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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

Quantitative suspended sediment mapping using aircraft remotely sensed multispectral data

Suspended sediment is an important environmental parameter for monitoring water quality, water movement, and land use. Quantitative suspended sediment determinations were made from analysis of aircraft remotely sensed multispectral digital data. A statistical analysis and derived regression equation were used to determine and plot quantitative suspended sediment concentration contours in the tidal James River, Virginia, on May 28, 1974. From the analysis, a single band, Band 8 (0.70-0.74 microns), was adequate for determining suspended sediment concentrations. A correlation coefficient of 0.89 was obtained with a mean inaccuracy of 23.5 percent for suspended sediment concentrations up to about 50 mg/l. Other water quality parameters - secchi disc depth and chlorophyll - also had high correlations with the remotely sensed data. Particle size distribution had only a fair correlation with the remotely sensed data.

Johnson, R. W.↗
NASA NTRS1992

Estimates of surface roughness derived from synthetic aperture radar (SAR) data

Radar remote sensing data provide a unique perspective of the earth's crust and the processes that have influenced its evolution. Physically based models are required, however, to relate the geophysical quantities being measured by the radar sensor to useful geologic information. In this study, synthetic aperture radar (SAR) data over the Cima volcanic field in the Mojave Desert of California are quantitatively connected with microtopography through inversion of a radar backscatter model. Changes in surface roughness inferred from the derived microtopography are modeled and found to be consistent with aeolian mantling as surfaces age. Estimated rates of aeolian deposition for the Cima area are compared to the Lunar Crater volcanic field in Nevada. Rates of deposition appear to be higher at Cima volcanic field, most likely because of its proximity to Soda Lake, the main source of the aeolian material.

Evans, Diane L.↗
NASA NTRS1998

Thermal Infrared Remote Sensing for Analysis of Landscape Ecological Processes: Methods and Applications

Thermal Infrared (TIR) remote sensing data can provide important measurements of surface energy fluxes and temperatures, which are integral to understanding landscape processes and responses. One example of this is the successful application of TIR remote sensing data to estimate evapotranspiration and soil moisture, where results from a number of studies suggest that satellite-based measurements from TIR remote sensing data can lead to more accurate regional-scale estimates of daily evapotranspiration. With further refinement in analytical techniques and models, the use of TIR data from airborne and satellite sensors could be very useful for parameterizing surface moisture conditions and developing better simulations of landscape energy exchange over a variety of conditions and space and time scales. Thus, TIR remote sensing data can significantly contribute to the observation, measurement, and analysis of energy balance characteristics (i.e., the fluxes and redistribution of thermal energy within and across the land surface) as an implicit and important aspect of landscape dynamics and landscape functioning. The application of TIR remote sensing data in landscape ecological studies has been limited, however, for several fundamental reasons that relate primarily to the perceived difficulty in use and availability of these data by the landscape ecology community, and from the fragmentation of references on TIR remote sensing throughout the scientific literature. It is our purpose here to provide evidence from work that has employed TIR remote sensing for analysis of landscape characteristics to illustrate how these data can provide important data for the improved measurement of landscape energy response and energy flux relationships. We examine the direct or indirect use of TIR remote sensing data to analyze landscape biophysical characteristics, thereby offering some insight on how these data can be used more robustly to further the understanding and modeling of landscape ecological processes.

Quattrochi, Dale A.↗
NASA NTRS2000

A Decision Support Information System for Urban Landscape Management Using Thermal Infrared Data

In this paper, we describe efforts to use remote sensing data within the purview of an information support system, to assess urban thermal landscape characteristics as a means for developing more robust models of the Urban Heat Island (UHI) effect. We also present a rationale on how we have successfully translated the results from the study of urban thermal heating and cooling regimes as identified from remote sensing data, to decision-makers, planners, government officials, and the public at large in several US cities to facilitate better understanding of how the UHI affects air quality. Additionally, through the assessment of the spatial distribution of urban thermal landscape characteristics using remote sensing data, it is possible to develop strategies to mitigate the UHI that hopefully will in turn, drive down ozone levels and improve overall urban air quality. Four US cities have been the foci for intensive analysis as part of our studies: Atlanta, GA, Baton Rouge, LA, Salt Lake City, UT, and Sacramento, CA. The remote sensing data for each of these cities has been used to generate a number of products for use by "stakeholder" working groups to convey information on what the effects are of the UHI and what measures can be taken to mitigate it. In turn, these data products are used to both educate and inform policy-makers, planners, and the general public about what kinds of UHI mitigation strategies are available.

