Abstract
The performance of both the Landsat-4 TM and MSS sensors is evaluated through the analysis of image and digital data simultaneously acquired over agricultural and forestry study sites in California. Spectral statistics extracted for selected cover types include band means, variances, coefficients of variation, range values, skewness, kurtosis, and covariance and correlation matrices. Spectral characteristics are evaluated through analysis of these statistics and interpretation of image products. Image products are used to visually represent significant spectral variations between the TM bands. Significant results include: 1) the overall spectral, spatial, and radiometric quality of the TM data are excellent; 2) discrimination of crop types on single-date image data is significantly improved by the addition of the first short-wave infrared band (TM5); 3) the thermal infrared data (TM6) allows the discrimination of agricultural and forestry cover types based on differences in their radiant temperature responses; and 4) the higher TM spatial resolution (28.5 m versus 57 m) provides the ability to discriminate small agricultural fields and boundaries, forest stand boundary conditions, road and stream networks in rough terrain, and small clearings resulting from various forest management practices.
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