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At least 1,189 records · Page 66

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NASA NTRS1981

The evaluation of alternate methodologies for land cover classification in an urbanizing area

The usefulness of LANDSAT in classifying land cover and in identifying and classifying land use change was investigated using an urbanizing area as the study area. The question of what was the best technique for classification was the primary focus of the study. The many computer-assisted techniques available to analyze LANDSAT data were evaluated. Techniques of statistical training (polygons from CRT, unsupervised clustering, polygons from digitizer and binary masks) were tested with minimum distance to the mean, maximum likelihood and canonical analysis with minimum distance to the mean classifiers. The twelve output images were compared to photointerpreted samples, ground verified samples and a current land use data base. Results indicate that for a reconnaissance inventory, the unsupervised training with canonical analysis-minimum distance classifier is the most efficient. If more detailed ground truth and ground verification is available, the polygons from the digitizer training with the canonical analysis minimum distance is more accurate.

Smekofski, R. M.↗

NASA NTRS1981

A land cover classification for Vermont

A university project to develop land cover classification capabilities is discussed. Specific objectives were to: (1) demonstrate the feasibility of using LANDSAT data for resource management; (2) establish projects and meet state needs for specific resource information; and (3) assist state personnel in obtaining technical expertise in the processing and analysis of LANDSAT data.

Whitmore, R. A., Jr.↗

NASA NTRS1983

Effects of autocorrelation upon LANDSAT classification accuracy

Richmond, Virginia and Denver, Colorado were study sites in an effort to determine the effect of autocorrelation on the accuracy of a parallelopiped classifier of LANDSAT digital data. The autocorrelation was assumed to decay to insignificant levels when sampled at distances of at least ten pixels. Spectral themes developed using blocks of adjacent pixels, and using groups of pixels spaced at least 10 pixels apart were used. Effects of geometric distortions were minimized by using only pixels from the interiors of land cover sections. Accuracy was evaluated for three classes; agriculture, residential and "all other"; both type 1 and type 2 errors were evaluated by means of overall classification accuracy. All classes give comparable results. Accuracy is approximately the same in both techniques; however, the variance in accuracy is significantly higher using the themes developed from autocorrelated data. The vectors of mean spectral response were nearly identical regardless of sampling method used. The estimated variances were much larger when using autocorrelated pixels.

Craig, R. G.↗

NASA NTRS1983

Continental land cover classification using meteorological satellite data

The use of the National Oceanic and Atmospheric Administration's advanced very high resolution radiometer satellite data for classifying land cover and monitoring of vegetation dynamics over an extremely large area is demonstrated for the continent of Africa. Data from 17 imaging periods of 21 consecutive days each were composited by a technique sensitive to the in situ green-leaf biomass to provide cloud-free imagery for the whole continent. Virtually cloud-free images were obtainable even for equatorial areas. Seasonal variation in the density and extent of green leaf vegetation corresponded to the patterns of rainfall associated with the inter-tropical convergence zone. Regional variations, such as the 1982 drought in east Africa, were also observed. Integration of the weekly satellite data with respect to time produced a remotely sensed assessment of biological activity based upon density and duration of green-leaf biomass. Two of the 21-day composited data sets were used to produce a general land cover classification. The resultant land cover distributions correspond well to those of existing maps.

Tucker, C. J.↗

NASA NTRS1981

Contextual classification of multispectral image data

A general method is presented for exploiting both spatial and spectral information when classifying multispectral image data. This statistical classification algorithm utilizes the tendency of certain ground cover classes to be more likely to occur in some contexts than others. The theoretical model assumes the two-dimensional array of random observations and a 0-1 loss function, a distribution of the p-context array that is spatially invariant, and class-conditional independence for the observations. The problems that prevent the immediate use of this context classifier are the need for a generally applicable method for making adequate estimates of the context distribution and a reduction in the computational intensivity of the classifier. The former problem is being approached by a method that raises the relative frequency value for each class configuration to a power and uses the result as the context distribution estimate. The second is being approached by searching for a less computationally intensive algorithm.

Tilton, J. C.↗

NASA NTRS1983

Classification by thresholding

A procedure is given which substantially reduces the processing time needed to perform maximum likelihood classification on large data sets. The given method uses a set of fixed thresholds which, if exceeded by one probability density function, makes it unnecessary to evaluate a competing density function. Proofs are given of the existence and optimality of these thresholds for the class of continuous, unimodal, and quasi-concave density functions (which includes the multivariable normal), and a method for computing the thresholds is provided for the specific case of multivariate normal densities. An example with remote sensing data consisting of some 20,000 observations of four-dimensional data from nine ground-cover classes shows that by using thresholds, one could cut the processing time almost in half.

