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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

The use of multidate multichannel radiance data in urban feature analysis

Two images were obtained from thematic mappers on Landsats 4 and 5 over the Washington, DC area during November 1982 and March 1984. Selected training areas containing different types of urban land use were examined,one area consisting entirely of forest. Mean digital radiance values for each bandpass in each image were examined, and variances, standard deviations, and covariances between bandpasses were calculated. It has been found that two bandpasses caused forested areas to stand out from other land use types, especially for the November 1982 image. In order to evaluate quantitatively the possible utility of the principal components analysis in selected feature extraction, the eigenvectors were evaluated for principal axes rotations which rendered each selected land use type most separable from all other land use types. The evaluated eigenvectors were plotted as a function of land use type, whose order was decided by considering anticipated shadow component and by examining the relative loadings indicative of vegetation for each of the principal components for the different features considered. The analysis was performed for each seven-band image separately and for the two combined images. It was found that by combining the two images, more dramatic land use type separation could be obtained.

Duggin, M. J.↗
NASA NTRS1982

An initial analysis of LANDSAT 4 Thematic Mapper data for the classification of agricultural, forested wetland, and urban land covers

An initial analysis of LANDSAT 4 thematic mapper (TM) data for the delineation and classification of agricultural, forested wetland, and urban land covers was conducted. A study area in Poinsett County, Arkansas was used to evaluate a classification of agricultural lands derived from multitemporal LANDSAT multispectral scanner (MSS) data in comparison with a classification of TM data for the same area. Data over Reelfoot Lake in northwestern Tennessee were utilized to evaluate the TM for delineating forested wetland species. A classification of the study area was assessed for accuracy in discriminating five forested wetland categories. Finally, the TM data were used to identify urban features within a small city. A computer generated classification of Union City, Tennessee was analyzed for accuracy in delineating urban land covers. An evaluation of digitally enhanced TM data using principal components analysis to facilitate photointerpretation of urban features was also performed.

Quattrochi, D. A.↗
NASA NTRS2001

Association of American Geographers, Remote Sensing Specialty Group Special Issue of Geocarto International

This special issue continues the precedence of the Association of American Geographers (AAG), Remote Sensing Specialty Group (RSSG) for publishing selected articles in Geocarto International as a by-product from the AAG annual meeting. As editors, we issued earlier this year, a solicitation for papers to be published in a special issue of Geocarto International that were presented in RSSG-sponsored sessions at the 2001 AAG annual meeting held in New York City on February 27-March 3. Although not an absolute requisite for publication, the vast majority of the papers in this special issue were presented at this year's AAG meeting in New York. Other articles in this issue that were not part of a paper or poster session at the 2001 AAG meeting are authored by RSSG members. Under the auspices of the RSSG, this special Geocarto International issue provides even more compelling evidence of the inextricable linkage between remote sensing and geography. The papers in this special issue fall into four general themes: 1) Urban Analysis and Techniques for Urban Analysis; 2) Land Use/Land Cover Analysis; 3) Fire Modeling Assessment; and 4) Techniques. The first four papers herein are concerned with the use of remote sensing for analysis of urban areas, and with use or development of techniques to better characterize urban areas using remote sensing data. As the lead paper in this grouping, Rashed et al., examine the usage of spectral mixture analysis (SMA) for analyzing satellite imagery of urban areas as opposed to more 'standard' methods of classification. Here SMA has been applied to IRS-1C satellite multispectral imagery to extract measures that better describe the 'anatomy' of the greater Cairo, Egypt region. Following this paper, Weng and Lo describe how Landsat TM data have been used to monitor land cover types and to estimate biomass parameters within an urban environment. The research reported in this paper applies an integrated GIS (Geographic Information System) approach for detecting urban growth and assessing its impact on biomass in the Zhujiang Delta, China. The remaining two papers in this first grouping deal with improved techniques for characterizing and analyzing urban areas using remote sensing data. Myint examines the use of texture analysis to better classify urban features. Here wavelet analysis has been employed to assist in deriving a more robust classification of the urban environment from high spatial resolution, multispectral aircraft data. Mesev provides insight on how through the modification of the standard maximum likelihood image analysis technique, population census data can be used enhance the overall robustness of urban image classification through the modification of the standard maximum likelihood image analysis technique.

