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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

LANDSAT applications by the Adirondack Park Agency for land cover analyses and forest cover change

The New York State Adirondack Park Agency is using LANDSAT imagery to provide current, consistent parkwide data on forest cover, forest change and other land cover characteristics for the Adirondack Park, an area of 9,375 sq. miles (24,280 sq km). Boundaries of the study area were digitized and the data were enhanced and geographically rectified. A classification scheme was devised which emphasized the basic land cover types of the Park: hardwoods, spruce-fir, pine, wet conifer, brushland, grassland, agricultural areas, exposed earth, urban areas, and water bodies. Cover type classifications for disturbed forest land were also chosen: cut hardwoods, regenerating hardwoods, and cut spruce fir. Field verification of 1978 classification revealed an accurate differentiation of forest types within types and between nonforested/forested areas. The classification accurately detects forest land disturbances; however, it is not always descriptive of the level of disturbance.

Banta, J. S.↗
NASA NTRS1984

Evaluation of the radar response to land surfaces and volumes: Examination of theoretical models, target statistics, and applications

Four areas of L-band radar remote sensing of terrain were examined: (1) the behavior of the radar backscatter coefficient of distributed surface and volumes as a function of the targets' dielectric and geometric parameters and as a fnction of their physical parameters; (2) the correspondence of the angular behavior of the relative backscatter coefficient as extracted from SIR-B digital imagery and truck mounted L-band scatterometer measurements for about 100 fields; (3) the statistical behavior of SIR-B image density for targets that appear homogeneous on Thematic Mapper (TM) optical imagery and/or color IR photography; and (4) the applicability of SIR-B imagery both alone and in conjunction with TM imagery for the classification and monitoring of land cover and renewable resources.

Ulaby, F. T.↗
NASA NTRS1970

The use of space and high altitude aerial photography to classify forest land and to detect forest disturbances

In October 1969, an investigation was begun near Atlanta, Georgia, to explore the possibilities of developing predictors for forest land and stand condition classifications using space photography. It has been found that forest area can be predicted with reasonable accuracy on space photographs using ocular techniques. Infrared color film is the best single multiband sensor for this purpose. Using the Apollo 9 infrared color photographs taken in March 1969 photointerpreters were able to predict forest area for small units consistently within 5 to 10 percent of ground truth. Approximately 5,000 density data points were recorded for 14 scan lines selected at random from five study blocks. The mean densities and standard deviations were computed for 13 separate land use classes. The results indicate that forest area cannot be separated from other land uses with a high degree of accuracy using optical film density alone. If, however, densities derived by introducing red, green, and blue cutoff filters in the optical system of the microdensitometer are combined with their differences and their ratios in regression analysis techniques, there is a good possibility of discriminating forest from all other classes.

Aldrich, R. C.↗
NASA NTRS1981

Evaluation of multiband, multitemporal, and transformed LANDSAT MSS data for land cover area estimation

Sample segments of ground-verified land cover data collected in conjunction with the USDA/ESS June Enumerative Survey were merged with LANDSAT data and served as a focus for unsupervised spectral class development and accuracy assessment. Multitemporal data sets were created from single-date LANDSAT MSS acquisitions from a nominal scene covering an eleven-county area in north central Missouri. Classification accuracies for the four land cover types predominant in the test site showed significant improvement in going from unitemporal to multitemporal data sets. Transformed LANDSAT data sets did not significantly improve classification accuracies. Regression estimators yielded mixed results for different land covers. Misregistration of two LANDSAT data sets by as much and one half pixels did not significantly alter overall classification accuracies. Existing algorithms for scene-to scene overlay proved adequate for multitemporal data analysis as long as statistical class development and accuracy assessment were restricted to field interior pixels.

Stoner, E. R.↗
NASA NTRS1973

Remote sensing techniques for support of coastal zone resource management.

Description of remote sensing studies carried out for the purpose of developing and/or demonstrating techniques which can be employed for land use inventory, marsh vegetation classification, and water characteristics surveys. Attention is given to results obtained with (1) photo interpretation techniques and procedures for the development of land use information from high-altitude aircraft and satellite imagery, (2) computer based pattern recognition techniques utilizing multispectral scanner data for marsh vegetation classification, and (3) infrared and microwave techniques for the monitoring and surveying of coastal water temperature and salinity characteristics.

