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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

An improvement in land cover classification achieved by merging microwave data with Landsat multispectral scanner data

The improvement in land cover classification achieved by merging microwave data with Landsat MSS data is examined. To produce a merged data set for analysis and comparison, a registration procedure by which a set of Seasat SAR digital data was merged with the MSS data is described. The Landsat MSS data and the merged Landsat/Seasat data sets were processed using conventional multichannel spectral pattern recognition techniques. An analysis of the classified data sets indicates that while Landsat data delineate different forest types (i.e., deciduous/coniferous) and allow some species separation, SAR data provide additional information related to plant canopy configuration and vegetation density as associated with varying water regimes, and therefore allow for further subdivision in the classification of forested wetlands of the coastal region of the southern United States.

Wu, S. T.↗
NASA NTRS1983

Urban land use classification using synthetic aperture radar

Several approaches to the use of radar imagery for land use classification of urban and near-urban areas are presented. The use of L band, horizontal transmit, and horizontal receive data is emphasized because of their general availability. For urban area studies using imaging radar, the effects of processing in an off-zero Doppler or squint mode, of the presence of large diffuse scatters, and of the possibility of height measurements are discussed. Data from the Los Angeles area are illustratively used.

Bryan, M. L.↗
NASA NTRS1977

Necessity to adapt land use and land cover classification systems to readily accept radar data

A hierarchial, four level, standardized system for classifying land use/land cover primarily from remote-sensor data (USGS system) is described. The USGS system was developed for nonmicrowave imaging sensors such as camera systems and line scanners. The USGS system is not compatible with the land use/land cover classifications at different levels that can be made from radar imagery, and particularly from synthetic-aperture radar (SAR) imagery. The use of radar imagery for classifying land use/land cover at different levels is discussed, and a possible revision of the USGS system to more readily accept land use/land cover classifications from radar imagery is proposed.

Drake, B.↗
NASA NTRS1973

Comparative evaluation of spatial features in automatic land use classification from photographic imagery

Five spatial features have been evaluated for their applicability in automatic land use classification of photographic imagery. Data arrays of approximately 10,000 square meters were classified on the basis of an 8 by 8 point data array. Statistical features, information features, sequency features and texture features were evaluated using a distance to prototype classifier and an adaptive classifier. The results indicate approximately 70% accuracy in the classification.

Herzog, J. H.↗
NASA NTRS1975

Study of USGS/NASA land use classification system

The results of a computer mapping project using LANDSAT data and the USGS/NASA land use classification system are summarized. During the computer mapping portion of the project, accuracies of 67 percent to 79 percent were achieved using Level II of the classification system and a 4,000 acre test site centered on Douglasville, Georgia. Analysis of response to a questionaire circulated to actual and potential LANDSAT data users reveals several important findings: (1) there is a substantial desire for additional information related to LANDSAT capabilities; (2) a majority of the respondents feel computer mapping from LANDSAT data could aid present or future projects; and (3) the costs of computer mapping are substantially less than those of other methods.

Spann, G. W.↗
NASA NTRS1973

Image interpretation for a multilevel land use classification system

The potential use is discussed of three remote sensors for developing a four level land use classification system. Three types of imagery for photointerpretation are presented: ERTS-1 satellite imagery, high altitude photography, and medium altitude photography. Suggestions are given as to which remote sensors and imagery scales may be most effectively employed to provide data on specific types of land use.

Source record↗
NASA NTRS1982

Urban land use classification using synthetic aperture radar

In the cases of such land uses as the commercial and industrial, the incorporation of microwave data to that available from the visible and near visible regions may increase classification accuracy, provided that the limitations inherent in radar data are taken into consideration. Attention is presently given to several approaches to the use of radar data in land use classification of urban and near urban areas, with emphasis on L-band horizontal transmit and horizontal receive data available from Seasat-A. The Los Angeles, CA metropolitan area provides the geographic focus for this study.