Allen, Thomas R.↗
NASA NTRS2001

Remote Sensing, GIS, and Vector-Borne Disease

The concept of global climate change encompasses more than merely an alteration in temperature; it also includes spatial and temporal covariations in precipitation and humidity, and more frequent occurrence of extreme weather events. The impact of these variations, which can occur at a variety of temporal and spatial scales, could have a direct impact on disease transmission through their environmental consequences for pathogen, vector, and host survival, as well as indirectly through human demographic and behavioral responses. New and future sensor systems will allow scientists to investigate the relationships between climate change and environmental risk factors at multiple spatial, temporal and spectral scales. Higher spatial resolution will provide better opportunities for mapping urban features previously only possible with high resolution aerial photography. These opportunities include housing quality (e.g., Chagas'disease, leishmaniasis) and urban mosquito habitats (e.g., dengue fever, filariasis, LaCrosse encephalitis). There are or will be many new sensors that have higher spectral resolution, enabling scientists to acquire more information about parameters such as soil moisture, soil type, better vegetation discrimination, and ocean color, to name a few. Although soil moisture content is now detectable using Landsat, the new thermal, shortwave infrared, and radar sensors will be able to provide this information at a variety of scales not achievable using Landsat. Soil moisture could become a key component in transmission risk models for Lyme disease (tick survival), helminthiases (worm habitat), malaria (vector-breeding habitat), and schistosomiasis (snail habitat).

Beck, Louisa R.↗
NASA NTRS1973

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

The author has identified the following significant results. In soil association mapping, computerized analysis of ERTS-1 MSS data has yielded images which will prove useful in the ongoing Cooperative Soil Survey program, involving the Soil Conservation Service of USDA and other state and local agencies. In the present mode of operation, a soil survey for a county may take up to 5 years to be completed. Results indicate that a great deal of soils information can be extracted from ERTS-1 data by computer analysis. This information is expected to be very valuable in the premapping conference phase of a soil survey, resulting in more efficient field operations during the actual mapping. In the earth surface features mapping effort it was found that temporal data improved the classification accuracy of forest classification in Tippecanoe County, Indiana. In water resources study a severe scanner look angle effect was observed in the aircraft scanner data of a test lake which was not present in ERTS-1 data of the same site. This effect was greatly accentuated by surface roughness caused by strong winds. Quantitative evaluation of urban features classification in ERTS-1 data was obtained. An 87.1% test accuracy was obtained for eight categories in Marion County, Indiana.

Landgrebe, D. A.↗
NASA NTRS1973

A comparison of land-use determinations using data from ERTS-1 and high altitude aircraft

A manual interpretation of ERTS-1 MSS system corrected imagery has been performed on a study area within the Houston Area Test Site to classify land use using the Level 1 categories proposed by the Department of the Interior. The two types of imagery used included: (1) black and white transparencies of each band enlarged to a scale of approximately 1:250,000 and (2) color transparencies composited from the computer compatible tapes using the film recorder on a multispectral data analysis station. The results of this interpretation have been compared with the 1970 land use inventory of HATS which was compiled using color ektachrome imagery from high altitude aircraft (scale 1:120,000). Urban data from the same scene was also analyzed using a computer-aided (clustering) technique. The resulting clusters, representing areas of similar content, were compared with existing land use patterns in Houston. A technique was developed to correlate the spectral clusters to specific urban features on aircraft imagery by the location of specific, high contrast objects in particular resolution elements. It was concluded that ERTS-1 data could be used to develop Level 1 and many Level 2 land use categories for regional inventories and perhaps to some degree on a local level.

Lundelius, M. A.↗
NASA NTRS1975

Satellite information on Orlando, Florida

A land use map of Orange County, Florida was prepared from EREP photography while LANDSAT and EREP multispectral scanner data were used to provide more detailed information on Orlando and its suburbs. The generalized maps were prepared by tracing the patterns on an overlay, using an enlarging viewer. Digital analysis of the multispectral scanner data was basically the maximum likelihood classification method with training sample input and computer printer mapping of the results. Urban features delineated by the maps are discussed. It is concluded that computer classification, accompanied by human interpretation and manual simplification can produce land use maps which are useful on a regional, county, and city basis.

