Mapping Fire-Induced Vegetation Mortality Using Landsat Thematic Mapper Data: A Comparison of Linear Transformation Techniques

Forests in the U.S. southwest experience large, intense wildfires. Fire severity maps can assist management of such fire-scarred landscapes. Remote sensing appears suitable for wildfire mapping, provided data have sufficient spatial, radiometric, and spectral resolutions. Using a 1995 Thematic Mapper (TM) post-fire scene of the 8900 ha Rattlesnake Fire in southeastern Arizona as a case study, two linear transformation techniques, the Kauth–Thomas (KT) and principal components analysis (PC) transforms were invoked to enhance Thematic Mapper data prior to supervised classification. The KT and PC transformations were selected to enhance fire-related brightness, greenness, and wetness variations in the image, detecting the extent of different fire severities. The KT transform produced 17% higher overall classification accuracies than the PC transform. The higher accuracy recorded by the KT transform results from brightness, greenness, and wetness variations which, in this case, are associated with fire severity.