Feiveson, A. H.↗

NASA NTRS1983

Vegetation classification based on Advanced Very High Resolution Radiometer /AVHRR/ satellite imagery

Data from the NOAA-6 spacecraft Advanced Very High Resolution Radiometer (AVHRR) were tested for effectiveness for vegetation classification. Vegetation, climatological, and meteorological data were gathered for three days over 12 locations, and the normalized differences between the AVHRR bands 1 and 2 were determined. A vegetative greenness index was compared with a hydrologic factor and vegetation characteristics as measured by ground truth. A multivariate vegetation gradient model was formulated, incorporating AVHRR and climatological data. The hydrologic factor was calculated in terms of the precipitation, evaporation, maximum and minimum temperatures, and the hydrologic capacity. The observations were taken over Texas, which has a wide range of climates. A high correlation was found in the vegetation-HF index. The AVHRR data are concluded to be an effective tool for analysis of vegetation/climate relationships.

Norwine, J.↗

NASA NTRS1983

Classification of the Allan Hills A77307 meteorite

Thermoluminescence (TL) measurements on Allan Hills A77307 (AH), a carbonaceous chondrite found in Antarctica, are compared with those on other chondrite and applied to its classification. Two lithologically different 250-mg samples were ground, freed of magnetic material, and ground again to pass a 100-micron sieve. Aliquots of 4 mg were heated to 500 C, exposed to beta radiation from a Sr-90 source, and heated at a rate of 7.3 C/sec in N2. TL was measured with a photomultiplier tube fitted with thermal and blue filters. Glow curves for AH and for seven other, established CO-type chondrites area presented, all exhibiting two major peaks of TL sensitivity. The peaks for the seven CO-type chondrites are found at 91 + or - 7 C and at 203 + or - 11 C; those for AH at 170 + or - 17 C and at approximately 250 C. This difference is considered significant and not due to random fluctuation or a typical sampling. From this comparison and from consideration of the weathering, preterrestrial-alteration, petrological and compositional evidence on AH, it is concluded that AH is a unique chondrite, possessing both similarities to and differences from the CO class.

Sears, D. W. G.↗

NASA NTRS1982

The effects of seasonal differences in climatic conditions on Landsat spectral signatures and associated land cover classification

Unsupervised classification algorithms are used to analyze Landsat computer-compatible tape data for an area of approximately 840 sq km in central Oklahoma, over the period from June 12 to August 4, 1979. The results obtained show that changes in remotely sensed spectral signatures and land cover classes are associated with a period of transition from moisture availability in late spring to moisture deficit in midsummer, with the latter being marked by greater visible spectrum reflectance and greater near-IR absorption, although each surface cover type has responded differently to the seasonal change in water availability. Consideration of these results has led to the identification of important factors in the use of multidate satellite data in environmental change monitoring. Naturally induced trends in surface albedo introduce noise into studies aimed at identifying anthropogenic land cover change. Specific problems associated with prairie-forest ecotonal areas in the southern Great Plains involve the seasonally induced differences in separability of forest, bush, and grassland cover types.

Harrington, J. A., Jr.↗

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 NTRS1982

Development of visible/infrared/microwave agriculture classification and biomass estimation algorithms, volume 2

Agricultural crop classification models using two or more spectral regions (visible through microwave) were developed and tested and biomass was estimated by including microwave with visible and infrared data. The study was conducted at Guymon, Oklahoma and Dalhart, Texas utilizing aircraft multispectral data and ground truth soil moisture and biomass information. Results indicate that inclusion of C, L, and P band active microwave data from look angles greater than 35 deg from nadir with visible and infrared data improved crop discrimination and biomass estimates compared to results using only visible and infrared data. The active microwave frequencies were sensitive to different biomass levels. In addition, two indices, one using only active microwave data and the other using data from the middle and near infrared bands, were well correlated to total biomass.

Rosenthal, W. D.↗

NASA NTRS1983

Frequency of Fast, Narrow Gamma-ray Bursts and Burst Classification

Evidence from the Vela satellites that very brief, approximately 0.1 s, gamma-ray bursts constitute a class distinct from the longer, highly structured bursts has been strengthened by the results of the Venera 11 and 12 KKONUS experiments. The Goodard ISEE-3 gamma-ray burst spectrometer, utilizing a trigger criterion which is more likely to be independent of duration than previous experiments, detected a sample of events which enhances this bimodal distribution. The ISEE-3 result is corroborated by an increase in the frequency of detection of short bursts in the KONUS 13/14 database over KONUS 11/12, an effect attributable to the use of a shorter trigger integration time in the later experiments. Considerations such as repeating bursters complicate a simple dichotomous classification of gamma-ray bursts.

