Based on performing various sorts of image processing on the original 9 bands of ASTER sensors, the authors objectively adopted the quantitative indicator of average separability to determine the optimal combinations of features most suitable for classification. In conjunction with the signature or prototype data for each class, the maximum likelihood classifier, BP neural network classifier and decision tree classifier based on data mining software of See 5.0 were respectively implemented to characterize the spatial distribution patterns of major land cover types over the entire study area. The final classification results based on field validation with 379 actual observations show that the decision tree algorithm possesses the best performance of extraction, with an overall accuracy of 84.4% and a kappa coefficient of 0.822, followed by the BP network algorithm, and that the maximum likelihood classifier has the worst performance of classification. In comparison with the traditional establishment and classification procedures which have been embedded into ENVI 4.1 and ERDAS 8.7, the automated decision tree algorithm used in this study is based on See 5.0 and Cart module (Classification and Regression tree). Due to its objectivity, high efficiency, reliability and high accuracy, the automated decision tree deserves more attention in future practice of classification.