Hannah, J. W.↗
NASA NTRS1976

An analysis of metropolitan land-use by machine processing of earth resources technology satellite data

A successful application of state-of-the-art remote sensing technology in classifying an urban area into its broad land use classes is reported. This research proves that numerous urban features are amenable to classification using ERTS multispectral data automatically processed by computer. Furthermore, such automatic data processing (ADP) techniques permit areal analysis on an unprecedented scale with a minimum expenditure of time. Also, classification results obtained using ADP procedures are consistent, comparable, and replicable. The results of classification are compared with the proposed U. S. G. S. land use classification system in order to determine the level of classification that is feasible to obtain through ERTS analysis of metropolitan areas.

Mausel, P. W.↗
NASA NTRS1993

SAR terrain classifier and mapper of biophysical attributes

In preparation for the launch of SIR-C/X-SAR and design studies for future orbital SAR, a program has made considerable progress in the development of an SAR terrain classifier and algorithms for quantification of biophysical attributes. The goal of this program is to produce a generalized software package for terrain classification and estimation of biophysical attributes and to make this package available to the larger scientific community. The basic elements of the SAR (Synthetic Aperture Radar) terrain classifier are outlined. An SAR image is calibrated with respect to known system and processor gains and external targets (if available). A Level 1 classifier operates on the data to differentiate: urban features, surfaces and tall and short vegetation. Level 2 classifiers further subdivide these classes on the basis of structure. Finally, biophysical and geophysical inversions are applied to each class to estimate attributes of interest. The process used to develop the classifiers and inversions is shown. Radar scattering models developed from theory and from empirical data obtained by truck-mounted polarimeters and the JPL AirSAR are validated. The validated models are used in sensitivity studies to understand the roles of various scattering sources (i.e., surface trunk, branches, etc.) in determining net backscatter. Model simulations of sigma (sup o) as functions of the wave parameters (lambda, polarization and angle of incidence) and the geophysical and biophysical attributes are used to develop robust classifiers. The classifiers are validated using available AirSAR data sets. Specific estimators are developed for each class on the basis of the scattering models and empirical data sets. The candidate algorithms are tested with the AirSAR data sets. The attributes of interest include: total above ground biomass, woody biomass, soil moisture and soil roughness.

Ulaby, Fawwaz T.↗
NASA NTRS2001

Spectral Dimensionality and Scale of Urban Radiance

Characterization of urban radiance and reflectance is important for understanding the effects of solar energy flux on the urban environment as well as for satellite mapping of urban settlement patterns. Spectral mixture analyses of Landsat and Ikonos imagery suggest that the urban radiance field can very often be described with combinations of three or four spectral endmembers. Dimensionality estimates of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) radiance measurements of urban areas reveal the existence of 30 to 60 spectral dimensions. The extent to which broadband imagery collected by operational satellites can represent the higher dimensional mixing space is a function of both the spatial and spectral resolution of the sensor. AVIRIS imagery offers the spatial and spectral resolution necessary to investigate the scale dependence of the spectral dimensionality. Dimensionality estimates derived from Minimum Noise Fraction (MNF) eigenvalue distributions show a distinct scale dependence for AVIRIS radiance measurements of Milpitas, California. Apparent dimensionality diminishes from almost 40 to less than 10 spectral dimensions between scales of 8000 m and 300 m. The 10 to 30 m scale of most features in urban mosaics results in substantial spectral mixing at the 20 m scale of high altitude AVIRIS pixels. Much of the variance at pixel scales is therefore likely to result from actual differences in surface reflectance at pixel scales. Spatial smoothing and spectral subsampling of AVIRIS spectra both result in substantial loss of information and reduction of apparent dimensionality, but the primary spectral endmembers in all cases are analogous to those found in global analyses of Landsat and Ikonos imagery of other urban areas.

Small, Christopher↗
NASA NTRS1973

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

The author has identified the following significant results. Nine projects are defined, five ERTS data applications experiments and four supporting technology tasks. The most significant applications results were achieved in the soil association mapping, earth surface feature identification, and urban land use mapping efforts. Four soil association boundaries were accurately delineated from ERTS-1 imagery. A data bank has been developed to test surface feature classifications obtained from ERTS-1 data. Preliminary forest cover classifications indicated that the number of acres estimated tended to be greater than actually existed by 25%. Urban land use analysis of ERTS-1 data indicated highly accurate classification could be obtained for many urban catagories. The wooded residential category tended to be misclassified as woods or agricultural land. Further statistical analysis revealed that these classes could be separated using sample variance.