Bryan, M. L.↗
NASA NTRS2000

BOREAS AFM-12 1-km AVHRR Seasonal Land Cover Classification

The Boreal Ecosystem-Atmosphere Study (BOREAS) Airborne Fluxes and Meteorology (AFM)-12 team's efforts focused on regional scale Surface Vegetation and Atmosphere (SVAT) modeling to improve parameterization of the heterogeneous BOREAS landscape for use in larger scale Global Circulation Models (GCMs). This regional land cover data set was developed as part of a multitemporal one-kilometer Advanced Very High Resolution Radiometer (AVHRR) land cover analysis approach that was used as the basis for regional land cover mapping, fire disturbance-regeneration, and multiresolution land cover scaling studies in the boreal forest ecosystem of central Canada. This land cover classification was derived by using regional field observations from ground and low-level aircraft transits to analyze spectral-temporal clusters that were derived from an unsupervised cluster analysis of monthly Normalized Difference Vegetation Index (NDVI) image composites (April-September 1992). This regional data set was developed for use by BOREAS investigators, especially those involved in simulation modeling, remote sensing algorithm development, and aircraft flux studies. Based on regional field data verification, this multitemporal one-kilometer AVHRR land cover mapping approach was effective in characterizing the biome-level land cover structure, embedded spatially heterogeneous landscape patterns, and other types of key land cover information of interest to BOREAS modelers.The land cover mosaics in this classification include: (1) wet conifer mosaic (low, medium, and high tree stand density), (2) mixed coniferous-deciduous forest (80% coniferous, codominant, and 80% deciduous), (3) recent visible bum, vegetation regeneration, or rock outcrops-bare ground-sparsely vegetated slow regeneration bum (four classes), (4) open water and grassland marshes, and (5) general agricultural land use/ grasslands (three classes). This land cover mapping approach did not detect small subpixel-scale landscape features such as fens, bogs, and small water bodies. Field observations and comparisons with Landsat Thematic Mapper (TM) suggest a minimum effective resolution of these land cover classes in the range of three to four kilometers, in part, because of the daily to monthly compositing process. In general, potential accuracy limitations are mitigated by the use of conservative parameterization rules such as aggregation of predominant land cover classes within minimum horizontal grid cell sizes of ten kilometers. The AFM-12 one-kilometer AVHRR seasonal land cover classification data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Steyaert, Lou↗
NASA NTRS1975

Land use classification for hydrologic models using interactive machine classification of LANDSAT data

Models designed to simulate the hydrology of urban areas require input parameters describing the land use and degree of imperviousness of the watershed. Unfortunately, the magnitude and spatial distribution of these parameters are rather difficult to estimate when a large watershed is involved. Trade-offs between accuracy of the model parameters and the time or money available for their determination must be made. Because of the necessity of such trade-offs, a study was developed to investigate the use of computer aided analysis of LANDSAT multispectral data in estimating percent of imperviousness and associated land uses needed in urban hydrologic modeling. An interactive computer was used to delineate seven land use classifications in the 342 sq. km. Maryland portion of the Anacostia River Basin from LANDSAT data. These results compared favorably with those of an earlier study which obtained the same information through analysis of aerial photographs having a scale of 1:4800. Approximately 94 man days were required to complete the land use analysis using the aerial photographs while less than three man days were required to accomplish similar tasks using the LANDSAT data.

Thomas J. Jackson↗
NASA NTRS1978

Procedures for gathering ground truth information for a supervised approach to a computer-implemented land cover classification of LANDSAT-acquired multispectral scanner data

Procedures for gathering ground truth information for a supervised approach to a computer-implemented land cover classification of LANDSAT acquired multispectral scanner data are provided in a step by step manner. Criteria for determining size, number, uniformity, and predominant land cover of training sample sites are established. Suggestions are made for the organization and orientation of field team personnel, the procedures used in the field, and the format of the forms to be used. Estimates are made of the probable expenditures in time and costs. Examples of ground truth forms and definitions and criteria of major land cover categories are provided in appendixes.

Joyce, A. T.↗
NASA NTRS1985

Evaluation of space SAR as a land-cover classification

The multidimensional approach to the mapping of land cover, crops, and forests is reported. Dimensionality is achieved by using data from sensors such as LANDSAT to augment Seasat and Shuttle Image Radar (SIR) data, using different image features such as tone and texture, and acquiring multidate data. Seasat, Shuttle Imaging Radar (SIR-A), and LANDSAT data are used both individually and in combination to map land cover in Oklahoma. The results indicates that radar is the best single sensor (72% accuracy) and produces the best sensor combination (97.5% accuracy) for discriminating among five land cover categories. Multidate Seasat data and a single data of LANDSAT coverage are then used in a crop classification study of western Kansas. The highest accuracy for a single channel is achieved using a Seasat scene, which produces a classification accuracy of 67%. Classification accuracy increases to approximately 75% when either a multidate Seasat combination or LANDSAT data in a multisensor combination is used. The tonal and textural elements of SIR-A data are then used both alone and in combination to classify forests into five categories.

Brisco, B.↗
NASA NTRS1977

Application of Landsat data to wetland study and land use classification in West Tennessee

Landsat data were employed in determining land use of a 32,300-hectare watershed area within the Obion-Forked Deer River Basin in northwest Tennessee. Black and white transparency chips for all four wavelength bands were interpreted by use of a video-input analog/digital automatic analysis and classification facility; densitometric methods showed that wetlands, urban areas, agricultural lands and forests could be discriminated by analysis of band 6 or 7 together with band 4 or 5. Comparison with high- and low-altitude photography indicated that the Landsat data could provide sufficiently accurate resource information and determine drainage trends.