Piland, R. O.↗
NASA NTRS1987

Monsoon flood boundary delineation and damage assessment using space borne imaging radar and Landsat data

Space-borne synthetic aperture radar (SAR) data acquired by the Shuttle Imaging Radar-B (SIR-B) Program and Landsat Multispectral Scanner Subsystem (MSS) Data from Landsat 4 were used to map flood boundaries for the assessment of flood damage in the Peoples Republic of Bangladesh. The cloud penetrating capabilities of the L-band radar provided a clear picture of the hydrologic conditions of the surface during a period of inclement weather at the end of the wet phase of the 1984 monsoon. The radar image data were digitally processed to geometrically rectify the pixel geometry and were filtered to subdue radar image speckle effects. Contrast enhancement techniques and density slicing were used to create discrete land-cover categories corresponding to surface conditions present at the time of the Shuttle overflight. The radar image classification map was digitally registered to a spectral signature classification map of the area derived from Landsat MSS data collected two weeks prior to the SIR-B mission. Classification accuracy comparisons were made between the radar and MSS classification maps, and flood boundary and flood damage assessment measurements were made with the merged data by adding the classifications and inventorying the land-cover classes inundated at the time of flooding.

Imhoff, Marc L.↗
NASA NTRS1980

Analysis of urban area land cover using SEASAT Synthetic Aperture Radar data

Digitally processed SEASAT synthetic aperture raar (SAR) imagery of the Denver, Colorado urban area was examined to explore the potential of SAR data for mapping urban land cover and the compatability of SAR derived land cover classes with the United States Geological Survey classification system. The imagery is examined at three different scales to determine the effect of image enlargement on accuracy and level of detail extractable. At each scale the value of employing a simplistic preprocessing smoothing algorithm to improve image interpretation is addressed. A visual interpretation approach and an automated machine/visual approach are employed to evaluate the feasibility of producing a semiautomated land cover classification from SAR data. Confusion matrices of omission and commission errors are employed to define classification accuracies for each interpretation approach and image scale.

Henderson, F. M.↗
NASA NTRS1973

A study of the utilization of ERTS-1 data from the Wabash River Basin

The author has identified the following significant results. For the urban land use analysis project, classification results were good to excellent for the following classes: single family residential, commercial/industrial, cloud, cloud shadow, trees, and water. Grassy areas were defined fairly well, but residential areas located between multi-family residential and single-family residential were misclassified as grassy. Residential areas dominated by tree cover, termed wooded residential, were unable to be classified in a single class. Data points in such areas were classified randomly as either grassy or trees.

Landgrebe, D. A.↗
NASA NTRS1976

Tri-county pilot study

The author has identified the following significant results. An area inventory was performed for three southeast Texas counties (Montgomery, Walker, and San Jacinto) totaling 0.65 million hectares. The inventory was performed using a two level hierarchy. Level 1 was divided into forestland, rangeland, and other land. Forestland was separated into Level 2 categories: pine, hardwood, and mixed; rangeland was not separated further. Results consisted of area statistics for each county and for the entire study site for pine, hardwood, mixed, rangeland, and other land. Color coded county classification maps were produced for the May data set, and procedures were developed and tested.

Reeves, C. A.↗
NASA NTRS1977

Automated image processing of Landsat II digital data for watershed runoff prediction

Digital image processing of Landsat data from a 230 sq km area was examined as a possible means of generating soil cover information for use in the watershed runoff prediction of Kern County, California. The soil cover information included data on brush, grass, pasture lands and forests. A classification accuracy of 94% for the Landsat-based soil cover survey suggested that the technique could be applied to the watershed runoff estimate. However, problems involving the survey of complex mountainous environments may require further attention

Sasso, R. R.↗
NASA NTRS1977

Microwave remote sensing from space for earth resource surveys

The concepts of radar remote sensing and microwave radiometry are discussed and their utility in earth resource sensing is examined. The direct relationship between the character of the remotely sensed data and the level of decision making for which the data are appropriate is considered. Applications of active and a passive microwave sensing covered include hydrology, land use, mapping, vegetation classification, environmental monitoring, coastal features and processes, geology, and ice and snow. Approved and proposed microwave sensors are described and the use of space shuttle as a development platform is evaluated.