Landgrebe, D. A.↗
NASA NTRS2015

Continental Spatio-Temporal Data Analysis with Linear Spectral Mixture Model Using FOSS

This work demonstrates the development and implementation of a Fully Constrained Least Squares (FCLS) unmixing model developed in C++ programming language with OpenCV package and boost C++ libraries in the NASA Earth Exchange (NEX). Visualization of the results is supported by GRASS GIS and statistical analysis is carried in R in a Linux system environment. FCLS was first tested on computer simulated data with Gaussian noise of various signal-to-noise ratio, and Landsat data of an agricultural scenario and an urban environment using a set of global end members of substrate (soils, sediments, rocks, and non-photosynthetic vegetation), vegetation that includes green photosynthetic plants and dark objects which encompasses absorptive substrate materials, clear water, deep shadows, etc. For the agricultural scenario, a spectrally diverse collection of 11 scenes of Level 1 terrain corrected, cloud free Landsat-5 TM data of Fresno, California, USA were unmixed and the results were validated with the corresponding ground data. To study an urbanized landscape, a clear sky Landsat-5 TM data were unmixed and validated with coincident World View-2 abundance maps (of 2 m spatial resolution) for an area of San Francisco, California, USA. The results were evaluated using descriptive statistics, correlation coefficient, RMSE, probability of success, boxplot and bivariate distribution function. Finally, FCLS was used for sub-pixel land cover analysis of the monthly WELD (Wen-enabled Landsat data) repository from 2008 to 2011 of North America. The abundance maps in conjunction with DMSP-OLS nighttime lights data were used to extract the urban land cover features and analyze their spatial-temporal growth.

Landsat Satellites↗
NASA NTRS2007

Robotic Vision-Based Localization in an Urban Environment

A system of electronic hardware and software, now undergoing development, automatically estimates the location of a robotic land vehicle in an urban environment using a somewhat imprecise map, which has been generated in advance from aerial imagery. This system does not utilize the Global Positioning System and does not include any odometry, inertial measurement units, or any other sensors except a stereoscopic pair of black-and-white digital video cameras mounted on the vehicle. Of course, the system also includes a computer running software that processes the video image data. The software consists mostly of three components corresponding to the three major image-data-processing functions: Visual Odometry This component automatically tracks point features in the imagery and computes the relative motion of the cameras between sequential image frames. This component incorporates a modified version of a visual-odometry algorithm originally published in 1989. The algorithm selects point features, performs multiresolution area-correlation computations to match the features in stereoscopic images, tracks the features through the sequence of images, and uses the tracking results to estimate the six-degree-of-freedom motion of the camera between consecutive stereoscopic pairs of images (see figure). Urban Feature Detection and Ranging Using the same data as those processed by the visual-odometry component, this component strives to determine the three-dimensional (3D) coordinates of vertical and horizontal lines that are likely to be parts of, or close to, the exterior surfaces of buildings. The basic sequence of processes performed by this component is the following: 1. An edge-detection algorithm is applied, yielding a set of linked lists of edge pixels, a horizontal-gradient image, and a vertical-gradient image. 2. Straight-line segments of edges are extracted from the linked lists generated in step 1. Any straight-line segments longer than an arbitrary threshold (e.g., 30 pixels) are assumed to belong to buildings or other artificial objects. 3. A gradient-filter algorithm is used to test straight-line segments longer than the threshold to determine whether they represent edges of natural or artificial objects. In somewhat oversimplified terms, the test is based on the assumption that the gradient of image intensity varies little along a segment that represents the edge of an artificial object.

Mchenry, Michael↗
NASA NTRS1986

Follow-on proposal identifying environmental features for land management decisions

Urban morphology (an examination of spatial fabric and structure), natural ecosystem (investigations emphasizing biophysical processes and patterns), and human ecosystem (emphasizing socio-economic and engineering parameters) were studied. The most critical variable, transpiration, in the ASPCON model, created by Jaynes (1978), describing the hydrology of aspen to conifer succession was studied to improve the accuracy. Transpiration is determined by a canopy transpiration model which estimates consumptive water use (CWU) for specific species and a plant activity index. Also studied was Pinyon-Juniper woodland erosion.