Quattrochi, Dale A.↗
NASA NTRS1987

NASA's Earth Resources Laboratory - Seventeen years of using remotely sensed satellite data in land applications

The activities of the Earth Resources Laboratoy (ERL) for the past seventeen years are reviewed with particular reference to four typical applications demonstrating the use of remotely sensed data in a geobased information system context. The applications discussed are: a fire control model for the Olympic National Park; wildlife habitat modeling; a resource inventory system including a potential soil erosion model; and a corridor analysis model for locating routes between geographical locations. Some future applications are also discussed.

Cashion, Kenneth D.↗
NASA NTRS1989

Combining multispectral and ancillary data in remote sensing using interval-valued probabilities

This paper presents a method for classifying multisource data in remote sensing and geographic information systems using interval-valued probabilities. In this method, each data source is considered as an information source which provides a body of statistical evidence. In order to integrate information obtained from multiple data sources, the method adopts Dempster's rule for combining multiple bodies of evidence. Preliminary experiments have been undertaken to illustrate the use of the method in a supervised ground-cover classification on multispectral data combined with digital elevation data. They demonstrate the ability of the method in capturing information provided by inexact and incomplete evidence when there are not enough training samples to estimate statistical parameters.

Kim, H.↗
NASA NTRS2012

Customizing NASA's Earth Science Research Products for addressing MENA Water Challenges

As projected by IPCC 2007 report, by the end of this century the Middle East North Mrica (MENA) region is projected to experience an increase of 3 C to 5 C rise in mean temperatures and a 20% decline in precipitation. This poses a serious problem for this geographic zone especially when majority of the hydrological consumption is for the agriculture sector and the remaining amount is for domestic consumption. In late 2011, the World Bank, USAID and NASA have joined hands to establishing integrated, modem, up to date NASA developed capabilities for various countries in the MENA region for addressing water resource issues and adapting to climate change impacts for improved decision making for societal benefits. The main focus of this undertaking is to address the most pressing societal issues which can be modeled and solved by utilizing NASA Earth Science remote sensing data products and hydrological models. The remote sensing data from space is one of the best ways to study such complex issues and further feed into the decision support systems. NASA's fleet of Earth Observing satellites offer a great vantage point from space to look at the globe and provide vital signs necessary to maintain healthy and sustainable ecosystem. NASA has over fifteen satellites and thirty instruments operating on these space borne platforms and generating over 2000 different science products on a daily basis. Some of these products are soil moisture, global precipitation, aerosols, cloud cover, normalized difference vegetation index, land cover/use, ocean altimetry, ocean salinity, sea surface winds, sea surface temperature, ozone and atmospheric gasses, ice and snow measurements, and many more. All of the data products, models and research results are distributed via the Internet freely through out the world. This project will utilize several NASA models such as global Land Data Assimilation System (LDAS) to generate hydrological states and fluxes in near real time. These LDAS products will then be further compared with other NASA satellite observations (MODIS, VIIRS, TRMM, etc.) and other discrete models to compare and optimize evapotranspiration, soil moisture and crop irrigation, drought assessment and water balance. The floods being a critical disaster in many of the MENA countries, NASA's global flood mapping and modeling framework (CREST) will be customized for country specific needs and delivered to the remote sensing organizations for their future use. Training is an important component under this activity and adequate level of training will be offered to build basic capacity to work with NASA provided data products, models for their future use. This paper provides a comprehensive introduction to NASA's Earth Science mission for understanding the behavior of our home Planet, projecting its health for future generations and applying research results solving societal issues.

Habib, Shahid↗
NASA NTRS1986

SAFE/DAE: Modal test in space

In September of 1984, NASA performed a series of experiments on orbit with a large solar wing attached to the Space Shuttle orbiter. These experiments, the Solar Array Flight Experiment (SAFE), mark the first tests of a large space structure in space. Extension, retraction, and dynamic tests had to be performed in space due to the fragility of the solar array. Due to the extendable and retractable design of the solar array, accelerometers and associated wires could not be used; therefore, remote sensing, the Dynamics Augmentation Experiment (DAE), was added to the SAFE program. The DAE uses a remote sensor based on star tracker technology to measure the dynamic response of the solar array. The DAE sensor tracked 18 targets on the solar array during free-decay response to a transient excitation. An overview of the SAFE/DAE is presented, highlighting analysis results from the remotely sensed data. Modal parameter estimates from the remotely sensed data were computed using the complex exponential and polyreference techniques.

