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A fixed effect, three factor (two levels per factor) analysis of variance was used to quantitatively assess the significance of the improved spectral, spatial and radiometric resolution capabilities of the LANDSAT-4 thematic mapper sensor relative to the familiar MSS sensor. TM data acquired over the Washington, D.C. area were progressively degraded in spectral, spatial and radiometric characteristics to simulate the MSS, and classification accuracies were derived in a consistent manner for all eight treatments in the ANOVA design. Statistical testing of the significance of differences in classification accuracies between treatments indicated that the increased number of spectral bands and the improved quantization capabilities afforded by the TM sensor design would lead to significant improvements in classification accuracies attainable relative to MSS. In contrast, however, the improved spatial resolution provided by the TM sensor did not enhance classification accuracy. This latter result was felt to be more a function of the type of classification algorithms available.

Classification capabilities with TM data result from the interactive effects of all of the sensor's attributes which complicates a more quantitative evaluation of the effects of individual sensor improvements. An experiment conducted to quantify the effect of individual sensor parameters (e.g., spectral, spatial, and radiometric resolution) on classification accuracy is described on classification accuracy. Preliminary results obtained using TM data acquired over the Washington, D.C., area indicate that the additional number of spectral bands and quantization levels of the TM relative to the MSS increase capabilities for the recognition and discrimination of land cover/use categories by per-pixel maximum likelihood classification. The refinement of spatial resolution, however, seems to hinder classification.

A fixed factor analysis-of-variance (ANOVA) approach was used to quantify the impact of each TM sensor characteristic (spectral, spatial, and radiometric resolutions) on classification accuracy. All assessments were made relative to MSS sensor characteristics, and the impact of each characteristic was assessed individually, and in all possible combinations. Thematic Mapper data acquired over the Washington, D.C. area on November 2, 1982 were utilized to conduct the experiment. The following results were obtained: the reduction of quantization level from eight bits to six bits caused a decrease in overall accuracy (7%); the use of only three bands (TM 2, 3 and 4) covering the isible and near infrared portion of the spectrum caused a decrease in overall accuracy (7%); and the decrease in spatial resolution resulted in an increase in overall accuracy (4%). Results indicate that the increased radiometric and spectral resolution of the TM instrument do provide increased information content. The result of the spatial resolution degradation is somewhat misleading, in that the result is more a function of spatial resolution.

A three factor (spectral, spatial, and radiometric resolution), two level (TM and MSS) analysis of variance (ANOVA) approach allowed evaluation of the effects of each factor individually and in all possible combinations. Digital classification accuracy was used as the figure of merit. Nine study sites in Washington, D.C. each of approximately 256 x 256 TM pixels, were randomly selected from the full scene for analysis. These results strongly suggest that the quantization level improvements and the addition of new spectral bands in the visible and middle IR regions (both afforded by the TM sensor design) can result in improved capabilities to accurately delineate land cover categories using a per point Gaussian maximum likelihood classifier. On the other hand, results indicate that the increase in spatial resolution to 30m does not significantly enhance classification accuracy. The spatial result points to an inherent limitation of a per point classifier and to the need to improve data analysis techniques to handle high spatial resolution data.

On July 16, 1982, the second decade of land remote sensing from space was inaugurated with the successful launch of Landsat-4. This satellite carries the Multispectral Scanner (MSS) and a new sensor, the Thematic Mapper (TM). The TM represents the result of an effort in which all of the major improvements in remote-sensing capability were simultaneously integrated into one system. An experiment was developed and conducted to quantify the effect of each TM sensor parameter on classification accuracy. This paper discusses the experimental design and summarizes the results obtained using TM data acquired over the Washington, DC area on November 2, 1982. Attention is given to a study site/data description, the experimental design, photointerpretation and digitization, spectral simulation, radiometric simulation, and spatial simulation.

A three factor (spectral, spatial, and radiometric resolution), two level (TM and MSS) analysis of variance (ANOVA) approach allowed evaluation of the effects of each factor individually and in all possible combinations. Digital classification accuracy was used as the figure of merit. Nine study sites in Washington, DC, each of approximately 256 x 256 TM pixels, were randomly selected from the full scene for analysis. These results strongly suggest that the quantization level improvements and the addition of new spectral bands in the visible and middle IR regions (both afforded by the TM sensor design) can result in improved capabilities to accurately delineate land cover categories using a per point Gaussian maximum likelihood classifier. On the other hand, results indicate that the increase in spatial resolution to 30 m does not significantly enhance classification accuracy. The spatial result points to an inherent limitation of a per point classifier and to the need to improve data analysis techniques to handle high spatial resolution 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.

Preliminary experimental results of airborne thematic mapper (TM) data taken to quantify the effect of three major TM sensor parameters, spectral, spatial, and radiometric resolution, six months after launch of Landsat-4 are reported. The flight took place on Nov. 2, 1982 over Washington, D.C., and data gathered were compared with ground reference data from color airborne photography on a 1:40,000 scale. Analyses proceeded by deleting one band from each of four data sets, thus making the data equivalent to MSS data. Attention was directed to land cover/use classes in a quick-look format. A per-pixel maximum likelihood scheme was found to increase the recognition and dicrimination categorization capabilities. Finer spatial resolution, however, impeded classification due to increased within-class variability of the field-center pixels, which also incresed class overlap in the spectral data base. Improved data analyses techniques are therefore needed to exploit the available higher spatial resolution of the TM.

Registered, multitemporal Landsat data of a study area in central Pennsylvania were analyzed to detect and assess changes in the forest canopy resulting from insect defoliation. Images taken July 19, 1976, and June 27, 1977, were chosen specifically to represent forest canopy conditions before and after defoliation, respectively. Several image manipulation and data transformation techniques, developed primarily for estimating agricultural and rangeland standing green biomass, were applied to these data. The applicability of each technique for estimating the severity of forest canopy defoliation was then evaluated. All techniques tested had highly correlated results. In all cases, heavy defoliation was discriminated from healthy forest. Areas of moderate defoliation were confused with healthy forest on northwest (NW) aspects, but were distinct from healthy forest conditions on southeast (SE)-facing slopes.

Image differencing was investigated as a technique for use with LANDSAT digital data to delineate areas of land cover change in an urban environment. LANDSAT data collected in April 1973 and April 1975 for Austin, Texas, were geometrically corrected and precisely registered to United States Geological Survey 7.5-minute quadrangle maps. At each pixel location reflectance values for the corresponding bands were subtracted to produce four difference images. Areas of major reflectance differences are isolated by thresholding each of the difference images. The resulting images are combined to obtain an image data set to total change. These areas of reflectance differences were found, in general, to correspond to areas of land cover change. Information on areas of land cover change was incorporated into a procedure to mask out all nonchange areas and perform an unsupervised classification only for data in the change areas. This procedure identified three broad categories: (1) areas of high reflectance (construction or extractive), (2) changes in agricultural areas, and (3) areas of confusion between agricultural and other areas.

The overall objective of a research effort at NASA's Goddard Space Flight Center is to develop and evaluate digital image processing techniques that will facilitate the assessment of the intensity and spatial distribution of forest insect damage in Northeastern U.S. forests using remotely sensed data from Landsats 1, 2 and C. Automated change detection techniques are presently being investigated as a method of isolating the areas of change in the forest canopy resulting from pest outbreaks. In order to follow the change detection approach, Landsat scene correction and overlay capabilities are utilized to provide multispectral/multitemporal image files of 'defoliation' and 'nondefoliation' forest stand conditions.