Abstract: Vegetation plays an important role in land use and land cover. Vegetation classification has a great reference value for watershed ecology research. Jianghe river basin in western Hubei is an important water source for the Middle Route of the South-to-North Water Transfer Project. Because of its large altitude range, complicated terrain, a large number of complex shadows exist in remote sensing images. It is difficult to classify the vegetation types in the shadow. This paper constructed a stratified analysis for vegetation classification. The stratified classification is a method based on the idea of division of layers step by step and different criteria and methods in each layer. The criteria included the vegetation indices, water index and shadow index, which were calculated from Landsat OLI image of 2017 and DEM.The vegetation indices included NDVI (Normalized Difference Vegetation Index) and RVI (Ratio Vegetation Index).The study selected Modified Normalized Difference Water Index (NMDWI) to indicate water. The topographic shadow was calculated from the hill-shade module based on ArcGIS. The input parameters of hill-shade model consisted of solar azimuth angle, solar zenith angle and ASTER GDEM. The results showed that: (1)Using the intelligent decision tree algorithm, the overall accuracy of the classification reached 92.93%, with a Kappa coefficient of 0.912; (2) For the same vegetation, there were higher NDVI and RVI in exposed halves of hills than that in shaded halves. It was consistent with the phenomenon of same object with different spectra. The value of RVI from high to low was: deciduous shrub, thedeciduous shrub and evergreen coniferous mixed forest and evergreen coniferous forest. (3) Because of different solar azimuth angle and solar zenith angle changing the distribution of shadow, each remote sensing image had a certain shadow shape. The accuracy of vegetation classification is largely reduced. Overall, this vegetation classification method combined the decision tree and hill-shade model to categorize the Jianghe river basin into six classes. As an intelligent decision tree classification algorithm, the method has the advantages of identifying vegetation categories in shaded halves and is of high application value, especially in low mountains and hills. There is still space to improve the classification accuracy, i.e., an optimized vegetation index, the increasing of vegetation samples in high altitude and large slope regions, and the use of higher spatial and temporal resolution images.