CAR). Our visualization program also provides functionalities for untangling complex surface/atmosphere features embedded in pixel-based remote sensing measurements, such as the FCC imagery generation of BRDF measurements of grasslands in the presence of wild fire smoke and clouds. Furthermore, PolarBRDF also provides quantitative information of the angular distribution of scattered surface/atmosphere radiation, in the form of relevant BRDF variables such as sunglint, hotspot and scattering statistics.

Remote Sensing Information Science Research

NASA Technical Reports Server (NTRS)

Clarke, Keith C.; Scepan, Joseph; Hemphill, Jeffrey; Herold, Martin; Husak, Gregory; Kline, Karen; Knight, Kevin

2002-01-01

This document is the final report summarizing research conducted by the Remote Sensing Research Unit, Department of Geography, University of California, Santa Barbara under National Aeronautics and Space Administration Research Grant NAG5-10457. This document describes work performed during the period of 1 March 2001 thorough 30 September 2002. This report includes a survey of research proposed and performed within RSRU and the UCSB Geography Department during the past 25 years. A broad suite of RSRU research conducted under NAG5-10457 is also described under themes of Applied Research Activities and Information Science Research. This research includes: 1. NASA ESA Research Grant Performance Metrics Reporting. 2. Global Data Set Thematic Accuracy Analysis. 3. ISCGM/Global Map Project Support. 4. Cooperative International Activities. 5. User Model Study of Global Environmental Data Sets. 6. Global Spatial Data Infrastructure. 7. CIESIN Collaboration. 8. On the Value of Coordinating Landsat Operations. 10. The California Marine Protected Areas Database: Compilation and Accuracy Issues. 11. Assessing Landslide Hazard Over a 130-Year Period for La Conchita, California Remote Sensing and Spatial Metrics for Applied Urban Area Analysis, including: (1) IKONOS Data Processing for Urban Analysis. (2) Image Segmentation and Object Oriented Classification. (3) Spectral Properties of Urban Materials. (4) Spatial Scale in Urban Mapping. (5) Variable Scale Spatial and Temporal Urban Growth Signatures. (6) Interpretation and Verification of SLEUTH Modeling Results. (7) Spatial Land Cover Pattern Analysis for Representing Urban Land Use and Socioeconomic Structures. 12. Colorado River Flood Plain Remote Sensing Study Support. 13. African Rainfall Modeling and Assessment. 14. Remote Sensing and GIS Integration.

Web Access to RSIG Data

EPA Pesticide Factsheets

Remote Sensing Information Gateway, a tool that allows scientists, researchers and decision makers to access a variety of multi-terabyte, environmental datasets and to subset the data and obtain only needed variables, greatly improving the download time.

Tips for Running RSIG2D

EPA Pesticide Factsheets

Remote Sensing Information Gateway, a tool that allows scientists, researchers and decision makers to access a variety of multi-terabyte, environmental datasets and to subset the data and obtain only needed variables, greatly improving the download time.

Instantaneous and daily values of the surface energy balance over agricultural fields using remote sensing and a reference field in an arid environment

USGS Publications Warehouse

Kustas, William P.; Moran, M.S.; Jackson, R. D.; Gay, L.W.; Duell, L.F.W.; Kunkel, K.E.; Matthias, A.D.

1990-01-01

Remotely sensed surface temperature and reflectance in the visible and near infrared wavebands along with ancilliary meteorological data provide the capability of computing three of the four surface energy balance components (i.e., net radiation, soil heat flux, and sensible heat flux) at different spatial and temporal scales. As a result, under nonadvective conditions, this enables the estimation of the remaining term (i.e., the latent heat flux). One of the practical applications with this approach is to produce evapotranspiration (ET) maps for agricultural regions which consist of an array of fields containing different crops at varying stages of growth and soil moisture conditions. Such a situation exists in the semiarid southwest at the University of Arizona Maricopa Agricultural Center, south of Phoenix. For one day (14 June 1987), surface temperature and reflectance measurements from an aircraft 150 m above ground level (agl) were acquired over fields from zero to nearly full cover at four times between 1000 MST and 1130 MST. The diurnal pattern of the surface energy balance was measured over four fields, which included alfalfa at 60% cover, furrowed cotton at 20% and 30% cover, and partially plowed what stubble. Instantaneous and daily values of ET were estimated for a representative area around each flux site with an energy balance model that relies on a reference ET. This reference value was determined with remotely sensed data and several meteorological inputs. The reference ET was adjusted to account for the different surface conditions in the other fields using only remotely sensed variables. A comparison with the flux measurements suggests the model has difficulties with partial canopy conditions, especially related to the estimation of the sensible heat flux. The resulting errors for instantaneous ET were on the order of 100 W m-2 and for daily values of order 2 mm day-1. These findings suggest future research should involve development of methods to account for the variability of meteorological parameters brought about by changes in surface conditions and improvements in the modeling of sensible heat transfer across the surface-atmosphere interface for partial canopy conditions using remote sensing information. ?? 1990.

Soil-vegetation-atmosphere energy fluxes: Land Surface Temperature evaluation by Terra/MODIS satellite images

NASA Astrophysics Data System (ADS)

Telesca, V.; Copertino, V. A.; Scavone, G.; Pastore, V.; Dal Sasso, S.

2009-04-01

Most of the hydrological models are by now founded on field and satellite data integration. In fact, the use of remote sensing techniques supplies the frequent lack of field-measured variables and parameters required to apply evaluation models of the hydrological cycle components at a regional scale. These components are very sensitive to the climatic and surface features and conditions. Remote sensing represent a complementary contribution to in situ investigation methodologies, furnishing repeated and real time observations. Naturally, the interest of these techniques is tied up to the existence of a solid correlation among the greatness to evaluate and the remote sensing information obtainable from the images. In this context, satellite remote sensing has become a basic tool since it allows the regular monitoring of extensive areas. Different surface variables and parameters can be extracted from the combination of the multi-spectral information contained in a satellite image. Land Surface Temperature (LST) is a fundamental parameter to estimate most of the components of the hydrological cycle and the soil-atmosphere energy balance, such as the net radiation, the sensible heat flux and the actual evapotranspiration. Besides, LST maps can be used in models for the fire monitoring and prevention. The aim of this work is to realize, exploiting the contribution of the remote sensing, some Land Surface Temperature maps, applying different "Split Windows" algorithms and to compare them with the "Day/Night" LST/MODIS, to select the best algorithm to apply in a Two-Source Energy Balance model (STSEB). Integrated into a rainfall/runoff model, it can contribute to cope with problems of land management for the protection from natural hazards. In particular, the energy balance procedure will be included into a model for the ‘in continuous' simulation and the forecast of floods. Another important application of our model is tied up to the forecast of scenarios connected to drought problems. In this context, they can contribute to the planning and the realization of mitigation interventions for the desertification risk.

Remote sensing of forest dynamics and land use in Amazonia

NASA Astrophysics Data System (ADS)

Toomey, Michael Paul

The rich, vast Amazonian ecosystem is directly and indirectly threatened by human activities; remote sensing serves as an essential tool for monitoring, understanding and mitigating these threats. A multi-faceted body of work is described here, addressing three major issues that employ and advance remote sensing techniques for the study of Amazonia and other tropical rainforest regions. In Chapter 2, canopy reflectance modeling and satellite observations were used to quantify the effect of epiphylls on remote sensing of humid forests. Modeling simulations demonstrated sensitivity of canopy-level near infrared and green reflectance to epiphylls on leaves. Time series of Moderate Resolution Imaging Spectrometer (MODIS) data corroborated the modeling results, suggesting a degree of coupling between epiphyll cover and vegetation indices which must be accounted for when using optical remote sensing in humid forests. In Chapter 4, 11 years (2000--2010) of MODIS land surface temperature (LST) data covering the entire Amazon basin were used to ascertain the role of heat stress during droughts in 2005 and 2010. Preliminary accuracy assessments showed that LST data provided reasonably accurate estimates of daytime air temperatures (RMSE = 1.45°C; Chapter 3). There were moderate to strong correlations between LST-based air temperature estimates and tower measurements (mean r = 0.64), illustrating a sensitivity to temporal variability. During both droughts, MODIS LST data detected anomalously high daytime and nighttime canopy temperatures throughout drought-affected regions. Multivariate linear models of LST and precipitation anomalies explained 65.1% of the variability in forest biomass losses, as determined from a wide network of forest inventory plots. These results suggest that models should incorporate both heat and moisture to predict drought effects on tropical forests. In Chapter 5, I performed high spatial and temporal resolution modeling of carbon stocks and fluxes in the state of Rondonia, Brazil for the period 1985--2009. Based on this analysis, Rondonia contributed ˜4% of pan-tropical humid forest deforestation emissions while carbon uptake by secondary forest was negligible due to limited spatial extent and high turnover rates. Spatial analysis of land cover change demonstrated the necessity for fine resolution carbon monitoring in tropical regions dominated by non-mechanized, smallholder land uses.

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.

System and method for evaluating wind flow fields using remote sensing devices

DOEpatents

Schroeder, John; Hirth, Brian; Guynes, Jerry

2016-12-13

The present invention provides a system and method for obtaining data to determine one or more characteristics of a wind field using a first remote sensing device and a second remote sensing device. Coordinated data is collected from the first and second remote sensing devices and analyzed to determine the one or more characteristics of the wind field. The first remote sensing device is positioned to have a portion of the wind field within a first scanning sector of the first remote sensing device. The second remote sensing device is positioned to have the portion of the wind field disposed within a second scanning sector of the second remote sensing device.

Spatial Inference for Distributed Remote Sensing Data

NASA Astrophysics Data System (ADS)

Braverman, A. J.; Katzfuss, M.; Nguyen, H.

2014-12-01

Remote sensing data are inherently spatial, and a substantial portion of their value for scientific analyses derives from the information they can provide about spatially dependent processes. Geophysical variables such as atmopsheric temperature, cloud properties, humidity, aerosols and carbon dioxide all exhibit spatial patterns, and satellite observations can help us learn about the physical mechanisms driving them. However, remote sensing observations are often noisy and incomplete, so inferring properties of true geophysical fields from them requires some care. These data can also be massive, which is both a blessing and a curse: using more data drives uncertainties down, but also drives costs up, particularly when data are stored on different computers or in different physical locations. In this talk I will discuss a methodology for spatial inference on massive, distributed data sets that does not require moving large volumes of data. The idea is based on a combination of ideas including modeling spatial covariance structures with low-rank covariance matrices, and distributed estimation in sensor or wireless networks.

A framework for nowcasting and forecasting of rainfall-triggered landslide activity using remotely sensed data

NASA Astrophysics Data System (ADS)

Kirschbaum, Dalia; Stanley, Thomas

2016-04-01

Remote sensing data offers the unique perspective to provide situational awareness of hydrometeorological hazards over large areas in a way that is impossible to achieve with in situ data. Recent work has shown that rainfall-triggered landslides, while typically local hazards that occupy small spatial areas, can be approximated over regional or global scales in near real-time. This work presents a regional and global approach to approximating potential landslide activity using the landslide hazard assessment for situational awareness (LHASA) model. This system couples remote sensing data, including Global Precipitation Measurement rainfall data, Shuttle Radar Topography Mission and other surface variables to estimate where and when landslide activity may be likely. This system also evaluates the effectiveness of quantitative precipitation estimates from the Goddard Earth Observing System Model, Version 5 to provide a 24 forecast of potential landslide activity. Preliminary results of the LHASA model and implications for are presented for a regional version of this system in Central America as well as a prototype global approach.

The interaction of light with phytoplankton in the marine environment

NASA Technical Reports Server (NTRS)

Carder, Kendall L.; Collins, Donald J.; Perry, Mary Jane; Clark, H. Lawrence; Mesias, Jorge M.

1986-01-01

In many regions of the ocean, the phytoplankton population dominates both the attenuation and scattering of light. In other regions, non-phytoplankton contributions to the absorption and scattering may change the remote sensing reflectance and thus affect the ability to interpret remotely sensed ocean color. Hence, variations in the composition of both the phytoplankton population and of the non-phytoplankton material in the water can affect the optical properties of the sea. The effects of these contributions to the remote sensing reflectance and the submarine light field are modeled using scattering and absorption measurements of phytoplankton cultures obtained at the Friday Harbor Laboratory of the University of Washington. These measurements are used to develop regional chlorophyll algorithms specific to the summer waters of Puget Sound for the Coastal Zone Color Scanner, Thematic Mapper and future Ocean Color Imager, and their accuracies are compared for high chlorophyll waters with little or no Gelbstoff, but with variable detrital and suspended material.

Exploring Models and Data for Remote Sensing Image Caption Generation

NASA Astrophysics Data System (ADS)

Lu, Xiaoqiang; Wang, Binqiang; Zheng, Xiangtao; Li, Xuelong

2018-04-01

Inspired by recent development of artificial satellite, remote sensing images have attracted extensive attention. Recently, noticeable progress has been made in scene classification and target detection.However, it is still not clear how to describe the remote sensing image content with accurate and concise sentences. In this paper, we investigate to describe the remote sensing images with accurate and flexible sentences. First, some annotated instructions are presented to better describe the remote sensing images considering the special characteristics of remote sensing images. Second, in order to exhaustively exploit the contents of remote sensing images, a large-scale aerial image data set is constructed for remote sensing image caption. Finally, a comprehensive review is presented on the proposed data set to fully advance the task of remote sensing caption. Extensive experiments on the proposed data set demonstrate that the content of the remote sensing image can be completely described by generating language descriptions. The data set is available at https://github.com/201528014227051/RSICD_optimal

Research on active imaging information transmission technology of satellite borne quantum remote sensing

NASA Astrophysics Data System (ADS)

Bi, Siwen; Zhen, Ming; Yang, Song; Lin, Xuling; Wu, Zhiqiang

2017-08-01

According to the development and application needs of Remote Sensing Science and technology, Prof. Siwen Bi proposed quantum remote sensing. Firstly, the paper gives a brief introduction of the background of quantum remote sensing, the research status and related researches at home and abroad on the theory, information mechanism and imaging experiments of quantum remote sensing and the production of principle prototype.Then, the quantization of pure remote sensing radiation field, the state function and squeezing effect of quantum remote sensing radiation field are emphasized. It also describes the squeezing optical operator of quantum light field in active imaging information transmission experiment and imaging experiments, achieving 2-3 times higher resolution than that of coherent light detection imaging and completing the production of quantum remote sensing imaging prototype. The application of quantum remote sensing technology can significantly improve both the signal-to-noise ratio of information transmission imaging and the spatial resolution of quantum remote sensing .On the above basis, Prof.Bi proposed the technical solution of active imaging information transmission technology of satellite borne quantum remote sensing, launched researches on its system composition and operation principle and on quantum noiseless amplifying devices, providing solutions and technical basis for implementing active imaging information technology of satellite borne Quantum Remote Sensing.

Temporal Variability of Canopy Light Use Efficiency and its Environmental Controls in a Subtropical Mangrove Wetland

NASA Astrophysics Data System (ADS)

Zhu, X.

2016-12-01

Mangrove wetlands play an important role in global carbon cycle due to their strong carbon sequestration resulting from high plant carbon assimilation and low soil respiration. However, temporal variability of carbon sequestration in mangrove wetlands is less understood since carbon processes of mangrove wetlands are influenced by many complicated and concurrent environmental controls including tidal activities, site climate and soil conditions. Canopy light use efficiency (LUE), is the most important plant physiological parameter that can be used to describe the temporal dynamics of canopy photosynthesis, and therefore a better characterization of temporal variability of canopy LUE will improve our understanding in mangrove photosynthesis and carbon balance. One of our aims is to study the temporal variability of canopy LUE and its environmental controls in a subtropical mangrove wetland. Half-hourly canopy LUE is derived from eddy covariance (EC) carbon flux and photosynthesis active radiation observations, and half-hourly environmental controls we measure include temperature, humidity, precipitation, radiation, tidal height, salinity, etc. Another aim is to explore the links between canopy LUE and spectral indices derived from near-surface tower-based remote sensing (normalized difference vegetation index, enhanced vegetation index, photochemical reflectance index, solar-induced chlorophyll fluorescence, etc.), and then identify potential quantitative relationships for developing remote sensing-based estimation methods of canopy LUE. At present, some instruments in our in-situ observation system have not yet been installed (planned in next months) and therefore we don't have enough measurements to support our analysis. However, a preliminary analysis of our historical EC and climate observations in past several years indicates that canopy LUE shows strong temporal variability and is greatly affected by environmental factors such as tidal activity. Detailed and systematic analyses of temporal variability of canopy LUE and its environmental controls and potential remote sensing estimation methods will be conducted when our in-situ observation system is ready in near future.

Introduction to the physics and techniques of remote sensing

NASA Technical Reports Server (NTRS)

Elachi, Charles

1987-01-01

This book presents a comprehensive overview of the basics behind remote-sensing physics, techniques, and technology. The physics of wave/matter interactions, techniques of remote sensing across the electromagnetic spectrum, and the concepts behind remote sensing techniques now established and future ones under development are discussed. Applications of remote sensing are described for a wide variety of earth and planetary atmosphere and surface sciences. Solid surface sensing across the electromagnetic spectrum, ocean surface sensing, basic principles of atmospheric sensing and radiative transfer, and atmospheric remote sensing in the microwave, millimeter, submillimeter, and infrared regions are examined.

Generalized Radiative Transfer as an Efficient Computational Tool for Spatial and/or Spectral Integration over Unresolved Variability in Multi-Angle Observations

NASA Astrophysics Data System (ADS)

Davis, A. B.; Xu, F.; Diner, D. J.

2017-12-01

Two perennial problems in applied theoretical and computational radiative transfer (RT) are: (1) the impact of unresolved spatial variability on large-scale fluxes (in climate models) or radiances (in remote sensing); and (2) efficient-yet-accurate estimation of broadband spectral integrals in radiant energy budget estimation as well as in remote sensing, in particular, of trace gases.Generalized RT (GRT) is a modification of classic RT in an optical medium with uniform extinction where Beer's exponential law for direct transmission is replaced by a monotonically decreasing function with a slower power-law decay. In a convenient parameterized version of GRT, mean extinction replaces the uniform value and just one new property is introduced. As a non-dimensional metric for the unresolved variability, we use the square of the mean extinction coefficient divided by its variance. This parameter is also the exponent of the power-law tail of the modified transmission law.This specific form of sub-exponential transmission has explored for almost two decades in application to spatial variability in the presence of long-range correlations, much like in turbulent media such as clouds, with a focus on multiple scattering. It has also been proposed by Conley and Collins (JQSRT, 112, 1525-, 2011) to improve on the standard (weak-line) implementation of the correlated-k technique for efficient spectral integration.We have merged these two applications within a rigorous formulation of the combined problem, and solve the new integral RT equations in the single-scattering limit. The result is illustrated by addressing practical problems in multi-angle remote sensing of aerosols using the O2 A-band, an emerging methodology for passive profiling of coarse aerosols and clouds.

Remotely Sensed Environmental Conditions and Malaria Mortality in Three Malaria Endemic Regions in Western Kenya.

PubMed

Sewe, Maquins Odhiambo; Ahlm, Clas; Rocklöv, Joacim

2016-01-01

Malaria is an important cause of morbidity and mortality in malaria endemic countries. The malaria mosquito vectors depend on environmental conditions, such as temperature and rainfall, for reproduction and survival. To investigate the potential for weather driven early warning systems to prevent disease occurrence, the disease relationship to weather conditions need to be carefully investigated. Where meteorological observations are scarce, satellite derived products provide new opportunities to study the disease patterns depending on remotely sensed variables. In this study, we explored the lagged association of Normalized Difference Vegetation Index (NVDI), day Land Surface Temperature (LST) and precipitation on malaria mortality in three areas in Western Kenya. The lagged effect of each environmental variable on weekly malaria mortality was modeled using a Distributed Lag Non Linear Modeling approach. For each variable we constructed a natural spline basis with 3 degrees of freedom for both the lag dimension and the variable. Lag periods up to 12 weeks were considered. The effect of day LST varied between the areas with longer lags. In all the three areas, malaria mortality was associated with precipitation. The risk increased with increasing weekly total precipitation above 20 mm and peaking at 80 mm. The NDVI threshold for increased mortality risk was between 0.3 and 0.4 at shorter lags. This study identified lag patterns and association of remote- sensing environmental factors and malaria mortality in three malaria endemic regions in Western Kenya. Our results show that rainfall has the most consistent predictive pattern to malaria transmission in the endemic study area. Results highlight a potential for development of locally based early warning forecasts that could potentially reduce the disease burden by enabling timely control actions.

Investigation of remote sensing techniques of measuring soil moisture

NASA Technical Reports Server (NTRS)

Newton, R. W. (Principal Investigator); Blanchard, A. J.; Nieber, J. L.; Lascano, R.; Tsang, L.; Vanbavel, C. H. M.

1981-01-01

Major activities described include development and evaluation of theoretical models that describe both active and passive microwave sensing of soil moisture, the evaluation of these models for their applicability, the execution of a controlled field experiment during which passive microwave measurements were acquired to validate these models, and evaluation of previously acquired aircraft microwave measurements. The development of a root zone soil water and soil temperature profile model and the calibration and evaluation of gamma ray attenuation probes for measuring soil moisture profiles are considered. The analysis of spatial variability of soil information as related to remote sensing is discussed as well as the implementation of an instrumented field site for acquisition of soil moisture and meteorologic information for use in validating the soil water profile and soil temperature profile models.

Chlorophyll-a specific volume scattering function of phytoplankton.

PubMed

Tan, Hiroyuki; Oishi, Tomohiko; Tanaka, Akihiko; Doerffer, Roland; Tan, Yasuhiro

2017-06-12

Chlorophyll-a specific light volume scattering functions (VSFs) by cultured phytoplankton in visible spectrum range is presented. Chlorophyll-a specific VSFs were determined based on the linear least squares method using a measured VSFs with different chlorophyll-a concentrations. We found obvious variability of it in terms of spectral and angular shapes of VSF between cultures. It was also presented that chlorophyll-a specific scattering significantly affected on spectral variation of the remote sensing reflectance, depending on spectral shape of b. This result is useful for developing an advance algorithm of ocean color remote sensing and for deep understanding of light in the sea.

Spectral Resolution and Coverage Impact on Advanced Sounder Information Content

NASA Technical Reports Server (NTRS)

Larar, Allen M.; Liu, Xu; Zhou, Daniel K.; Smith, William L.

2010-01-01

Advanced satellite sensors are tasked with improving global measurements of the Earth s atmosphere, clouds, and surface to enable enhancements in weather prediction, climate monitoring capability, and environmental change detection. Achieving such measurement improvements requires instrument system advancements. This presentation focuses on the impact of spectral resolution and coverage changes on remote sensing system information content, with a specific emphasis on thermodynamic state and trace species variables obtainable from advanced atmospheric sounders such as the Infrared Atmospheric Sounding Interferometer (IASI) and Cross-track Infrared Sounder (CrIS) systems on the MetOp and NPP/NPOESS series of satellites. Key words: remote sensing, advanced sounders, information content, IASI, CrIS

The use of remotely sensed soil moisture data in large-scale models of the hydrological cycle

NASA Technical Reports Server (NTRS)

Salomonson, V. V.; Gurney, R. J.; Schmugge, T. J.

1985-01-01

Manabe (1982) has reviewed numerical simulations of the atmosphere which provided a framework within which an examination of the dynamics of the hydrological cycle could be conducted. It was found that the climate is sensitive to soil moisture variability in space and time. The challenge arises now to improve the observations of soil moisture so as to provide up-dated boundary condition inputs to large scale models including the hydrological cycle. Attention is given to details regarding the significance of understanding soil moisture variations, soil moisture estimation using remote sensing, and energy and moisture balance modeling.

Environmental mapping and monitoring of Iceland by remote sensing (EMMIRS)

NASA Astrophysics Data System (ADS)

Pedersen, Gro B. M.; Vilmundardóttir, Olga K.; Falco, Nicola; Sigurmundsson, Friðþór S.; Rustowicz, Rose; Belart, Joaquin M.-C.; Gísladóttir, Gudrun; Benediktsson, Jón A.

2016-04-01

Iceland is exposed to rapid and dynamic landscape changes caused by natural processes and man-made activities, which impact and challenge the country. Fast and reliable mapping and monitoring techniques are needed on a big spatial scale. However, currently there is lack of operational advanced information processing techniques, which are needed for end-users to incorporate remote sensing (RS) data from multiple data sources. Hence, the full potential of the recent RS data explosion is not being fully exploited. The project Environmental Mapping and Monitoring of Iceland by Remote Sensing (EMMIRS) bridges the gap between advanced information processing capabilities and end-user mapping of the Icelandic environment. This is done by a multidisciplinary assessment of two selected remote sensing super sites, Hekla and Öræfajökull, which encompass many of the rapid natural and man-made landscape changes that Iceland is exposed to. An open-access benchmark repository of the two remote sensing supersites is under construction, providing high-resolution LIDAR topography and hyperspectral data for land-cover and landform classification. Furthermore, a multi-temporal and multi-source archive stretching back to 1945 allows a decadal evaluation of landscape and ecological changes for the two remote sensing super sites by the development of automated change detection techniques. The development of innovative pattern recognition and machine learning-based approaches to image classification and change detection is one of the main tasks of the EMMIRS project, aiming to extract and compute earth observation variables as automatically as possible. Ground reference data collected through a field campaign will be used to validate the implemented methods, which outputs are then inferred with geological and vegetation models. Here, preliminary results of an automatic land-cover classification based on hyperspectral image analysis are reported. Furthermore, the EMMIRS project investigates the complex landscape dynamics between geological and ecological processes. This is done through cross-correlation of mapping results and implementation of modelling techniques that simulate geological and ecological processes in order to extrapolate the landscape evolution

Remote sensing using MIMO systems

DOEpatents

Bikhazi, Nicolas; Young, William F; Nguyen, Hung D

2015-04-28

A technique for sensing a moving object within a physical environment using a MIMO communication link includes generating a channel matrix based upon channel state information of the MIMO communication link. The physical environment operates as a communication medium through which communication signals of the MIMO communication link propagate between a transmitter and a receiver. A spatial information variable is generated for the MIMO communication link based on the channel matrix. The spatial information variable includes spatial information about the moving object within the physical environment. A signature for the moving object is generated based on values of the spatial information variable accumulated over time. The moving object is identified based upon the signature.

[Thematic Issue: Remote Sensing.

ERIC Educational Resources Information Center

Howkins, John, Ed.

1978-01-01

Four of the articles in this publication discuss the remote sensing of the Earth and its resources by satellites. Among the topics dealt with are the development and management of remote sensing systems, types of satellites used for remote sensing, the uses of remote sensing, and issues involved in using information obtained through remote…

75 FR 65304 - Advisory Committee on Commercial Remote Sensing (ACCRES); Request for Nominations

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-22

... Commercial Remote Sensing (ACCRES); Request for Nominations AGENCY: National Oceanic and Atmospheric... Commercial Remote Sensing (ACCRES). SUMMARY: The Advisory Committee on Commercial Remote Sensing (ACCRES) was... Atmosphere, on matters relating to the U.S. commercial remote sensing industry and NOAA's activities to carry...

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.

14 CFR 35.21 - Variable and reversible pitch propellers.

Code of Federal Regulations, 2010 CFR

2010-01-01

... is shown to be extremely remote under § 35.15. (b) For propellers incorporating a method to select... manual. The method for sensing and indicating the propeller blade pitch position must be such that its...

14 CFR 35.21 - Variable and reversible pitch propellers.

Code of Federal Regulations, 2012 CFR

2012-01-01

... is shown to be extremely remote under § 35.15. (b) For propellers incorporating a method to select... manual. The method for sensing and indicating the propeller blade pitch position must be such that its...

14 CFR 35.21 - Variable and reversible pitch propellers.

Code of Federal Regulations, 2011 CFR

2011-01-01

... is shown to be extremely remote under § 35.15. (b) For propellers incorporating a method to select... manual. The method for sensing and indicating the propeller blade pitch position must be such that its...

14 CFR 35.21 - Variable and reversible pitch propellers.

Code of Federal Regulations, 2014 CFR

2014-01-01

... is shown to be extremely remote under § 35.15. (b) For propellers incorporating a method to select... manual. The method for sensing and indicating the propeller blade pitch position must be such that its...

14 CFR 35.21 - Variable and reversible pitch propellers.

Code of Federal Regulations, 2013 CFR

2013-01-01

... is shown to be extremely remote under § 35.15. (b) For propellers incorporating a method to select... manual. The method for sensing and indicating the propeller blade pitch position must be such that its...

Method to estimate drag coefficient at the air/ice interface over drifting open pack ice from remotely sensed data

NASA Technical Reports Server (NTRS)

Feldman, U.

1984-01-01

A knowledge in near real time, of the surface drag coefficient for drifting pack ice is vital for predicting its motions. And since this is not routinely available from measurements it must be replaced by estimates. Hence, a method for estimating this variable, as well as the drag coefficient at the water/ice interface and the ice thickness, for drifting open pack ice was developed. These estimates were derived from three-day sequences of LANDSAT-1 MSS images and surface weather charts and from the observed minima and maxima of these variables. The method was tested with four data sets in the southeastern Beaufort sea. Acceptable results were obtained for three data sets. Routine application of the method depends on the availability of data from an all-weather air or spaceborne remote sensing system, producing images with high geometric fidelity and high resolution.

Expanding understanding of optical variability in Lake Superior with a 4-year dataset

NASA Astrophysics Data System (ADS)

Mouw, Colleen B.; Ciochetto, Audrey B.; Grunert, Brice; Yu, Angela

2017-07-01

Lake Superior is one of the largest freshwater lakes on our planet, but few optical observations have been made to allow for the development and validation of visible spectral satellite remote sensing products. The dataset described here focuses on coincidently observing inherent and apparent optical properties along with biogeochemical parameters. Specifically, we observe remote sensing reflectance, absorption, scattering, backscattering, attenuation, chlorophyll concentration, and suspended particulate matter over the ice-free months of 2013-2016. The dataset substantially increases the optical knowledge of the lake. In addition to visible spectral satellite algorithm development, the dataset is valuable for characterizing the variable light field, particle, phytoplankton, and colored dissolved organic matter distributions, and helpful in food web and carbon cycle investigations. The compiled data can be freely accessed at https://seabass.gsfc.nasa.gov/archive/URI/Mouw/LakeSuperior/.

Remotely sensed vegetation moisture as explanatory variable of Lyme borreliosis incidence

NASA Astrophysics Data System (ADS)

Barrios, J. M.; Verstraeten, W. W.; Maes, P.; Clement, J.; Aerts, J. M.; Farifteh, J.; Lagrou, K.; Van Ranst, M.; Coppin, P.

2012-08-01

The strong correlation between environmental conditions and abundance and spatial spread of the tick Ixodes ricinus is widely documented. I. ricinus is in Europe the main vector of the bacterium Borrelia burgdorferi, the pathogen causing Lyme borreliosis (LB). Humidity in vegetated systems is a major factor in tick ecology and its effects might translate into disease incidence in humans. Time series of two remotely sensed indices with sensitivity to vegetation greenness and moisture were tested as explanatory variables of LB incidence. Wavelet-based multiresolution analysis allowed the examination of these signals at different temporal scales in study sites in Belgium, where increases in LB incidence were reported in recent years. The analysis showed the potential of the tested indices for disease monitoring, the usefulness of analyzing the signal in different time frames and the importance of local characteristics of the study area for the selection of the vegetation index.

