John Rogan

This paper presents preliminary results of research to improve upon an existing operational forest change detection monitoring strategy in California. Comparisons were. made between Landsat 5 TM and Landsat 7 ETM scene normalization techniques (absolute versus, relative). Prior to normalization, scenes containing wildfire smoke plumes were successfully corrected using a space-varying haze equalization algorithm. Simple dark object subtraction provided improved performance over relative (pseudo-invariant feature) approaches. A decision tree classifier produced high change map overall accuracy (86%) for five categories of forest cover change

Several remote sensing techniques have been used successfully to map the areas of wildfire burn scars. Burn severity mapping, however, presents a suite of problems, caused by spectral confusion between vegetation affected by surface fire and unburned vegetation, between moderately burned vegetation and sparse vegetation, and between burned shaded and unburned shaded vegetation. A single date Landsat-7 Enhanced Thematic Mapper image was used to map five burn severity classes in two areas affected by wildfire in southern California in June 1999. Spectral mixture analysis (SMA), using four reference endmembers (vegetation, soil, shade, nonphotosynthetic vegetation) and a single (charcoal-ash) image endmember, was used to enhance the image prior to supervised classification of burn severity. SMA provided a robust technique for mapping fire-affected areas due to its ability to extract subpixel information and minimize the effects of topography on single date satellite data. Overall kappa classification accuracy was high (0.81 and 0.72, respectively) for the burned areas, using five burn severity classes. Individual severity class accuracies ranged from 0.53 to 0.94