Abstract: This paper describes the technique to be needed for rapidly and accurately identifying the burned area by forest fires,following the catastrophic fires by the vegetation index CART decision tree methods using the wide coverage image of GF-1(GF-1 WFV).They were compared between the maximum likelihood classification of supervised and unsupervised classification(ISODATA),within burned area indexes,to improve the accuracy of the burned area,shaded vegetation index,global environment monitoring index,improved shadows and bare commission or omission burned phenomenon.The results showed that the decision tree classification method based on CART algorithms for burned area identification has significantly improved the overall accuracy by 4.38% compared with the maximum likelihood method;Kappa coefficient increased by 0.1024.GF-1 satellite imagery for unsupervised classification(ISODATA)identifies the burned area poorly,the overall accuracy and Kappa coefficient are low,the map making accuracy and user accuracy have not reached 1%.

Key words: GF-1 satellite images, forest disaster, burned area, vegetation index, the decision tree model