Remote Sensing Information Gateway: A free application and web service for fast, convenient, interoperable access to large repositories of atmospheric data

NASA Astrophysics Data System (ADS)

Plessel, T.; Szykman, J.; Freeman, M.

2012-12-01

EPA's Remote Sensing Information Gateway (RSIG) is a widely used free applet and web service for quickly and easily retrieving, visualizing and saving user-specified subsets of atmospheric data - by variable, geographic domain and time range. Petabytes of available data include thousands of variables from a set of NASA and NOAA satellites, aircraft, ground stations and EPA air-quality models. The RSIG applet is used by atmospheric researchers and uses the rsigserver web service to obtain data and images. The rsigserver web service is compliant with the Open Geospatial Consortium Web Coverage Service (OGC-WCS) standard to facilitate data discovery and interoperability. Since rsigserver is publicly accessible, it can be (and is) used by other applications. This presentation describes the architecture and technical implementation details of this successful system with an emphasis on achieving convenience, high-performance, data integrity and security.

Rapid Assessment of In Situ Wheat Straw Residue Via Remote Sensing Platforms

NASA Technical Reports Server (NTRS)

Sullivan, D. G.; Shaw, J. N.; Mask, P. L.; Rickman, D.; Luvall, J.; Wersinger, J. M.; Guertal, E. A.

2003-01-01

Crop residues influence near surface soil organic carbon content (SOC), impact our ability to remotely assess soil properties, and play a role in global carbon budgets. Methods that measure crop residues are laborious, and largely inappropriate for regional estimates. The objective of this study was to evaluate remote sensing (RS) data for rapid quantification of residue cover. In March 2000 and April 2001, residue plots (15 m x 15 m) were established in the Coastal Plain and Appalachian Plateau physiographic regions of Alabama. Treatments consisted of five wheat (Triticum aestivum L.) straw cover rates (0, 10, 20, 50, and 80%) replicated 3 times. Soil water content and residue decomposition were monitored. Spectral measurements were acquired via spectroradiometer (350 - 1050 nm), Airborne Terrestrial Applications Sensor (ATLAS) (400 - 12,500 nm), airborne color photography (400 - 600 nm), and IKONOS satellite (450 - 900 nm). Spectroradiometer data were acquired monthly, aircraft images yearly, and satellite per availability. Results showed all platforms successfully estimated residue cover variability using red, near infrared (NIR) and thermal infrared (TIR) regions of the spectrum. Airborne ATLAS imagery was best explaining as much as 98% of the variability in wheat straw cover. Spectroradiometer, color infrared photography, and IKONOS imagery accounted for 84, 56, and 24% of the variability, respectively.

Literature relevant to remote sensing of water quality

NASA Technical Reports Server (NTRS)

Middleton, E. M.; Marcell, R. F.

1983-01-01

References relevant to remote sensing of water quality were compiled, organized, and cross-referenced. The following general categories were included: (1) optical properties and measurement of water characteristics; (2) interpretation of water characteristics by remote sensing, including color, transparency, suspended or dissolved inorganic matter, biological materials, and temperature; (3) application of remote sensing for water quality monitoring; (4) application of remote sensing according to water body type; and (5) manipulation, processing and interpretation of remote sensing digital water data.

Learning Methods of Remote Sensing In the 2013 Curriculum of Secondary School

NASA Astrophysics Data System (ADS)

Lili Somantri, Nandi

2016-11-01

The new remote sensing material included in the subjects of geography in the curriculum of 1994. For geography teachers generation of 90s and over who in college do not get the material remote sensing, for teaching is a tough matter. Most teachers only give a theoretical matter, and do not carry out practical reasons in the lack of facilities and infrastructure of computer laboratories. Therefore, in this paper studies the importance about the method or manner of teaching remote sensing material in schools. The purpose of this paper is 1) to explain the position of remote sensing material in the study of geography, 2) analyze the Geography Curriculum 2013 Subjects related to remote sensing material, 3) describes a method of teaching remote sensing material in schools. The method used in this paper is a descriptive analytical study supported by the literature. The conclusion of this paper that the position of remote sensing in the study of geography is a method or a way to obtain spatial data earth's surface. In the 2013 curriculum remote sensing material has been applied to the study of land use and transportation. Remote sensing methods of teaching must go through a practicum, which starts from the introduction of the theory of remote sensing, data extraction phase of remote sensing imagery to produce maps, both visually and digitally, field surveys, interpretation of test accuracy, and improved maps.

JPRS Report, Science & Technology, China, Remote Sensing Systems, Applications.

DTIC Science & Technology

1991-01-17

Partial Contents: Short Introduction to Nation’s Remote Sensing Units, Domestic Airborne Remote - Sensing System, Applications in Monitoring Natural...Disasters, Applications of Imagery From Experimental Satellites Launched in 1985, 1986, Current Status, Future Prospects for Domestic Remote - Sensing -Satellite...Ground Station, and Radar Remote - Sensing Technology Used to Monitor Yellow River Delta,

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

NASA Astrophysics Data System (ADS)

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

2013-04-01

The use of crop models allows simulating plant development, growth and yield under different environmental and management conditions. When combined with high spatial and temporal resolution remote sensing data, these models provide new perspectives for crop monitoring at regional scale. We propose here an approach to estimate time courses of dry aboveground biomass, yield and evapotranspiration (ETR) for summer (maize, sunflower) and winter crops (wheat) by assimilating Green Area Index (GAI) data, obtained from satellite observations, into a simple crop model. Only high spatial resolution and gap-free satellite time series can provide enough information for efficient crop monitoring applications. The potential of remote sensing data is often limited by cloud cover and/or gaps in observation. Data from different sensor systems need then to be combined. For this work, we employed a unique set of Formosat-2 and SPOT images (164 images) and in-situ measurements, acquired from 2006 to 2010 in southwest France. Among the several land surface biophysical variables accessible from satellite observations, the GAI is the one that has a key role in soil-plant-atmosphere interactions and in biomass accumulation process. Many methods have been developed to relate GAI to optical remote sensing signal. Here, seasonal dynamics of remotely sensed GAI were estimated by applying a method based on the inversion of a radiative transfer model using artificial neural networks. The modelling approach is based on the Simple Algorithm for Yield and Evapotranspiration estimate (SAFYE) model, which couples the FAO-56 model with an agro-meteorological model, based on Monteith's light-use efficiency theory. The SAFYE model is a daily time step crop model that simulates time series of GAI, dry aboveground biomass, grain yield and ETR. Crop and soil model parameters were determined using both in-situ measurements and values found in the literature. Phenological parameters were calibrated by the assimilation of the remotely sensed GAI time series. The calibration process led to accurate spatial estimates of GAI, ETR as well as of biomass and yield over the study area (24 km x 24 km window). The results highlight the interest of using a combined approach (crop model coupled with high spatial and temporal resolution remote sensing data) for the estimation of agronomical variables. At local scale, the model reproduced correctly the biomass production and ETR for summer crops (with relative RMSE of 29% and 35%, respectively). At regional scale, estimated yield and water requirement for irrigation were compared to regional statistics of yield and irrigation inventories provided by the local water agency. Results showed good agreements for inter-annual dynamics of yield estimates. Differences between water requirement for irrigation and actual supply were lower than 10% and inter-annual variability was well represented as well. The work, initially focused on summer crops, is being adapted to winter crops.

Biomass and health based forest cover delineation using spectral un-mixing

Treesearch

Mohan Tiruveedhula; Joseph Fan; Ravi R. Sadasivuni; Surya S. Durbha; David L. Evans

2009-01-01

Remote sensing is a well-suited source of information on various forest characteristics such as forest cover type, leaf area, biomass, and health. The use of appropriate layers helps to quantify the variables of interest. For example, normalized difference vegetation index (NDVI) and greenness help explain variability in biomass as well as health of forests....

On the temporal and spatial variability of near-surface soil moisture for the identification of representative in situ soil moisture monitoring stations

USDA-ARS?s Scientific Manuscript database

The high spatio-temporal variability of soil moisture complicates the validation of remotely sensed soil moisture products using in-situ monitoring stations. Therefore, a standard methodology for selecting the most repre- sentative stations for the purpose of validating satellites and land surface ...

A COMPARISON OF THE SALINITY REGIME ALONG THE TEXAS COAST WITH TERRESTRIAL VEGETATION GREENNESS AND WATER USE IN THE GALVESTON BAY WATERSHED USING REMOTING SENSING

EPA Science Inventory

Variability in vegetation greenness was determined for the Galveston Bay watershed using biweekly Normalized Difference Vegetation Index (NDVI) data derived from the Advanced Very High Resolution Radiometer (A VHRR) flown on NOAA satellites. NDVI variability was compared with reg...

Load sensing system

DOEpatents

Sohns, Carl W.; Nodine, Robert N.; Wallace, Steven Allen

1999-01-01

A load sensing system inexpensively monitors the weight and temperature of stored nuclear material for long periods of time in widely variable environments. The system can include an electrostatic load cell that encodes weight and temperature into a digital signal which is sent to a remote monitor via a coaxial cable. The same cable is used to supply the load cell with power. When multiple load cells are used, vast

[A review on polarization information in the remote sensing detection].

PubMed

Gong, Jie-Qiong; Zhan, Hai-Gang; Liu, Da-Zhao

2010-04-01

Polarization is one of the inherent characteristics. Because the surface of the target structure, internal structure, and the angle of incident light are different, the earth's surface and any target in atmosphere under optical interaction process will have their own characteristic nature of polarization. Polarimetric characteristics of radiation energy from the targets are used in polarization remote sensing detection as detective information. Polarization remote sensing detection can get the seven-dimensional information of targets in complicated backgrounds, detect well-resolved outline of targets and low-reflectance region of objectives, and resolve the problems of atmospheric detection and identification camouflage detection which the traditional remote sensing detection can not solve, having good foreground in applications. This paper introduces the development of polarization information in the remote sensing detection from the following four aspects. The rationale of polarization remote sensing detection is the base of polarization remote sensing detection, so it is firstly introduced. Secondly, the present researches on equipments that are used in polarization remote sensing detection are particularly and completely expatiated. Thirdly, the present exploration of theoretical simulation of polarization remote sensing detection is well detailed. Finally, the authors present the applications research home and abroad of the polarization remote sensing detection technique in the fields of remote sensing, atmospheric sounding, sea surface and underwater detection, biology and medical diagnosis, astronomical observation and military, summing up the current problems in polarization remote sensing detection. The development trend of polarization remote sensing detection technology in the future is pointed out in order to provide a reference for similar studies.

Cybernetic Basis and System Practice of Remote Sensing and Spatial Information Science

NASA Astrophysics Data System (ADS)

Tan, X.; Jing, X.; Chen, R.; Ming, Z.; He, L.; Sun, Y.; Sun, X.; Yan, L.

2017-09-01

Cybernetics provides a new set of ideas and methods for the study of modern science, and it has been fully applied in many areas. However, few people have introduced cybernetics into the field of remote sensing. The paper is based on the imaging process of remote sensing system, introducing cybernetics into the field of remote sensing, establishing a space-time closed-loop control theory for the actual operation of remote sensing. The paper made the process of spatial information coherently, and improved the comprehensive efficiency of the space information from acquisition, procession, transformation to application. We not only describes the application of cybernetics in remote sensing platform control, sensor control, data processing control, but also in whole system of remote sensing imaging process control. We achieve the information of output back to the input to control the efficient operation of the entire system. This breakthrough combination of cybernetics science and remote sensing science will improve remote sensing science to a higher level.

REMOTE AND PROXIMATE SENSING IN SUPPORT OF SUSTAINABLE WATERSHED MANAGEMENT

EPA Science Inventory

Inter-generationally prudent management of watershed resources will require attention to a complex array of interdependent variables. An interdisciplinary team of investigators from four national research laboratories in EPA's ORD are collaborating to develop stratagems for water...

Remote Distributed Vibration Sensing Through Opaque Media Using Permanent Magnets

DOE PAGES

Chen, Yi; Mazumdar, Anirban; Brooks, Carlton F.; ...

2018-04-05

Vibration sensing is critical for a variety of applications from structural fatigue monitoring to understanding the modes of airplane wings. In particular, remote sensing techniques are needed for measuring the vibrations of multiple points simultaneously, assessing vibrations inside opaque metal vessels, and sensing through smoke clouds and other optically challenging environments. Here, in this paper, we propose a method which measures high-frequency displacements remotely using changes in the magnetic field generated by permanent magnets. We leverage the unique nature of vibration tracking and use a calibrated local model technique developed specifically to improve the frequency-domain estimation accuracy. The results showmore » that two-dimensional local models surpass the dipole model in tracking high-frequency motions. A theoretical basis for understanding the effects of electronic noise and error due to correlated variables is generated in order to predict the performance of experiments prior to implementation. Simultaneous measurements of up to three independent vibrating components are shown. The relative accuracy of the magnet-based displacement tracking with respect to the video tracking ranges from 40 to 190 μm when the maximum displacements approach ±5 mm and when sensor-to-magnet distances vary from 25 to 36 mm. Finally, vibration sensing inside an opaque metal vessel and mode shape changes due to damage on an aluminum beam are also studied using the wireless permanent-magnet vibration sensing scheme.« less

Remote Distributed Vibration Sensing Through Opaque Media Using Permanent Magnets

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

Chen, Yi; Mazumdar, Anirban; Brooks, Carlton F.

Vibration sensing is critical for a variety of applications from structural fatigue monitoring to understanding the modes of airplane wings. In particular, remote sensing techniques are needed for measuring the vibrations of multiple points simultaneously, assessing vibrations inside opaque metal vessels, and sensing through smoke clouds and other optically challenging environments. Here, in this paper, we propose a method which measures high-frequency displacements remotely using changes in the magnetic field generated by permanent magnets. We leverage the unique nature of vibration tracking and use a calibrated local model technique developed specifically to improve the frequency-domain estimation accuracy. The results showmore » that two-dimensional local models surpass the dipole model in tracking high-frequency motions. A theoretical basis for understanding the effects of electronic noise and error due to correlated variables is generated in order to predict the performance of experiments prior to implementation. Simultaneous measurements of up to three independent vibrating components are shown. The relative accuracy of the magnet-based displacement tracking with respect to the video tracking ranges from 40 to 190 μm when the maximum displacements approach ±5 mm and when sensor-to-magnet distances vary from 25 to 36 mm. Finally, vibration sensing inside an opaque metal vessel and mode shape changes due to damage on an aluminum beam are also studied using the wireless permanent-magnet vibration sensing scheme.« less

Expanding dryland ecosystem flux datasets enable novel quantification of water availability and carbon exchange in Southwestern North America

NASA Astrophysics Data System (ADS)

Biederman, J. A.; Scott, R. L.; Smith, W. K.; Litvak, M. E.; MacBean, N.

2017-12-01

Global-scale studies suggest that water-limited dryland ecosystems dominate the increasing trend in magnitude and interannual variability of the land CO2 sink. However, the terrestrial biosphere models and remote sensing models used in large-scale analyses are poorly constrained by flux measurements in drylands, which are under-represented in global datasets. In this talk, I will address this gap with eddy covariance data from 30 ecosystems across the Southwest of North America with observed ranges in annual precipitation of 100 - 1000 mm, annual temperatures of 2 - 25 °C, and records of 3 - 10 years each (160 site-years). This extensive dryland dataset enables new approaches including 1) separation of temporal and spatial patterns to infer fast and slow ecosystem responses to change, and 2) partitioning of precipitation into hydrologic losses, evaporation, and ecosystem-available water. I will then compare direct flux measurements with models and remote sensing used to scale fluxes regionally. Combining eddy covariance and streamflow measurements, I will show how evapotranspiration (ET), which is the efflux of soil moisture remaining after hydrologic losses, is a better metric than precipitation of water available to drive ecosystem CO2 exchange. Furthermore, I will present a novel method to partition ET into evaporation and transpiration using the tight coupling of transpiration and photosynthesis. In contrast with typical carbon sink function in wetter, more-studied regions, dryland sites express an annual net carbon uptake varying from -350 to +330 gC m-2. Due to less respiration losses relative to photosynthesis gains during winter, declines in winter precipitation across the Southwest since 1999 are reducing annual net CO2 uptake. Interannual variability of net uptake is larger than for wetter regions, and half the sites pivot between sinks in wet years to sources in dry years. Biospheric and remote sensing models capture only 20-30 % of interannual variability in ET and CO2 fluxes, suggesting the impact of dryland regions on the variability of global CO2 may be up to 3 - 5 times larger than current estimates. Finally, I will highlight progress in ongoing work to develop improved remote sensing models of dryland CO2 uptake using novel indices including solar-induced fluorescence.

Sea ice-atmosphere interaction: Application of multispectral satellite data in polar surface energy flux estimates

NASA Technical Reports Server (NTRS)

Steffen, K.; Schweiger, A.; Maslanik, J.; Key, J.; Weaver, R.; Barry, R.

1990-01-01

The application of multi-spectral satellite data to estimate polar surface energy fluxes is addressed. To what accuracy and over which geographic areas large scale energy budgets can be estimated are investigated based upon a combination of available remote sensing and climatological data sets. The general approach was to: (1) formulate parameterization schemes for the appropriate sea ice energy budget terms based upon the remotely sensed and/or in-situ data sets; (2) conduct sensitivity analyses using as input both natural variability (observed data in regional case studies) and theoretical variability based upon energy flux model concepts; (3) assess the applicability of these parameterization schemes to both regional and basin wide energy balance estimates using remote sensing data sets; and (4) assemble multi-spectral, multi-sensor data sets for at least two regions of the Arctic Basin and possibly one region of the Antarctic. The type of data needed for a basin-wide assessment is described and the temporal coverage of these data sets are determined by data availability and need as defined by parameterization scheme. The titles of the subjects are as follows: (1) Heat flux calculations from SSM/I and LANDSAT data in the Bering Sea; (2) Energy flux estimation using passive microwave data; (3) Fetch and stability sensitivity estimates of turbulent heat flux; and (4) Surface temperature algorithm.

Mapping a Better Vintage

NASA Technical Reports Server (NTRS)

2003-01-01

Vineyards such as those located in California's Napa Valley tend to be subdivided into relatively large fields or blocks that often encompass heterogeneous physical conditions. Since growers typically treat the entire block as a single minimum management unit for cultivation and harvest, mapping and monitoring the variability within a block is a concern. Over the last decade, an increasing number of vineyard managers have utilized digital remote sensing and geographic information systems (GIS) to visualize the variability within their blocks. With computer software designed to overlay remotely sensed imagery with environmental and agronomic geographic data on a map, GIS helps growers recognize and explain patterns that might not have been obvious otherwise. GIS can also serve as a valuable archiving mechanism for future reference. To further develop the use of image technology and GIS for vineyard management support, NASA's Earth Science Enterprise partnered with the U.S. wine and commercial remote sensing industries for a project known as the Viticultural Integration of NASA Technologies for Assessment of the Grapevine Environment (VINTAGE). With project investigators from NASA's Ames Research Center, the California State University at Monterey Bay, and the University of Montana, several prototype products have been developed to support agricultural decisions concerning canopy management and irrigation practice. One key VINTAGE aspect involved the evaluation of satellite and airborne multispectral imagery for delineation of sub-block management zones within a vineyard.

[Modeling of species distribution using topography and remote sensing data, with vascular plants of the Tukuringra Range low mountain belt (Zeya state Nature Reserve, Amur Region) as a case study].

PubMed

Dudov, S V

2016-01-01

On the basis of maximum entropy method embedded in MaxEnt software, the cartographic models are designed for spatial distribution of 63 species of vascular plants inhabiting low mountain belt of the Tukuringra Range. Initial data for modeling were actual points of a species occurrence, data on remote sensing (multispectral space snapshots by Landsat), and a digital topographic model. It is found out that the structure of factors contributing to the model is related to species ecological amplitude. The distribution of stenotopic species is determined, mainly, by the topography, which thermal and humidity conditions of habitats are associated with. To the models for eurytopic species, variables formed on the basis of remote sensing contribute significantly, those variables encompassing the parameters of the soil-vegetable cover. In course of the obtained models analyzing, three principal groups of species are revealed that have similar distribution pattern. Species of the first group are restricted in their distribution by the slopes of the. River Zeya and River Giluy gorges. Species of the second group are associated with the southern macroslope of the range and with southern slopes of large rivers' valleys. The third group incorporates those species that are distributed over the whole territory under study.

Validation of Cloud Optical Parameters from Passive Remote Sensing in the Arctic by using the Aircraft Measurements

NASA Astrophysics Data System (ADS)

Chen, H.; Schmidt, S.; Coddington, O.; Wind, G.; Bucholtz, A.; Segal-Rosenhaimer, M.; LeBlanc, S. E.

2017-12-01

Cloud Optical Parameters (COPs: e.g., cloud optical thickness and cloud effective radius) and surface albedo are the most important inputs for determining the Cloud Radiative Effect (CRE) at the surface. In the Arctic, the COPs derived from passive remote sensing such as from the Moderate Resolution Imaging Spectroradiometer (MODIS) are difficult to obtain with adequate accuracy owing mainly to insufficient knowledge about the snow/ice surface, but also because of the low solar zenith angle. This study aims to validate COPs derived from passive remote sensing in the Arctic by using aircraft measurements collected during two field campaigns based in Fairbanks, Alaska. During both experiments, ARCTAS (Arctic Research of the Composition of the Troposphere from Aircraft and Satellites) and ARISE (Arctic Radiation-IceBridge Sea and Ice Experiment), the Solar Spectral Flux Radiometer (SSFR) measured upwelling and downwelling shortwave spectral irradiances, which can be used to derive surface and cloud albedo, as well as the irradiance transmitted by clouds. We assess the variability of the Arctic sea ice/snow surfaces albedo through these aircraft measurements and incorporate this variability into cloud retrievals for SSFR. We then compare COPs as derived from SSFR and MODIS for all suitable aircraft underpasses of the satellites. Finally, the sensitivities of the COPs to surface albedo and solar zenith angle are investigated.

Evaluation of SPOT imagery for the estimation of grassland biomass

NASA Astrophysics Data System (ADS)

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

2015-06-01

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

Airborne remote sensing in precision viticolture: assessment of quality and quantity vineyard production using multispectral imagery: a case study in Velletri, Rome surroundings (central Italy)

NASA Astrophysics Data System (ADS)

Tramontana, Gianluca; Papale, Dario; Girard, Filippo; Belli, Claudio; Pietromarchi, Paolo; Tiberi, Domenico; Comandini, Maria C.

2009-09-01

During 2008 an experimental study aimed to investigate the capabilities of a new Airborne Remote sensing platform as an aid in precision viticulture was conducted. The study was carried out on 2 areas located in the town of Velletri, near Rome; the acquisitions were conducted on 07-08-2008 and on 09-09-2008, using ASPIS (Advanced Spectroscopic Imager System) the new airborne multispectral sensor, capable to acquire 12 narrow spectral bands (10 nm) located in the visible and near-infrared region. Several vegetation indices, for a total of 22 independent variables, were tested for the estimation of different oenological parameters. Anova test showed that several oenochemical parameters, such as sugars and acidity, differ according to the variety taken into consideration. The remotely sensed data were significantly correlated with the following oenochemical parameters: Leaf Surface Exposed (SFE) (correlation coefficient R2 ~ 0.8), wood pruning (R2 ~ 0.8), reducing sugars (R2 ~ 0.6 and Root Mean Square Error ~ 5g/l), total acidity (R2 ~ 0.6 and RMSE ~ 0.5 g/l), polyphenols (R2~ 0.9) and anthocyanins content (R2 ~ 0.89) in order to provide "prescriptives" thematic maps related to the oenological variables of interest, the relationships previously carried out have been applied to the vegetation indices.

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models

PubMed Central

Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties–sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen–in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models–multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)–were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources. PMID:28114334

Ground and surface temperature variability for remote sensing of soil moisture in a heterogeneous landscape

USGS Publications Warehouse

Giraldo, M.A.; Bosch, D.; Madden, M.; Usery, L.; Finn, M.

2009-01-01

At the Little River Watershed (LRW) heterogeneous landscape near Tifton Georgia US an in situ network of stations operated by the US Department of Agriculture-Agriculture Research Service-Southeast Watershed Research Lab (USDA-ARS-SEWRL) was established in 2003 for the long term study of climatic and soil biophysical processes. To develop an accurate interpolation of the in situ readings that can be used to produce distributed representations of soil moisture (SM) and energy balances at the landscape scale for remote sensing studies, we studied (1) the temporal and spatial variations of ground temperature (GT) and infra red temperature (IRT) within 30 by 30 m plots around selected network stations; (2) the relationship between the readings from the eight 30 by 30 m plots and the point reading of the network stations for the variables SM, GT and IRT; and (3) the spatial and temporal variation of GT and IRT within agriculture landuses: grass, orchard, peanuts, cotton and bare soil in the surrounding landscape. The results showed high correlations between the station readings and the adjacent 30 by 30 m plot average value for SM; high seasonal independent variation in the GT and IRT behavior among the eight 30 by 30 m plots; and site specific, in-field homogeneity in each 30 by 30 m plot. We found statistical differences in the GT and IRT between the different landuses as well as high correlations between GT and IRT regardless of the landuse. Greater standard deviations for IRT than for GT (in the range of 2-4) were found within the 30 by 30 m, suggesting that when a single point reading for this variable is selected for the validation of either remote sensing data or water-energy models, errors may occur. The results confirmed that in this landscape homogeneous 30 by 30 m plots can be used as landscape spatial units for soil moisture and ground temperature studies. Under this landscape conditions small plots can account for local expressions of environmental processes, decreasing the errors and uncertainties in remote sensing estimates caused by landscape heterogeneity.

High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models.

PubMed

Forkuor, Gerald; Hounkpatin, Ozias K L; Welp, Gerhard; Thiel, Michael

2017-01-01

Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.

Incorporation of satellite remote sensing pan-sharpened imagery into digital soil prediction and mapping models to characterize soil property variability in small agricultural fields

NASA Astrophysics Data System (ADS)

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

2017-01-01

Soil prediction models based on spectral indices from some multispectral images are too coarse to characterize spatial pattern of soil properties in small and heterogeneous agricultural lands. Image pan-sharpening has seldom been utilized in Digital Soil Mapping research before. This research aimed to analyze the effects of pan-sharpened (PAN) remote sensing spectral indices on soil prediction models in smallholder farm settings. This research fused the panchromatic band and multispectral (MS) bands of WorldView-2, GeoEye-1, and Landsat 8 images in a village in Southern India by Brovey, Gram-Schmidt and Intensity-Hue-Saturation methods. Random Forest was utilized to develop soil total nitrogen (TN) and soil exchangeable potassium (Kex) prediction models by incorporating multiple spectral indices from the PAN and MS images. Overall, our results showed that PAN remote sensing spectral indices have similar spectral characteristics with soil TN and Kex as MS remote sensing spectral indices. There is no soil prediction model incorporating the specific type of pan-sharpened spectral indices always had the strongest prediction capability of soil TN and Kex. The incorporation of pan-sharpened remote sensing spectral data not only increased the spatial resolution of the soil prediction maps, but also enhanced the prediction accuracy of soil prediction models. Small farms with limited footprint, fragmented ownership and diverse crop cycle should benefit greatly from the pan-sharpened high spatial resolution imagery for soil property mapping. Our results show that multiple high and medium resolution images can be used to map soil properties suggesting the possibility of an improvement in the maps' update frequency. Additionally, the results should benefit the large agricultural community through the reduction of routine soil sampling cost and improved prediction accuracy.

Use of multispectral satellite remote sensing to assess mixing of suspended sediment downstream of large river confluences

NASA Astrophysics Data System (ADS)

Umar, M.; Rhoads, Bruce L.; Greenberg, Jonathan A.

2018-01-01

Although past work has noted that contrasts in turbidity often are detectable on remotely sensed images of rivers downstream from confluences, no systematic methodology has been developed for assessing mixing over distance of confluent flows with differing surficial suspended sediment concentrations (SSSC). In contrast to field measurements of mixing below confluences, satellite remote-sensing can provide detailed information on spatial distributions of SSSC over long distances. This paper presents a methodology that uses remote-sensing data to estimate spatial patterns of SSSC downstream of confluences along large rivers and to determine changes in the amount of mixing over distance from confluences. The method develops a calibrated Random Forest (RF) model by relating training SSSC data from river gaging stations to derived spectral indices for the pixels corresponding to gaging-station locations. The calibrated model is then used to predict SSSC values for every river pixel in a remotely sensed image, which provides the basis for mapping of spatial variability in SSSCs along the river. The pixel data are used to estimate average surficial values of SSSC at cross sections spaced uniformly along the river. Based on the cross-section data, a mixing metric is computed for each cross section. The spatial pattern of change in this metric over distance can be used to define rates and length scales of surficial mixing of suspended sediment downstream of a confluence. This type of information is useful for exploring the potential influence of various controlling factors on mixing downstream of confluences, for evaluating how mixing in a river system varies over time and space, and for determining how these variations influence water quality and ecological conditions along the river.

Malaria Modeling using Remote Sensing and GIS Technologies

NASA Technical Reports Server (NTRS)

Kiang, Richard

2004-01-01

Malaria has been with the human race since the ancient time. In spite of the advances of biomedical research and the completion of genomic mapping of Plasmodium falciparum, the exact mechanisms of how the various strains of parasites evade the human immune system and how they have adapted and become resistant to multiple drugs remain elusive. Perhaps because of these reasons, effective vaccines against malaria are still not available. Worldwide, approximately one to three millions deaths are attributed to malaria annually. With the increased availability of remotely sensed data, researchers in medical entomology, epidemiology and ecology have started to associate environmental and ecological variables with malaria transmission. In several studies, it has been shown that transmission correlates well with certain environmental and ecological parameters, and that remote sensing can be used to measure these determinants. In a NASA project, we have taken a holistic approach to examine how remote sensing and GIs can contribute to vector and malaria controls. To gain a better understanding of the interactions among the possible promoting factors, we have been developing a habitat model, a transmission model, and a risk prediction model, all using remote sensing data as input. Our objectives are: 1) To identify the potential breeding sites of major vector species and the locations for larvicide and insecticide applications in order to reduce costs, lessen the chance of developing pesticide resistance, and minimize the damage to the environment; 2) To develop a malaria transmission model characterizing the interactions among hosts, vectors, parasites, landcover and environment in order to identify the key factors that sustain or intensify malaria transmission, and 3) To develop a risk model to predict the occurrence of malaria and its transmission intensity using epidemiological data and satellite-derived or ground-measured environmental and meteorological data.

Water Dynamics in Fogera and the Upper Blue Nile - Farmers perspectives and remote sensing

NASA Astrophysics Data System (ADS)

Chemin, Yann; Desalegn, Mengistu; Curnow, Jayne; Johnston, Robyn

2015-04-01

This research work is about finding the connection between farmers perspectives on changes of water conditions in their socio-agricultural environment and satellite remote sensing analysis. Key informant surveys were conducted to investigate localised views on water scarcity as a counterpoint to the physical measurement of water availability. Does a numerical or mapped image identifying water scarcity always equate to a dearth of water for agriculture? To push the limits of the relationship between human and physical data we sought to ground-truth GIS results with the practical experience and knowledge of people living in the area. We data-mined public domain satellite data with FOSS (GDAL, GRASS GIS) and produced water-related spatio-temporal domains for our study area and the larger Upper Nile Basin. Accumulated remote sensing information was then cross-referenced with informant's accounts of water availability for the same space and time. During the survey fieldwork the team also took photographs electronically stamped with GPS coordinates to compare and contrast the views of informants and the remote sensing information with high resolution images of the landscape. We found that farmers perspective on the Spring maize crop sensibility to variability of rainfall can be quantified in space and time by remote sensing cumulative transpiration. A crop transpiration gap of 1-2.5 mm/day for about 20 days is to be overcome, a full amount of 20 to 50 mm, depending on the type of year deficit. Such gap can be overcome, even by temporary supplemental irrigation practices, however, the economical and cultural set up is already developed in another way, as per sesonal renting of higher soil profile water retention capacity fields.