Source record↗
NASA NTRS1986

Use of remotely sensed data for assessing forest stand conditions in the Eastern United States

Techniques for the detection, classification, and measurement of forest disturbances, using digital Landsat data for three study areas (Pennsynvania, North Carolina, and Maine) are reported. Results with respect to (1) the delineation and assessment of forest damage due to the use of two forest insect defoliators, and (2) qualitative assessment of the Multispectral Scanner (MSS) and the Thematic Mapper data for delineating forest stand characteristics are presented. Key results include a development of a statewide MSS digital data base and associated image-processing techniques for accurately delineating insect-damaged and healthy forest areas. For classification of broad land-cover classes which are spectrally homogeneous, the accuracy yielded by the use of either MSS data or TM Simulator data is similar. However, the TMS data provided 20 percent accuracy improvement over the MSS results when detailed (Level III) forest classes were mapped.

Williams, D. L.↗
NASA NTRS2007

Potential of VIIRS Time Series Data for Aiding the USDA Forest Service Early Warning System for Forest Health Threats: A Gypsy Moth Defoliation Case Study

This report details one of three experiments performed during FY 2007 for the NASA RPC (Rapid Prototyping Capability) at Stennis Space Center. This RPC experiment assesses the potential of VIIRS (Visible/Infrared Imager/Radiometer Suite) and MODIS (Moderate Resolution Imaging Spectroradiometer) data for detecting and monitoring forest defoliation from the non-native Eurasian gypsy moth (Lymantria dispar). The intent of the RPC experiment was to assess the degree to which VIIRS data can provide forest disturbance monitoring information as an input to a forest threat EWS (Early Warning System) as compared to the level of information that can be obtained from MODIS data. The USDA Forest Service (USFS) plans to use MODIS products for generating broad-scaled, regional monitoring products as input to an EWS for forest health threat assessment. NASA SSC is helping the USFS to evaluate and integrate currently available satellite remote sensing technologies and data products for the EWS, including the use of MODIS products for regional monitoring of forest disturbance. Gypsy moth defoliation of the mid-Appalachian highland region was selected as a case study. Gypsy moth is one of eight major forest insect threats listed in the Healthy Forest Restoration Act (HFRA) of 2003; the gypsy moth threatens eastern U.S. hardwood forests, which are also a concern highlighted in the HFRA of 2003. This region was selected for the project because extensive gypsy moth defoliation occurred there over multiple years during the MODIS operational period. This RPC experiment is relevant to several nationally important mapping applications, including agricultural efficiency, coastal management, ecological forecasting, disaster management, and carbon management. In this experiment, MODIS data and VIIRS data simulated from MODIS were assessed for their ability to contribute broad, regional geospatial information on gypsy moth defoliation. Landsat and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data were used to assess the quality of gypsy moth defoliation mapping products derived from MODIS data and from simulated VIIRS data. The project focused on use of data from MODIS Terra as opposed to MODIS Aqua mainly because only MODIS Terra data was collected during 2000 and 2001-years with comparatively high amounts of gypsy moth defoliation within the study area. The project assessed the quality of VIIRS data simulation products. Hyperion data was employed to assess the quality of MODIS-based VIIRS simulation datasets using image correlation analysis techniques. The ART (Application Research Toolbox) software was used for data simulation. Correlation analysis between MODIS-simulated VIIRS data and Hyperion-simulated VIIRS data for red, NIR (near-infrared), and NDVI (Normalized Difference Vegetation Index) image data products collectively indicate that useful, effective VIIRS simulations can be produced using Hyperion and MODIS data sources. The r(exp 2) for red, NIR, and NDVI products were 0.56, 0.63, and 0.62, respectively, indicating a moderately high correlation between the 2 data sources. Temporal decorrelation from different data acquisition times and image misregistration may have lowered correlation results. The RPC experiment also generated MODIS-based time series data products using the TSPT (Time Series Product Tool) software. Time series of