Abstract: The classification of vegetation has attracted much attention for study of ecological effects on the Qinghai-Tibetan Plateau. Previous studies have mostly focused on decision tree classifiers, and much research has been done to test this classification on a small scale. In this study, we consider a large scale method (object-oriented classification), which can also be integrated with a conventional decision tree classifier. However, the rules of classification have only utilized the information from decision tree classifiers. This approach comprehensively considered information of position, terrain and texture from TM (thematic mapper), DEM (digital elevation model), EVI (enhanced vegetation index) and LST (land surface temperature) in Yushu, and then segmented or merged the type of steppe. The overall accuracy is 49.32%, and Kappa coefficient is 0.373 5. Our study suggested that this method could overcome the disadvantages of scattered pixels when division is by the type of vegetation. Compared to the conventional decision tree classifier, the overall precision of our method is low. However our method maintained the statistical relationship between factors derived from the environment and geography, and vegetation types to reduce the salt and pepper effects. In addition, the physical process, parameter calculations and environmental factor collection of vegetation models are complicated. In this paper, a simple and quick way of division of vegetation types is provided by our method.