Remote sensing, paleoecology, and the archaeology of human migration during the Pleistocene in central Asia and western China

NASA Astrophysics Data System (ADS)

Glantz, Michelle M.; Todd, Lawrence

2003-07-01

Remote sensing used in the context of global information systems has enormous applications within archaeology. This technology enables the discovery of new archaeological features and promotes an understanding of the relationship between ecosystem and cultural dynamics. Archaeologists are able to add a time dimension to 'creeping environmental changes' that other areas of scientific inquiry concerned with climate change often lack. Remote sensing and other aerial prospecting has been used successfully to model land use and population expansions during relatively recent archaeological eras, such as the Bronze and Iron Ages. Although satellite image databases exist for numerous areas of the New and Old World, very little research has been conducted in Central Asia or western China. This region is historically significant because of its position along the important trading route called the Silk Road. The purpose of the present research is to investigate another poorly understood period of human history that would benefit from the application of remote sensing and associated ground truthing techniques. The migration of hominids out of Africa during the late Pliocene/early Pleistocene and their subsequent colonization of north-central, east, and south-east Asia is relatively well documented in the archaeological record and marks the beginning of the long-term process of human impacts on the region. However, the trajectory of dispersal of Homo erectus, Neandertals, and early modern humans and the ways by which ecosystem vagaries affected this dispersal across Eurasia is unknown. Our purpose is to summarize what is currently known about the geological indicators of ecosystem changes that remote sensing techniques provide and how ecosystem variables may allow us to model human migration as that of an invasive species through this important geographic crossroads of the Old World.

Ground-based Remote Sensing for Quantifying Subsurface and Surface Co-variability to Scale Arctic Ecosystem Functioning

NASA Astrophysics Data System (ADS)

Oktem, R.; Wainwright, H. M.; Curtis, J. B.; Dafflon, B.; Peterson, J.; Ulrich, C.; Hubbard, S. S.; Torn, M. S.

2016-12-01

Predicting carbon cycling in Arctic requires quantifying tightly coupled surface and subsurface processes including permafrost, hydrology, vegetation and soil biogeochemistry. The challenge has been a lack of means to remotely sense key ecosystem properties in high resolution and over large areas. A particular challenge has been characterizing soil properties that are known to be highly heterogeneous. In this study, we exploit tightly-coupled above/belowground ecosystem functioning (e.g., the correlations among soil moisture, vegetation and carbon fluxes) to estimate subsurface and other key properties over large areas. To test this concept, we have installed a ground-based remote sensing platform - a track-mounted tram system - along a 70 m transect in the ice-wedge polygonal tundra near Barrow, Alaska. The tram carries a suite of near-surface remote sensing sensors, including sonic depth, thermal IR, NDVI and multispectral sensors. Joint analysis with multiple ground-based measurements (soil temperature, active layer soil moisture, and carbon fluxes) was performed to quantify correlations and the dynamics of above/belowground processes at unprecedented resolution, both temporally and spatially. We analyzed the datasets with particular focus on correlating key subsurface and ecosystem properties with surface properties that can be measured by satellite/airborne remote sensing over a large area. Our results provided several new insights about system behavior and also opens the door for new characterization approaches. We documented that: (1) soil temperature (at >5 cm depth; critical for permafrost thaw) was decoupled from soil surface temperature and was influenced strongly by soil moisture, (2) NDVI and greenness index were highly correlated with both soil moisture and gross primary productivity (based on chamber flux data), and (3) surface deformation (which can be measured by InSAR) was a good proxy for thaw depth dynamics at non-inundated locations.

Regional comparison of tundra carbon budget response over the Alaska North Slope to varying environmental conditions as informed by in situ and flux tower measurements, remote sensing and biophysical modeling

NASA Astrophysics Data System (ADS)

Shirley, S.; Watts, J. D.; Kimball, J. S.; Zhang, Z.; Poulter, B.; Klene, A. E.; Jones, L. A.; Kim, Y.; Oechel, W. C.; Zona, D.; Euskirchen, E. S.

2017-12-01

A warming Arctic climate is contributing to shifts in landscape moisture and temperature regimes, a shortening of the non-frozen season, and increases in the depth of annual active layer. The changing environmental conditions make it difficult to determine whether tundra ecosystems are a carbon sink or source. At present, eddy covariance flux towers and biophysical measurements within the tower footprint provide the most direct assessment of change to the tundra carbon balance. However, these measurements have a limited spatial footprint and exist over relatively short timescales. Thus, terrestrial ecosystem models are needed to provide an improved understanding of how changes in landscape environmental conditions impact regional carbon fluxes. This study examines the primary drivers thought to affect the magnitude and variability of tundra-atmosphere CO2 and CH4 fluxes over the Alaska North Slope. Also investigated is the ability of biophysical models to capture seasonal flux characteristics over the 9 tundra tower sites examined. First, we apply a regression tree approach to ascertain which remotely sensed environmental variables best explain observed variability in the tower fluxes. Next, we compare flux estimates obtained from multiple process models including Terrestrial Carbon Flux (TCF) and the Lund-Potsdam-Jena Wald Schnee und Landschaft (LPJ-wsl), and Soil Moisture Active Passive Level 4 Carbon (SMAP L4_C) products. Our results indicate that out of 7 variables examined vegetation greenness, temperature, and moisture are more significant predictors of carbon flux magnitude over the tundra tower sites. This study found that satellite data-driven models, due to the ability of remote sensing instruments to capture the physical principles and processes driving tundra carbon flux, are more effective at estimating the magnitude and spatiotemporal variability of CO2 and CH4 fluxes in northern high latitude ecosystems.

Remote Sensing of Vegetation Recovery from Disturbance in Drylands

NASA Astrophysics Data System (ADS)

Poitras, T. B.; Villarreal, M. L.; Waller, E.; Duniway, M.; Nauman, T.

2016-12-01

Characteristics of dryland ecosystems such as climatic extremes and water limitations render semi-arid regions vulnerable to disturbance and slow to recover. Land surface monitoring over time through the use of remote sensing may have potential for identifying dryland ecosystem recovery after anthropogenic and natural disturbance. However, semi-arid vegetation cover is challenging to measure using remote sensing techniques due to low vegetation cover and confusion between bright and variable soils and non-photosynthetic vegetation (NPV). We therefore evaluated the ability of various multispectral indices to distinguish bare ground from total vegetation cover, in order to determine those that can detect changes over time in heavily disturbed sites. We calculated nine spectral indices from Landsat TM using Google Earth Engine (March through October, 2006 through 2008) and tested relationships between index values and ground measurements from long-term monitoring data collected in and around Canyonlands National Park in Utah. We also tested multivariate models, with some showing improvement under cross-validation. We found that indices that included shortwave infrared bands and soil brightness were important for capturing gradients in bare ground, and vegetation cover was best quantified with near-infrared bands. These results will be used to help assess the landscape-scale impacts of oil and gas development in dryland ecosystems and to measure response to restoration efforts. Keywords: remote sensing, landsat, drylands

Biodiversity and agriculture in dynamic landscapes: Integrating ground and remotely-sensed baseline surveys.

PubMed

Gillison, Andrew N; Asner, Gregory P; Fernandes, Erick C M; Mafalacusser, Jacinto; Banze, Aurélio; Izidine, Samira; da Fonseca, Ambrósio R; Pacate, Hermenegildo

2016-07-15

Sustainable biodiversity and land management require a cost-effective means of forecasting landscape response to environmental change. Conventional species-based, regional biodiversity assessments are rarely adequate for policy planning and decision making. We show how new ground and remotely-sensed survey methods can be coordinated to help elucidate and predict relationships between biodiversity, land use and soil properties along complex biophysical gradients that typify many similar landscapes worldwide. In the lower Zambezi valley, Mozambique we used environmental, gradient-directed transects (gradsects) to sample vascular plant species, plant functional types, vegetation structure, soil properties and land-use characteristics. Soil fertility indices were derived using novel multidimensional scaling of soil properties. To facilitate spatial analysis, we applied a probabilistic remote sensing approach, analyzing Landsat 7 satellite imagery to map photosynthetically active and inactive vegetation and bare soil along each gradsect. Despite the relatively low sample number, we found highly significant correlations between single and combined sets of specific plant, soil and remotely sensed variables that permitted testable spatial projections of biodiversity and soil fertility across the regional land-use mosaic. This integrative and rapid approach provides a low-cost, high-return and readily transferable methodology that permits the ready identification of testable biodiversity indicators for adaptive management of biodiversity and potential agricultural productivity. Copyright © 2016 Elsevier Ltd. All rights reserved.

[Fluorescence peak shift corresponding to high chlorophyll concentrations in inland water].

PubMed

Duan, Hong-Tao; Ma, Rong-Hua; Zhang, Yuan-Zhi; Zhang, Bai

2009-01-01

Hyperspectral remote sensing offers the potential to detect water quality variables such as Chl-a by using narrow spectral channels of less than 10 nm, which could otherwise be masked by broadband satellites such as Landsat TM. Fluorescence peak of the red region is very important for the remote sensing of inland and coastal waters, which is unique to phytoplankton Chl-a that takes place in this region. Based on in situ water sampling and field spectral measurement from 2004 to 2006 in Nanhu Lake, the features of the spectral reflectance were analyzed in detail with peak position shift. The results showed: An exponential fitting model, peak position = a(Chl-a)b, was developed between chlorophyll-a concentration and fluorescence peak shift, where a varies between 686.11 and 686.29, while b between 0.0062 and 0.0065. It was found that the better the spectral resolution, the higher the precision of the model. Except that, the average of peak shift showed a high correlation with the average of different Chl-a grades, and the determination coefficient (R2) was higher than 0.81. It contributed significantly to the increase in the accuracy of the derivation of chlorophyll values from remote sensing data in Nanhu Lake. There is satisfactory correspondence between hyperspectral models and chl-a concentration, therefore, it is possible to monitor the water quality of Nanhu lake throngh the hyperspetral remote sensing data.

Spatial analysis of vector-borne infectious diseases and ecological indicators using GIS and remote sensing

NASA Astrophysics Data System (ADS)

Anh, N. K.; Liou, Y. A.

2017-12-01

Ecological and climate indicators play a vital role in defining patterns of human activities and behaviors, such as seasonal features, migration, winter-summer lifestyles, which in turn might be associated with vector-borne disease habitats and transmission risks. Remote sensing has been instrumental in deriving environmental variables and indicators. GIS is shown to be a powerful tool in spatiotemporal visualization and distribution of vector-borne diseases and for analysis of associations between environmental conditions and characteristics of vector-borne habitats. Vietnam is in the sub-tropical climate zone with high humidity and abundant precipitation, while the distribution of precipitation is uneven leading to frequently annual occurrence of drought and flood disasters. Moreover, urban heat island effect is significantly enhanced in urbanized areas in recent years. The increase in the frequency and magnitude of severity of weather extremes that are potentially linked to climate change and anthropogenic processes have highlighted the demand of research into health risk assessment and adaptive capacity. This research focuses on the analysis of physical features of environmental indicators and its association with vector-borne diseases as well as adaptive capacity. The study illustrates how remotely sensed data has been utilized in geohealth applications, surveillance, and health risk mapping. In addition, promising possibilities of allowing disease early-warning systems with citizen participation platform will be proposed. Keywords: Vector-borne diseases; environmental indicators; remote sensing; GIS; Vietnam.

Remote sensing of land use and water quality relationships - Wisconsin shore, Lake Michigan

NASA Technical Reports Server (NTRS)

Haugen, R. K.; Marlar, T. L.

1976-01-01

This investigation assessed the utility of remote sensing techniques in the study of land use-water quality relationships in an east central Wisconsin test area. The following types of aerial imagery were evaluated: high altitude (60,000 ft) color, color infrared, multispectral black and white, and thermal; low altitude (less than 5000 ft) color infrared, multispectral black and white, thermal, and passive microwave. A non-imaging hand-held four-band radiometer was evaluated for utility in providing data on suspended sediment concentrations. Land use analysis includes the development of mapping and quantification methods to obtain baseline data for comparison to water quality variables. Suspended sediment loads in streams, determined from water samples, were related to land use differences and soil types in three major watersheds. A multiple correlation coefficient R of 0.85 was obtained for the relationship between the 0.6-0.7 micrometer incident and reflected radiation data from the hand-held radiometer and concurrent ground measurements of suspended solids in streams. Applications of the methods and baseline data developed in this investigation include: mapping and quantification of land use; input to watershed runoff models; estimation of effects of land use changes on stream sedimentation; and remote sensing of suspended sediment content of streams. High altitude color infrared imagery was found to be the most acceptable remote sensing technique for the mapping and measurement of land use types.

Image Mining in Remote Sensing for Coastal Wetlands Mapping: from Pixel Based to Object Based Approach

NASA Astrophysics Data System (ADS)

Farda, N. M.; Danoedoro, P.; Hartono; Harjoko, A.

2016-11-01

The availably of remote sensing image data is numerous now, and with a large amount of data it makes “knowledge gap” in extraction of selected information, especially coastal wetlands. Coastal wetlands provide ecosystem services essential to people and the environment. The aim of this research is to extract coastal wetlands information from satellite data using pixel based and object based image mining approach. Landsat MSS, Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI images located in Segara Anakan lagoon are selected to represent data at various multi temporal images. The input for image mining are visible and near infrared bands, PCA band, invers PCA bands, mean shift segmentation bands, bare soil index, vegetation index, wetness index, elevation from SRTM and ASTER GDEM, and GLCM (Harralick) or variability texture. There is three methods were applied to extract coastal wetlands using image mining: pixel based - Decision Tree C4.5, pixel based - Back Propagation Neural Network, and object based - Mean Shift segmentation and Decision Tree C4.5. The results show that remote sensing image mining can be used to map coastal wetlands ecosystem. Decision Tree C4.5 can be mapped with highest accuracy (0.75 overall kappa). The availability of remote sensing image mining for mapping coastal wetlands is very important to provide better understanding about their spatiotemporal coastal wetlands dynamics distribution.

Near-earth orbital guidance and remote sensing

NASA Technical Reports Server (NTRS)

Powers, W. F.

1972-01-01

The curriculum of a short course in remote sensing and parameter optimization is presented. The subjects discussed are: (1) basics of remote sensing and the user community, (2) multivariant spectral analysis, (3) advanced mathematics and physics of remote sensing, (4) the atmospheric environment, (5) imaging sensing, and (6)nonimaging sensing. Mathematical models of optimization techniques are developed.

Operational programs in forest management and priority in the utilization of remote sensing

NASA Technical Reports Server (NTRS)

Douglass, R. W.

1978-01-01

A speech is given on operational remote sensing programs in forest management and the importance of remote sensing in forestry is emphasized. Forest service priorities in using remote sensing are outlined.

Remote sensing, land use, and demography - A look at people through their effects on the land

NASA Technical Reports Server (NTRS)

Paul, C. K.; Landini, A. J.

1976-01-01

Relevant causes of failure by the remote sensing community in the urban scene are analyzed. The reasons for the insignificant role of remote sensing in urban land use data collection are called the law of realism, the incompatibility of remote sensing and urban management system data formats is termed the law of nominal/ordinal systems compatibility, and the land use/population correlation dilemma is referred to as the law of missing persons. The study summarizes the three laws of urban land use information for which violations, avoidance, or ignorance have caused the decline of present remote sensing research. Particular attention is given to the rationale for urban land use information and for remote sensing. It is shown that remote sensing of urban land uses compatible with the three laws can be effectively developed by realizing the 10 percent contribution of remote sensing to urban land use planning data collection.

Thematic Conference on Geologic Remote Sensing, 8th, Denver, CO, Apr. 29-May 2, 1991, Proceedings. Vols. 1 & 2

NASA Technical Reports Server (NTRS)

1991-01-01

The proceedings contain papers discussing the state-of-the-art exploration, engineering, and environmental applications of geologic remote sensing, along with the research and development activities aimed at increasing the future capabilities of this technology. The following topics are addressed: spectral geology, U.S. and international hydrocarbon exporation, radar and thermal infrared remote sensing, engineering geology and hydrogeology, mineral exploration, remote sensing for marine and environmental applications, image processing and analysis, geobotanical remote sensing, and data integration and geographic information systems. Particular attention is given to spectral alteration mapping with imaging spectrometers, mapping the coastal plain of the Congo with airborne digital radar, applications of remote sensing techniques to the assessment of dam safety, remote sensing of ferric iron minerals as guides for gold exploration, principal component analysis for alteration mappping, and the application of remote sensing techniques for gold prospecting in the north Fujian province.

Methods of training the graduate level and professional geologist in remote sensing technology

NASA Technical Reports Server (NTRS)

Kolm, K. E.

1981-01-01

Requirements for a basic course in remote sensing to accommodate the needs of the graduate level and professional geologist are described. The course should stress the general topics of basic remote sensing theory, the theory and data types relating to different remote sensing systems, an introduction to the basic concepts of computer image processing and analysis, the characteristics of different data types, the development of methods for geological interpretations, the integration of all scales and data types of remote sensing in a given study, the integration of other data bases (geophysical and geochemical) into a remote sensing study, and geological remote sensing applications. The laboratories should stress hands on experience to reinforce the concepts and procedures presented in the lecture. The geologist should then be encouraged to pursue a second course in computer image processing and analysis of remotely sensed data.

Remote sensing of Earth terrain

NASA Technical Reports Server (NTRS)

Kong, J. A.

1993-01-01

Progress report on remote sensing of Earth terrain covering the period from Jan. to June 1993 is presented. Areas of research include: radiative transfer model for active and passive remote sensing of vegetation canopy; polarimetric thermal emission from rough ocean surfaces; polarimetric passive remote sensing of ocean wind vectors; polarimetric thermal emission from periodic water surfaces; layer model with tandom spheriodal scatterers for remote sensing of vegetation canopy; application of theoretical models to active and passive remote sensing of saline ice; radiative transfer theory for polarimetric remote sensing of pine forest; scattering of electromagnetic waves from a dense medium consisting of correlated mie scatterers with size distributions and applications to dry snow; variance of phase fluctuations of waves propagating through a random medium; polarimetric signatures of a canopy of dielectric cylinders based on first and second order vector radiative transfer theory; branching model for vegetation; polarimetric passive remote sensing of periodic surfaces; composite volume and surface scattering model; and radar image classification.

Use of change detection in assessing development plans - A Philippine example. [aircraft/Landsat remote sensing information system for regional planning

NASA Technical Reports Server (NTRS)

Coiner, J. C.; Bruce, R. C.

1978-01-01

An aircraft/Landsat change-detection study conducted 1948-1972 on Marinduque Province, Republic of the Philippines, is discussed, and a procedure using both remote sensing and information systems for collection, spatial analysis, and display of periodic data is described. Each of the 4,008 25-hectare cells representing Marinduque were observed, and changes in and between variables were measured and tested using nonparametric statistics to determine the effect of specific land cover changes. Procedures using Landsat data to obtain a more continuous updating of the data base are considered. The system permits storage and comparison of historical and current data.

A modified JPEG-LS lossless compression method for remote sensing images

NASA Astrophysics Data System (ADS)

Deng, Lihua; Huang, Zhenghua

2015-12-01

As many variable length source coders, JPEG-LS is highly vulnerable to channel errors which occur in the transmission of remote sensing images. The error diffusion is one of the important factors which infect its robustness. The common method of improving the error resilience of JPEG-LS is dividing the image into many strips or blocks, and then coding each of them independently, but this method reduces the coding efficiency. In this paper, a block based JPEP-LS lossless compression method with an adaptive parameter is proposed. In the modified scheme, the threshold parameter RESET is adapted to an image and the compression efficiency is close to that of the conventional JPEG-LS.

A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model. Part 1; Overview

NASA Technical Reports Server (NTRS)

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

1998-01-01

Knowledge of the amount of water in the soil is of great importance to many earth science disciplines. Soil moisture is a key variable in controlling the exchange of water and energy between the land surface and the atmosphere. Thus, soil moisture information is valuable in a wide range of applications including weather and climate, runoff potential and flood control, early warning of droughts, irrigation, crop yield forecasting, soil erosion, reservoir management, geotechnical engineering, and water quality. Despite the importance of soil moisture information, widespread and continuous measurements of soil moisture are not possible today. Although many earth surface conditions can be measured from satellites, we still cannot adequately measure soil moisture from space. Research in soil moisture remote sensing began in the mid 1970s shortly after the surge in satellite development. Recent advances in remote sensing have shown that soil moisture can be measured, at least qualitatively, by several methods. Quantitative measurements of moisture in the soil surface layer have been most successful using both passive and active microwave remote sensing, although complications arise from surface roughness and vegetation type and density. Early attempts to measure soil moisture from space-borne microwave instruments were hindered by what is now considered sub-optimal wavelengths (shorter than 5 cm) and the coarse spatial resolution of the measurements. L-band frequencies between 1 and 3 GHz (10-30 cm) have been deemed optimal for detection of soil moisture in the upper few centimeters of soil. The Electronically Steered Thinned Array Radiometer (ESTAR), an aircraft-based instrument operating a 1,4 GHz, has shown great promise for soil moisture determination. Initiatives are underway to develop a similar instrument for space. Existing space-borne synthetic aperture radars (SARS) operating at C- and L-band have also shown some potential to detect surface wetness. The advantage of radar is its much higher resolution than passive microwave systems, but it is currently hampered by surface roughness effects and the lack of a good algorithm based on a single frequency and single polarization. In addition, its repeat frequency is generally low (about 40 days). In the meantime, two new radiometers offer some hope for remote sensing of soil moisture from space. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), launched in November 1997, possesses a 10.65 GHz channel and the Advanced Microwave Scanning Radiometer (AMSR) on both the ADEOS-11 and Earth Observing System AM-1 platforms to be launched in 1999 possesses a 6.9 GHz channel. Aside from issues about interference from vegetation, the coarse resolution of these data will provide considerable challenges pertaining to their application. The resolution of TMI is about 45 km and that of AMSR is about 70 km. These resolutions are grossly inconsistent with the scale of soil moisture processes and the spatial variability of factors that control soil moisture. Scale disparities such as these are forcing us to rethink how we assimilate data of various scales in hydrologic models. Of particular interest is how to assimilate soil moisture data by reconciling the scale disparity between what we can expect from present and future remote sensing measurements of soil moisture and modeling soil moisture processes. It is because of this disparity between the resolution of space-based sensors and the scale of data needed for capturing the spatial variability of soil moisture and related properties that remote sensing of soil moisture has not met with more widespread success. Within a single footprint of current sensors at the wavelengths optimal for this application, in most cases there is enormous heterogeneity in soil moisture created by differences in landcover, soils and topography, as well as variability in antecedent precipitation. It is difficult to interpret the meaning of 'mean' soil moisture under such conditions and even more difficult to apply such a value. Because of the non-linear relationships between near-surface soil moisture and other variables of interest, such as surface energy fluxes and runoff, mean soil moisture has little applicability at such large scales. It is for these reasons that the use of remote sensing in conjunction with a hydrologic model appears to be of benefit in capturing the complete spatial and temporal structure of soil moisture. This paper is Part I of a four-part series describing a method for intermittently assimilating remotely-sensed soil moisture information to improve performance of a distributed land surface hydrology model. The method, summarized in section II, involves the following components, each of which is detailed in the indicated section of the paper or subsequent papers in this series: Forward radiative transfer model methods (section II and Part IV); Use of a Kalman filter to assimilate remotely-sensed soil moisture estimates with the model profile (section II and Part IV); Application of a soil hydrology model to capture the continuous evolution of the soil moisture profile within and below the root zone (section III); Statistical aggregation techniques (section IV and Part II); Disaggregation techniques using a neural network approach (section IV and Part III); and Maximum likelihood and Bayesian algorithms for inversely solving for the soil moisture profile in the upper few cm (Part IV).

Remote sensing by satellite - Technical and operational implications for international cooperation

NASA Technical Reports Server (NTRS)

Doyle, S. E.

1976-01-01

International cooperation in the U.S. Space Program is discussed and related to the NASA program for remote sensing of the earth. Satellite remote sensing techniques are considered along with the selection of the best sensors and wavelength bands. The technology of remote sensing satellites is considered with emphasis on the Landsat system configuration. Future aspects of remote sensing satellites are considered.

Assessing Wetland Hydroperiod and Soil Moisture With Remote Sensing: A Demonstration for the NASA Plum Brook Station Year 2

NASA Technical Reports Server (NTRS)

Brooks, Colin; Bourgeau-Chavez, Laura; Endres, Sarah; Battaglia, Michael; Shuchman, Robert

2015-01-01

Primary Goal: Assist with the evaluation and measuring of wetlands hydroperiod at the PlumBrook Station using multi-source remote sensing data as part of a larger effort on projecting climate change-related impacts on the station's wetland ecosystems. MTRI expanded on the multi-source remote sensing capabilities to help estimate and measure hydroperiod and the relative soil moisture of wetlands at NASA's Plum Brook Station. Multi-source remote sensing capabilities are useful in estimating and measuring hydroperiod and relative soil moisture of wetlands. This is important as a changing regional climate has several potential risks for wetland ecosystem function. The year two analysis built on the first year of the project by acquiring and analyzing remote sensing data for additional dates and types of imagery, combined with focused field work. Five deliverables were planned and completed: 1) Show the relative length of hydroperiod using available remote sensing datasets 2) Date linked table of wetlands extent over time for all feasible non-forested wetlands 3) Utilize LIDAR data to measure topographic height above sea level of all wetlands, wetland to catchment area radio, slope of wetlands, and other useful variables 4) A demonstration of how analyzed results from multiple remote sensing data sources can help with wetlands vulnerability assessment 5) A MTRI style report summarizing year 2 results. This report serves as a descriptive summary of our completion of these our deliverables. Additionally, two formal meetings were held with Larry Liou and Amanda Sprinzl to provide project updates and receive direction on outputs. These were held on 2/26/15 and 9/17/15 at the Plum Brook Station. Principal Component Analysis (PCA) is a multivariate statistical technique used to identify dominant spatial and temporal backscatter signatures. PCA reduces the information contained in the temporal dataset to the first few new Principal Component (PC) images. Some advantages of PCA include the ability to filter out temporal autocorrelation and reduce speckle to the higher order PC images. A PCA was performed using ERDAS Imagine on a time series of PALSAR dates. Hydroperiod maps were created by separating the PALSAR dates into two date ranges, 2006-2008 and 2010, and performing an unsupervised classification on the PCAs.

Drought monitoring using remote sensing of evapotranspiration

USDA-ARS?s Scientific Manuscript database

Drought assessment is a complex endeavor, requiring monitoring of deficiencies in multiple components of the hydrologic budget. Precipitation anomalies reflect variability in water supply to the land surface, while soil moisture (SM), ground and surface water anomalies reflect deficiencies in moist...

Prediction of Coral Bleaching in the Florida Keys Using Remotely Sensed Data

EPA Science Inventory

Coral bleaching has been attributed to extremes or stressful synergy in several physical variables of the coral habitat. Of particular concern have been temperature, ultraviolet radiation, and photosynthetically available radiation. Satellite observing systems allow synoptic-sca...

Variable Selection Strategies for Small-area Estimation Using FIA Plots and Remotely Sensed Data

Treesearch

Andrew Lister; Rachel Riemann; James Westfall; Mike Hoppus

2005-01-01

The USDA Forest Service's Forest Inventory and Analysis (FIA) unit maintains a network of tens of thousands of georeferenced forest inventory plots distributed across the United States. Data collected on these plots include direct measurements of tree diameter and height and other variables. We present a technique by which FIA plot data and coregistered...

Remote sensing in operational range management programs in Western Canada

NASA Technical Reports Server (NTRS)

Thompson, M. D.

1977-01-01

A pilot program carried out in Western Canada to test remote sensing under semi-operational conditions and display its applicability to operational range management programs was described. Four agencies were involved in the program, two in Alberta and two in Manitoba. Each had different objectives and needs for remote sensing within its range management programs, and each was generally unfamiliar with remote sensing techniques and their applications. Personnel with experience and expertise in the remote sensing and range management fields worked with the agency personnel through every phase of the pilot program. Results indicate that these agencies have found remote sensing to be a cost effective tool and will begin to utilize remote sensing in their operational work during ensuing seasons.

Study for urbanization corresponding to socio-economic activities in Savannaket, Laos using satellite remote sensing

NASA Astrophysics Data System (ADS)

Kimijiama, S.; Nagai, M.

2014-06-01

In Greater Mekong Sub-region (GMS), economic liberalization and deregulation facilitated by GMS Regional Economic Corporation Program (GMS-ECP) has triggered urbanization in the region. However, the urbanization rate and its linkage to socio-economic activities are ambiguous. The objectives of this paper are to: (a) determine the changes in urban area from 1972 to 2013 using remote sensing data, and (b) analyse the relationships between urbanization with respect to socio-economic activities in central Laos. The study employed supervised classification and human visible interpretation to determine changes in urbanization rate. Regression analysis was used to analyze the correlation between the urbanization rate and socio-economic variables. The result shows that the urban area increased significantly from 1972 to 2013. The socio-economic variables such as school enrollment, labour force, mortality rate, water source and sanitation highly correlated with the rate of urbanization during the period. The study concluded that identifying the highly correlated socio-economic variables with urbanization rate could enable us to conduct a further urbanization simulation. The simulation helps in designing policies for sustainable development.

The role of C3 and C4 grasses to interannual variability in remotely sensed ecosystem performance over the US Great Plains

USGS Publications Warehouse

Ricotta, C.; Reed, B.C.; Tieszen, L.T.

2003-01-01

Time integrated normalized difference vegetation index (??NDVI) derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) multi-temporal imagery over a 10-year period (1989-1998) was used as a surrogate for primary production to investigate the impact of interannual climate variability on grassland performance for central and northern US Great Plains. First, the contribution of C3 and C4 species abundance to the major grassland ecosystems of the US Great Plains is described. Next, the relation between mean ??NDVI and the ??NDVI coefficient of variation (CV ??NDVI) used as a proxy for interranual climate variability is analysed. Results suggest that the differences in the long-term climate control over ecosystem performance approximately coincide with changes between C3- and C4-dominant grassland classes. Variation in remotely sensed net primary production over time is higher for the southern and western plains grasslands (primary C4 grasslands), whereas the C3-dominated classes in the northern and eastern portion of the US Great Plains, generally show lower CV ??NDVI values.

Correlating Species and Spectral Diversity using Remote Sensing in Successional Fields in Virginia

NASA Astrophysics Data System (ADS)

Aneece, I.; Epstein, H. E.

