Mapping burn severity in southern California using spectral mixture analysis

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.