Spectral characterization and regression-based classification of forest damage in Norway spruce stands in the Czech Republic using Landsat Thematic Mapper data

Abstract

Abstract This study assessed the ability of Landsat Thematic Mapper (TM) sensor data to discriminate among three damage categories of Norway spruce in the Krusne Hory mountains using dichotomous logit regressions. Moderate and light damage stands, being the most spectrally similar, were separated with 83 per cent accuracy using TM1, TM4 and TM7. Moderate and heavy categories were best separated by TM3 (accuracy=88 per cent). Light and heavy damage classes were separated with up to 95 per cent accuracy. Ratios and indices did not improve the regression accuracies. The regression equations, when used to classify three categories of damage, accurately classified 71–75 per cent of Norway spruce stands.