2015-12-01

Conserving biodiversity can help preserve ecosystem properties and function. As the increasing prevalence of invasive plant species threatens biodiversity, advances in remote sensing technology can help monitor invasive species and their effects on ecosystems and plant communities. To assess whether we could study the effects of invasive species on biodiversity using remote sensing, we asked whether species diversity was positively correlated with spectral diversity, and whether correlations differed among spectral regions along the visible and near-infrared range. To answer these questions, we established community plots in secondary successional fields at the Blandy Experimental Farm in northern Virginia and collected vegetation surveys and ground-level hyperspectral data from 350 to 1025 nm wavelengths. Pearson correlation analysis revealed a positive correlation between spectral diversity and species diversity in the visible ranges of 350-499 nm (Pearson correlation=0.69, p=0.01), 500-589 nm (Pearson=0.64, p=0.03), and 590-674 nm (Pearson=0.70, p=0.01), slight positive correlation in the red edge range of 675-754 nm (Pearson=0.56, p=0.06), and no correlation in the near-infrared ranges of 755-924 nm (Pearson=-0.06, p=0.85) and 925-1025 nm (Pearson=0.30, p=0.34). These differences in correlations across spectral regions may be due to the elements that contribute to signatures in those regions and spectral data transformation methods. To investigate the role of pigment variability in these correlations, we estimated chlorophyll, carotenoid, and anthocyanin concentrations of five dominant species in the plots using vegetation indices. Although interspecific variability in pigment levels exceeded intraspecific variability, chlorophyll (F value=118) was more varied within species than carotenoids (F=322) and anthocyanins (F=126), perhaps contributing to the lack of correlation between species diversity and spectral diversity in the red edge region. Interspecific differences in pigment levels, however, make it possible to differentiate species remotely.

The energy balance of an urban area: Examining temporal and spatial variability through measurements, remote sensing and modeling

NASA Astrophysics Data System (ADS)

Offerle, Brian

Urban environmental problems related to air quality, thermal stress, issues of water demand and quality, all of which are linked directly or indirectly to urban climate, are emerging as major environmental concerns at the start of the 21st century. Thus there are compelling social, political and economic, and scientific reasons that make the study and understanding of the fundamental causes of urban climates critically important. This research addresses these topics through an intensive study of the surface energy balance of Lodz, Poland. The research examines the temporal variability in long-term measurements of urban surface-atmosphere exchange at a downtown location and the spatial variability of this exchange over distinctly different neighborhoods using shorter-term observations. These observations provide the basis for an evaluation of surface energy balance models. Monthly patterns in energy exchange are consistent from year-to-year with variability determined by net radiation and the timing and amount of precipitation. Spatial variability can be determined from plan area fractions of vegetation and impervious surface, though heat storage exerts a strong control on shorter term variability of energy exchange, within and between locations in an urban area. Anthropogenic heat fluxes provide most of the energy driving surface-atmosphere exchange in winter, From a modeling perspective, sensible heat fluxes can be reliably determined from radiometrically sensed surface temperatures and spatially representative surface-atmosphere exchange in an urban area can be determined from satellite remote sensing products. Models of the urban surface energy balance showed good agreement with mean values of energy exchange and under most conditions represented the temporal variability due to synoptic and shorter time scale forcing well.

Constraining the dynamics of the water budget at high spatial resolution in the world's water towers using models and remote sensing data; Snake River Basin, USA

NASA Astrophysics Data System (ADS)

Watson, K. A.; Masarik, M. T.; Flores, A. N.

2016-12-01

Mountainous, snow-dominated basins are often referred to as the water towers of the world because they store precipitation in seasonal snowpacks, which gradually melt and provide water supplies to downstream communities. Yet significant uncertainties remain in terms of quantifying the stores and fluxes of water in these regions as well as the associated energy exchanges. Constraining these stores and fluxes is crucial for advancing process understanding and managing these water resources in a changing climate. Remote sensing data are particularly important to these efforts due to the remoteness of these landscapes and high spatial variability in water budget components. We have developed a high resolution regional climate dataset extending from 1986 to the present for the Snake River Basin in the northwestern USA. The Snake River Basin is the largest tributary of the Columbia River by volume and a critically important basin for regional economies and communities. The core of the dataset was developed using a regional climate model, forced by reanalysis data. Specifically the Weather Research and Forecasting (WRF) model was used to dynamically downscale the North American Regional Reanalysis (NARR) over the region at 3 km horizontal resolution for the period of interest. A suite of satellite remote sensing products provide independent, albeit uncertain, constraint on a number of components of the water and energy budgets for the region across a range of spatial and temporal scales. For example, GRACE data are used to constrain basinwide terrestrial water storage and MODIS products are used to constrain the spatial and temporal evolution of evapotranspiration and snow cover. The joint use of both models and remote sensing products allows for both better understanding of water cycle dynamics and associated hydrometeorologic processes, and identification of limitations in both the remote sensing products and regional climate simulations.

PROCEEDINGS OF THE FOURTH SYMPOSIUM ON REMOTE SENSING OF ENVIRONMENT; 12, 13, 14 APRIL 1966.

DTIC Science & Technology

The symposium was conducted as part of a continuing program investigating the field of remote sensing , its potential in scientific research and...information on all aspects of remote sensing , with special emphasis on such topics as needs for remotely sensed data, data management, and the special... remote sensing programs, data acquisition, data analysis and application, and equipment design, were presented. (Author)

Remote sensing and image interpretation

NASA Technical Reports Server (NTRS)

Lillesand, T. M.; Kiefer, R. W. (Principal Investigator)

1979-01-01

A textbook prepared primarily for use in introductory courses in remote sensing is presented. Topics covered include concepts and foundations of remote sensing; elements of photographic systems; introduction to airphoto interpretation; airphoto interpretation for terrain evaluation; photogrammetry; radiometric characteristics of aerial photographs; aerial thermography; multispectral scanning and spectral pattern recognition; microwave sensing; and remote sensing from space.

Probability theory for 3-layer remote sensing radiative transfer model: univariate case.

PubMed

Ben-David, Avishai; Davidson, Charles E

2012-04-23

A probability model for a 3-layer radiative transfer model (foreground layer, cloud layer, background layer, and an external source at the end of line of sight) has been developed. The 3-layer model is fundamentally important as the primary physical model in passive infrared remote sensing. The probability model is described by the Johnson family of distributions that are used as a fit for theoretically computed moments of the radiative transfer model. From the Johnson family we use the SU distribution that can address a wide range of skewness and kurtosis values (in addition to addressing the first two moments, mean and variance). In the limit, SU can also describe lognormal and normal distributions. With the probability model one can evaluate the potential for detecting a target (vapor cloud layer), the probability of observing thermal contrast, and evaluate performance (receiver operating characteristics curves) in clutter-noise limited scenarios. This is (to our knowledge) the first probability model for the 3-layer remote sensing geometry that treats all parameters as random variables and includes higher-order statistics. © 2012 Optical Society of America

Sizing up human health through remote sensing: uses and misuses.

PubMed

Herbreteau, V; Salem, G; Souris, M; Hugot, J P; Gonzalez, J P

2005-03-01

Following the launch of new satellites, remote sensing (RS) has been increasingly implicated in human health research for thirty years, providing a growing availability of images with higher resolution and spectral ranges. However, the scope of applications, beyond theoretical large potentialities, appears limited both by their technical nature and the models developed. An exhaustive review of RS applications in human health highlights the real implication thus far regarding the diversity and range of health issues, remotely sensed data, processes and interpretations. The place of RS is far under its expected potential, revealing fundamental barriers in its implementation for health applications. The selection of images is done by practical considerations as trivial as price and availability, which are often not relevant to addressing health questions requiring suitable resolutions and spatio-temporal range. The relationships of environmental variables from RS, geospatial data from other sources for health investigations are poorly addressed and usually simplified. A discussion covering the potential of RS for human health is developed here to assist health scientists deal with spatial and temporal dynamics of health, by finding the most relevant data and analysis procedures.

Atmospheric Radiative Transfer for Satellite Remote Sensing: Validation and Uncertainty

NASA Technical Reports Server (NTRS)

Marshak, Alexander

2007-01-01

My presentation will begin with the discussion of the Intercomparison of three-dimensional (3D) Radiative Codes (13RC) project that has been started in 1997. I will highlight the question of how well the atmospheric science community can solve the 3D radiative transfer equation. Initially I3RC was focused only on algorithm intercomparison; now it has acquired a broader identity providing new insights and creating new community resources for 3D radiative transfer calculations. Then I will switch to satellite remote sensing. Almost all radiative transfer calculations for satellite remote sensing are one-dimensional (1D) assuming (i) no variability inside a satellite pixel and (ii) no radiative interactions between pixels. The assumptions behind the 1D approach will be checked using cloud and aerosol data measured by the MODerate Resolution Imaging Spectroradiometer (MODIS) on board of two NASA satellites TERRA and AQUA. In the discussion, I will use both analysis technique: statistical analysis over large areas and time intervals, and single scene analysis to validate how well the 1D radiative transfer equation describes radiative regime in cloudy atmospheres.

Remote sensing and water quality indicators in the Korean West coast: Spatio-temporal structures of MODIS-derived chlorophyll-a and total suspended solids.

PubMed

Kim, Hae-Cheol; Son, Seunghyun; Kim, Yong Hoon; Khim, Jong Seong; Nam, Jungho; Chang, Won Keun; Lee, Jung-Ho; Lee, Chang-Hee; Ryu, Jongseong

2017-08-15

The Yellow Sea is a shallow marginal sea with a large tidal range. In this study, ten areas located along the western coast of the Korean Peninsula are investigated with respect to remotely sensed water quality indicators derived from NASA MODIS aboard of the satellite Aqua. We found that there was a strong seasonal trend with spatial heterogeneity. In specific, a strong six-month phase-lag was found between chlorophyll-a and total suspended solid owing to their inversed seasonality, which could be explained by different dynamics and environmental settings. Chlorophyll-a concentration seemed to be dominantly influenced by temperature, while total suspended solid was largely governed by local tidal forcing and bottom topography. This study demonstrated the potential and applicability of satellite products in coastal management, and highlighted find that remote-sensing would be a promising tool in resolving orthogonality of large spatio-temporal scale variabilities when combining with proper time series analyses. Copyright © 2017 Elsevier Ltd. All rights reserved.

Geological remote sensing

NASA Astrophysics Data System (ADS)

Bishop, Charlotte; Rivard, Benoit; de Souza Filho, Carlos; van der Meer, Freek

2018-02-01

Geology is defined as the 'study of the planet Earth - the materials of which it is made, the processes that act on these materials, the products formed, and the history of the planet and its life forms since its origin' (Bates and Jackson, 1976). Remote sensing has seen a number of variable definitions such as those by Sabins and Lillesand and Kiefer in their respective textbooks (Sabins, 1996; Lillesand and Kiefer, 2000). Floyd Sabins (Sabins, 1996) defined it as 'the science of acquiring, processing and interpreting images that record the interaction between electromagnetic energy and matter' while Lillesand and Kiefer (Lillesand and Kiefer, 2000) defined it as 'the science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation'. Thus Geological Remote Sensing can be considered the study of, not just Earth given the breadth of work undertaken in planetary science, geological features and surfaces and their interaction with the electromagnetic spectrum using technology that is not in direct contact with the features of interest.

Evaporation estimation of rift valley lakes: comparison of models.

PubMed

Melesse, Assefa M; Abtew, Wossenu; Dessalegne, Tibebe

2009-01-01

Evapotranspiration (ET) accounts for a substantial amount of the water flux in the arid and semi-arid regions of the World. Accurate estimation of ET has been a challenge for hydrologists, mainly because of the spatiotemporal variability of the environmental and physical parameters governing the latent heat flux. In addition, most available ET models depend on intensive meteorological information for ET estimation. Such data are not available at the desired spatial and temporal scales in less developed and remote parts of the world. This limitation has necessitated the development of simple models that are less data intensive and provide ET estimates with acceptable level of accuracy. Remote sensing approach can also be applied to large areas where meteorological data are not available and field scale data collection is costly, time consuming and difficult. In areas like the Rift Valley regions of Ethiopia, the applicability of the Simple Method (Abtew Method) of lake evaporation estimation and surface energy balance approach using remote sensing was studied. The Simple Method and a remote sensing-based lake evaporation estimates were compared to the Penman, Energy balance, Pan, Radiation and Complementary Relationship Lake Evaporation (CRLE) methods applied in the region. Results indicate a good correspondence of the models outputs to that of the above methods. Comparison of the 1986 and 2000 monthly lake ET from the Landsat images to the Simple and Penman Methods show that the remote sensing and surface energy balance approach is promising for large scale applications to understand the spatial variation of the latent heat flux.

A remote sensing based vegetation classification logic for global land cover analysis

USGS Publications Warehouse

Running, Steven W.; Loveland, Thomas R.; Pierce, Lars L.; Nemani, R.R.; Hunt, E. Raymond

1995-01-01

This article proposes a simple new logic for classifying global vegetation. The critical features of this classification are that 1) it is based on simple, observable, unambiguous characteristics of vegetation structure that are important to ecosystem biogeochemistry and can be measured in the field for validation, 2) the structural characteristics are remotely sensible so that repeatable and efficient global reclassifications of existing vegetation will be possible, and 3) the defined vegetation classes directly translate into the biophysical parameters of interest by global climate and biogeochemical models. A first test of this logic for the continental United States is presented based on an existing 1 km AVHRR normalized difference vegetation index database. Procedures for solving critical remote sensing problems needed to implement the classification are discussed. Also, some inferences from this classification to advanced vegetation biophysical variables such as specific leaf area and photosynthetic capacity useful to global biogeochemical modeling are suggested.

Geotechnical applications of remote sensing and remote data transmission; Proceedings of the Symposium, Cocoa Beach, FL, Jan. 31-Feb. 1, 1986

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

Johnson, A.I.; Pettersson, C.B.

1988-01-01

Papers and discussions concerning the geotechnical applications of remote sensing and remote data transmission, sources of remotely sensed data, and glossaries of remote sensing and remote data transmission terms, acronyms, and abbreviations are presented. Aspects of remote sensing use covered include the significance of lineaments and their effects on ground-water systems, waste-site use and geotechnical characterization, the estimation of reservoir submerging losses using CIR aerial photographs, and satellite-based investigation of the significance of surficial deposits for surface mining operations. Other topics presented include the location of potential ground subsidence and collapse features in soluble carbonate rock, optical Fourier analysis ofmore » surface features of interest in geotechnical engineering, geotechnical applications of U.S. Government remote sensing programs, updating the data base for a Geographic Information System, the joint NASA/Geosat Test Case Project, the selection of remote data telemetry methods for geotechnical applications, the standardization of remote sensing data collection and transmission, and a comparison of airborne Goodyear electronic mapping system/SAR with satelliteborne Seasat/SAR radar imagery.« less

Education in Environmental Remote Sensing: Potentials and Problems.

ERIC Educational Resources Information Center

Kiefer, Ralph W.; Lillesand, Thomas M.

1983-01-01

Discusses remote sensing principles and applications and the status and needs of remote sensing education in the United States. A summary of the fundamental policy issues that will determine remote sensing's future role in environmental and resource managements is included. (Author/BC)

THE EPA REMOTE SENSING ARCHIVE

EPA Science Inventory

What would you do if you were faced with organizing 30 years of remote sensing projects that had been haphazardly stored at two separate locations for years then combined? The EPA Remote Sensing Archive, currently located in Las Vegas, Nevada. contains the remote sensing data and...

Characterizing Open Water Bodies and Their Color Properties Through Optical Remote Sensing to Identify Areas of Vector-Borne Disease Risk

NASA Astrophysics Data System (ADS)

Podest, E.; De La Torre Juarez, M.; McDonald, K. C.; Jensen, K.; Ceccato, P.

2014-12-01

Predicting the risk of vector-borne disease outbreaks is a required step towards their control and eradication. Satellite observations can provide needed data to support agency decisions with respect to deployment of preventative measures and control resources. The coverage and persistence of open water is one of the primary indicators of conditions suitable for mosquito breeding habitats. This is currently a poorly measured variable due to its spatial and temporal variability across landscapes, especially in remote areas. Here we develop a methodology for monitoring these conditions through optical remote sensing images from Landsat. We pansharpen the images and apply a decision tree classification approach using Random Forests to generate 15 meter resolution maps of open water. In addition, since some mosquitos breed in clear water while others in turbid water, we classify water bodies according to their water color properties and we validate the results using field knowledge. We focus in East Africa where we assses the usefulness of these products to improve prediction of malaria outbreaks. Portions of this work were carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

Research on remote sensing image pixel attribute data acquisition method in AutoCAD

NASA Astrophysics Data System (ADS)

Liu, Xiaoyang; Sun, Guangtong; Liu, Jun; Liu, Hui

2013-07-01

The remote sensing image has been widely used in AutoCAD, but AutoCAD lack of the function of remote sensing image processing. In the paper, ObjectARX was used for the secondary development tool, combined with the Image Engine SDK to realize remote sensing image pixel attribute data acquisition in AutoCAD, which provides critical technical support for AutoCAD environment remote sensing image processing algorithms.

Bibliography of Remote Sensing Techniques Used in Wetland Research.

DTIC Science & Technology

1993-01-01

remote sensing technology for detecting changes in wetland environments. This report documents a bibliographic search conducted as part of that work unit on applications of remote sensing techniques in wetland research. Results were used to guide research efforts on the use of remote sensing technology for wetland change detection and assessment. The citations are presented in three appendixes, organized by wetland type, sensor type, and author.... Change detection, Wetland assessment, Remote sensing ,

Development of low-altitude remote sensing systems for crop production management

USDA-ARS?s Scientific Manuscript database

Precision agriculture accounts for within-field variability for targeted treatment rather than uniform treatment of an entire field. Precision agriculture is built on agricultural mechanization and state-of-the-art technologies of geographical information systems (GIS), global positioning systems (G...

Into the environment of mosquito-borne disease: A spatial analysis of vector distribution using traditional and remotely sensed methods

NASA Astrophysics Data System (ADS)

Brown, Heidi E.

Spatially explicit information is increasingly available for infectious disease modeling. However, such information is reluctantly or inappropriately incorporated. My dissertation research uses spatially explicit data to assess relationships between landscape and mosquito species distribution and discusses challenges regarding accurate predictive risk modeling. The goal of my research is to use remotely sensed environmental information and spatial statistical methods to better understand mosquito-borne disease epidemiology for improvement of public health responses. In addition to reviewing the progress of spatial infectious disease modeling, I present four research projects. I begin by evaluating the biases in surveillance data and build up to predictive modeling of mosquito species presence. In the first study I explore how mosquito surveillance trap types influence estimations of mosquito populations. Then. I use county-based human surveillance data and landscape variables to identify risk factors for West Nile virus disease. The third study uses satellite-based vegetation indices to identify spatial variation among West Nile virus vectors in an urban area and relates the variability to virus transmission dynamics. Finally, I explore how information from three satellite sensors of differing spatial and spectral resolution can be used to identify and distinguish mosquito habitat across central Connecticut wetlands. Analyses presented here constitute improvements to the prediction of mosquito distribution and therefore identification of disease risk factors. Current methods for mosquito surveillance data collection are labor intensive and provide an extremely limited, incomplete picture of the species composition and abundance. Human surveillance data offers additional challenges with respect to reporting bias and resolution, but is nonetheless informative in identifying environmental risk factors and disease transmission dynamics. Remotely sensed imagery supports mosquito and human disease surveillance data by providing spatially explicit, line resolution information about environmental factors relevant to vector-borne disease processes. Together, surveillance and remotely sensed environmental data facilitate improved description and modeling of disease transmission. Remote sensing can be used to develop predictive maps of mosquito distribution in relation to disease risk. This has implications for increased accuracy of mosquito control efforts. The projects presented in this dissertation enhance current public health capacities by examining the applications of spatial modeling with respect to mosquito-borne disease.

Remote sensing of trend and seasonal variability of greenhouse gas emissions from the Los Angeles basin using an FTS on Mount Wilson

NASA Astrophysics Data System (ADS)

Wong, C.; Fu, D.; Pongetti, T. J.; Newman, S.; Yung, Y. L.; Sander, S. P.

2013-12-01

Cities, such as Los Angeles, are significant sources of anthropogenic greenhouse gases (GHGs). With the growth of populations in cities worldwide, GHG emissions will increase, and monitoring the temporal trends will provide crucial data for global climate models as well as assessments of the effectiveness of control policies. Currently, continuous GHG observations in the Los Angeles basin are limited to a few in situ measurements, which are shown to be sensitive to local emissions and do not represent the Los Angeles basin well. To quantify GHG emissions from the metropolitan area, which tend to have heterogeneous characteristics, it is important to perform measurements which provide both continuous temporal and spatial coverage of the domain. Here we present observations of the major greenhouse gases, CO2 and CH4, using a spectroscopic remote sensing technique from the California Laboratory for Atmospheric Remote Sensing (CLARS) at Mount Wilson, California (1.7 km elevation). A Fourier Transform Spectrometer (FTS) deployed at the CLARS site points downward at 28 selected land surfaces in the Los Angeles basin to measure the slant column abundances of CO2, CH4, N2O, CO and O2 using reflected sunlight in the near-infrared and shortwave infrared regions. This remote sensing technique provides continuous temporal and spatial measurements in the Los Angeles basin to achieve the goal of quantifying emissions of GHGs and CO. It also serves as a test-bed for future geostationary satellite missions to measure GHGs from space such as JPL's Geostationary Carbon Process Investigation (GCPI). The path-averaged dry-air mixing ratio, XCO2 and XCH4, observed by the CLARS FTS, show significant diurnal variability that arises from emissions in the Los Angeles basin and atmospheric transport processes. High-precision data have been collected since August 2011. Here we analyze the annual and seasonal trend of the ratio XCH4:XCO2 in the Los Angeles basin observed by the CLARS FTS from August 2011 to present. This work demonstrates the ability to quantify and track GHG emissions in a megacity using ground-based remote sensing from an elevated platform and the potential for future geostationary satellite missions, such as GCPI, to monitor carbon fluxes in cities.

Enhancing Remotely Sensed TIR Data for Public Health Applications: Is West Nile Virus Heat-Related?

NASA Astrophysics Data System (ADS)

Weng, Q.; Liu, H.; Jiang, Y.

2014-12-01

Public health studies often require thermal infrared (TIR) images at both high temporal and spatial resolution to retrieve LST. However, currently, no single satellite sensors can deliver TIR data at both high temporal and spatial resolution. This technological limitation prevents the wide usage of remote sensing data in epidemiological studies. To solve this issue, we have developed a few image fusion techniques to generate high temporally-resolved image data. We downscaled GOES LST data to 15-minute 1-km resolution to assess community-based heat-related risk in Los Angeles County, California and simulated ASTER datasets by fusing ASTER and MODIS data to derive biophysical variables, including LST, NDVI, and normalized difference water index, to examine the effects of those environmental characteristics on WNV outbreak and dissemination. A spatio-temporal analysis of WNV outbreak and dissemination was conducted by synthesizing the remote sensing variables and mosquito surveillance data, and by focusing on WNV risk areas in July through September due to data sufficiency of mosquito pools. Moderate- and high-risk areas of WNV infections in mosquitoes were identified for five epidemiological weeks. These identified WNV-risk areas were then collocated in GIS with heat hazard, exposure, and vulnerability maps to answer the question of whether WNV is a heat related virus. The results show that elevation and built-up conditions were negatively associated with the WNV propagation, while LST positively correlated with the viral transmission. NDVI was not significantly associated with WNV transmission. San Fernando Valley was found to be the most vulnerable to mosquito infections of WNV. This research provides important insights into how high temporal resolution remote sensing imagery may be used to study time-dependant events in public health, especially in the operational surveillance and control of vector-borne, water-borne, or other epidemic diseases.

Remote Sensing of Vegetation Species Diversity: The Utility of Integrated Airborne Hyperspectral and Lidar Data

NASA Astrophysics Data System (ADS)

Krause, Keith Stuart

The change, reduction, or extinction of species is a major issue currently facing the Earth. Efforts are underway to measure, monitor, and protect habitats that contain high species diversity. Remote sensing technology shows extreme value for monitoring species diversity by mapping ecosystems and using those land cover maps or other derived data as proxies to species number and distribution. The National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) consists of remote sensing instruments such as an imaging spectrometer, a full-waveform lidar, and a high-resolution color camera. AOP collected data over the Ordway-Swisher Biological Station (OSBS) in May 2014. A majority of the OSBS site is covered by the Sandhill ecosystem, which contains a very high diversity of vegetation species and is a native habitat for several threatened fauna species. The research presented here investigates ways to analyze the AOP data to map ecosystems at the OSBS site. The research attempts to leverage the high spatial resolution data and study the variability of the data within a ground plot scale along with integrating data from the different sensors. Mathematical features are derived from the data and brought into a decision tree classification algorithm (rpart), in order to create an ecosystem map for the site. The hyperspectral and lidar features serve as proxies for chemical, functional, and structural differences in the vegetation types for each of the ecosystems. K-folds cross validation shows a training accuracy of 91%, a validation accuracy of 78%, and a 66% accuracy using independent ground validation. The results presented here represent an important contribution to utilizing integrated hyperspectral and lidar remote sensing data for ecosystem mapping, by relating the spatial variability of the data within a ground plot scale to a collection of vegetation types that make up a given ecosystem.

Kite Aerial Photography as a Tool for Remote Sensing

ERIC Educational Resources Information Center

Sallee, Jeff; Meier, Lesley R.

2010-01-01

As humans, we perform remote sensing nearly all the time. This is because we acquire most of our information about our surroundings through the senses of sight and hearing. Whether viewed by the unenhanced eye or a military satellite, remote sensing is observing objects from a distance. With our current technology, remote sensing has become a part…

Remote sensing for detecting and mapping whitefly (Bemisia tabaci) infestations

USDA-ARS?s Scientific Manuscript database

Remote sensing technology has long been used for detecting insect infestations on agricultural crops. With recent advances in remote sensing sensors and other spatial information technologies such as Global Position Systems (GPS) and Geographic Information Systems (GIS), remote sensing is finding mo...

Reflections on Earth--Remote-Sensing Research from Your Classroom.

ERIC Educational Resources Information Center

Campbell, Bruce A.

2001-01-01

Points out the uses of remote sensing in different areas, and introduces the program "Reflections on Earth" which provides access to basic and instructional information on remote sensing to students and teachers. Introduces students to concepts related to remote sensing and measuring distances. (YDS)

Remote-Sensing Practice and Potential

DTIC Science & Technology

1974-05-01

Six essential processes that must be accomplished if use of a remote - sensing system is to result in useful information are defined as problem...to be useful in remote - sensing projects are described. An overview of the current state-of-the-art of remote sensing is presented.

History and future of remote sensing technology and education

NASA Technical Reports Server (NTRS)

Colwell, R. N.

1980-01-01

A historical overview of the discovery and development of photography, related sciences, and remote sensing technology is presented. The role of education to date in the development of remote sensing is discussed. The probable future and potential of remote sensing and training is described.

Role of remote sensing in documenting living resources

NASA Technical Reports Server (NTRS)

Wagner, P. E.; Anderson, R. R.; Brun, B.; Eisenberg, M.; Genys, J. B.; Lear, D. W., Jr.; Miller, M. H.

1978-01-01

Specific cases of known or potentially useful applications of remote sensing in assessing biological resources are discussed. It is concluded that the more usable remote sensing techniques relate to the measurement of population fluctuations in aquatic systems. Sensing of the flora and the fauna of the Bay is considered with emphasis on direct sensing of aquatic plant populations and of water quality. Recommendations for remote sensing projects are given.

Commercial future: making remote sensing a media event

NASA Astrophysics Data System (ADS)

Lurie, Ian

1999-12-01

The rapid growth of commercial remote sensing has made high quality digital sensing data widely available -- now, remote sensing must become and remain a strong, commercially viable industry. However, this new industry cannot survive without an educated consumer base. To access markets, remote sensing providers must make their product more accessible, both literally and figuratively: Potential customers must be able to find the data they require, when they require it, and they must understand the utility of the information available to them. The Internet and the World Wide Web offer the perfect medium to educate potential customers and to sell remote sensing data to those customers. A well-designed web presence can provide both an information center and a market place for companies offering their data for sale. A very high potential web-based market for remote sensing lies in media. News agencies, web sites, and a host of other visual media services can use remote sensing data to provide current, relevant information regarding news around the world. This paper will provide a model for promotion and sale of remote sensing data via the Internet.

Evidence of a robust relationship between solar-induced chlorophyll fluorescence and gross primary productivity across dryland ecosystems of southwestern North America

NASA Astrophysics Data System (ADS)

Smith, W. K.; Biederman, J. A.; Scott, R. L.; Moore, D. J.; Kimball, J. S.; He, M.; Yan, D.; Hudson, A.; Barnes, M.; MacBean, N.; Fox, A. M.; Litvak, M. E.

2017-12-01

Satellite remote sensing provides unmatched spatiotemporal information on multiple facets of vegetation dynamics including seasonal to interannual total photosynthesis, termed gross primary productivity (GPP). Yet, our understanding of the relationship between GPP and remote sensing observations - and how this relationship changes with scale, biophysical constraint, vegetation type, etc. - remains limited. This knowledge gap is especially apparent for dryland ecosystems, which have high spatial and temporal variability and are under-represented by long-term, continuous field measurements. Here, utilizing a new synthesis of eddy covariance flux tower data for southwestern North America, we present a first assessment of the ability of novel satellite remote sensing vegetation proxies to accurately capture seasonal to interannual GPP dynamics across the region. We evaluate the greenness-based Enhanced Vegetation Index (EVI) and emerging proxies linked to plant physiological function, Solar-Induced Fluorescence (SIF) and Photochemical Reflectivity Index (PRI). We find that SIF observations more consistently correlate with seasonal GPP dynamics (R = 0.90) compared to EVI (R = 0.85) and PRI (R = 0.78). More, we find that SIF observations are also more sensitive to interannual GPP variability (linear slope = 0.80) relative to EVI (linear slope = 0.63) and PRI (linear slope = 0.35). This is likely due to increased sensitivity of SIF to GPP during periods of decoupling between greenness and photosynthesis due to water-limitation / stomatal closure. Conversely, EVI and PRI observations better capture spatial GPP variability between flux tower sites. These results suggest that combinations of these independent vegetation growth proxies could yield synergistic improvements in satellite-based GPP estimates.

CO Seasonal Variability and Trend over Paris Megacity Using Ground-Based QualAir FTS and Satellite IASI-MetOp Measurements

NASA Astrophysics Data System (ADS)

Te, Yao; Jeseck, Pascal; Hadji-Lazaro, Juliette

2012-11-01

In a growing world with more than 7 billion inhabitants and big emerging countries such as China, Brazil and India, emissions of anthropogenic pollutants are increasing continuously. Monitoring and control of atmospheric pollutants in megacities have become a major challenge for scientists and public health authorities in environmental research area. The QualAir platform at University Pierre et Marie Curie (UPMC), is an innovating experimental research platform dedicated to survey urban atmospheric pollution and air quality. A Bruker Optics IFS 125HR Fourier transform spectrometer belonged to the Laboratoire de Physique Moléculaire pour l'Atmosphère et l'Astrophysique (LPMAA), was adapted for ground-based atmospheric measurements. As one of the major instruments of the QualAir platform, this ground-based Fourier transform spectrometer (QualAir FTS) analyses the composition of the urban atmosphere of Paris, which is the third largest European megacity. The continuous monitoring of atmospheric pollutants is essential to improve the understanding of urban air pollution processes. Associated with a sun-tracker, the QualAir remote sensing FTS operates in solar infrared absorption and enables to monitor many trace gases, and to follow up their variability in the Ile-de-France region. Concentrations of atmospheric pollutants are retrieved by the radiative transfer model PROFFIT. These ground-based remote sensing measurements are compared to ground in-situ measurements and to satellite data from IASI-MetOp (Infrared Atmospheric Sounding Interferometer). The remote sensing total column of the carbon monoxide (CO) obtained from January 2009 to June 2012, has a seasonal variability with a maximum in April and a minimum in October. While, after 2008, the mean CO level is quite stable (no significant decrease as before 2008).

