To facilitate the identification of appropriate post-fire watershed treatments and minimize erosion effects after socio-economically important fires, Interagency Burned Area Emergency Rehabilitation (BAER) teams produce initial timely estimates of the fire perimeter and classifications of burn severity, forest mortality, and vegetation mortality. Accurate, cost-effective, and minimal time-consuming methods of mapping fire are desirable to assist rehabilitation efforts immediately after containment of the fire. BAER teams often derive their products by manually interpreting color infrared aerial photos and/or field analysis. Automated classification of multispectral satellite data are examined to determine whether they can provide improved accuracy over manually digitized aerial photographs. In addition, pre-fire vegetation data are incorporated to determine whether further gains in accuracy of mapped canopy consumption can be made. BAER team classifications from the Cerro Grande Fire were compared to estimates of overstory consumption produced using a pre-fire vegetation classification, and a change detection algorithm using bands 4 and 7 from July 1997 pre-fire Landsat Thematic Mapper (TM) and July 2000 post-fire Enhanced Thematic Mapper (ETM) data. BAER team classifications are highly correlated to overstory consumption and should produce high Kappa statistics when verified using the same dataset. Our three-class supervised classification of the change image incorporating a pre-fire vegetation classification yielded the highest Kappa at 0.86. A three-class unsupervised classification of the change image yielded a lower Kappa of 0.72. BAER team classifications yielded Kappas ranging from 0.38 to 0.63 using the same verification dataset. (C) 2002 Elsevier Science Inc. All rights reserved.
