TY - JOUR
T1 - Mapping wildfire burn severity in Southern California forests and shrublands using enhanced thematic mapper imagery
AU - Rogan, John
AU - Franklin, Janet
N1 - Funding Information:
This work was supported by NASA Grant #LCLUC99-0002-0126. The authors wish to thank J. Miller, D. Stow, A. Hope and S. Aitken of San Diego State University, A. Kroeger and D. A. Roberts, University of California Santa Barbara, L. Levien, USDA Forest Service and C. Fischer, California Department of Forestry and Fire Protection, for their help in this research. The manuscript was greatly improved by the comments of the reviewers.
PY - 2001
Y1 - 2001
N2 - Wildfire is a major disturbance agent in Mediterranean Type Ecosystems (MTEs). Providing reliable, quantitative information on the area of burns and the level of damage caused is therefore important both for guiding resource management and global change monitoring. Previous studies have successfully mapped burn severity using remote sensing, but reliable accuracy has yet to be gained using standard methods over different vegetation types. The objective of this research was to classify burn severity across several vegetation types using Landsat ETM imagery in two areas affected by wildfire in southern California in June 1999. Spectral mixture analysis (SMA) using four reference endmembers (vegetation, soil, shade, non-photosynthetic vegetation) and a single (charcoal-ash) image endmember were used to enhance imagery prior to burn severity classification using decision trees. SMA provided a robust technique for enhancing fire-affected areas due to its ability to extract sub-pixel information and minimize the effects of topography on single date satellite data. Overall kappa classification accuracy results were high (0.71 and 0.85, respectively) for the burned areas, using five canopy consumption classes. Individual severity class accuracies ranged from 0.5 to 0.94.
AB - Wildfire is a major disturbance agent in Mediterranean Type Ecosystems (MTEs). Providing reliable, quantitative information on the area of burns and the level of damage caused is therefore important both for guiding resource management and global change monitoring. Previous studies have successfully mapped burn severity using remote sensing, but reliable accuracy has yet to be gained using standard methods over different vegetation types. The objective of this research was to classify burn severity across several vegetation types using Landsat ETM imagery in two areas affected by wildfire in southern California in June 1999. Spectral mixture analysis (SMA) using four reference endmembers (vegetation, soil, shade, non-photosynthetic vegetation) and a single (charcoal-ash) image endmember were used to enhance imagery prior to burn severity classification using decision trees. SMA provided a robust technique for enhancing fire-affected areas due to its ability to extract sub-pixel information and minimize the effects of topography on single date satellite data. Overall kappa classification accuracy results were high (0.71 and 0.85, respectively) for the burned areas, using five canopy consumption classes. Individual severity class accuracies ranged from 0.5 to 0.94.
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U2 - 10.1080/10106040108542218
DO - 10.1080/10106040108542218
M3 - Article
AN - SCOPUS:33746607869
SN - 1010-6049
VL - 16
SP - 91
EP - 106
JO - Geocarto International
JF - Geocarto International
IS - 4
ER -