Wright, P. M.↗
NASA NTRS2020

Cambridge Urban Development - Quantifying Changes in Urban Albedo with NASA Earth Observations to Reduce Urban Heat Island Effect in Cambridge, Massachusetts

The urban heat island (UHI) effect occurs when urban areas have temperatures that are warmer on average than the surrounding suburban and rural regions. Low albedo surfaces traditionally found in urban landscapes, such as dark asphalt and rooftops, absorb solar irradiance and reemit heat, which contributes to the UHI effect. Cambridge,Massachusetts,part of the Boston metropolitan area,expects severe impacts on human health,infrastructure, and local environmental features due to increasing urban heat. The Massachusetts – Boston NASA DEVELOP team partnered with the City of Cambridge Community Development Department and the American Geophysical Union’s Thriving Earth Exchange to inform ongoing planning and heat mitigation efforts. High Resolution Orthoimagery (HRO) and National Agriculture Imagery Program (NAIP) scenes were analyzed in conjunction with ancillary datasets to assess changes in rooftop albedo between 2008 and 2018. Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to determine nighttime land surface temperature (LST) between 2003 and 2019. Lastly, temperature anomalies were calculated using seasonally averaged nighttime LST values obtained from Aqua MODIS to display ‘hot spots’ for summers between 2004 and 2019. On average, albedo increased by 0.12 across the city, with the majority of positive change in commercial areas. Analysis of mean summer nighttime LST between 2003 and 2019 revealed a non-significant increase in temperature, although temperature anomaly maps demonstrated that Cambridge was warmer on average than rural areas. These results were incorporated in the interactive Cambridge Urban Heat Dashboard, which allows users to explore temporal trends in albedo.

Nicole Ramberg-Pihl↗
NASA NTRS2020

Cambridge Urban Development: Quantifying Changes in Urban Albedo with NASA Earth Observations to Reduce Urban Heat Island Effect in Cambridge, Massachusetts

The urban heat island (UHI) effect occurs when urban areas have temperatures that are warmer on average than the surrounding suburban and rural regions. Low albedo surfaces traditionally found in urban landscapes, such as dark asphalt and rooftops, absorb solar irradiance and reemit heat, which contributes to the UHI effect. Cambridge, Massachusetts, part of the Boston metropolitan area, expects severe impacts on human health, infrastructure, and local environmental features due to increasing urban heat. The Massachusetts – Boston NASA DEVELOP team partnered with the City of Cambridge Community Development Department and the American Geophysical Union’s Thriving Earth Exchange to inform ongoing planning and heat mitigation efforts. High Resolution Orthoimagery (HRO) and National Agriculture Imagery Program (NAIP) scenes were analyzed in conjunction with ancillary datasets to assess changes in rooftop albedo between 2008 and 2018. Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to determine nighttime land surface temperature (LST) between 2003 and 2019. Lastly, temperature anomalies were calculated using seasonally averaged nighttime LST values obtained from Aqua MODIS to display ‘hot spots’ for summers between 2004 and 2019. On average, albedo increased by 0.12 across the city, with the majority of positive change in commercial areas. Analysis of mean summer nighttime LST between 2003 and 2019 revealed a non-significant increase in temperature, although temperature anomaly maps demonstrated that Cambridge was warmer on average than rural areas. These results were incorporated in the interactive Cambridge Urban Heat Dashboard, which allows users to explore temporal trends in albedo.

Nicole Ramberg-Pihl↗
NASA NTRS1974

The ERTS-1 investigation (ER-600). Volume 5: ERTS-1 urban land use analysis

The Urban Land Use Team conducted a year's investigation of ERTS-1 MSS data to determine the number of Land Use categories in the Houston, Texas, area. They discovered unusually low classification accuracies occurred when a spectrally complex urban scene was classified with extensive rural areas containing spectrally homogeneous features. Separate computer processing of only data in the urbanized area increased classification accuracies of certain urban land use categories. Even so, accuracies of urban landscape were in the 40-70 percent range compared to 70-90 percent for the land use categories containing more homogeneous features (agriculture, forest, water, etc.) in the nonurban areas.

Erb, R. B.↗
NASA NTRS1983

Spaceborne radar research in the 1980's

The SEASAT SAR and Shuttle Imaging Radar SIR-A experiments demonstrated that spaceborne synthetic aperture radars provide synoptic images of land and ocean features. Radar images clearly show geologic structures, morphologic features, clear cutting, subsurface features (in very arid regions), agricultural and urban land use, ocean surface waves, current boundaries, internal waves, ice floes and numerous other ocean features which affect the surface roughness.

Elachi, C.↗