An assessment of the effectiveness of a random forest classifier for land-cover classification

NASA Astrophysics Data System (ADS)

Rodriguez-Galiano, V. F.; Ghimire, B.; Rogan, J.; Chica-Olmo, M.; Rigol-Sanchez, J. P.

2012-01-01

Land cover monitoring using remotely sensed data requires robust classification methods which allow for the accurate mapping of complex land cover and land use categories. Random forest (RF) is a powerful machine learning classifier that is relatively unknown in land remote sensing and has not been evaluated thoroughly by the remote sensing community compared to more conventional pattern recognition techniques. Key advantages of RF include: their non-parametric nature; high classification accuracy; and capability to determine variable importance. However, the split rules for classification are unknown, therefore RF can be considered to be black box type classifier. RF provides an algorithm for estimating missing values; and flexibility to perform several types of data analysis, including regression, classification, survival analysis, and unsupervised learning. In this paper, the performance of the RF classifier for land cover classification of a complex area is explored. Evaluation was based on several criteria: mapping accuracy, sensitivity to data set size and noise. Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land categories in the south of Spain. Results show that the RF algorithm yields accurate land cover classifications, with 92% overall accuracy and a Kappa index of 0.92. RF is robust to training data reduction and noise because significant differences in kappa values were only observed for data reduction and noise addition values greater than 50 and 20%, respectively. Additionally, variables that RF identified as most important for classifying land cover coincided with expectations. A McNemar test indicates an overall better performance of the random forest model over a single decision tree at the 0.00001 significance level.

77 FR 39220 - Advisory Committee on Commercial Remote Sensing (ACCRES); Charter Renewal

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-02

... Commercial Remote Sensing (ACCRES); Charter Renewal AGENCY: National Oceanic and Atmospheric Administration... Committee on Commercial Remote Sensing (ACCRES) was renewed on March 14, 2012. SUPPLEMENTARY INFORMATION: In... Commercial Remote Sensing (ACCRES) is in the public interest in connection with the performance of duties...

76 FR 66042 - Advisory Committee on Commercial Remote Sensing (ACCRES); Request for Nominations

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-25

... Commercial Remote Sensing (ACCRES); Request for Nominations ACTION: Notice requesting nominations for the Advisory Committee on Commercial Remote Sensing (ACCRES). SUMMARY: The Advisory Committee on Commercial Remote Sensing (ACCRES) was established to advise the Secretary of Commerce, through the Under Secretary...

An introduction to quantitative remote sensing. [data processing

NASA Technical Reports Server (NTRS)

Lindenlaub, J. C.; Russell, J.

1974-01-01

The quantitative approach to remote sensing is discussed along with the analysis of remote sensing data. Emphasis is placed on the application of pattern recognition in numerically oriented remote sensing systems. A common background and orientation for users of the LARS computer software system is provided.

Advancing High Spatial and Spectral Resolution Remote Sensing for Observing Plant Community Response to Environmental Variability and Change in the Alaskan Arctic

NASA Astrophysics Data System (ADS)

Vargas Zesati, Sergio A.

The Arctic is being impacted by climate change more than any other region on Earth. Impacts to terrestrial ecosystems have the potential to manifest through feedbacks with other components of the Earth System. Of particular concern is the potential for the massive store of soil organic carbon to be released from arctic permafrost to the atmosphere where it could exacerbate greenhouse warming and impact global climate and biogeochemical cycles. Even though substantial gains to our understanding of the changing Arctic have been made, especially over the past decade, linking research results from plot to regional scales remains a challenge due to the lack of adequate low/mid-altitude sampling platforms, logistic constraints, and the lack of cross-scale validation of research methodologies. The prime motivation of this study is to advance observational capacities suitable for documenting multi-scale environmental change in arctic terrestrial landscapes through the development and testing of novel ground-based and low altitude remote sensing methods. Specifically this study addressed the following questions: • How well can low-cost kite aerial photography and advanced computer vision techniques model the microtopographic heterogeneity of changing tundra surfaces? • How does imagery from kite aerial photography and fixed time-lapse digital cameras (pheno-cams) compare in their capacity to monitor plot-level phenological dynamics of arctic vegetation communities? • Can the use of multi-scale digital imaging systems be scaled to improve measurements of ecosystem properties and processes at the landscape level? • How do results from ground-based and low altitude digital remote sensing of the spatiotemporal variability in ecosystem processes compare with those from satellite remote sensing platforms? Key findings from this study suggest that cost-effective alternative digital imaging and remote sensing methods are suitable for monitoring and quantifying plot to landscape level ecosystem structure and phenological dynamics at multiple temporal scales. Overall, this study has furthered our knowledge of how tundra ecosystems in the Arctic change seasonally and how such change could impact remote sensing studies conducted from multiple platforms and across multiple spatial scales. Additionally, this study also highlights the urgent need for research into the validation of satellite products in order to better understand the causes and consequences of the changing Arctic and its potential effects on global processes. This study focused on sites located in northern Alaska and was formed in collaboration with Florida International University (FIU) and Grand Valley State University (GVSU) as a contribution to the US Arctic Observing Network (AON). All efforts were supported through the National Science Foundation (NSF), the Cyber-ShARE Center of Excellence, and the International Tundra Experiment (ITEX).

Using remote sensing, ecological niche modeling, and Geographic Information Systems for Rift Valley fever risk assessment in the United States

NASA Astrophysics Data System (ADS)

Tedrow, Christine Atkins

The primary goal in this study was to explore remote sensing, ecological niche modeling, and Geographic Information Systems (GIS) as aids in predicting candidate Rift Valley fever (RVF) competent vector abundance and distribution in Virginia, and as means of estimating where risk of establishment in mosquitoes and risk of transmission to human populations would be greatest in Virginia. A second goal in this study was to determine whether the remotely-sensed Normalized Difference Vegetation Index (NDVI) can be used as a proxy variable of local conditions for the development of mosquitoes to predict mosquito species distribution and abundance in Virginia. As part of this study, a mosquito surveillance database was compiled to archive the historical patterns of mosquito species abundance in Virginia. In addition, linkages between mosquito density and local environmental and climatic patterns were spatially and temporally examined. The present study affirms the potential role of remote sensing imagery for species distribution prediction, and it demonstrates that ecological niche modeling is a valuable predictive tool to analyze the distributions of populations. The MaxEnt ecological niche modeling program was used to model predicted ranges for potential RVF competent vectors in Virginia. The MaxEnt model was shown to be robust, and the candidate RVF competent vector predicted distribution map is presented. The Normalized Difference Vegetation Index (NDVI) was found to be the most useful environmental-climatic variable to predict mosquito species distribution and abundance in Virginia. However, these results indicate that a more robust prediction is obtained by including other environmental-climatic factors correlated to mosquito densities (e.g., temperature, precipitation, elevation) with NDVI. The present study demonstrates that remote sensing and GIS can be used with ecological niche and risk modeling methods to estimate risk of virus establishment in mosquitoes and transmission to humans. Maps delineating the geographic areas in Virginia with highest risk for RVF establishment in mosquito populations and RVF disease transmission to human populations were generated in a GIS using human, domestic animal, and white-tailed deer population estimates and the MaxEnt potential RVF competent vector species distribution prediction. The candidate RVF competent vector predicted distribution and RVF risk maps presented in this study can help vector control agencies and public health officials focus Rift Valley fever surveillance efforts in geographic areas with large co-located populations of potential RVF competent vectors and human, domestic animal, and wildlife hosts. Keywords. Rift Valley fever, risk assessment, Ecological Niche Modeling, MaxEnt, Geographic Information System, remote sensing, Pearson's Product-Moment Correlation Coefficient, vectors, mosquito distribution, mosquito density, mosquito surveillance, United States, Virginia, domestic animals, white-tailed deer, ArcGIS

Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists

PubMed Central

Wang, Kai; Franklin, Steven E.; Guo, Xulin; Cattet, Marc

2010-01-01

Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS). PMID:22163432

Remote sensing of ecology, biodiversity and conservation: a review from the perspective of remote sensing specialists.

PubMed

Wang, Kai; Franklin, Steven E; Guo, Xulin; Cattet, Marc

2010-01-01

Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS).

Remote Sensing and Reflectance Profiling in Entomology.

PubMed

Nansen, Christian; Elliott, Norman

2016-01-01

Remote sensing describes the characterization of the status of objects and/or the classification of their identity based on a combination of spectral features extracted from reflectance or transmission profiles of radiometric energy. Remote sensing can be benchtop based, and therefore acquired at a high spatial resolution, or airborne at lower spatial resolution to cover large areas. Despite important challenges, airborne remote sensing technologies will undoubtedly be of major importance in optimized management of agricultural systems in the twenty-first century. Benchtop remote sensing applications are becoming important in insect systematics and in phenomics studies of insect behavior and physiology. This review highlights how remote sensing influences entomological research by enabling scientists to nondestructively monitor how individual insects respond to treatments and ambient conditions. Furthermore, novel remote sensing technologies are creating intriguing interdisciplinary bridges between entomology and disciplines such as informatics and electrical engineering.

Long-term remote monitoring of salt marsh biomass

NASA Astrophysics Data System (ADS)

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

1990-12-01

An objective of NASA's Biospheric Research Program is to understand biogeochemical cycling on a global scale. Being both very biologically productive and anoxic, wetlands are major sites of carbon dioxide, mean, and sulfur gas flux on a per area basis. Biogeochemical cycling in wetlands is intricately linked to vegetation biomass production. We have been monitoring biomass dynamics of the dominant salt marsh grass Spartina alterniflora for over ten years using remote sensing. Live above ground biomass is highly correlated (r = .79) with Laridsat Thematic Mapper ('IN) and SPOT spectral data transformed into normalized difference vegetation indices. Live belowg round biomass is, in turn, highly correlated (r = .86) with live above ground biomass. Therefore, below ground biomass, a source of carbon substrates for microbial gas production, can be measured using remote sensing indirectly. These relationships have been tested over a wide latitudinal range (from Georgia to Nova Scotia). Analysis of TM and SPOT satellite images from several years has revealed substantial interannual variability in mean live aerial biomass of this species in a 580ha Delaware marsh. Additionally, interannual spatial variability in biomass distribution within the marsh is evident and seems to be linked to precipitation. The aerial biomass of high salinity areas least influenced by upland runoff is the most sensitive to precipitation, whereas marsh areas adjacent to large upland areas or freshwater creeks are the least sensitive. In summary, remote sensing is an effective tool for studying aboveground and belowground biomass in salt marshes. Once the relationship between gas flux data and vegetation biomass is better understood, satellite data could be used to estimate biomass arid gas flux over large regions of the world.

The use of satellite data for monitoring temporal and spatial patterns of fire: a comprehensive review

NASA Astrophysics Data System (ADS)

Lasaponara, R.

2009-04-01

Remotely sensed (RS) data can fruitfully support both research activities and operative monitoring of fire at different temporal and spatial scales with a synoptic view and cost effective technologies. "The contribution of remote sensing (RS) to forest fires may be grouped in three categories, according to the three phases of fire management: (i) risk estimation (before fire), (ii) detection (during fire) and (iii) assessment (after fire)" Chuvieco (2006). Relating each phase, wide research activities have been conducted over the years. (i) Risk estimation (before fire) has been mainly based on the use of RS data for (i) monitoring vegetation stress and assessing variations in vegetation moisture content, (ii) fuel type mapping, at different temporal and spatial scales from global, regional down to a local scale (using AVHRR, MODIS, TM, ASTER, Quickbird images and airborne hyperspectral and LIDAR data). Danger estimation has been mainly based on the use of AVHRR (onborad NOAA), MODIS (onboard TERRA and AQUA), VEGETATION (onboard SPOT) due to the technical characteristics (i.e. spectral, spatial and temporal resolution). Nevertheless microwave data have been also used for vegetation monitoring. (ii) Detection: identification of active fires, estimation of fire radiative energy and fire emission. AVHRR was one of the first satellite sensors used for setting up fire detection algorithms. The availbility of MODIS allowed us to obtain global fire products free downloaded from NASA web site. Sensors onboard geostationary satellite platforms, such as GOES, SEVIRI, have been used for fire detection, to obtain a high temporal resolution (at around 15 minutes) monitoring of active fires. (iii) Post fire damage assessment includes: burnt area mapping, fire emission, fire severity, vegetation recovery, fire resilience estimation, and, more recently, fire regime characterization. Chuvieco E. L. Giglio, C. Justice, 2008 Global charactrerization of fire activity: toward defining fire regimes from Earth observation data Global Change Biology vo. 14. doi: 10.1111/j.1365-2486.2008.01585.x 1-15, Chuvieco E., P. Englefield, Alexander P. Trishchenko, Yi Luo Generation of long time series of burn area maps of the boreal forest from NOAA-AVHRR composite data. Remote Sensing of Environment, Volume 112, Issue 5, 15 May 2008, Pages 2381-2396 Chuvieco Emilio 2006, Remote Sensing of Forest Fires: Current limitations and future prospects in Observing Land from Space: Science, Customers and Technology, Advances in Global Change Research Vol. 4 pp 47-51 De Santis A., E. Chuvieco Burn severity estimation from remotely sensed data: Performance of simulation versus empirical models, Remote Sensing of Environment, Volume 108, Issue 4, 29 June 2007, Pages 422-435. De Santis A., E. Chuvieco, Patrick J. Vaughan, Short-term assessment of burn severity using the inversion of PROSPECT and GeoSail models, Remote Sensing of Environment, Volume 113, Issue 1, 15 January 2009, Pages 126-136 García M., E. Chuvieco, H. Nieto, I. Aguado Combining AVHRR and meteorological data for estimating live fuel moisture content Remote Sensing of Environment, Volume 112, Issue 9, 15 September 2008, Pages 3618-3627 Ichoku C., L. Giglio, M. J. Wooster, L. A. Remer Global characterization of biomass-burning patterns using satellite measurements of fire radiative energy. Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 2950-2962. Lasaponara R. and Lanorte, On the capability of satellite VHR QuickBird data for fuel type characterization in fragmented landscape Ecological Modelling Volume 204, Issues 1-2, 24 May 2007, Pages 79-84 Lasaponara R., A. Lanorte, S. Pignatti,2006 Multiscale fuel type mapping in fragmented ecosystems: preliminary results from Hyperspectral MIVIS and Multispectral Landsat TM data, Int. J. Remote Sens., vol. 27 (3) pp. 587-593. Lasaponara R., V. Cuomo, M. F. Macchiato, and T. Simoniello, 2003 .A self-adaptive algorithm based on AVHRR multitemporal data analysis for small active fire detection.n International Journal of Remote Sensing, vol. 24, No 8, 1723-1749. Minchella A., F. Del Frate, F. Capogna, S. Anselmi, F. Manes Use of multitemporal SAR data for monitoring vegetation recovery of Mediterranean burned areas Remote Sensing of Environment, In Press Næsset E., T. Gobakken Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 3079-3090 Peterson S. H, Dar A. Roberts, Philip E. Dennison Mapping live fuel moisture with MODIS data: A multiple regression approach, Remote Sensing of Environment, Volume 112, Issue 12, 15 December 2008, Pages 4272-4284. Schroeder Wilfrid, Elaine Prins, Louis Giglio, Ivan Csiszar, Christopher Schmidt, Jeffrey Morisette, Douglas Morton Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data Remote Sensing of Environment, Volume 112, Issue 5, 15 May 2008, Pages 2711-2726 Shi J., T. Jackson, J. Tao, J. Du, R. Bindlish, L. Lu, K.S. Chen Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E Remote Sensing of Environment, Volume 112, Issue 12, 15 December 2008, Pages 4285-4300 Tansey, K., Grégoire, J-M., Defourny, P., Leigh, R., Pekel, J-F., van Bogaert, E. and Bartholomé, E., 2008 A New, Global, Multi-Annual (2000-2007) Burnt Area Product at 1 km Resolution and Daily Intervals Geophysical Research Letters, VOL. 35, L01401, doi:10.1029/2007GL031567, 2008. Telesca L. and Lasaponara R., 2006; "Pre-and Post- fire Behaviural trends revealed in satellite NDVI time series" Geophysical Research Letters,., 33, L14401, doi:10.1029/2006GL026630 Telesca L. and Lasaponara R 2005 Discriminating Dynamical Patterns in Burned and Unburned Vegetational Covers by Using SPOT-VGT NDVI Data. Geophysical Research Letters,, 32, L21401, doi:10.1029/2005GL024391. Telesca L. and Lasaponara R. Investigating fire-induced behavioural trends in vegetation covers , Communications in Nonlinear Science and Numerical Simulation, 13, 2018-2023, 2008 Telesca L., A. Lanorte and R. Lasaponara, 2007. Investigating dynamical trends in burned and unburned vegetation covers by using SPOT-VGT NDVI data. Journal of Geophysics and Engineering, Vol. 4, pp. 128-138, 2007 Telesca L., R. Lasaponara, and A. Lanorte, Intra-annual dynamical persistent mechanisms in Mediterranean ecosystems revealed SPOT-VEGETATION Time Series, Ecological Complexity, 5, 151-156, 2008 Verbesselt, J., Somers, B., Lhermitte, S., Jonckheere, I., van Aardt, J., and Coppin, P. (2007) Monitoring herbaceous fuel moisture content with SPOT VEGETATION time-series for fire risk prediction in savanna ecosystems. Remote Sensing of Environment 108: 357-368. Zhang X., S. Kondragunta Temporal and spatial variability in biomass burned areas across the USA derived from the GOES fire product Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 2886-2897 Zhang X., Shobha Kondragunta Temporal and spatial variability in biomass burned areas across the USA derived from the GOES fire product Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 2886-2897

Remote Sensing in Geography in the New Millennium: Prospects, Challenges, and Opportunities

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Jensen, John R.; Morain, Stanley A.; Walsh, Stephen J.; Ridd, Merrill K.

1999-01-01

Remote sensing science contributes greatly to our understanding of the Earth's ecosystems and cultural landscapes. Almost all the natural and social sciences, including geography, rely heavily on remote sensing to provide quantitative, and indispensable spatial information. Many geographers have made significant contributions to remote sensing science since the 1970s, including the specification of advanced remote sensing systems, improvements in analog and digital image analysis, biophysical modeling, and terrain analysis. In fact, the Remote Sensing Specialty Group (RSSG) is one of the largest specialty groups within the AAG with over 500 members. Remote sensing in concert with a geographic information systems, offers much value to geography as both an incisive spatial-analytical tool and as a scholarly pursuit that adds to the body of geographic knowledge on the whole. The "power" of remote sensing as a research endeavor in geography lies in its capabilities for obtaining synoptic, near-real time data at many spatial and temporal scales, and in many regions of the electromagnetic spectrum - from microwave, to RADAR, to visible, and reflective and thermal infrared. In turn, these data present a vast compendium of information for assessing Earth attributes and characte6stics that are at the very core of geography. Here we revisit how remote sensing has become a fundamental and important tool for geographical research, and how with the advent of new and improved sensing systems to be launched in the near future, remote sensing will further advance geographical analysis in the approaching New Millennium.

Estimating cavity tree and snag abundance using negative binomial regression models and nearest neighbor imputation methods

Treesearch

Bianca N.I. Eskelson; Hailemariam Temesgen; Tara M. Barrett

2009-01-01

Cavity tree and snag abundance data are highly variable and contain many zero observations. We predict cavity tree and snag abundance from variables that are readily available from forest cover maps or remotely sensed data using negative binomial (NB), zero-inflated NB, and zero-altered NB (ZANB) regression models as well as nearest neighbor (NN) imputation methods....

Development of a drought forecasting model for the Asia-Pacific region using remote sensing and climate data: Focusing on Indonesia

NASA Astrophysics Data System (ADS)

Rhee, Jinyoung; Kim, Gayoung; Im, Jungho

2017-04-01

Three regions of Indonesia with different rainfall characteristics were chosen to develop drought forecast models based on machine learning. The 6-month Standardized Precipitation Index (SPI6) was selected as the target variable. The models' forecast skill was compared to the skill of long-range climate forecast models in terms of drought accuracy and regression mean absolute error (MAE). Indonesian droughts are known to be related to El Nino Southern Oscillation (ENSO) variability despite of regional differences as well as monsoon, local sea surface temperature (SST), other large-scale atmosphere-ocean interactions such as Indian Ocean Dipole (IOD) and Southern Pacific Convergence Zone (SPCZ), and local factors including topography and elevation. Machine learning models are thus to enhance drought forecast skill by combining local and remote SST and remote sensing information reflecting initial drought conditions to the long-range climate forecast model results. A total of 126 machine learning models were developed for the three regions of West Java (JB), West Sumatra (SB), and Gorontalo (GO) and six long-range climate forecast models of MSC_CanCM3, MSC_CanCM4, NCEP, NASA, PNU, POAMA as well as one climatology model based on remote sensing precipitation data, and 1 to 6-month lead times. When compared the results between the machine learning models and the long-range climate forecast models, West Java and Gorontalo regions showed similar characteristics in terms of drought accuracy. Drought accuracy of the long-range climate forecast models were generally higher than the machine learning models with short lead times but the opposite appeared for longer lead times. For West Sumatra, however, the machine learning models and the long-range climate forecast models showed similar drought accuracy. The machine learning models showed smaller regression errors for all three regions especially with longer lead times. Among the three regions, the machine learning models developed for Gorontalo showed the highest drought accuracy and the lowest regression error. West Java showed higher drought accuracy compared to West Sumatra, while West Sumatra showed lower regression error compared to West Java. The lower error in West Sumatra may be because of the smaller sample size used for training and evaluation for the region. Regional differences of forecast skill are determined by the effect of ENSO and the following forecast skill of the long-range climate forecast models. While shown somewhat high in West Sumatra, relative importance of remote sensing variables was mostly low in most cases. High importance of the variables based on long-range climate forecast models indicates that the forecast skill of the machine learning models are mostly determined by the forecast skill of the climate models.

TEMPORAL CORRELATION OF CLASSIFICATIONS IN REMOTE SENSING

EPA Science Inventory

A bivariate binary model is developed for estimating the change in land cover from satellite images obtained at two different times. The binary classifications of a pixel at the two times are modeled as potentially correlated random variables, conditional on the true states of th...

Method of determining forest production from remotely sensed forest parameters

DOEpatents

Corey, J.C.; Mackey, H.E. Jr.

1987-08-31

A method of determining forest production entirely from remotely sensed data in which remotely sensed multispectral scanner (MSS) data on forest 5 composition is combined with remotely sensed radar imaging data on forest stand biophysical parameters to provide a measure of forest production. A high correlation has been found to exist between the remotely sensed radar imaging data and on site measurements of biophysical 10 parameters such as stand height, diameter at breast height, total tree height, mean area per tree, and timber stand volume.

Removal of Surface-Reflected Light for the Measurement of Remote-Sensing Reflectance from an Above-Surface Platform

DTIC Science & Technology

2010-12-01

remote - sensing reflectance) can be highly inaccurate if a spectrally constant value is applied (although errors can be reduced by carefully filtering measured raw data). To remove surface-reflected light in field measurements of remote sensing reflectance, a spectral optimization approach was applied, with results compared with those from remote sensing models and from direct measurements. The agreement from different determinations suggests that reasonable results for remote sensing reflectance of clear

Removal of Surface-Reflected Light for the Measurement of Remote-Sensing Reflectance from an Above-Surface Platform

DTIC Science & Technology

2010-12-06

remote - sensing reflectance) can be highly inaccurate if a spectrally constant value is applied (although errors can be reduced by carefully filtering measured raw data). To remove surface-reflected light in field measurements of remote sensing reflectance, a spectral optimization approach was applied, with results compared with those from remote sensing models and from direct measurements. The agreement from different determinations suggests that reasonable results for remote sensing reflectance of clear

Estimation of photosynthetic capacity using MODIS polarization: 1988 proposal to NASA Headquarters

NASA Technical Reports Server (NTRS)

Vanderbilt, Vern C.

1992-01-01

The remote sensing community has clearly identified the utility of NDVI (normalized difference vegetation index) and SR (simple ratio) and other vegetation indices for estimating such metrics of landscape ecology as green foliar biomass, photosynthetic capacity, and net primary production. Both theoretical and empirical investigations have established cause and effect relationships between the photosynthetic process in plant canopies and these combinations of remotely sensed data. Yet it has also been established that the relationships exhibit considerable variability that appears to be ecosystem-dependent and may represent a source of ecologically important information. The overall hypothesis of this proposal is that the ecosystem-dependent variability in the various vegetation indices is in part attributable to the effects of specular reflection. The polarization channels on MODIS provide the potential to estimate this specularly reflected light and allow the modification of the vegetation indices to better measure the photosynthetic process in plant canopies. In addition, these polarization channels potentially provide additional ecologically important information about the plant canopy.

Sampling design for an integrated socioeconomic and ecological survey by using satellite remote sensing and ordination

PubMed Central

Binford, Michael W.; Lee, Tae Jeong; Townsend, Robert M.

2004-01-01

Environmental variability is an important risk factor in rural agricultural communities. Testing models requires empirical sampling that generates data that are representative in both economic and ecological domains. Detrended correspondence analysis of satellite remote sensing data were used to design an effective low-cost sampling protocol for a field study to create an integrated socioeconomic and ecological database when no prior information on ecology of the survey area existed. We stratified the sample for the selection of tambons from various preselected provinces in Thailand based on factor analysis of spectral land-cover classes derived from satellite data. We conducted the survey for the sampled villages in the chosen tambons. The resulting data capture interesting variations in soil productivity and in the timing of good and bad years, which a purely random sample would likely have missed. Thus, this database will allow tests of hypotheses concerning the effect of credit on productivity, the sharing of idiosyncratic risks, and the economic influence of environmental variability. PMID:15254298

Estimating crop yields and crop evapotranspiration distributions from remote sensing and geospatial agricultural data

NASA Astrophysics Data System (ADS)

Smith, T.; McLaughlin, D.

2017-12-01

Growing more crops to provide a secure food supply to an increasing global population will further stress land and water resources that have already been significantly altered by agriculture. The connection between production and resource use depends on crop yields and unit evapotranspiration (UET) rates that vary greatly, over both time and space. For regional and global analyses of food security it is appropriate to treat yield and UET as uncertain variables conditioned on climatic and soil properties. This study describes how probability distributions of these variables can be estimated by combining remotely sensed land use and evapotranspiration data with in situ agronomic and soils data, all available at different resolutions and coverages. The results reveal the influence of water and temperature stress on crop yield at large spatial scales. They also provide a basis for stochastic modeling and optimization procedures that explicitly account for uncertainty in the environmental factors that affect food production.

Responsiveness of Food Security Reporting to Environmental Variability and Agricultural Production Deficits

NASA Astrophysics Data System (ADS)

Brickley, E. B.; Brown, M. E.

2010-12-01

This paper uses 1342 food security update reports from the US Agency for International Development (USAID)’s Famine Early Warning System Network (FEWS NET) in an analysis that focuses on the environmental, market, and livelihood influences on the food security in 17 countries in Africa from 2000-2009. A textual analysis was conducted using the reports as a primary data source to evaluate the responsiveness of food security analysis to environmental variability and food production deficits. The research shows that FEWS NET analysts demonstrate a consistent approach across all 17 countries as to the discussion and use of rainfall information, agricultural production, food prices and food access parameters. There are significant differences in the use of remote sensing and other technical information between East, West and Southern African country analysts, with satellite remote sensing of vegetation being used 28% of the time, rainfall imagery 84% and gridded crop models only 10% of the time. Significantly more discussion of biophysical information was seen during the rainy season than during the dry season, and different satellite products were used during periods of drought than periods of adequate moisture. As the demand for early warning information grows to more countries in different ecosystems, there is likely to be an increased need for the effective utilization of remote sensing, market, and livelihood data, and it is also probable that this information will be critical for improved policy-making regarding climate extremes in the future.

Extrapolating active layer thickness measurements across Arctic polygonal terrain using LiDAR and NDVI data sets.

PubMed

Gangodagamage, Chandana; Rowland, Joel C; Hubbard, Susan S; Brumby, Steven P; Liljedahl, Anna K; Wainwright, Haruko; Wilson, Cathy J; Altmann, Garrett L; Dafflon, Baptiste; Peterson, John; Ulrich, Craig; Tweedie, Craig E; Wullschleger, Stan D

2014-08-01

Landscape attributes that vary with microtopography, such as active layer thickness ( ALT ), are labor intensive and difficult to document effectively through in situ methods at kilometer spatial extents, thus rendering remotely sensed methods desirable. Spatially explicit estimates of ALT can provide critically needed data for parameterization, initialization, and evaluation of Arctic terrestrial models. In this work, we demonstrate a new approach using high-resolution remotely sensed data for estimating centimeter-scale ALT in a 5 km 2 area of ice-wedge polygon terrain in Barrow, Alaska. We use a simple regression-based, machine learning data-fusion algorithm that uses topographic and spectral metrics derived from multisensor data (LiDAR and WorldView-2) to estimate ALT (2 m spatial resolution) across the study area. Comparison of the ALT estimates with ground-based measurements, indicates the accuracy (r 2  = 0.76, RMSE ±4.4 cm) of the approach. While it is generally accepted that broad climatic variability associated with increasing air temperature will govern the regional averages of ALT , consistent with prior studies, our findings using high-resolution LiDAR and WorldView-2 data, show that smaller-scale variability in ALT is controlled by local eco-hydro-geomorphic factors. This work demonstrates a path forward for mapping ALT at high spatial resolution and across sufficiently large regions for improved understanding and predictions of coupled dynamics among permafrost, hydrology, and land-surface processes from readily available remote sensing data.

Spatial heterogeneity of leaf area index across scales from simulation and remote sensing

NASA Astrophysics Data System (ADS)

Reichenau, Tim G.; Korres, Wolfgang; Montzka, Carsten; Schneider, Karl

2016-04-01

Leaf area index (LAI, single sided leaf area per ground area) influences mass and energy exchange of vegetated surfaces. Therefore LAI is an input variable for many land surface schemes of coupled large scale models, which do not simulate LAI. Since these models typically run on rather coarse resolution grids, LAI is often inferred from coarse resolution remote sensing. However, especially in agriculturally used areas, a grid cell of these products often covers more than a single land-use. In that case, the given LAI does not apply to any single land-use. Therefore, the overall spatial heterogeneity in these datasets differs from that on resolutions high enough to distinguish areas with differing land-use. Detailed process-based plant growth models simulate LAI for separate plant functional types or specific species. However, limited availability of observations causes reduced spatial heterogeneity of model input data (soil, weather, land-use). Since LAI is strongly heterogeneous in space and time and since processes depend on LAI in a nonlinear way, a correct representation of LAI spatial heterogeneity is also desirable on coarse resolutions. The current study assesses this issue by comparing the spatial heterogeneity of LAI from remote sensing (RapidEye) and process-based simulations (DANUBIA simulation system) across scales. Spatial heterogeneity is assessed by analyzing LAI frequency distributions (spatial variability) and semivariograms (spatial structure). Test case is the arable land in the fertile loess plain of the Rur catchment near the Germany-Netherlands border.

NEON Airborne Remote Sensing of Terrestrial Ecosystems

NASA Astrophysics Data System (ADS)

Kampe, T. U.; Leisso, N.; Krause, K.; Karpowicz, B. M.