Norris, J. P.↗

NASA NTRS1984

A tectonic geomorphological classification of the walls of Valles Marineris

Viking 1 imagery of the Coprates NW quadrangle was used in an attempt to develop a geomorphic classification scheme for the canyon walls of Valles Marineris analogous to that devised to evaluate the relative tectonic activity of terrestrial mountain fronts. The four classes of walls established are described and mapped. Regions where a class cannot be assigned owing to the presence of intra canyon sediments, landslides, or landslide debris; and apparent fault scarps that occur on the canyon floor rather than at the wall base are also shown. The most striking feature is the concentration of active tectonic features within lus Chasma, and to a lesser extent in Tithonium Chasma, as well as along the north walls of Coprates and East Candor.

Spencer, J. R.↗

NASA NTRS1983

Evaluation of the effects of the seasonal variation of solar elevation angle and azimuth on the processes of digital filtering and thematic classification of relief units

The effects of the seasonal variation of illumination over digital processing of LANDSAT images are evaluated. Two sets of LANDSAT data referring to the orbit 150 and row 28 were selected with illumination parameters varying from 43 deg to 64 deg for azimuth and from 30 deg to 36 deg for solar elevation respectively. IMAGE-100 system permitted the digital processing of LANDSAT data. Original images were transformed by means of digital filtering so as to enhance their spatial features. The resulting images were used to obtain an unsupervised classification of relief units. Topographic variables (declivity, altitude, relief range and slope length) were used to identify the true relief units existing on the ground. The LANDSAT over pass data show that digital processing is highly affected by illumination geometry, and there is no correspondence between relief units as defined by spectral features and those resulting from topographic features.

Parada, N. D. J.↗

NASA NTRS1984

A simulation of remote sensor systems and data processing algorithms for spectral feature classification

A computational model of the deterministic and stochastic processes involved in multispectral remote sensing was designed to evaluate the performance of sensor systems and data processing algorithms for spectral feature classification. Accuracy in distinguishing between categories of surfaces or between specific types is developed as a means to compare sensor systems and data processing algorithms. The model allows studies to be made of the effects of variability of the atmosphere and of surface reflectance, as well as the effects of channel selection and sensor noise. Examples of these effects are shown.

Arduini, R. F.↗

NASA NTRS1984

Petrography and classification of Ca, Al-rich and olivine-rich inclusions in the Allende CV3 chondrite

The results of a detailed, systematic petrographic survey of Ca, Al-rich and olivine-rich inclusions in the Allende CV3 chondrite are reported, and a new classification system based on clearly defined and readily applied petrographic criteria is presented. Most Allende inclusions are aggregates containing one or more of three distinct constituents: (1) rimmed concentric objects enriched in Al- and Ti-rich oxide minerals and various amounts of Ca-rich silicates; (2) porous, 'fine-grained' chaotic material enriched in Ca-rich silicates, especially clinopyroxenes and garnets; and (3) porous, 'fine-grained', mafic inclusion matrix, enriched in olivine, pyroxene, and feldspathoids. Two texturally distinct varieties of inclusions consist primarily of inclusion matrix: unrimmed olivine aggregates and rimmed olivine aggregates. Ca, Al-rich inclusions are classified on the basis of the size and abundance of their constituent concentric objects. Some fundamental relationships among Allende inclusions that previusly have not been emphasized are discussed.

Kormacki, A. S.↗

NASA NTRS1983

The statewide forest/nonforest classification of Pennsylvania using Landsat MSS data

A procedure is described for processing the large volume of data needed to generate a Landsat-derived forest resource map (forest/nonforest mask) for the state of Pennsylvania, for use in a defoliation assessment program. Landsat coverage of the 28 million acres encompassing Pennsylvania requires portions of ten Landsat frames, totalling approximately 76 million pixels. A prime effort of this project was to efficiently and accurately classify this data into forest or nonforest categories. A specialized approach to Bayesian classification was developed which involved the use of MSS5 and MSS7; the identification of a single class, forest; and the use of the confidence map to generate the forest/nonforest mask. Statewide, for 7-acre contiguous forest areas, the overall agreement achieved between the mask and reference data was 90 percent.

Russo, S. A.↗

NASA NTRS1983

Automated vegetation classification using Thematic Mapper Simulation data

The present investigation is concerned with the results of a study of Thematic Mapper Simulation (TMS) data. One of the objectives of the study was related to an evaluation of the usefulness of the Thematic Mapper's (TM) improved spatial resolution and spectral coverage. The study was undertaken as part of a preparation for the efficient incorporation of Landsat 4 data into ongoing technology development in remote sensing. The study included an application of automated Landsat vegetation classification technology to TMS data. Results of comparing TMS data to Multispectral Scanner (MSS) data were found to indicate that all field definition, crop type discrimination, and subsequent proportion estimation may be greatly increased with the availability of TM data.

Nedelman, K. S.↗