Jones, N. L.↗
NASA NTRS1985

African land-cover classification using satellite data

Data from the advanced very high resolution radiometer sensor on the National Oceanic and Atmospheric Administration's operational series of meteorological satellites were used to classify land cover and monitor vegetation dynamics for Africa over a 19-month period. There was a correspondence between seasonal variations in the density and extent of green leaf vegetation and the patterns of rainfall associated with the movement of the Intertropical Convergence Zone. Regional variations, such as the 1983 drought in the Sahel of western Africa, were observed. Integration of the weekly satellite data with respect to time for a 12-month period produced a remotely sensed estimate of primary production based upon the density and duration of green leaf biomass. Eight of the 21-day composited data sets covering an 11-month period were used to produce a general land-cover classification that corresponded well with those of existing maps.

Tucker, C. J.↗
NASA NTRS2003

Understanding the Effect of Land Cover Classification on Model Estimates of Regional Carbon Cycling in the Boreal Forest Biome

The original objectives of this proposed 3-year project were to: 1) quantify the respective contributions of land cover and disturbance (i.e., wild fire) to uncertainty associated with regional carbon source/sink estimates produced by a variety of boreal ecosystem models; 2) identify the model processes responsible for differences in simulated carbon source/sink patterns for the boreal forest; 3) validate model outputs using tower and field- based estimates of NEP and NPP; and 4) recommend/prioritize improvements to boreal ecosystem carbon models, which will better constrain regional source/sink estimates for atmospheric C02. These original objectives were subsequently distilled to fit within the constraints of a 1 -year study. This revised study involved a regional model intercomparison over the BOREAS study region involving Biome-BGC, and TEM (A.D. McGuire, UAF) ecosystem models. The major focus of these revised activities involved quantifying the sensitivity of regional model predictions associated with land cover classification uncertainties. We also evaluated the individual and combined effects of historical fire activity, historical atmospheric CO2 concentrations, and climate change on carbon and water flux simulations within the BOREAS study region.

Kimball, John↗
NASA NTRS1997

On the Implementation of a Land Cover Classification System for SAR Images Using Khoros

The Synthetic Aperture Radar (SAR) sensor is widely used to record data about the ground under all atmospheric conditions. The SAR acquired images have very good resolution which necessitates the development of a classification system that process the SAR images to extract useful information for different applications. In this work, a complete system for the land cover classification was designed and programmed using the Khoros, a data flow visual language environment, taking full advantages of the polymorphic data services that it provides. Image analysis was applied to SAR images to improve and automate the processes of recognition and classification of the different regions like mountains and lakes. Both unsupervised and supervised classification utilities were used. The unsupervised classification routines included the use of several Classification/Clustering algorithms like the K-means, ISO2, Weighted Minimum Distance, and the Localized Receptive Field (LRF) training/classifier. Different texture analysis approaches such as Invariant Moments, Fractal Dimension and Second Order statistics were implemented for supervised classification of the images. The results and conclusions for SAR image classification using the various unsupervised and supervised procedures are presented based on their accuracy and performance.

Medina Revera, Edwin J.↗
NASA NTRS1987

Land cover/use classification of Cairns, Queensland, Australia: A remote sensing study involving the conjunctive use of the airborne imaging spectrometer, the large format camera and the thematic mapper simulator

In an attempt to improve the land cover/use classification accuracy obtainable from remotely sensed multispectral imagery, Airborne Imaging Spectrometer-1 (AIS-1) images were analyzed in conjunction with Thematic Mapper Simulator (NS001) Large Format Camera color infrared photography and black and white aerial photography. Specific portions of the combined data set were registered and used for classification. Following this procedure, the resulting derived data was tested using an overall accuracy assessment method. Precise photogrammetric 2D-3D-2D geometric modeling techniques is not the basis for this study. Instead, the discussion exposes resultant spectral findings from the image-to-image registrations. Problems associated with the AIS-1 TMS integration are considered, and useful applications of the imagery combination are presented. More advanced methodologies for imagery integration are needed if multisystem data sets are to be utilized fully. Nevertheless, research, described herein, provides a formulation for future Earth Observation Station related multisensor studies.

Heric, Matthew↗
NASA NTRS1973

Land use classification and change analysis using ERTS-1 imagery in CARETS

Land use detail in the CARETS area obtainable from ERTS exceeds the expectations of the Interagency Steering Committee and the USGS proposed standardized classification, which presents Level 1 categories for ERTS and Level 2 for high altitude aircraft data. Some Levels 2 and 3, in addition to Level 1, categories were identified on ERTS data. Significant land use changes totaling 39.2 sq km in the Norfolk-Portsmouth SMSA were identified and mapped at Level 2 detail using a combination of procedures employing ERTS and high altitude aircraft data.

Alexander, R. H.↗