2012-12-01

The National Ecological Observatory Network (NEON) is the continental-scale research platform that will collect information on ecosystems across the United States to advance our understanding and ability to forecast environmental change at the continental scale. One of NEON's observing systems, the Airborne Observation Platform (AOP), will fly an instrument suite consisting of a high-fidelity visible-to-shortwave infrared imaging spectrometer, a full waveform small footprint LiDAR, and a high-resolution digital camera on a low-altitude aircraft platform. NEON AOP is focused on acquiring data on several terrestrial Essential Climate Variables including bioclimate, biodiversity, biogeochemistry, and land use products. These variables are collected throughout a network of 60 sites across the Continental United States, Alaska, Hawaii and Puerto Rico via ground-based and airborne measurements. Airborne remote sensing plays a critical role by providing measurements at the scale of individual shrubs and larger plants over hundreds of square kilometers. The NEON AOP plays the role of bridging the spatial scales from that of individual organisms and stands to the scale of satellite-based remote sensing. NEON is building 3 airborne systems to facilitate the routine coverage of NEON sites and provide the capacity to respond to investigator requests for specific projects. The first NEON imaging spectrometer, a next-generation VSWIR instrument, was recently delivered to NEON by JPL. This instrument has been integrated with a small-footprint waveform LiDAR on the first NEON airborne platform (AOP-1). A series of AOP-1 test flights were conducted during the first year of NEON's construction phase. The goal of these flights was to test out instrument functionality and performance, exercise remote sensing collection protocols, and provide provisional data for algorithm and data product validation. These test flights focused the following questions: What is the optimal remote sensing data collection protocol to meet NEON science requirements? How do aircraft altitude, spatial sampling, spatial resolution, and LiDAR instrument configuration affect data retrievals? What are appropriate algorithms to derive ECVs from AOP data? What methodology should be followed to validate AOP remote sensing products and how should ground truth data be collected? Early test flights were focused on radiometric and geometric calibration as well as processing from raw data to Level-1 products. Subsequent flights were conducted focusing on collecting vegetation chemistry and structure measurements. These test flights that were conducted during 2012 have proved to be extremely valuable for verifying instrument functionality and performance, exercising remote sensing collection protocols, and providing data for algorithm and science product validation. Results from these early flights are presented, including the radiometric and geometric calibration of the AOP instruments. These 2012 flight campaigns are just the first of a series of test flights that will take place over the next several years as part of the NEON observatory construction. Lessons learned from these early campaigns will inform both airborne and ground data collection methodologies for future campaigns as well as guide the AOP sampling strategy before NEON enters full science operations.

A Novel Methodology for Improving Plant Pest Surveillance in Vineyards and Crops Using UAV-Based Hyperspectral and Spatial Data.

PubMed

Vanegas, Fernando; Bratanov, Dmitry; Powell, Kevin; Weiss, John; Gonzalez, Felipe

2018-01-17

Recent advances in remote sensed imagery and geospatial image processing using unmanned aerial vehicles (UAVs) have enabled the rapid and ongoing development of monitoring tools for crop management and the detection/surveillance of insect pests. This paper describes a (UAV) remote sensing-based methodology to increase the efficiency of existing surveillance practices (human inspectors and insect traps) for detecting pest infestations (e.g., grape phylloxera in vineyards). The methodology uses a UAV integrated with advanced digital hyperspectral, multispectral, and RGB sensors. We implemented the methodology for the development of a predictive model for phylloxera detection. In this method, we explore the combination of airborne RGB, multispectral, and hyperspectral imagery with ground-based data at two separate time periods and under different levels of phylloxera infestation. We describe the technology used-the sensors, the UAV, and the flight operations-the processing workflow of the datasets from each imagery type, and the methods for combining multiple airborne with ground-based datasets. Finally, we present relevant results of correlation between the different processed datasets. The objective of this research is to develop a novel methodology for collecting, processing, analising and integrating multispectral, hyperspectral, ground and spatial data to remote sense different variables in different applications, such as, in this case, plant pest surveillance. The development of such methodology would provide researchers, agronomists, and UAV practitioners reliable data collection protocols and methods to achieve faster processing techniques and integrate multiple sources of data in diverse remote sensing applications.

Estimating the Relative Water Content of Single Leaves from Optical Polarization Measurements

NASA Technical Reports Server (NTRS)

Vanderbilt, Vern; Daughtry, Craig; Dahlgren, Robert

2016-01-01

Remotely sensing the water status of plants and the water content of canopies remain long-term goals of remote sensing research. For monitoring canopy water status, existing approaches such as the Crop Water Stress Index and the Equivalent Water Thickness have limitations. The CWSI does not work well in humid regions, requires estimates of the vapor pressure deficit near the canopy during the remote sensing over-flight and, once stomata close, provides little information regarding the canopy water status. The EWI is based upon the physics of water-light interaction, not plant physiology. In this research, we applied optical polarization techniques to monitor the VISNIR light reflected from the leaf interior, R, as well as the leaf transmittance, T, as the relative water content (RWC) of corn (Zea mays) leaves decreased. Our results show that R and T both changed nonlinearly as each leaf dried, R increasing and T decreasing. Our results tie changes in the VISNIR R and T to leaf physiological changes linking the light scattered out of the drying leaf interior to its relative water content and to changes in leaf cellular structure and pigments. Our results suggest remotely sensing the physiological water status of a single leaf and perhaps of a plant canopy might be possible in the future. However, using our approach to estimate the water status of a leaf does not appear possible at present, because our results display too much variability that we do not yet understand.

Remote sensing of aerosols in the Arctic for an evaluation of global climate model simulations

PubMed Central

Glantz, Paul; Bourassa, Adam; Herber, Andreas; Iversen, Trond; Karlsson, Johannes; Kirkevåg, Alf; Maturilli, Marion; Seland, Øyvind; Stebel, Kerstin; Struthers, Hamish; Tesche, Matthias; Thomason, Larry

2014-01-01

In this study Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua retrievals of aerosol optical thickness (AOT) at 555 nm are compared to Sun photometer measurements from Svalbard for a period of 9 years. For the 642 daily coincident measurements that were obtained, MODIS AOT generally varies within the predicted uncertainty of the retrieval over ocean (ΔAOT = ±0.03 ± 0.05 · AOT). The results from the remote sensing have been used to examine the accuracy in estimates of aerosol optical properties in the Arctic, generated by global climate models and from in situ measurements at the Zeppelin station, Svalbard. AOT simulated with the Norwegian Earth System Model/Community Atmosphere Model version 4 Oslo global climate model does not reproduce the observed seasonal variability of the Arctic aerosol. The model overestimates clear-sky AOT by nearly a factor of 2 for the background summer season, while tending to underestimate the values in the spring season. Furthermore, large differences in all-sky AOT of up to 1 order of magnitude are found for the Coupled Model Intercomparison Project phase 5 model ensemble for the spring and summer seasons. Large differences between satellite/ground-based remote sensing of AOT and AOT estimated from dry and humidified scattering coefficients are found for the subarctic marine boundary layer in summer. Key Points Remote sensing of AOT is very useful in validation of climate models PMID:25821664

Vegetation Coverage and Impervious Surface Area Estimated Based on the Estarfm Model and Remote Sensing Monitoring

NASA Astrophysics Data System (ADS)

Hu, Rongming; Wang, Shu; Guo, Jiao; Guo, Liankun

2018-04-01

Impervious surface area and vegetation coverage are important biophysical indicators of urban surface features which can be derived from medium-resolution images. However, remote sensing data obtained by a single sensor are easily affected by many factors such as weather conditions, and the spatial and temporal resolution can not meet the needs for soil erosion estimation. Therefore, the integrated multi-source remote sensing data are needed to carry out high spatio-temporal resolution vegetation coverage estimation. Two spatial and temporal vegetation coverage data and impervious data were obtained from MODIS and Landsat 8 remote sensing images. Based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), the vegetation coverage data of two scales were fused and the data of vegetation coverage fusion (ESTARFM FVC) and impervious layer with high spatiotemporal resolution (30 m, 8 day) were obtained. On this basis, the spatial variability of the seepage-free surface and the vegetation cover landscape in the study area was measured by means of statistics and spatial autocorrelation analysis. The results showed that: 1) ESTARFM FVC and impermeable surface have higher accuracy and can characterize the characteristics of the biophysical components covered by the earth's surface; 2) The average impervious surface proportion and the spatial configuration of each area are different, which are affected by natural conditions and urbanization. In the urban area of Xi'an, which has typical characteristics of spontaneous urbanization, landscapes are fragmented and have less spatial dependence.

Estimating the Relative Water Content of Single Leaves from Optical Polarization Measurements.

NASA Astrophysics Data System (ADS)

Vanderbilt, V. C.; Daughtry, C. S. T.; Dahlgren, R. P.

2016-12-01

Remotely sensing the water status of plants and the water content of canopies remain long term goals of remote sensing research. For monitoring canopy water status, existing approaches such as the Crop Water Stress Index and the Equivalent Water Thickness have limitations. The CWSI does not work well in humid regions, requires estimates of the vapor pressure deficit near the canopy during the remote sensing over-flight and, once stomata close, provides little information regarding the canopy water status. The EWI is based upon the physics of water-light interaction, not plant physiology. In this research, we applied optical polarization techniques to monitor the VIS/NIR light reflected from the leaf interior, R, as well as the leaf transmittance, T, as the relative water content (RWC) of corn (Zea mays) leaves decreased. Our results show that R and T both changed nonlinearly as each leaf dried, R increasing and T decreasing. Our results tie changes in the VIS/NIR R and T to leaf physiological changes - linking the light scattered out of the drying leaf interior to its relative water content and to changes in leaf cellular structure and pigments. Our results suggest remotely sensing the physiological water status of a single leaf - and perhaps of a plant canopy - might be possible in the future. However, using our approach to estimate the water status of a leaf does not appear possible at present, because our results display too much variability that we do not yet understand.

Aircraft remote sensing of phytoplankton spatial patterns during the 1989 Joint Global Ocean Flux Study (JGOFS) North Atlantic bloom experiment

NASA Technical Reports Server (NTRS)

Yoder, James A.; Hoge, Frank E.

1991-01-01

Mesoscale phytoplankton chlorophyll variability near the Joint Global Ocean Flux study sites along the 20 W meridian at 34 N, 47 N, and 59 N is discussed. The NASA P-3 aircraft and the Airborne Oceanographic Lidar (AOL) system provides remote sensing support for the North Atlantic Bloom Experiment. The principal instrument of the AOL system is the blue-green laser that stimulates fluorescence from photoplankton chlorophyll, the principal photosynthetic pigment. Other instruments on the NASA P-3 aircraft include up- and down-looking spectrometers, PRT-5 for infrared measurements to determine sea surface temperature, and a system to deploy and record AXBTs to measure subsurface temperature structure.

Development of a ground hydrology model suitable for global climate modeling using soil morphology and vegetation cover, and an evaluation of remotely sensed information

NASA Technical Reports Server (NTRS)

Zobler, L.; Lewis, R.

1988-01-01

The long-term purpose was to contribute to scientific understanding of the role of the planet's land surfaces in modulating the flows of energy and matter which influence the climate, and to quantify and monitor human-induced changes to the land environment that may affect global climate. Highlights of the effort include the following: production of geo-coded, digitized World Soil Data file for use with the Goddard Institute for Space Studies (GISS) climate model; contribution to the development of a numerical physically-based model of ground hydrology; and assessment of the utility of remote sensing for providing data on hydrologically significant land surface variables.

The IEEE GRSS Standardized Remote Sensing Data Website: A Step Towards "Science 2.0" in Remote Sensing

NASA Astrophysics Data System (ADS)

Dell'Acqua, Fabio; Iannelli, Gianni Cristian; Kerekes, John; Lisini, Gianni; Moser, Gabriele; Ricardi, Niccolo; Pierce, Leland

2016-08-01

The issue of homogeneity in performance assessment of proposed algorithms for information extraction is generally perceived also in the Earth Observation (EO) domain. Different authors propose different datasets to test their developed algorithms and to the reader it is frequently difficult to assess which is better for his/her specific application, given the wide variability in test sets that makes pure comparison of e.g. accuracy values less meaningful than one would desire. With our work, we gave a modest contribution to ease the problem by making it possible to automatically distribute a limited set of possible "standard" open datasets, together with some ground truth info, and automatically assess processing results provided by the users.

Spaceborne studies of ocean circulation

NASA Technical Reports Server (NTRS)

Patzert, W. C.

1984-01-01

The history and near-term future of ocean remote sensing to study ocean circulation are examined. Seasat provided the first-ever global data sets of sea surface topography (altimeter) and marine winds (scatterometer) and laid the foundation for the next generation of satellite missions planned for the late 1980s. The future missions are the next generation of altimeter and scatterometer to be flown aboard TOPEX (TOPography EXperiment) and NROSS (Navy Remote Sensing System), respectively. The data from these satellites will be coordinated with measurements made at sea to determine the driving forces of ocean circulation and to study the oceans' role in climate variability. The significance of such studies to such matters as climatic changes, fisheries, commerce, waste disposal, and national defense is noted.

Remote sensing applied to numerical modelling. [water resources pollution

NASA Technical Reports Server (NTRS)

Sengupta, S.; Lee, S. S.; Veziroglu, T. N.; Bland, R.

1975-01-01

Progress and remaining difficulties in the construction of predictive mathematical models of large bodies of water as ecosystems are reviewed. Surface temperature is at present the only variable than can be measured accurately and reliably by remote sensing techniques, but satellite infrared data are of sufficient resolution for macro-scale modeling of oceans and large lakes, and airborne radiometers are useful in meso-scale analysis (of lakes, bays, and thermal plumes). Finite-element and finite-difference techniques applied to the solution of relevant coupled time-dependent nonlinear partial differential equations are compared, and the specific problem of the Biscayne Bay and environs ecosystem is tackled in a finite-differences treatment using the rigid-lid model and a rigid-line grid system.

Field Data Collection: an Essential Element in Remote Sensing Applications

NASA Technical Reports Server (NTRS)

Pettinger, L. R.

1971-01-01

Field data collected in support of remote sensing projects are generally used for the following purposes: (1) calibration of remote sensing systems, (2) evaluation of experimental applications of remote sensing imagery on small test sites, and (3) designing and evaluating operational regional resource studies and inventories which are conducted using the remote sensing imagery obtained. Field data may be used to help develop a technique for a particular application, or to aid in the application of that technique to a resource evaluation or inventory problem for a large area. Scientists at the Forestry Remote Sensing Laboratory have utilized field data for both purposes. How meaningful field data has been collected in each case is discussed.

Remote sensing and eLearning 2.0 for school education

NASA Astrophysics Data System (ADS)

Voss, Kerstin; Goetzke, Roland; Hodam, Henryk

2010-10-01

The "Remote Sensing in Schools" project aims at improving the integration of "Satellite remote sensing" into school teaching. Therefore, it is the project's overall objective to teach students in primary and secondary schools the basics and fields of application of remote sensing. Existing results show that many teachers are interested in remote sensing and at same time motivated to integrate it into their teaching. Despite the good intention, in the end, the implementation often fails due to the complexity and poor set-up of the information provided. Therefore, a comprehensive and well-structured learning platform on the topic of remote sensing is developed. The platform shall allow a structured introduction to the topic.

Ecosystem properties of semiarid savanna grassland in West Africa and its relationship with environmental variability.

PubMed

Tagesson, Torbern; Fensholt, Rasmus; Guiro, Idrissa; Rasmussen, Mads Olander; Huber, Silvia; Mbow, Cheikh; Garcia, Monica; Horion, Stéphanie; Sandholt, Inge; Holm-Rasmussen, Bo; Göttsche, Frank M; Ridler, Marc-Etienne; Olén, Niklas; Lundegard Olsen, Jørgen; Ehammer, Andrea; Madsen, Mathias; Olesen, Folke S; Ardö, Jonas

2015-01-01

The Dahra field site in Senegal, West Africa, was established in 2002 to monitor ecosystem properties of semiarid savanna grassland and their responses to climatic and environmental change. This article describes the environment and the ecosystem properties of the site using a unique set of in situ data. The studied variables include hydroclimatic variables, species composition, albedo, normalized difference vegetation index (NDVI), hyperspectral characteristics (350-1800 nm), surface reflectance anisotropy, brightness temperature, fraction of absorbed photosynthetic active radiation (FAPAR), biomass, vegetation water content, and land-atmosphere exchanges of carbon (NEE) and energy. The Dahra field site experiences a typical Sahelian climate and is covered by coexisting trees (~3% canopy cover) and grass species, characterizing large parts of the Sahel. This makes the site suitable for investigating relationships between ecosystem properties and hydroclimatic variables for semiarid savanna ecosystems of the region. There were strong interannual, seasonal and diurnal dynamics in NEE, with high values of ~-7.5 g C m(-2)  day(-1) during the peak of the growing season. We found neither browning nor greening NDVI trends from 2002 to 2012. Interannual variation in species composition was strongly related to rainfall distribution. NDVI and FAPAR were strongly related to species composition, especially for years dominated by the species Zornia glochidiata. This influence was not observed in interannual variation in biomass and vegetation productivity, thus challenging dryland productivity models based on remote sensing. Surface reflectance anisotropy (350-1800 nm) at the peak of the growing season varied strongly depending on wavelength and viewing angle thereby having implications for the design of remotely sensed spectral vegetation indices covering different wavelength regions. The presented time series of in situ data have great potential for dryland dynamics studies, global climate change related research and evaluation and parameterization of remote sensing products and dynamic vegetation models. © 2014 John Wiley & Sons Ltd.

Food Price Volatility and Decadal Climate Variability

NASA Astrophysics Data System (ADS)

Brown, M. E.

2013-12-01

The agriculture system is under pressure to increase production every year as global population expands and more people move from a diet mostly made up of grains, to one with more meat, dairy and processed foods. Weather shocks and large changes in international commodity prices in the last decade have increased pressure on local food prices. This paper will review several studies that link climate variability as measured with satellite remote sensing to food price dynamics in 36 developing countries where local monthly food price data is available. The focus of the research is to understand how weather and climate, as measured by variations in the growing season using satellite remote sensing, has affected agricultural production, food prices and access to food in agricultural societies. Economies are vulnerable to extreme weather at multiple levels. Subsistence small holders who hold livestock and consume much of the food they produce are vulnerable to food production variability. The broader society, however, is also vulnerable to extreme weather because of the secondary effects on market functioning, resource availability, and large-scale impacts on employment in trading, trucking and wage labor that are caused by weather-related shocks. Food price variability captures many of these broad impacts and can be used to diagnose weather-related vulnerability across multiple sectors. The paper will trace these connections using market-level data and analysis. The context of the analysis is the humanitarian aid community, using the guidance of the USAID Famine Early Warning Systems Network and the United Nation's World Food Program in their response to food security crises. These organizations have worked over the past three decades to provide baseline information on food production through satellite remote sensing data and agricultural yield models, as well as assessments of food access through a food price database. Econometric models and spatial analysis are used to describe the connection between shocks and food prices, and to demonstrate the importance of these metrics in overall outcomes in food-insecure communities.

Remote sensing programs and courses in engineering and water resources

NASA Technical Reports Server (NTRS)

Kiefer, R. W.

1981-01-01

The content of typical basic and advanced remote sensing and image interpretation courses are described and typical remote sensing graduate programs of study in civil engineering and in interdisciplinary environmental remote sensing and water resources management programs are outlined. Ideally, graduate programs with an emphasis on remote sensing and image interpretation should be built around a core of five courses: (1) a basic course in fundamentals of remote sensing upon which the more specialized advanced remote sensing courses can build; (2) a course dealing with visual image interpretation; (3) a course dealing with quantitative (computer-based) image interpretation; (4) a basic photogrammetry course; and (5) a basic surveying course. These five courses comprise up to one-half of the course work required for the M.S. degree. The nature of other course work and thesis requirements vary greatly, depending on the department in which the degree is being awarded.

Remote sensing research in geographic education: An alternative view

NASA Technical Reports Server (NTRS)

Wilson, H.; Cary, T. K.; Goward, S. N.

1981-01-01

It is noted that within many geography departments remote sensing is viewed as a mere technique a student should learn in order to carry out true geographic research. This view inhibits both students and faculty from investigation of remotely sensed data as a new source of geographic knowledge that may alter our understanding of the Earth. The tendency is for geographers to accept these new data and analysis techniques from engineers and mathematicians without questioning the accompanying premises. This black-box approach hinders geographic applications of the new remotely sensed data and limits the geographer's contribution to further development of remote sensing observation systems. It is suggested that geographers contribute to the development of remote sensing through pursuit of basic research. This research can be encouraged, particularly among students, by demonstrating the links between geographic theory and remotely sensed observations, encouraging a healthy skepticism concerning the current understanding of these data.

Research on assessment and improvement method of remote sensing image reconstruction

NASA Astrophysics Data System (ADS)

Sun, Li; Hua, Nian; Yu, Yanbo; Zhao, Zhanping

2018-01-01

Remote sensing image quality assessment and improvement is an important part of image processing. Generally, the use of compressive sampling theory in remote sensing imaging system can compress images while sampling which can improve efficiency. A method of two-dimensional principal component analysis (2DPCA) is proposed to reconstruct the remote sensing image to improve the quality of the compressed image in this paper, which contain the useful information of image and can restrain the noise. Then, remote sensing image quality influence factors are analyzed, and the evaluation parameters for quantitative evaluation are introduced. On this basis, the quality of the reconstructed images is evaluated and the different factors influence on the reconstruction is analyzed, providing meaningful referential data for enhancing the quality of remote sensing images. The experiment results show that evaluation results fit human visual feature, and the method proposed have good application value in the field of remote sensing image processing.

Unmanned aerial vehicle: A unique platform for low-altitude remote sensing for crop management

USDA-ARS?s Scientific Manuscript database

Unmanned aerial vehicles (UAV) provide a unique platform for remote sensing to monitor crop fields that complements remote sensing from satellite, aircraft and ground-based platforms. The UAV-based remote sensing is versatile at ultra-low altitude to be able to provide an ultra-high-resolution imag...

Application of the remote-sensing communication model to a time-sensitive wildfire remote-sensing system

Treesearch

Christopher D. Lippitt; Douglas A. Stow; Philip J. Riggan

2016-01-01

Remote sensing for hazard response requires a priori identification of sensor, transmission, processing, and distribution methods to permit the extraction of relevant information in timescales sufficient to allow managers to make a given time-sensitive decision. This study applies and demonstrates the utility of the Remote Sensing Communication...

75 FR 32360 - Proposed Information Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-08

... Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems AGENCY: National Oceanic and.... Abstract NOAA has established requirements for the licensing of private operators of remote-sensing space... Land Remote- Sensing Policy Act of 1992 and with the national security and international obligations of...

78 FR 44536 - Proposed Information Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-24

... Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems AGENCY: National Oceanic and... for the licensing of private operators of remote-sensing space systems. The information in applications and subsequent reports is needed to ensure compliance with the Land Remote- Sensing Policy Act of...

Advancement of China’s Visible Light Remote Sensing Technology In Aerospace,

DTIC Science & Technology

1996-03-19

Aerospace visible light film systems were among the earliest space remote sensing systems to be developed in China. They have been applied very well...makes China the third nation in the world to master space remote sensing technology, it also puts recoverable remote sensing satellites among the first

Polarimetric passive remote sensing of periodic surfaces

NASA Technical Reports Server (NTRS)

Veysoglu, Murat E.; Yueh, H. A.; Shin, R. T.; Kong, J. A.

1991-01-01

The concept of polarimetry in active remote sensing is extended to passive remote sensing. The potential use of the third and fourth Stokes parameters U and V, which play an important role in polarimetric active remote sensing, is demonstrated for passive remote sensing. It is shown that, by the use of the reciprocity principle, the polarimetric parameters of passive remote sensing can be obtained through the solution of the associated direct scattering problem. These ideas are applied to study polarimetric passive remote sensing of periodic surfaces. The solution of the direct scattering problem is obtained by an integral equation formulation which involves evaluation of periodic Green's functions and normal derivative of those on the surface. Rapid evaluation of the slowly convergent series associated with these functions is observed to be critical for the feasibility of the method. New formulas, which are rapidly convergent, are derived for the calculation of these series. The study has shown that the brightness temperature of the Stokes parameter U can be significant in passive remote sensing. Values as high as 50 K are observed for certain configurations.

Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS)

NASA Astrophysics Data System (ADS)

Liu, H.; Jin, Y.; Devine, S.; Dahlgren, R. A.; Covello, S.; Larsen, R.; O'Geen, A. T.

2017-12-01

California rangelands cover 23 million hectares and support a $3.4 billion annual cattle industry. Rangeland forage production varies appreciably from year-to-year and across short distances on the landscape. Spatially explicit and near real-time information on forage production at a high resolution is critical for effective rangeland management, especially during an era of climatic extremes. We here integrated a multispectral MicaSense RedEdge camera with a 3DR solo quad-copter and acquired time-series images during the 2017 growing season over a topographically complex 10-hectare rangeland in San Luis Obispo County, CA. Soil moisture and temperature sensors were installed at 16 landscape positions, and vegetation clippings were collected at 36 plots to quantify forage dry biomass. We built four centimeter-level models for forage production mapping using time series of sUAS images and ground measurements of forage biomass and soil temperature and moisture. The biophysical model based on Monteith's eco-physiological plant growth theory estimated forage production reasonably well with a coefficient of determination (R2) of 0.86 and a root-mean-square error (RMSE) of 424 kg/ha when the soil parameters were included, and a R2 of 0.79 and a RMSE of 510 kg/ha when only remote sensing and topographical variables were included. We built two empirical models of forage production using a stepwise variable selection technique, one with soil variables. Results showed that cumulative absorbed photosynthetically active radiation (APAR) and elevation were the most important variables in both models, explaining more than 40% of the spatio-temporal variance in forage production. Soil moisture accounted for an additional 29% of the variance. Illumination condition was selected as a proxy for soil moisture in the model without soil variables, and accounted for 18% of the variance. We applied the remote sensing-based models to map daily forage production at 30-cm resolution for the whole study area during the 2017 growing season. The forage maps captured similar seasonal and spatial patterns of forage production as ground measured dry biomass. This study demonstrated a near real-time monitoring tool for ranchers to estimate forage production with sUAS technology and improved watershed-scale rangeland management.

From planets to crops and back: Remote sensing makes sense

NASA Astrophysics Data System (ADS)

Mustard, John F.

2017-04-01

Remotely sensed data and the instruments that acquire them are core parts of Earth and planetary observation systems. They are used to quantify the Earth's interconnected systems, and remote sensing is the only way to get a daily, or more frequent, snapshot of the status of the Earth. It really is the Earth's stethoscope. In a similar manner remote sensing is the rock hammer of the planetary scientist and the only way comprehensive data sets can be acquired. To risk offending many remotely sensed data acquired across the electromagnetic spectrum, it is the tricorder to explore known and unknown planets. Arriving where we are today in the use of remotely sensed data in the solar system has been a continually evolving synergy between Earth observation, planetary exploration, and fundamental laboratory work.

Remote sensing of on-road vehicle emissions: Mechanism, applications and a case study from Hong Kong

NASA Astrophysics Data System (ADS)

Huang, Yuhan; Organ, Bruce; Zhou, John L.; Surawski, Nic C.; Hong, Guang; Chan, Edward F. C.; Yam, Yat Shing

2018-06-01

Vehicle emissions are a major contributor to air pollution in cities and have serious health impacts to their inhabitants. On-road remote sensing is an effective and economic tool to monitor and control vehicle emissions. In this review, the mechanism, accuracy, advantages and limitations of remote sensing were introduced. Then the applications and major findings of remote sensing were critically reviewed. It was revealed that the emission distribution of on-road vehicles was highly skewed so that the dirtiest 10% vehicles accounted for over half of the total fleet emissions. Such findings highlighted the importance and effectiveness of using remote sensing for in situ identification of high-emitting vehicles for further inspection and maintenance programs. However, the accuracy and number of vehicles affected by screening programs were greatly dependent on the screening criteria. Remote sensing studies showed that the emissions of gasoline and diesel vehicles were significantly reduced in recent years, with the exception of NOx emissions of diesel vehicles in spite of greatly tightened automotive emission regulations. Thirdly, the experience and issues of using remote sensing for identifying high-emitting vehicles in Hong Kong (where remote sensing is a legislative instrument for enforcement purposes) were reported. That was followed by the first time ever identification and discussion of the issue of frequent false detection of diesel high-emitters using remote sensing. Finally, the challenges and future research directions of on-road remote sensing were elaborated.

Remote sensing of natural resources: Quarterly literature review

NASA Technical Reports Server (NTRS)

1976-01-01

A quarterly review of technical literature concerning remote sensing techniques is presented. The format contains indexed and abstracted materials with emphasis on data gathering techniques performed or obtained remotely from space, aircraft, or ground-based stations. Remote sensor applications including the remote sensing of natural resources are presented.

Water quality parameters of harbors of Charlotte Amalie, St. Thomas, Virgin Islands: Acquisition of in situ water data, intercorrelation of selected water parameters, and initial correlation of these in situ biological, chemical and physical data with ERTS-1 bulk CCT MSS band 5 data

NASA Technical Reports Server (NTRS)

Coulbourn, W. C.; Olsen, D. A. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Remote sensing by the ERTS-1 satellite was compared with selected water quality parameters including pH, salinity, conductivity, dissolved oxygen, water depth, water temperature, turbidity, plankton concentration, current variables, chlorophylla, total carotenoids, and species diversity of the benthic community. Strong correlation between turbidity and MSS-sensed radiance was recorded and less strong correlations between the two plankton pigments and radiance. Turbidity and benthic species diversity were highly correlated furnishing an inferential tie between an easily sensed water quality variable and a sensitive indicator of average water quality conditions.

Status of Vegetation Classification in Redwood Ecosystems

Treesearch

Thomas M. Mahony; John D. Stuart

2007-01-01

Vegetation classifications, based primarily on physiognomic variability and canopy dominants and derived principally from remotely sensed imagery, have been completed for the entire redwood range (Eyre 1980, Fox 1989). However, systematic, quantitative, floristic-based vegetation classifications in old-growth redwood forests have not been completed for large portions...

Uncoupling the complexity of forest soil variation: influence of terrain attributes, spectral indices, and spatial variability

EPA Science Inventory

Growing concern over climate and management induced changes to soil nutrient status has prompted interest in understanding the spatial distribution of forest soil properties. Recent advancements in remotely sensed geospatial technologies are providing an increasing array of data...

Long term observation and validation of windsat soil moisture data

USDA-ARS?s Scientific Manuscript database

The surface soil moisture controls surface energy budget. It is a key environmental variable in the coupled atmospheric and hydrological processes that are related to drought, heat waves and monsoon formation. Satellite remote sensing of soil moisture provides information that can contribute to unde...

Magnitude and variability of land evaporation and its components at the global scale

USDA-ARS?s Scientific Manuscript database

A physics-based methodology is applied to estimate global land-surface evaporation from multi-satellite observations. GLEAM (Global Land-surface Evaporation: the Amsterdam Methodology) combines a wide range of remotely sensed observations within a Priestley and Taylor-based framework. Daily actual e...

USDA-ARS Hydrology Laboratory MISWG Hydrology Workshop

NASA Technical Reports Server (NTRS)

Jackson, T. J.

1982-01-01

Current research being conducted in remote sensing techniques for measuring hydrologic parameters and variables deals with runoff curve numbers (CN), evapotranspiration (ET), and soil moisture. The CN and ET research utilizes visible and infrared measurements. Soil moisture investigations focus on the microwave region of the electromagnetic spectrum.