simulated VIIRS NDVI products were produced at approximately 400-meter resolution GSD (Ground Sampling Distance) at nadir for comparison to MODIS NDVI products at either 250- or 500-meter GSD. The project also computed MODIS (MOD02) NDMI (Normalized Difference Moisture Index) products at 500-meter GSD for comparison to NDVI-based products. For each year during 2000-2006, MODIS and VIIRS (simulated from MOD02) time series were computed during the peak gypsy moth defoliation time frame in the study area (approximately June 10 through July 27). Gypsy moth defoliation mapping products from simated VIIRS and MOD02 time series were produced using multiple methods, including image classification and change detection via image differencing. The latter enabled an automated defoliation detection product computed using percent change in maximum NDVI for a peak defoliation period during 2001 compared to maximum NDVI across the entire 2000-2006 time frame. Final gypsy moth defoliation mapping products were assessed for accuracy using randomly sampled locations found on available geospatial reference data (Landsat and ASTER data in conjunction with defoliation map data from the USFS). Extensive gypsy moth defoliation patches were evident on screen displays of multitemporal color composites derived from MODIS data and from simulated VIIRS vegetation index data. Such defoliation was particularly evident for 2001, although widespread denuded forests were also seen for 2000 and 2003. These visualizations were validated using aforementioned reference data. Defoliation patches were visible on displays of MODIS-based NDVI and NDMI data. The viewing of apparent defoliation patches on all of these products necessitated adoption of a specialized temporal data processing method (e.g., maximum NDVI during the peak defoliation time frame). The frequency of cloud cover necessitated this approach. Multitemporal simulated VIIRS and MODIS Terra data both produced effective general classifications of defoliated forest versus other land cover. For 2001, the MOD02-simulated VIIRS 400-meter NDVI classification produced a similar yet slightly lower overall accuracy (87.28 percent with 0.72 Kappa) than the MOD02 250-meter NDVI classification (88.44 percent with 0.75 Kappa). The MOD13 250-meter NDVI classification had a lower overall accuracy (79.13 percent) and a much lower Kappa (0.46). The report discusses accuracy assessment results in much more detail, comparing overall classification and individual class accuracy statistics for simulated VIIRS 400-meter NDVI, MOD02 250-meter NDVI, MOD02-500 meter NDVI, MOD13 250-meter NDVI, and MOD02 500-meter NDMI classifications. Automated defoliation detection products from simulated VIIRS and MOD02 data for 2001 also yielded similar, relatively high overall classification accuracy (85.55 percent for the VIIRS 400-meter NDVI versus 87.28 percent for the MOD02 250-meter NDVI). In contrast, the USFS aerial sketch map of gypsy moth defoliation showed a lower overall classification accuracy at 73.64 percent. The overall classification Kappa values were also similar for the VIIRS (approximately 0.67 Kappa) versus the MOD02 (approximately 0.72 Kappa) automated defoliation detection product, which were much higher than the values exhibited by the USFS sketch map product (overall Kappa of approximately 0.47). The report provides additional details on the accuracy of automated gypsy moth defoliation detection products compared with USFS sketch maps. The results suggest that VIIRS data can be effectively simulated from MODIS data and that VIIRS data will produce gypsy moth defoliation mapping products that are similar to MODIS-based products. The results of the RPC experiment indicate that VIIRS and MODIS data products have good potential for integration into the forest threat EWS. The accuracy assessment was performed only for 2001 because of time constraints and a relative scarcity of cloud-free Landsat and ASTER data for the peak defoliation period of the other years in the 2000-2006 time series. Additional work should be performed to assess the accuracy of gypsy moth defoliation detection products for additional years.The study area (mid-Appalachian highlands) and application (gypsy moth forest defoliation) are not necessarily representative of all forested regions and of all forest threat disturbance agents. Additional work should be performed on other inland and coastal regions as well as for other major forest threats.

Spruce, Joseph P.↗
NASA NTRS2010

Satellite Sampling and Retrieval Errors in Regional Monthly Rain Estimates from TMI AMSR-E, SSM/I, AMSU-B and the TRMM PR

