Abstract: Based on the Google Earth Engine platform and Landsat OLI time-series data from 2014 to 2017, combined with vegetation index, topography and temperature related features, the forest type classification of Shangri-La was studied by using random forest classification classifier. The findings indicates that the different types have obvious phenological characteristics difference in various seasons, among which 4 forest types were the most in winter. Therefore, the time-series data can provide the phenological difference characteristics for classification, thus benefitting the classification of forest types. Accuracy assessment of forest/non forest classification indicates that the overall accuracy and Kappa coefficient are 97.17% and 0.943, respectively. And the forest type classification’s overall accuracy and Kappa coefficient are 87.78% and 0.80, respectively. The method can provide a forest classification product with high precision and is helpful for mapping forest types.