Assimilation of Remotely Sensed Evaporative Fraction for Improved Agricultural Irrigation Water Management

NASA Astrophysics Data System (ADS)

Lei, F.; Crow, W. T.; Kustas, W. P.; Yang, Y.; Anderson, M. C.

2017-12-01

Improving the water usage efficiency and maintaining water use sustainability is challenging under rapidly changed natural environments. For decades, extensive field investigations and conceptual/physical numerical modeling have been developed to quantify and track surface water and energy fluxes at different spatial and temporal scales. Meanwhile, with the development of satellite-based sensors, land surface eco-hydrological parameters can be retrieved remotely to supplement ground-based observations. However, both models and remote sensing retrievals contain various sources of errors and an accurate and spatio-temporally continuous simulation and forecasting system at the field-scale is crucial for the efficient water management in agriculture. Specifically, data assimilation technique can optimally integrate measurements acquired from various sources (including in-situ and remotely-sensed data) with numerical models through consideration of different types of uncertainties. In this presentation, we will focus on improving the estimation of water and energy fluxes over a vineyard in California, U.S. A high-resolution remotely-sensed Evaporative Fraction (EF) product from the Atmosphere-Land Exchange Inverse (ALEXI) model will be incorporated into a Soil Vegetation Atmosphere Transfer (SVAT) model via a 2-D data assimilation method. The results will show that both the accuracy and spatial variability of soil water content and evapotranspiration in SVAT model can be enhanced through the assimilation of EF data. Furthermore, we will demonstrate that by taking the optimized soil water flux as initial condition and combining it with weather forecasts, future field water status can be predicted under different irrigation scenarios. Finally, we will discuss the practical potential of these advances by leveraging our numerical experiment for the design of new irrigation strategies and water management techniques.

Forest mensuration with remote sensing: A retrospective and a vision for the future

Treesearch

Randolph H. Wynne

2004-01-01

Remote sensing, while occasionally oversold, has clear potential to reduce the overall cost of traditional forest inventories. Perhaps most important, some of the information needed for more intensive, rather than extensive, forest management is available from remote sensing. These new information needs may justify increased use and the increased cost of remote sensing...

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 15 Commerce and Foreign Trade 3 2010-01-01 2010-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

Remote Sensing: Analyzing Satellite Images to Create Higher Order Thinking Skills.

ERIC Educational Resources Information Center

Marks, Steven K.; And Others

1996-01-01

Presents a unit that uses remote-sensing images from satellites and other spacecraft to provide new perspectives of the earth and generate greater global awareness. Relates the levels of Bloom's hierarchy to different aspects of the remote sensing unit to confirm that the concepts and principles of remote sensing and related images belong in…

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 15 Commerce and Foreign Trade 3 2011-01-01 2011-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 15 Commerce and Foreign Trade 3 2012-01-01 2012-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 15 Commerce and Foreign Trade 3 2014-01-01 2014-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 15 Commerce and Foreign Trade 3 2013-01-01 2013-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

Annotated bibliography of remote sensing methods for monitoring desertification

USGS Publications Warehouse

Walker, A.S.; Robinove, Charles J.

1981-01-01

Remote sensing techniques are valuable for locating, assessing, and monitoring desertification. Remotely sensed data provide a permanent record of the condition of the land in a format that allows changes in land features and condition to be measured. The annotated bibliography of 118 items discusses remote sensing methods that may be applied to desertification studies.

Applied Remote Sensing Program (ARSP)

NASA Technical Reports Server (NTRS)

Johnson, J. D.; Foster, K. E.; Mouat, D. A.; Miller, D. A.; Conn, J. S.

1976-01-01

The activities and accomplishments of the Applied Remote Sensing Program during FY 1975-1976 are reported. The principal objective of the Applied Remote Sensing Program continues to be designed projects having specific decision-making impacts as a principal goal. These projects are carried out in cooperation and collaboration with local, state and federal agencies whose responsibilities lie with planning, zoning and environmental monitoring and/or assessment in the application of remote sensing techniques. The end result of the projects is the use by the involved agencies of remote sensing techniques in problem solving.

Communicating remote sensing concepts in an interdisciplinary environment

NASA Technical Reports Server (NTRS)

Chung, R.

1981-01-01

Although remote sensing is currently multidisciplinary in its applications, many of its terms come from the engineering sciences, particularly from the field of pattern recognition. Scholars from fields such as the social sciences, botany, and biology, may experience initial difficulty with remote sensing terminology, even though parallel concepts exist in their own fields. Some parallel concepts and terminologies from nonengineering fields, which might enhance the understanding of remote sensing concepts in an interdisciplinary situation are identified. Feedbacks which this analogue strategy might have on remote sensing itself are explored.

People, Places and Pixels: Remote Sensing in the Service of Society

NASA Technical Reports Server (NTRS)

Lulla, Kamlesh

2003-01-01

What is the role of Earth remote sensing and other geospatial technologies in our society? Recent global events have brought into focus the role of geospatial science and technology such as remote sensing, GIS, GPS in assisting the professionals who are responsible for operations such as rescue and recovery of sites after a disaster or a terrorist act. This paper reviews the use of recent remote sensing products from satellites such as IKONOS in these efforts. Aerial and satellite imagery used in land mine detection has been evaluated and the results of this evaluation will be discussed. Synopsis of current and future ISS Earth Remote Sensing capabilities will be provided. The role of future missions in humanitarian use of remote sensing will be explored.

Calculation of evapotranspiration using color-infrared photography. [remote sensing in Arizona

NASA Technical Reports Server (NTRS)

Jones, J. E.

1977-01-01

Data from 38 color-infrared photographic missions flown during a five year period over the Gila River Phreatophyte Project in southeastern Arizona were analyzed to determine the possibility of identifying and measuring vegetative parameters and their associated hydrologic variables by spectral analysis of the photographs. The derived spectra equations are discussed, and a table of 24 statistical parameters describing the spectral and hydrologic variables is included.

Hybrid Optimal Design of the Eco-Hydrological Wireless Sensor Network in the Middle Reach of the Heihe River Basin, China

PubMed Central

Kang, Jian; Li, Xin; Jin, Rui; Ge, Yong; Wang, Jinfeng; Wang, Jianghao

2014-01-01

The eco-hydrological wireless sensor network (EHWSN) in the middle reaches of the Heihe River Basin in China is designed to capture the spatial and temporal variability and to estimate the ground truth for validating the remote sensing productions. However, there is no available prior information about a target variable. To meet both requirements, a hybrid model-based sampling method without any spatial autocorrelation assumptions is developed to optimize the distribution of EHWSN nodes based on geostatistics. This hybrid model incorporates two sub-criteria: one for the variogram modeling to represent the variability, another for improving the spatial prediction to evaluate remote sensing productions. The reasonability of the optimized EHWSN is validated from representativeness, the variogram modeling and the spatial accuracy through using 15 types of simulation fields generated with the unconditional geostatistical stochastic simulation. The sampling design shows good representativeness; variograms estimated by samples have less than 3% mean error relative to true variograms. Then, fields at multiple scales are predicted. As the scale increases, estimated fields have higher similarities to simulation fields at block sizes exceeding 240 m. The validations prove that this hybrid sampling method is effective for both objectives when we do not know the characteristics of an optimized variables. PMID:25317762

The Thermocline Layer and Chlorophyll-a Concentration Variability during Southeast Monsoon in the Banda Sea

NASA Astrophysics Data System (ADS)

Pusparini, Nikita; Prasetyo, Budi; Ambariyanto; Widowati, Ita

2017-02-01

Thermocline layer and chlorophyll-a concentration can be used to investigate the upwelling region. This investigation is focused in the Banda Sea because the upwelling event in this area is quite large and has a longer upwelling duration than other waters in Indonesia. In addition, Banda Sea is also influenced by climatic factors such as monsoon. The aim of this research is to determine the validation of secondary data (from satellite imagery data and model) and in situ observation data (from research cruise) and to determine the variability of thermocline layer and chlorophyll-a concentration during Southeast Monsoon in the Banda Sea. The data used in this study were chlorophyll-a concentration, seawater vertical temperature at depths 0-400 meters, and sea surface temperature from remote sensing and in situ data. Spatial and temporal analysis of all parameters was conducted by quantitative descriptive method. The results showed that the variability of thermocline layer and the chlorophyll-a distribution were strongly related to seasonal pattern. In most cases, the estimates of thermocline layer and chlorophyll-a concentration using remote sensing algorithm were higher than in situ measured values. The greatest variability occurred in the eastern Banda Sea during the Southeast Monsoon with shallower thermocline layer, more abundance of chlorophyll-a concentration, and lower sea surface temperature.

Identifying optimal remotely-sensed variables for ecosystem monitoring in Colorado Plateau drylands

USGS Publications Warehouse

Poitras, Travis; Villarreal, Miguel; Waller, Eric K.; Nauman, Travis; Miller, Mark E.; Duniway, Michael C.

2018-01-01

Water-limited ecosystems often recover slowly following anthropogenic or natural disturbance. Multitemporal remote sensing can be used to monitor ecosystem recovery after disturbance; however, dryland vegetation cover can be challenging to accurately measure due to sparse cover and spectral confusion between soils and non-photosynthetic vegetation. With the goal of optimizing a monitoring approach for identifying both abrupt and gradual vegetation changes, we evaluated the ability of Landsat-derived spectral variables to characterize surface variability of vegetation cover and bare ground across a range of vegetation community types. Using three year composites of Landsat data, we modeled relationships between spectral information and field data collected at monitoring sites near Canyonlands National Park, UT. We also developed multiple regression models to assess improvement over single variables. We found that for all vegetation types, percent cover bare ground could be accurately modeled with single indices that included a combination of red and shortwave infrared bands, while near infrared-based vegetation indices like NDVI worked best for quantifying tree cover and total live vegetation cover in woodlands. We applied four models to characterize the spatial distribution of putative grassland ecological states across our study area, illustrating how this approach can be implemented to guide dryland ecosystem management.

Hybrid optimal design of the eco-hydrological wireless sensor network in the middle reach of the Heihe River Basin, China.

PubMed

Kang, Jian; Li, Xin; Jin, Rui; Ge, Yong; Wang, Jinfeng; Wang, Jianghao

2014-10-14

The eco-hydrological wireless sensor network (EHWSN) in the middle reaches of the Heihe River Basin in China is designed to capture the spatial and temporal variability and to estimate the ground truth for validating the remote sensing productions. However, there is no available prior information about a target variable. To meet both requirements, a hybrid model-based sampling method without any spatial autocorrelation assumptions is developed to optimize the distribution of EHWSN nodes based on geostatistics. This hybrid model incorporates two sub-criteria: one for the variogram modeling to represent the variability, another for improving the spatial prediction to evaluate remote sensing productions. The reasonability of the optimized EHWSN is validated from representativeness, the variogram modeling and the spatial accuracy through using 15 types of simulation fields generated with the unconditional geostatistical stochastic simulation. The sampling design shows good representativeness; variograms estimated by samples have less than 3% mean error relative to true variograms. Then, fields at multiple scales are predicted. As the scale increases, estimated fields have higher similarities to simulation fields at block sizes exceeding 240 m. The validations prove that this hybrid sampling method is effective for both objectives when we do not know the characteristics of an optimized variables.

The application of remote sensing techniques to the study of ophiolites

NASA Astrophysics Data System (ADS)

Khan, Shuhab D.; Mahmood, Khalid

2008-08-01

Satellite remote sensing methods are a powerful tool for detailed geologic analysis, especially in inaccessible regions of the earth's surface. Short-wave infrared (SWIR) bands are shown to provide spectral information bearing on the lithologic, structural, and geochemical character of rock bodies such as ophiolites, allowing for a more comprehensive assessment of the lithologies present, their stratigraphic relationships, and geochemical character. Most remote sensing data are widely available for little or no cost, along with user-friendly software for non-specialists. In this paper we review common remote sensing systems and methods that allow for the discrimination of solid rock (lithologic) components of ophiolite complexes and their structural relationships. Ophiolites are enigmatic rock bodies which associated with most, if not all, plate collision sutures. Ophiolites are ideal for remote sensing given their widely recognized diversity of lithologic types and structural relationships. Accordingly, as a basis for demonstrating the utility of remote sensing techniques, we briefly review typical ophiolites in the Tethyan tectonic belt. As a case study, we apply integrated remote sensing studies of a well-studied example, the Muslim Bagh ophiolite, located in Balochistan, western Pakistan. On this basis, we attempt to demonstrate how remote sensing data can validate and reconcile existing information obtained from field studies. The lithologic and geochemical diversity of Muslim Bagh are representative of Tethyan ophiolites. Despite it's remote location it has been extensively mapped and characterized by structural and geochemical studies, and is virtually free of vegetative cover. Moreover, integrating the remote sensing data with 'ground truth' information thus offers the potential of an improved template for interpreting remote sensing data sets of other ophiolites for which little or no field information is available.

Remote Sensing and Remote Control Activities in Europe and America: Part 2--Remote Sensing Ground Stations in Europe,

DTIC Science & Technology

1996-04-08

Development tasks and products of remote sensing ground stations in Europe are represented by the In-Sec Corporation and the Schlumberger Industries Corporation. The article presents the main products of these two corporations.

Yield variability prediction by remote sensing sensors with different spatial resolution

NASA Astrophysics Data System (ADS)

Kumhálová, Jitka; Matějková, Štěpánka

2017-04-01

Currently, remote sensing sensors are very popular for crop monitoring and yield prediction. This paper describes how satellite images with moderate (Landsat satellite data) and very high (QuickBird and WorldView-2 satellite data) spatial resolution, together with GreenSeeker hand held crop sensor, can be used to estimate yield and crop growth variability. Winter barley (2007 and 2015) and winter wheat (2009 and 2011) were chosen because of cloud-free data availability in the same time period for experimental field from Landsat satellite images and QuickBird or WorldView-2 images. Very high spatial resolution images were resampled to worse spatial resolution. Normalised difference vegetation index was derived from each satellite image data sets and it was also measured with GreenSeeker handheld crop sensor for the year 2015 only. Results showed that each satellite image data set can be used for yield and plant variability estimation. Nevertheless, better results, in comparison with crop yield, were obtained for images acquired in later phenological phases, e.g. in 2007 - BBCH 59 - average correlation coefficient 0.856, and in 2011 - BBCH 59-0.784. GreenSeeker handheld crop sensor was not suitable for yield estimation due to different measuring method.

Towards identification of relevant variables in the observed aerosol optical depth bias between MODIS and AERONET observations

NASA Astrophysics Data System (ADS)

Malakar, N. K.; Lary, D. J.; Gencaga, D.; Albayrak, A.; Wei, J.

2013-08-01

Measurements made by satellite remote sensing, Moderate Resolution Imaging Spectroradiometer (MODIS), and globally distributed Aerosol Robotic Network (AERONET) are compared. Comparison of the two datasets measurements for aerosol optical depth values show that there are biases between the two data products. In this paper, we present a general framework towards identifying relevant set of variables responsible for the observed bias. We present a general framework to identify the possible factors influencing the bias, which might be associated with the measurement conditions such as the solar and sensor zenith angles, the solar and sensor azimuth, scattering angles, and surface reflectivity at the various measured wavelengths, etc. Specifically, we performed analysis for remote sensing Aqua-Land data set, and used machine learning technique, neural network in this case, to perform multivariate regression between the ground-truth and the training data sets. Finally, we used mutual information between the observed and the predicted values as the measure of similarity to identify the most relevant set of variables. The search is brute force method as we have to consider all possible combinations. The computations involves a huge number crunching exercise, and we implemented it by writing a job-parallel program.

The role of C3 and C4 grasses to interannual variability in remotely sensed ecosystem performance over the US Great Plains

USGS Publications Warehouse

Ricotta, C.; Reed, Bradley C.; Tieszen, Larry L.

2003-01-01

Time integrated normalized difference vegetation index (ΣNDVI) derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) multi-temporal imagery over a 10-year period (1989–1998) was used as a surrogate for primary production to investigate the impact of interannual climate variability on grassland performance for central and northern US Great Plains. First, the contribution of C3 and C4 species abundance to the major grassland ecosystems of the US Great Plains is described. Next, the relation between mean ΣNDVI and the ΣNDVI coefficient of variation (CV ΣNDVI) used as a proxy for interannual climate variability is analysed. Results suggest that the differences in the long-term climatic control over ecosystem performance approximately coincide with changes between C3- and C4-dominant grassland classes. Variation in remotely sensed net primary production over time is higher for the southern and western plains grasslands (primarily C4 grasslands), whereas the C3-dominated classes in the northern and eastern portion of the US Great Plains, generally show lower CV ΣNDVI values.

Intensity-Duration-Frequency curves from remote sensing datasets: direct comparison of weather radar and CMORPH over the Eastern Mediterranean

NASA Astrophysics Data System (ADS)

Morin, Efrat; Marra, Francesco; Peleg, Nadav; Mei, Yiwen; Anagnostou, Emmanouil N.

2017-04-01

Rainfall frequency analysis is used to quantify the probability of occurrence of extreme rainfall and is traditionally based on rain gauge records. The limited spatial coverage of rain gauges is insufficient to sample the spatiotemporal variability of extreme rainfall and to provide the areal information required by management and design applications. Conversely, remote sensing instruments, even if quantitative uncertain, offer coverage and spatiotemporal detail that allow overcoming these issues. In recent years, remote sensing datasets began to be used for frequency analyses, taking advantage of increased record lengths and quantitative adjustments of the data. However, the studies so far made use of concepts and techniques developed for rain gauge (i.e. point or multiple-point) data and have been validated by comparison with gauge-derived analyses. These procedures add further sources of uncertainty and prevent from isolating between data and methodological uncertainties and from fully exploiting the available information. In this study, we step out of the gauge-centered concept presenting a direct comparison between at-site Intensity-Duration-Frequency (IDF) curves derived from different remote sensing datasets on corresponding spatial scales, temporal resolutions and records. We analyzed 16 years of homogeneously corrected and gauge-adjusted C-Band weather radar estimates, high-resolution CMORPH and gauge-adjusted high-resolution CMORPH over the Eastern Mediterranean. Results of this study include: (a) good spatial correlation between radar and satellite IDFs ( 0.7 for 2-5 years return period); (b) consistent correlation and dispersion in the raw and gauge adjusted CMORPH; (c) bias is almost uniform with return period for 12-24 h durations; (d) radar identifies thicker tail distributions than CMORPH and the tail of the distributions depends on the spatial and temporal scales. These results demonstrate the potential of remote sensing datasets for rainfall frequency analysis for management (e.g. warning and early-warning systems) and design (e.g. sewer design, large scale drainage planning)

Developing Remote Sensing Products for Monitoring and Modeling Great Lakes Coastal Wetland Vulnerability to Climate Change and Land Use

NASA Astrophysics Data System (ADS)

Bourgeau-Chavez, L. L.; Miller, M. E.; Battaglia, M.; Banda, E.; Endres, S.; Currie, W. S.; Elgersma, K. J.; French, N. H. F.; Goldberg, D. E.; Hyndman, D. W.

2014-12-01

Spread of invasive plant species in the coastal wetlands of the Great Lakes is degrading wetland habitat, decreasing biodiversity, and decreasing ecosystem services. An understanding of the mechanisms of invasion is crucial to gaining control of this growing threat. To better understand the effects of land use and climatic drivers on the vulnerability of coastal zones to invasion, as well as to develop an understanding of the mechanisms of invasion, research is being conducted that integrates field studies, process-based ecosystem and hydrological models, and remote sensing. Spatial data from remote sensing is needed to parameterize the hydrological model and to test the outputs of the linked models. We will present several new remote sensing products that are providing important physiological, biochemical, and landscape information to parameterize and verify models. This includes a novel hybrid radar-optical technique to delineate stands of invasives, as well as natural wetland cover types; using radar to map seasonally inundated areas not hydrologically connected; and developing new algorithms to estimate leaf area index (LAI) using Landsat. A coastal map delineating wetland types including monocultures of the invaders (Typha spp. and Phragmites austrailis) was created using satellite radar (ALOS PALSAR, 20 m resolution) and optical data (Landsat 5, 30 m resolution) fusion from multiple dates in a Random Forests classifier. These maps provide verification of the integrated model showing areas at high risk of invasion. For parameterizing the hydrological model, maps of seasonal wetness are being developed using spring (wet) imagery and differencing that with summer (dry) imagery to detect the seasonally wet areas. Finally, development of LAI remote sensing high resolution algorithms for uplands and wetlands is underway. LAI algorithms for wetlands have not been previously developed due to the difficulty of a water background. These products are being used to improve the hydrological model through higher resolution products and parameterization of variables that have previously been largely unknown.

International Space Station Data Collection for Disaster Response

NASA Technical Reports Server (NTRS)

Stefanov, William L.; Evans, Cynthia A.

2015-01-01

Remotely sensed data acquired by orbital sensor systems has emerged as a vital tool to identify the extent of damage resulting from a natural disaster, as well as providing near-real time mapping support to response efforts on the ground and humanitarian aid efforts. The International Space Station (ISS) is a unique terrestrial remote sensing platform for acquiring disaster response imagery. Unlike automated remote-sensing platforms it has a human crew; is equipped with both internal and externally-mounted remote sensing instruments; and has an inclined, low-Earth orbit that provides variable views and lighting (day and night) over 95 percent of the inhabited surface of the Earth. As such, it provides a useful complement to autonomous sensor systems in higher altitude polar orbits. NASA remote sensing assets on the station began collecting International Disaster Charter (IDC) response data in May 2012. The initial NASA ISS sensor systems responding to IDC activations included the ISS Agricultural Camera (ISSAC), mounted in the Window Observational Research Facility (WORF); the Crew Earth Observations (CEO) Facility, where the crew collects imagery using off-the-shelf handheld digital cameras; and the Hyperspectral Imager for the Coastal Ocean (HICO), a visible to near-infrared system mounted externally on the Japan Experiment Module Exposed Facility. The ISSAC completed its primary mission in January 2013. It was replaced by the very high resolution ISS SERVIR Environmental Research and Visualization System (ISERV) Pathfinder, a visible-wavelength digital camera, telescope, and pointing system. Since the start of IDC response in 2012 there have been 108 IDC activations; NASA sensor systems have collected data for thirty-two of these events. Of the successful data collections, eight involved two or more ISS sensor systems responding to the same event. Data has also been collected by International Partners in response to natural disasters, most notably JAXA and Roscosmos/Energia through the Urugan program.

[Estimation of desert vegetation coverage based on multi-source remote sensing data].

PubMed

Wan, Hong-Mei; Li, Xia; Dong, Dao-Rui

2012-12-01

Taking the lower reaches of Tarim River in Xinjiang of Northwest China as study areaAbstract: Taking the lower reaches of Tarim River in Xinjiang of Northwest China as study area and based on the ground investigation and the multi-source remote sensing data of different resolutions, the estimation models for desert vegetation coverage were built, with the precisions of different estimation methods and models compared. The results showed that with the increasing spatial resolution of remote sensing data, the precisions of the estimation models increased. The estimation precision of the models based on the high, middle-high, and middle-low resolution remote sensing data was 89.5%, 87.0%, and 84.56%, respectively, and the precisions of the remote sensing models were higher than that of vegetation index method. This study revealed the change patterns of the estimation precision of desert vegetation coverage based on different spatial resolution remote sensing data, and realized the quantitative conversion of the parameters and scales among the high, middle, and low spatial resolution remote sensing data of desert vegetation coverage, which would provide direct evidence for establishing and implementing comprehensive remote sensing monitoring scheme for the ecological restoration in the study area.

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.

An Approach of Registration between Remote Sensing Image and Electronic Chart Based on Coastal Line

NASA Astrophysics Data System (ADS)

Li, Ying; Yu, Shuiming; Li, Chuanlong

Remote sensing plays an important role marine oil spill emergency. In order to implement a timely and effective countermeasure, it is important to provide exact position of oil spills. Therefore it is necessary to match remote sensing image and electronic chart properly. Variance ordinarily exists between oil spill image and electronic chart, although geometric correction is applied to remote sensing image. It is difficult to find the steady control points on sea to make exact rectification of remote sensing image. An improved relaxation algorithm was developed for finding the control points along the coastline since oil spills occurs generally near the coast. A conversion function is created with the least square, and remote sensing image can be registered with the vector map based on this function. SAR image was used as the remote sensing data and shape format map as the electronic chart data. The results show that this approach can guarantee the precision of the registration, which is essential for oil spill monitoring.

The U.S. Geological Survey land remote sensing program

USGS Publications Warehouse

Saunders, T.; Feuquay, J.; Kelmelis, J.A.

2003-01-01

The U.S. Geological Survey has been a provider of remotely sensed information for decades. As the availability and use of satellite data has grown, USGS has placed increasing emphasis on expanding the knowledge about the science of remote sensing and on making remotely sensed data more accessible. USGS encourages widespread availability and distribution of these data and through its programs, encourages and enables a variety of research activities and the development of useful applications of the data. The science of remote sensing has great potential for assisting in the monitoring and assessment of the impacts of natural disasters, management and analysis of environmental, biological, energy, and mineral investigations, and supporting informed public policy decisions. By establishing the Land Remote Sensing Program (LRS) as a major unit of the USGS Geography Program, USGS has taken the next step to further increase support for the accessibility, understanding, and use of remotely sensed data. This article describes the LRS Program, its mission and objectives, and how the program has been structured to accomplish its goals.

Predicting Ecologically Important Vegetation Variables from Remotely Sensed Optical/Radar Data Using Neural Networks

NASA Technical Reports Server (NTRS)

Kimes, Daniel S.; Nelson, Ross F.

1998-01-01

A number of satellite sensor systems will collect large data sets of the Earth's surface during NASA's Earth Observing System (EOS) era. Efforts are being made to develop efficient algorithms that can incorporate a wide variety of spectral data and ancillary data in order to extract vegetation variables required for global and regional studies of ecosystem processes, biosphere-atmosphere interactions, and carbon dynamics. These variables are, for the most part, continuous (e.g. biomass, leaf area index, fraction of vegetation cover, vegetation height, vegetation age, spectral albedo, absorbed photosynthetic active radiation, photosynthetic efficiency, etc.) and estimates may be made using remotely sensed data (e.g. nadir and directional optical wavelengths, multifrequency radar backscatter) and any other readily available ancillary data (e.g., topography, sun angle, ground data, etc.). Using these types of data, neural networks can: 1) provide accurate initial models for extracting vegetation variables when an adequate amount of data is available; 2) provide a performance standard for evaluating existing physically-based models; 3) invert multivariate, physically based models; 4) in a variable selection process, identify those independent variables which best infer the vegetation variable(s) of interest; and 5) incorporate new data sources that would be difficult or impossible to use with conventional techniques. In addition, neural networks employ a more powerful and adaptive nonlinear equation form as compared to traditional linear, index transformations, and simple nonlinear analyses. These neural networks attributes are discussed in the context of the authors' investigations of extracting vegetation variables of ecological interest.

Site-specific management of cotton root rot using airborne and satellite imagery

USDA-ARS?s Scientific Manuscript database

Cotton root rot is a serious cotton disease that can now be effectively controlled with Topguard Terra Fungicide. The objectives of this research were to demonstrate how site-specific fungicide application could be implemented based on historical remote sensing imagery and variable rate technology. ...

Investigating trends in water use over the Choptank River watershed using a multi-satellite data fusion approach

USDA-ARS?s Scientific Manuscript database

Satellite remote sensing technologies have been widely used to map spatiotemporal variability in consumptive water use (or evapotranspiration; ET) for agricultural water management applications. However, current satellite-based sensors with the high spatial resolution required to map ET at sub-field...

Investigating water use over the Choptank River Watershed using a multi-satellite data fusion approach

USDA-ARS?s Scientific Manuscript database

Satellite remote sensing technologies have been widely used to map spatiotemporal variability in consumptive water use (or evapotranspiration; ET) for agricultural water management applications. However, current satellite-based sensors with the high spatial resolution required to map ET at sub-field...

Foreword to the Special Issue on Remote Sensing and Modeling of Surface Properties

USDA-ARS?s Scientific Manuscript database

CURRENTLY, the Numerical Weather Prediction (NWP) community is striving for better ways to extract information on the lower layer using current and future satellite systems to improve short-term to medium-range forecasts. The surface emissivity is highly variable and may cause biases in the forward ...

A Skylab program for the International Hydrological Decade (IHD). [Lake Ontario Basin

NASA Technical Reports Server (NTRS)

Polcyn, F. C. (Principal Investigator); Rebel, D. L.

1974-01-01

The author has identified the following significant results. The development of the algorithm (using real data) relating red and IR reflectance to surface soil moisture over regions of variable vegetation cover will enable remote sensing to make direct inputs into determination of this important hydrologic parameter.

Remote sensing support for national forest inventories

Treesearch

Ronald E. McRoberts; Erkki O. Tomppo

2007-01-01

National forest inventory programs are tasked to produce timely and accurate estimates for a wide range of forest resource variables for a variety of users and applications. Time, cost, and precision constraints cause these programs to seek technological innovations that contribute to measurement and estimation efficiencies and that facilitate the production and...

CHARACTERISTIC LENGTH SCALE OF INPUT DATA IN DISTRIBUTED MODELS: IMPLICATIONS FOR MODELING GRID SIZE. (R824784)

EPA Science Inventory

The appropriate spatial scale for a distributed energy balance model was investigated by: (a) determining the scale of variability associated with the remotely sensed and GIS-generated model input data; and (b) examining the effects of input data spatial aggregation on model resp...

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

USDA-ARS?s Scientific Manuscript database

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

Daily mapping of 30m LAI and NDVI for grape yield prediction in California vineyards

USDA-ARS?s Scientific Manuscript database

Wine grape quality and quantity are affected by vine growing conditions during critical phenological stages. Field observations of vine growth stages are normally very sparse and cannot capture the spatial variability of vine conditions. Remote sensing data acquired from visible and near infrared ba...

Remote sensing of perennial crop stand duration and pre-crop identification

USDA-ARS?s Scientific Manuscript database

Field to field variability in soil erosion and off-site transport of nutrients and pesticides in western Oregon in any single year is primarily driven by the question of whether individual fields were disturbed for planting of new crop stands or remained in production of established perennial crops...

Microwave Remote Sensing Modeling of Ocean Surface Salinity and Winds Using an Empirical Sea Surface Spectrum

NASA Technical Reports Server (NTRS)

Yueh, Simon H.

2004-01-01

Active and passive microwave remote sensing techniques have been investigated for the remote sensing of ocean surface wind and salinity. We revised an ocean surface spectrum using the CMOD-5 geophysical model function (GMF) for the European Remote Sensing (ERS) C-band scatterometer and the Ku-band GMF for the NASA SeaWinds scatterometer. The predictions of microwave brightness temperatures from this model agree well with satellite, aircraft and tower-based microwave radiometer data. This suggests that the impact of surface roughness on microwave brightness temperatures and radar scattering coefficients of sea surfaces can be consistently characterized by a roughness spectrum, providing physical basis for using combined active and passive remote sensing techniques for ocean surface wind and salinity remote sensing.

Online catalog access and distribution of remotely sensed information

NASA Astrophysics Data System (ADS)

Lutton, Stephen M.

1997-09-01

Remote sensing is providing voluminous data and value added information products. Electronic sensors, communication electronics, computer software, hardware, and network communications technology have matured to the point where a distributed infrastructure for remotely sensed information is a reality. The amount of remotely sensed data and information is making distributed infrastructure almost a necessity. This infrastructure provides data collection, archiving, cataloging, browsing, processing, and viewing for applications from scientific research to economic, legal, and national security decision making. The remote sensing field is entering a new exciting stage of commercial growth and expansion into the mainstream of government and business decision making. This paper overviews this new distributed infrastructure and then focuses on describing a software system for on-line catalog access and distribution of remotely sensed information.