Passive and active microwave rain sensors onboard earth-orbiting satellites estimate monthly rainfall from the instantaneous rain statistics collected during satellite overpasses. It is well known that climate-scale rain estimates from meteorological satellites incur sampling errors resulting from the process of discrete temporal sampling and statistical averaging. Sampling and retrieval errors ultimately become entangled in the estimation of the mean monthly rain rate. The sampling component of the error budget effectively introduces statistical noise into climate-scale rain estimates that obscure the error component associated with the instantaneous rain retrieval. Estimating the accuracy of the retrievals on monthly scales therefore necessitates a decomposition of the total error budget into sampling and retrieval error quantities. This paper presents results from a statistical evaluation of the sampling and retrieval errors for five different space-borne rain sensors on board nine orbiting satellites. Using an error decomposition methodology developed by one of the authors, sampling and retrieval errors were estimated at 0.25 resolution within 150 km of ground-based weather radars located at Kwajalein, Marshall Islands and Melbourne, Florida. Error and bias statistics were calculated according to the land, ocean and coast classifications of the surface terrain mask developed for the Goddard Profiling (GPROF) rain algorithm. Variations in the comparative error statistics are attributed to various factors related to differences in the swath geometry of each rain sensor, the orbital and instrument characteristics of the satellite and the regional climatology. The most significant result from this study found that each of the satellites incurred negative longterm oceanic retrieval biases of 10 to 30%.

Fisher, Brad↗
NASA NTRS2015

Final NASA Panel Recommendations for Definition of Acceptable Risk of Injury Due to Spaceflight Dynamic Events

A panel of experts was convened in 2010 to help define acceptable injury risk levels for space vehicle launches, landings, and abort scenarios. Classifications of spaceflight-relevant injuries were defined using four categories ranging from minor to severe injury. Limits for each injury category were agreed to, dependent on the expected number of crew exposures in a given vehicle and on whether the flight was considered nominal or off-nominal. Somers et al. captured the findings of this summit in a NASA technical memorandum. This panel was recently re-convened (December 1, 2014) to determine whether the previous recommended injury limits were applicable to newly-designed commercial space flight vehicles. In particular, previous limits were based in part on the number of crew exposures per vehicle and also were sensitive to a definition of nominal and off-nominal vehicle performance. Reconsideration of these aspects led to a new consensus on a definition of injury risk.

Somers, Jeffrey T.↗
NASA NTRS2022

Mesoamerica Ecological Forecasting: Assessing Land Cover Change to Inform Management Planning for the Mesoamerican Biological Corridor

In 1992, Central America and Mexico drew up an agreement to establish the Mesoamerican Biological Corridor (MBC) which defines natural corridors to connect nearly 600 protected areas. The MBC is home to 9% of the world's terrestrial species on 0.7% of the world's landmass, yet this biodiverse area has been impacted by great levels of deforestation. The MBC supports protected areas and the important conservation efforts that are tied into the area’s economic and sustainable development. The NASA DEVELOP team partnered with NASA SERVIR, Sistema de la Integración Centroamericana (SICA), Tropical Agriculture Research and High Education Center (CATIE), and Ministries of the Environment for Costa Rica, El Salvador, and Guatemala to assess forest cover change in the MBC. While the southern states of Mexico are included in the MBC, the team excluded Mexico in this study. The team acquired data from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), and Landsat 9 OLI-2 to develop a forest versus non-forest classification. This classification was used to create a Land Use Land Cover Change (LULC) trend map and Deforestation Detection Time Series analysis between 1992 and 2022. The team found that minimum distance classification was the most effective classifier for the project scope. Analysis showed that 6.18% of the study area experienced forest loss and 10.99% experienced forest growth. These observations will help partners visualize the evolution and severity of deforestation and allow decision making for future land management and transboundary conservation efforts.

Hanna Jung↗
NASA NTRS1980

Taxonomic classification of world map units in crop producing areas of Argentina and Brazil with representative US soil series and major land resource areas in which they occur

The most probable current U.S. taxonomic classification of the soils estimated to dominate world soil map units (WSM)) in selected crop producing states of Argentina and Brazil are presented. Representative U.S. soil series the units are given. The map units occurring in each state are listed with areal extent and major U.S. land resource areas in which similar soils most probably occur. Soil series sampled in LARS Technical Report 111579 and major land resource areas in which they occur with corresponding similar WSM units at the taxonomic subgroup levels are given.

Huckle, H. F.↗
NASA NTRS1982

Land use/land cover

The use of remote sensing multispectral systems in urban/suburban land use, and environmental impact from surface mines is discussed. The classification system used in conjunction with the land use/land cover data is included.

Source record↗