Nesman, T. E.↗
NASA NTRS1985

Orbital remote sensing - Space technology applications in south-east Asia

The evolution of remote sensing techniques in the developing countries of southeast Asia is reviewed. The use of the images for monitoring soil, water, and vegetation resources, in order to develop a national policy for conservation of the resources, is described. The remote sensing data are helpful in observing deforestation in southeast Asia; however, excessive cloud coverage does not allow accurate evaluation of the rice crop. The effects of the capabilities of the developing countries to process the data and remote sensing program of industrial countries on the future application of satellite imagery in developing countries are studied. The need for improved data banking and dissemination of the imagery is analyzed. Agreements on proprietary rights due to the improved ground resolution of orbital sensors are required. The designing of remote sensing equipment to meet the requirements of its users is discussed.

Malingreau, J.-P.↗
NASA NTRS2022

Improved representation of agricultural land use and crop management for large-scale hydrological impact simulation in Africa using SWAT+

To date, most regional and global hydrological models either ignore the representation of cropland or consider crop cultivation in a simplistic way or in abstract terms without any management practices. Yet, the water balance of cultivated areas is strongly influenced by applied management practices (e.g. planting, irrigation, fertilization, and harvesting). The SWAT+ (Soil and Water Assessment Tool) model represents agricultural land by default in a generic way, where the start of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and subtropical regions such as sub-Saharan Africa, where crop growth dynamics are mainly controlled by rainfall rather than temperature. In this study, we present an approach on how to incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a regional SWAT+ model for northeastern Africa. We evaluate the influence of the crop phenology representation on simulations of leaf area index (LAI) and evapotranspiration (ET) using LAI remote sensing data from Copernicus Global Land Service (CGLS) and WaPOR (Water Productivity through Open access of Remotely sensed derived data) ET data, respectively. Results show that a representation of crop phenology using global datasets leads to improved temporal patterns of LAI and ET simulations, especially for regions with a single cropping cycle. However, for regions with multiple cropping seasons, global phenology datasets need to be complemented with local data or remote sensing data to capture additional cropping seasons. In addition, the improvement of the cropping season also helps to improve soil erosion estimates, as the timing of crop cover controls erosion rates in the model. With more realistic growing seasons, soil erosion is largely reduced for most agricultural hydrologic response units (HRUs), which can be considered as a move towards substantial improvements over previous estimates. We conclude that regional and global hydrological models can benefit from improved representations of crop phenology and the associated management practices. Future work regarding the incorporation of multiple cropping seasons in global phenology data is needed to better represent cropping cycles in areas where they occur using regional to global hydrological models.

crop phenology↗
NASA NTRS1992

Atmospheric correction of remotely sensed image data by a simplified model

The interaction of radiation with the atmosphere is complex and has proved difficult to calculate without reference to measurements made at or close to the time and location of interest. In this paper we describe the use of data from an airborne-tracking sunphotometer mounted on a NASA C-130 aircraft to derive optical properties of the atmospheric aerosols above the aircraft when it is on the ground as well as when the aircraft is at altitude collecting remote sensing data. Furthermore, we describe the use of these optical properties for quantitative atmospheric correction of Landsat Thematic Mapper (TM) and NS001 TM simulator radiances in a simplified radiative transfer model appropriate for each pixel of entire images. Both qualitative and quantitative comparisons of results from use of the model show it provides good to excellent correction for atmospheric effects in TM and NS001 TM simulator data. Quantitative comparisons with measurements of surface radiances measured near the time of the TM overpass showed agreement within a few percent for bands 2, 3, and 4. Band 1 yielded radiances within 15 percent of the surface radiances. We think the simplified atmospheric correction model shows great promise for applying atmospheric corrections to entire images instead of individual points within images to yield surface radiances within a few percent in a computationally efficient manner.

Wrigley, R. C.↗
NASA NTRS2001

Narragansett Bay From Space: A Perspective for the 21st Century

In 1996, the NASA Administrator Dan Goldin and Rhode Island Congressman Patrick Kennedy challenged researchers in the Department of Geological Sciences at Brown University to developed a series of projects to apply remotely sensed data to problems of immediate concern to the State of Rhode Island. The result of that challenge was the project Narragansett Bay from Space: A Perspective for the 21st Century. The goals of the effort were to a) identify problems in coordination with state and local agencies, b) apply NASA technology to the problems and c) to involve small business that would benefit from incorporating remotely sensed data into their business operations. The overall effort was to serve two functions: help provide high quality science results based on remotely sensed data and increase the capacity of environmental managers and companies to use remotely sensed data. The effort has succeeded on both these fronts by providing new, quantitative information on the extent of environmental problems and developing a greater awareness and acceptance of remotely sensed data as a tool for monitoring and research.