Interannual variation of the surface temperature of tropical forests from satellite observations

DOE PAGES

Gao, Huilin; Zhang, Shuai; Fu, Rong; ...

2016-01-01

Land surface temperatures (LSTs) within tropical forests contribute to climate variations. However, observational data are very limited in such regions. This study used passive microwave remote sensing data from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS), providing observations under all weather conditions, to investigate the LST over the Amazon and Congo rainforests. The SSM/I and SSMIS data were collected from 1996 to 2012. The morning and afternoon observations from passive microwave remote sensing facilitate the investigation of the interannual changes of LST anomalies on a diurnal basis. As a result of the variability ofmore » cloud cover and the corresponding reduction of solar radiation, the afternoon LST anomalies tend to vary more than the morning LST anomalies. The dominant spatial and temporal patterns for interseasonal variations of the LST anomalies over the tropical rainforest were analyzed. The impacts of droughts and El Niños on this LST were also investigated. Lastly, the differences between early morning and late afternoon LST anomalies were identified by the remote sensing product, with the morning LST anomalies controlled by humidity (according to comparisons with the National Centers for Environmental Prediction (NCEP) reanalysis data).« less

Remote sensing and spatial statistical techniques for modelling Ommatissus lybicus (Hemiptera: Tropiduchidae) habitat and population densities

PubMed Central

Kwan, Paul; Welch, Mitchell

2017-01-01

In order to understand the distribution and prevalence of Ommatissus lybicus (Hemiptera: Tropiduchidae) as well as analyse their current biographical patterns and predict their future spread, comprehensive and detailed information on the environmental, climatic, and agricultural practices are essential. The spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental, and human factors. The main objective of this paper is to review remote sensing and relevant analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus. An exhaustive search of related literature revealed that there are very limited studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental, and human practice related variables. This review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance methods in designing both local and regional level integrated pest management strategies of palm tree and other affected cultivated crops. PMID:28875085

Remote sensing and spatial statistical techniques for modelling Ommatissus lybicus (Hemiptera: Tropiduchidae) habitat and population densities.

PubMed

Al-Kindi, Khalifa M; Kwan, Paul; R Andrew, Nigel; Welch, Mitchell

2017-01-01

In order to understand the distribution and prevalence of Ommatissus lybicus (Hemiptera: Tropiduchidae) as well as analyse their current biographical patterns and predict their future spread, comprehensive and detailed information on the environmental, climatic, and agricultural practices are essential. The spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental, and human factors. The main objective of this paper is to review remote sensing and relevant analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus . An exhaustive search of related literature revealed that there are very limited studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental, and human practice related variables. This review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance methods in designing both local and regional level integrated pest management strategies of palm tree and other affected cultivated crops.

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

Gao, Huilin; Zhang, Shuai; Fu, Rong

Land surface temperatures (LSTs) within tropical forests contribute to climate variations. However, observational data are very limited in such regions. This study used passive microwave remote sensing data from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS), providing observations under all weather conditions, to investigate the LST over the Amazon and Congo rainforests. The SSM/I and SSMIS data were collected from 1996 to 2012. The morning and afternoon observations from passive microwave remote sensing facilitate the investigation of the interannual changes of LST anomalies on a diurnal basis. As a result of the variability ofmore » cloud cover and the corresponding reduction of solar radiation, the afternoon LST anomalies tend to vary more than the morning LST anomalies. The dominant spatial and temporal patterns for interseasonal variations of the LST anomalies over the tropical rainforest were analyzed. The impacts of droughts and El Niños on this LST were also investigated. Lastly, the differences between early morning and late afternoon LST anomalies were identified by the remote sensing product, with the morning LST anomalies controlled by humidity (according to comparisons with the National Centers for Environmental Prediction (NCEP) reanalysis data).« less

Regional surface soil heat flux estimate from multiple remote sensing data in a temperate and semiarid basin

NASA Astrophysics Data System (ADS)

Li, Nana; Jia, Li; Lu, Jing; Menenti, Massimo; Zhou, Jie

2017-01-01

The regional surface soil heat flux (G0) estimation is very important for the large-scale land surface process modeling. However, most of the regional G0 estimation methods are based on the empirical relationship between G0 and the net radiation flux. A physical model based on harmonic analysis was improved (referred to as "HM model") and applied over the Heihe River Basin northwest China with multiple remote sensing data, e.g., FY-2C, AMSR-E, and MODIS, and soil map data. The sensitivity analysis of the model was studied as well. The results show that the improved model describes the variation of G0 well. Land surface temperature (LST) and thermal inertia (Γ) are the two key input variables to the HM model. Compared with in situ G0, there are some differences, mainly due to the differences between remote-sensed LST and the in situ LST. The sensitivity analysis shows that the errors from -7 to -0.5 K in LST amplitude and from -300 to 300 J m-2 K-1 s-0.5 in Γ will cause about 20% errors, which are acceptable for G0 estimation.

Hyperspectral Geobotanical Remote Sensing for Monitoring and Verifying CO 2 Containment Final Report CRADA No. TC-2036-02

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

Pickles, W. L.; Ebrom, D. A.

This collaborative effort was in support of the CO 2 Capture Project (CCP), to develop techniques that integrate overhead images of plant species, plant health, geological formations, soil types, aquatic, and human use spatial patterns for detection and discrimination of any CO 2 releases from underground storage formations. The goal of this work was to demonstrate advanced hyperspectral geobotanical remote sensing methods to assess potential leakage of CO 2 from underground storage. The timeframes and scales relevant to the long-term storage of CO 2 in the subsurface make remote sensing methods attractive. Moreover, it has been shown that individual fieldmore » measurements of gas composition are subject to variability on extremely small temporal and spatial scales. The ability to verify ultimate reservoir integrity and to place individual surface measurements into context will be crucial to successful long-term monitoring and verification activities. The desired results were to produce a defined and tested procedure that could be easily used for long-term monitoring of possible CO 2 leakage from underground CO 2 sequestration sites. This testing standard will be utilized on behalf of the oil industry.« less

Remote Sensing and the Environment.

ERIC Educational Resources Information Center

Osmers, Karl

1991-01-01

Suggests using remote sensing technology to help students make sense of the natural world. Explains that satellite information allows observation of environmental changes over time. Identifies possible student projects based on remotely sensed data. Recommends obtaining the assistance of experts and seeking funding through effective project…

Use of remote sensing in agriculture

NASA Technical Reports Server (NTRS)

Pettry, D. E.; Powell, N. L.; Newhouse, M. E.

1974-01-01

Remote sensing studies in Virginia and Chesapeake Bay areas to investigate soil and plant conditions via remote sensing technology are reported ant the results given. Remote sensing techniques and interactions are also discussed. Specific studies on the effects of soil moisture and organic matter on energy reflection of extensively occurring Sassafras soils are discussed. Greenhouse and field studies investigating the effects of chlorophyll content of Irish potatoes on infrared reflection are presented. Selected ground truth and environmental monitoring data are shown in summary form. Practical demonstrations of remote sensing technology in agriculture are depicted and future use areas are delineated.

Applications of remote sensing to watershed management

NASA Technical Reports Server (NTRS)

Rango, A.

1975-01-01

Aircraft and satellite remote sensing systems which are capable of contributing to watershed management are described and include: the multispectral scanner subsystem on LANDSAT and the basic multispectral camera array flown on high altitude aircraft such as the U-2. Various aspects of watershed management investigated by remote sensing systems are discussed. Major areas included are: snow mapping, surface water inventories, flood management, hydrologic land use monitoring, and watershed modeling. It is indicated that technological advances in remote sensing of hydrological data must be coupled with an expansion of awareness and training in remote sensing techniques of the watershed management community.

NASA Glenn OHIOVIEW FY01/02 Project

NASA Technical Reports Server (NTRS)

2003-01-01

The results of the research performed by the university principal investigators are herein compiled. OhioView's general goals were: 1) To increase remote sensing education for Ohio s undergraduate and graduate students, and also enhancing curriculum in the mathematics and science for K-12 students using the capabilities of remote sensing; 2) To conduct advanced research to develop novel remote sensing applications, i.e. to turn data into information for more applications; 3) To maximize the use of remote sensing technology by the general public through outreach and the development of tools for more user-friendly access to remote sensing data.

The availability of conventional forms of remotely sensed data

USGS Publications Warehouse

Sturdevant, James A.; Holm, Thomas M.

1982-01-01

For decades Federal and State agencies have been collecting aerial photographs of various film types and scales over parts of the United States. More recently, worldwide Earth resources data acquired by orbiting satellites have inundated the remote sensing community. Determining the types of remotely sensed data that are publicly available can be confusing to the land-resource manager, planner, and scientist. This paper is a summary of the more commonly used types of remotely sensed data (aircraft and satellite) and their public availability. Special emphasis is placed on the National High-Altitude Photography (NHAP) program and future remote-sensing satellites.

NASA's Applied Remote Sensing Training (ARSET) Webinar Series

Atmospheric Science Data Center

2016-07-12

NASA's Applied Remote Sensing Training (ARSET) Webinar Series Tuesday, July 12, 2016 ... you of a free training opportunity: Introduction to Remote Sensing for Air Quality Applications Webinar Series Beginning in ...

Tropospheric Passive Remote Sensing

NASA Technical Reports Server (NTRS)

Keafer, L. S., Jr. (Editor)

1982-01-01

The long term role of airborne/spaceborne passive remote sensing systems for tropospheric air quality research and the identification of technology advances required to improve the performance of passive remote sensing systems were discussed.

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)

Opportunities and problems in introducing or expanding the teaching of remote sensing in universities

NASA Technical Reports Server (NTRS)

Maxwell, E. L.

1980-01-01

The need for degree programs in remote sensing is considered. Any education program which claims to train remote sensing specialists must include expertise in the physical principles upon which remote sensing is based. These principles dictate the limits of engineering and design, computer analysis, photogrammetry, and photointerpretation. Faculty members must be hired to provide emphasis in those five areas.

Remote sensing of vegetation fires and its contribution to a fire management information system

Treesearch

Stephane P. Flasse; Simon N. Trigg; Pietro N. Ceccato; Anita H. Perryman; Andrew T. Hudak; Mark W. Thompson; Bruce H. Brockett; Moussa Drame; Tim Ntabeni; Philip E. Frost; Tobias Landmann; Johan L. le Roux

2004-01-01

In the last decade, research has proven that remote sensing can provide very useful support to fire managers. This chapter provides an overview of the types of information remote sensing can provide to the fire community. First, it considers fire management information needs in the context of a fire management information system. An introduction to remote sensing then...

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

Basic Remote Sensing Investigations for Beach Reconnaissance.

DTIC Science & Technology

Progress is reported on three tasks designed to develop remote sensing beach reconnaissance techniques applicable to the benthic, beach intertidal...and beach upland zones. Task 1 is designed to develop remote sensing indicators of important beach composition and physical parameters which will...ultimately prove useful in models to predict beach conditions. Task 2 is designed to develop remote sensing techniques for survey of bottom features in

Bridging the Scales from Field to Region with Practical Tools to Couple Time- and Space-Synchronized Data from Flux Towers and Networks with Proximal and Remote Sensing Data

NASA Astrophysics Data System (ADS)

Burba, G. G.; Avenson, T.; Burkart, A.; Gamon, J. A.; Guan, K.; Julitta, T.; Pastorello, G.; Sakowska, K.

2017-12-01

Many hundreds of flux towers are presently operational as standalone projects and as parts of regional networks. However, the vast majority of these towers do not allow straightforward coupling with remote sensing (drone, aircraft, satellite, etc.) data, and even fewer have optical sensors for validation of remote sensing products, and upscaling from field to regional levels. In 2016-2017, new tools to collect, process, and share time-synchronized flux data from multiple towers were developed and deployed globally. Originally designed to automate site and data management, and to streamline flux data analysis, these tools allow relatively easy matching of tower data with remote sensing data: GPS-driven PTP time protocol synchronizes instrumentation within the station, different stations with each other, and all of these to remote sensing data to precisely align remote sensing and flux data in time Footprint size and coordinates computed and stored with flux data help correctly align tower flux footprints and drone, aircraft or satellite motion to precisely align optical and flux data in space Full snapshot of the remote sensing pixel can then be constructed, including leaf-level, ground optical sensor, and flux tower measurements from the same footprint area, closely coupled with the remote sensing measurements to help interpret remote sensing data, validate models, and improve upscaling Additionally, current flux towers can be augmented with advanced ground optical sensors and can use standard routines to deliver continuous products (e.g. SIF, PRI, NDVI, etc.) based on automated field spectrometers (e.g., FloX and RoX, etc.) and other optical systems. Several dozens of new towers already operational globally can be readily used for the proposed workflow. Over 500 active traditional flux towers can be updated to synchronize their data with remote sensing measurements. This presentation will show how the new tools are used by major networks, and describe how this approach can be utilized for matching remote sensing and tower data to aid in ground truthing, improve scientific interactions, and promote joint grant writing and other forms of collaboration between the flux and remote sensing communities.

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.

Remote Sensing: A Film Review.

ERIC Educational Resources Information Center

Carter, David J.

1986-01-01

Reviews the content of 19 films on remote sensing published between 1973 and 1980. Concludes that they are overly simplistic, notably outdated, and generally too optimistic about the potential of remote sensing from space for resource exploration and environmental problem-solving. Provides names and addresses of more current remote sensing…

Large-scale experimental technology with remote sensing in land surface hydrology and meteorology

NASA Technical Reports Server (NTRS)

Brutsaert, Wilfried; Schmugge, Thomas J.; Sellers, Piers J.; Hall, Forrest G.

1988-01-01

Two field experiments to study atmospheric and land surface processes and their interactions are summarized. The Hydrologic-Atmospheric Pilot Experiment, which tested techniques for measuring evaporation, soil moisture storage, and runoff at scales of about 100 km, was conducted over a 100 X 100 km area in France from mid-1985 to early 1987. The first International Satellite Land Surface Climatology Program field experiment was conducted in 1987 to develop and use relationships between current satellite measurements and hydrologic, climatic, and biophysical variables at the earth's surface and to validate these relationships with ground truth. This experiment also validated surface parameterization methods for simulation models that describe surface processes from the scale of vegetation leaves up to scales appropriate to satellite remote sensing.

Northern Everglades, Florida, satellite image map

USGS Publications Warehouse

Thomas, Jean-Claude; Jones, John W.

2002-01-01

These satellite image maps are one product of the USGS Land Characteristics from Remote Sensing project, funded through the USGS Place-Based Studies Program with support from the Everglades National Park. The objective of this project is to develop and apply innovative remote sensing and geographic information system techniques to map the distribution of vegetation, vegetation characteristics, and related hydrologic variables through space and over time. The mapping and description of vegetation characteristics and their variations are necessary to accurately simulate surface hydrology and other surface processes in South Florida and to monitor land surface changes. As part of this research, data from many airborne and satellite imaging systems have been georeferenced and processed to facilitate data fusion and analysis. These image maps were created using image fusion techniques developed as part of this project.

Commercial Applications Multispectral Sensor System

NASA Technical Reports Server (NTRS)

Birk, Ronald J.; Spiering, Bruce

1993-01-01

NASA's Office of Commercial Programs is funding a multispectral sensor system to be used in the development of remote sensing applications. The Airborne Terrestrial Applications Sensor (ATLAS) is designed to provide versatility in acquiring spectral and spatial information. The ATLAS system will be a test bed for the development of specifications for airborne and spaceborne remote sensing instrumentation for dedicated applications. This objective requires spectral coverage from the visible through thermal infrared wavelengths, variable spatial resolution from 2-25 meters; high geometric and geo-location accuracy; on-board radiometric calibration; digital recording; and optimized performance for minimized cost, size, and weight. ATLAS is scheduled to be available in 3rd quarter 1992 for acquisition of data for applications such as environmental monitoring, facilities management, geographic information systems data base development, and mineral exploration.

Load sensing system

DOEpatents

Sohns, C.W.; Nodine, R.N.; Wallace, S.A.

1999-05-04

A load sensing system inexpensively monitors the weight and temperature of stored nuclear material for long periods of time in widely variable environments. The system can include an electrostatic load cell that encodes weight and temperature into a digital signal which is sent to a remote monitor via a coaxial cable. The same cable is used to supply the load cell with power. When multiple load cells are used, vast inventories of stored nuclear material can be continuously monitored and inventoried of minimal cost. 4 figs.

Educational activities of remote sensing archaeology (Conference Presentation)

NASA Astrophysics Data System (ADS)

Hadjimitsis, Diofantos G.; Agapiou, Athos; Lysandrou, Vasilki; Themistocleous, Kyriacos; Cuca, Branka; Nisantzi, Argyro; Lasaponara, Rosa; Masini, Nicola; Krauss, Thomas; Cerra, Daniele; Gessner, Ursula; Schreier, Gunter

2016-10-01

Remote sensing science is increasingly being used to support archaeological and cultural heritage research in various ways. Satellite sensors either passive or active are currently used in a systematic basis to detect buried archaeological remains and to systematic monitor tangible heritage. In addition, airborne and low altitude systems are being used for documentation purposes. Ground surveys using remote sensing tools such as spectroradiometers and ground penetrating radars can detect variations of vegetation and soil respectively, which are linked to the presence of underground archaeological features. Education activities and training of remote sensing archaeology to young people is characterized of highly importance. Specific remote sensing tools relevant for archaeological research can be developed including web tools, small libraries, interactive learning games etc. These tools can be then combined and aligned with archaeology and cultural heritage. This can be achieved by presenting historical and pre-historical records, excavated sites or even artifacts under a "remote sensing" approach. Using such non-form educational approach, the students can be involved, ask, read, and seek to learn more about remote sensing and of course to learn about history. The paper aims to present a modern didactical concept and some examples of practical implementation of remote sensing archaeology in secondary schools in Cyprus. The idea was built upon an ongoing project (ATHENA) focused on the sue of remote sensing for archaeological research in Cyprus. Through H2020 ATHENA project, the Remote Sensing Science and Geo-Environment Research Laboratory at the Cyprus University of Technology (CUT), with the support of the National Research Council of Italy (CNR) and the German Aerospace Centre (DLR) aims to enhance its performance in all these new technologies.

Developing spectral, structural, and phenological diversity proxies for monitoring biodiversity change across space and time using ESA's Sentinel satellites

NASA Astrophysics Data System (ADS)

Ma, X.; Mahecha, M. D.; Migliavacca, M.; Luo, Y.; Urban, M.; Bohn, F. J.; Huth, A.; Reichstein, M.

2017-12-01

A key challenge for monitoring biodiversity change is the lack of consistent measures of biodiversity across space and time. This challenge may be addressed by exploring the potentials provided by novel remote sensing observations. By continuously observing broad-scale patterns of vegetation and land surface parameters, remote sensing can complement the restricted coverage afforded by field measurements. Here we develop methods to infer spatial patterns of biodiversity at ecosystem level from ESA's next-generation Sentinel sensors (Sentinel-1: C-band radar & Sentinel-2: multispectral). Both satellites offer very high spatial (10 m) and temporal resolutions (5 days) measurements with global coverage. We propose and test several ecosystem biodiversity proxies, including landscape spectral diversity, phenological diversity, and canopy structural diversity. These diversity proxies are highly related to some key aspects of essential biodiversity variables (EBVs) as defined by GEO-BON, such as habitat structure, community composition, ecosystem function and structure. We verify spaceborne retrievals of these biodiversity proxies with in situ measurements from drone (spectral diversity), phenocam (phenological diversity), and airborne LiDAR (canopy structural diversity) over multiple flux tower sites within the Mediterranean region. We further compare our remote sensing retrievals of biodiversity proxies against several biodiversity indices as derived from field measurements (incl. ⍺-/β- diversity and Shannon-index) to explore the limitations and potentials of extending the RS proxies to a greater spatial extent. We expect the new concept as to maximize the potential of remote sensing information might help to monitor key aspects of EBVs on a global scale.

Evaluating Coupled Human-Hydrologic Systems in High Altitude Regions: A Case Study of the Arun Watershed, Eastern Nepal

NASA Astrophysics Data System (ADS)

Voss, K.; Bookhagen, B.; Tague, C.; Lopez-Carr, D.

2014-12-01

The Himalaya exhibit dynamic ecological, hydrological, and climatic extremes that magnify the variability and extent of natural hazards, resulting in destruction to both physical and human landscapes. Coupled with poverty, these factors intensify local communities' vulnerability to climate change. This study highlights the Arun watershed in eastern Nepal as a case study to evaluate how local communities in high altitude regions are managing their water for domestic and agricultural needs while coping with extreme events, such as floods and landslides. Remotely-sensed precipitation, snowpack and glacial extent data from the past decade are combined with preliminary results from extensive field-based community surveys in the Arun watershed. The analysis of remotely-sensed data will describe seasonal trends in water availability, glacial lake growth, and the spatial variation of these trends within the basin. These hydrologic changes will be linked to the human survey analysis, which will provide an understanding of locals' perceptions of water challenges and the current water management strategies within the basin. Particular attention will be given to a comparison between the eastern and western tributaries of the Arun River, where the catchments are mainly rain-fed (eastern) versus glacial-fed (western). This contrast will highlight how different hydrologic scenarios evidenced from remote-sensing data motivate diverse human water management responses as defined in field surveys. A particular focus will be given to management decisions related to agriculture expansion and hydropower development. This synthesis of remote-sensing and social research methodologies provides a valuable perspective on coupled human-hydrologic systems.

A Novel Methodology for Improving Plant Pest Surveillance in Vineyards and Crops Using UAV-Based Hyperspectral and Spatial Data

PubMed Central

Vanegas, Fernando; Weiss, John; Gonzalez, Felipe

2018-01-01

Recent advances in remote sensed imagery and geospatial image processing using unmanned aerial vehicles (UAVs) have enabled the rapid and ongoing development of monitoring tools for crop management and the detection/surveillance of insect pests. This paper describes a (UAV) remote sensing-based methodology to increase the efficiency of existing surveillance practices (human inspectors and insect traps) for detecting pest infestations (e.g., grape phylloxera in vineyards). The methodology uses a UAV integrated with advanced digital hyperspectral, multispectral, and RGB sensors. We implemented the methodology for the development of a predictive model for phylloxera detection. In this method, we explore the combination of airborne RGB, multispectral, and hyperspectral imagery with ground-based data at two separate time periods and under different levels of phylloxera infestation. We describe the technology used—the sensors, the UAV, and the flight operations—the processing workflow of the datasets from each imagery type, and the methods for combining multiple airborne with ground-based datasets. Finally, we present relevant results of correlation between the different processed datasets. The objective of this research is to develop a novel methodology for collecting, processing, analysing and integrating multispectral, hyperspectral, ground and spatial data to remote sense different variables in different applications, such as, in this case, plant pest surveillance. The development of such methodology would provide researchers, agronomists, and UAV practitioners reliable data collection protocols and methods to achieve faster processing techniques and integrate multiple sources of data in diverse remote sensing applications. PMID:29342101

Assessment of Wildfire Risk in Southern California with Live Fuel Moisture Measurement and Remotely Sensed Vegetation Water Content Proxies

NASA Astrophysics Data System (ADS)

Jia, S.; Kim, S. H.; Nghiem, S. V.; Kafatos, M.

2017-12-01

Live fuel moisture (LFM) is the water content of live herbaceous plants expressed as a percentage of the oven-dry weight of plant. It is a critical parameter in fire ignition in Mediterranean climate and routinely measured in sites selected by fire agencies across the U.S. Vegetation growing cycle, meteorological metrics, soil type, and topography all contribute to the seasonal and inter-annual variation of LFM, and therefore, the risk of wildfire. The optical remote sensing-based vegetation indices (VIs) have been used to estimate the LFM. Comparing to the VIs, microwave remote sensing products have advantages like less saturation effect in greenness and representing the water content of the vegetation cover. In this study, we established three models to evaluate the predictability of LFM in Southern California using MODIS NDVI, vegetation temperature condition index (VTCI) from downscaled Soil Moisture Active Passive (SMAP) products, and vegetation optical depth (VOD) derived by Land Parameter Retrieval Model. Other ancillary variables, such as topographic factors (aspects and slope) and meteorological metrics (air temperature, precipitation, and relative humidity), are also considered in the models. The model results revealed an improvement of LFM estimation from SMAP products and VOD, despite the uncertainties introduced in the downscaling and parameter retrieval. The estimation of LFM using remote sensing data can provide an assessment of wildfire danger better than current methods using NDVI-based growing seasonal index. Future study will test the VOD estimation from SMAP data using the multi-temporal dual channel algorithm (MT-DCA) and extend the LFM modeling to a regional scale.

Passive Microwave Remote Sensing of Soil Moisture

NASA Technical Reports Server (NTRS)

Njoku, Eni G.; Entekhabi, Dara

1996-01-01

Microwave remote sensing provides a unique capability for direct observation of soil moisture. Remote measurements from space afford the possibility of obtaining frequent, global sampling of soil moisture over a large fraction of the Earth's land surface. Microwave measurements have the benefit of being largely unaffected by cloud cover and variable surface solar illumination, but accurate soil moisture estimates are limited to regions that have either bare soil or low to moderate amounts of vegetation cover. A particular advantage of passive microwave sensors is that in the absence of significant vegetation cover soil moisture is the dominant effect on the received signal. The spatial resolutions of passive Microwave soil moisture sensors currently considered for space operation are in the range 10-20 km. The most useful frequency range for soil moisture sensing is 1-5 GHz. System design considerations include optimum choice of frequencies, polarizations, and scanning configurations, based on trade-offs between requirements for high vegetation penetration capability, freedom from electromagnetic interference, manageable antenna size and complexity, and the requirement that a sufficient number of information channels be available to correct for perturbing geophysical effects. This paper outlines the basic principles of the passive microwave technique for soil moisture sensing, and reviews briefly the status of current retrieval methods. Particularly promising are methods for optimally assimilating passive microwave data into hydrologic models. Further studies are needed to investigate the effects on microwave observations of within-footprint spatial heterogeneity of vegetation cover and subsurface soil characteristics, and to assess the limitations imposed by heterogeneity on the retrievability of large-scale soil moisture information from remote observations.

Mapping the impact of river regulation on carbon dynamics using coupled field surveys and remotely-sensed optical properties

NASA Astrophysics Data System (ADS)

Kuhn, C.; Butman, D. E.

2016-12-01

Many river-reservoir networks are already managed for ecological targets such as stream temperature regulation, but less is known about how management choices alter the quantity and composition of dissolved organic carbon as well as the concentration of dissolved carbon gases. Understanding these ecological impacts is critical to informing water resources management, especially in light of the global hydropower boom and the increased interest in dam removal in the United States. Here we present results from a field survey and remote sensing imagery analysis quantifying a suite of water quality variables. With this approach, we evaluate spatial differences in carbon signals above, and below eight mainstem dams located on the Columbia and Snake Rivers. Dissolved methane and carbon dioxide concentrations were in excess of atmospheric levels with occasional carbon dioxide undersaturation being observed in the Snake River. CH4 and CO2 δ13C values shifted between the mainstem and the tributaries reflecting changes in carbon sources and processes. Satellite-retrieved estimates of CDOM and chlorophyll-a were compared to in situ measurements to enable surface mapping of concentrations at broader spatial scales. Our technical approach blends cloud-based data fusion techniques and machine learning to link ground-collected observations to remote sensing imagery in order to produce spatially-explicit, cross-scale estimates of carbon dynamics in a large, highly regulated river system. These findings test the feasibility of coupling remote sensing with field-based measurements to observe the complex impacts of run-of-the river impoundments to aquatic carbon cycling.

Assimilation of Remotely-Sensed Snow information to improve streamflow predictions in the Southwestern US

NASA Astrophysics Data System (ADS)

López-Burgos, V.; Rajagopal, S.; Martinez Baquero, G. F.; Gupta, H. V.

2009-12-01

Rapidly growing population in the southwestern US is leading to increasing demand and decreasing availability of water, requiring a detailed quantification of hydrological processes. The integration of detailed spatial information of water fluxes from remote sensing platforms, and hydrological models coupled with ground based data is an important step towards this goal. This project is exploring the use of Snow Water Equivalent (SWE) estimates to update the snow component of the Variable Infiltration Capacity model (VIC). SWE estimates are obtained by combining SNOTEL data with MODIS Snow Cover Area (SCA) information. Because, cloud cover corrupts the estimates of SCA, a rule-based method is used to clean up the remotely sensed images. The rules include a time interpolation method, and the probability of a pixel for been covered with snow based on the relationships between elevation, temperature, lapse rate, aspect and topographic shading. The approach is used to improve streamflow predictions on two rivers managed by the Salt River Project, a water and energy supplier in central Arizona. This solution will help improve the management of reservoirs in the Salt and Verde River in Phoenix, Arizona (tributaries of the lower Colorado River basin), by incorporating physically based distributed models and remote sensing observations into their Decision Support Tools and planning tools. This research seeks to increase the knowledge base used to manage reservoirs and groundwater resources in a region affected by a long-term drought. It will be applicable and relevant for other water utility companies facing the challenges of climate change and decreasing water resources.

Use hyperspectral remote sensing technique to monitoring pine wood nomatode disease preliminary

NASA Astrophysics Data System (ADS)

Qin, Lin; Wang, Xianghong; Jiang, Jing; Yang, Xianchang; Ke, Daiyan; Li, Hongqun; Wang, Dingyi

2016-10-01

The pine wilt disease is a devastating disease of pine trees. In China, the first discoveries of the pine wilt disease on 1982 at Dr. Sun Yat-sen's Mausoleum in Nanjing. It occurred an area of 77000 hm2 in 2005, More than 1540000 pine trees deaths in the year. Many districts of Chongqing in Three Gorges Reservoir have different degrees of pine wilt disease occurrence. It is a serious threat to the ecological environment of the reservoir area. Use unmanned airship to carry high spectrum remote sensing monitoring technology to develop the study on pine wood nematode disease early diagnosis and early warning and forecasting in this study. The hyper spectral data and the digital orthophoto map data of Fuling District Yongsheng Forestry had been achieved In September 2015. Using digital image processing technology to deal with the digital orthophoto map, the number of disease tree and its distribution is automatic identified. Hyper spectral remote sensing data is processed by the spectrum comparison algorithm, and the number and distribution of disease pine trees are also obtained. Two results are compared, the distribution area of disease pine trees are basically the same, indicating that using low air remote sensing technology to monitor the pine wood nematode distribution is successful. From the results we can see that the hyper spectral data analysis results more accurate and less affected by environmental factors than digital orthophoto map analysis results, and more environment variable can be extracted, so the hyper spectral data study is future development direction.

Remote Sensing and the Earth.

ERIC Educational Resources Information Center

Brosius, Craig A.; And Others

This document is designed to help senior high school students study remote sensing technology and techniques in relation to the environmental sciences. It discusses the acquisition, analysis, and use of ecological remote data. Material is divided into three sections and an appendix. Section One is an overview of the basics of remote sensing.…

Microwave remote sensing of snowpack properties

NASA Technical Reports Server (NTRS)

Rango, A. (Editor)

1980-01-01

Topic concerning remote sensing capabilities for providing reliable snow cover data and measurement of snow water equivalents are discussed. Specific remote sensing technqiues discussed include those in the microwave region of the electromagnetic spectrum.