TY - JOUR
T1 - Mapping fire-induced vegetation mortality using landsat thematic mapper data
T2 - A comparison of linear transformation techniques
AU - Patterson, Mark W.
AU - Yool, Stephen R.
N1 - Funding Information:
We are grateful to Mark Jakubauskas (University of Oklahoma) and Stuart Marsh (University of Arizona) for their thoughtful comments on this article. The authors wish to thank Carl Edminster (Rocky Mountain Forest Research Station, Ft. Collins, CO) for research support and Brian Power, Ed Encinas, Chris Peterson (U.S. Forest Service, Douglas Ranger District, Douglas, AZ), Robert Brew, and John Rogan (University of Arizona) for their field assistance. This work was supported by U.S. Forest Service Cooperative Agreement 28-CS-893.
PY - 1998/8
Y1 - 1998/8
N2 - 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 brighthess, 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.
AB - 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 brighthess, 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.
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U2 - 10.1016/S0034-4257(98)00018-2
DO - 10.1016/S0034-4257(98)00018-2
M3 - Article
AN - SCOPUS:0032132325
SN - 0034-4257
VL - 65
SP - 132
EP - 142
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
IS - 2
ER -