Mustard, John F.↗
NASA NTRS2007

Data Fusion for Earth Science Remote Sensing

Beginning in 2004, NASA has supported the development of an international network of ground-based remote sensing installations for the measurement of greenhouse gas columns. This collaboration has been successful and is currently used in both carbon cycle investigations and in the efforts to validate the GOSAT space-based column observations of CO2 and CH4. With the support of a grant, this research group has established a network of ground-based column observations that provide an essential link between the satellite observations of CO2, CO, and CH4 and the extensive global in situ surface network. The Total Carbon Column Observing Network (TCCON) was established in 2004. At the time of this report seven sites, employing modern instrumentation, were operational or were expected to be shortly. TCCON is expected to expand. In addition to providing the most direct means of tying the in situ and remote sensing data sets together, TCCON provides a means of testing the retrieval algorithms of SCIAMACHY and GOSAT over the broadest variation in atmospheric state. TCCON provides a critically maintained and long timescale record for identification of temporal drift and spatial bias in the calibration of the space-based sensors. Finally, the global observations from TCCON are improving our understanding of how to use column observations to provide robust estimates of surface exchange of C02 and CH4 in advance of the launch of OCO and GOSAT. TCCON data are being used to better understand the impact of both regional fluxes and long-range transport on gradients in the C02 column. Such knowledge is essential for identifying the tools required to best use the space-based observations. The technical approach and methodology of retrieving greenhouse gas columns from near-IR solar spectra, data quality and process control are described. Additionally, the impact of and relevance to NASA of TCCON and satellite validation and carbon science are addressed.

data fusion↗
NASA NTRS1991

The least-squares mixing models to generate fraction images derived from remote sensing multispectral data

Constrained-least-squares and weighted-least-squares mixing models for generating fraction images derived from remote sensing multispectral data are presented. An experiment considering three components within the pixels-eucalyptus, soil (understory), and shade-was performed. The generated fraction images for shade (shade image) derived from these two methods were compared by considering the performance and computer time. The derived shade images are related to the observed variation in forest structure, i.e., the fraction of inferred shade in the pixel is related to different eucalyptus ages.

Shimabukuro, Yosio Edemir↗
NASA NTRS1972

Applications of remote sensor data to geologic and economic analysis on the Bonanza Test Site, Colorado

Recent studies conducted in the Bonanza Test Site, Colorado, area indicated that: (1) more geologic structural information is available from remote sensing data than from conventional techniques; (2) greater accuracy results from using remote sensing data; (3) all major structural features were detected; (4) of all structural interpretations, about 75% were correct; and (5) interpretation of remote sensing data will not supplant field work, but it enables field work to be done much more efficiently.

Reeves, R. G.↗
NASA NTRS1994

An investigation of current and future satellite and in-situ data for the remote sensing of the land surface energy balance

This final report from the University of Wisconsin-Madison Cooperative Institute for Meteorological Satellite Studies (CIMSS) summarizes a research program designed to improve our knowledge of the water and energy balance of the land surface through the application of remote sensing and in-situ data sources. The remote sensing data source investigations to be detailed involve surface radiometric ('skin') temperatures and also high-spectral-resolution infrared radiance data from atmospheric sounding instruments projected to be available at the end of the decade, which have shown promising results for evaluating the land-surface water and energy budget. The in-situ data types to be discussed are measurements of the temporal changes of the height of the planetary boundary layer and measurements of air temperature within the planetary boundary layer. Physical models of the land surface, planetary boundary layer and free atmosphere have been used as important tools to interpret the in-situ and remote sensing signals of the surface energy balance. A prototype 'optimal' system for combining multiple data sources into a three-dimensional estimate of the surface energy balance was developed and first results from this system will be detailed. Potential new sources of data for this system and suggested continuation research will also be discussed.

Diak, George R.↗
NASA NTRS1998

Predicting Ecologically Important Vegetation Variables from Remotely Sensed Optical/Radar Data Using Neural Networks

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.

Kimes, Daniel S.↗
NASA NTRS1979

Data and information requirement trends in remote sensing

Two characteristics of remote sensing - data volume and information volatility - are discussed. Emphasis is placed on the NASA Applications Data Service and Remotely Piloted Vehicle (RPV) television. It is noted that the data volume and information volatility in a given remote sensing system require a careful choice of the point in the end-to-end system at which data compression and/or information extraction take place.

Lynch, T. J.↗