Shangri-La is located in the eastern part of the Qinghai-Tibet Plateau, which has a fragile ecology. The plateau grassland has suffered from irreversible degradation under the influence of human activities. To address this issue, the Sentinel-2A data obtained is used in this study to calculate the RVI and build an inversion model of grassland degradation grade with GDI data, which was used to obtain the area and proportion of grassland degradation. Landscape indexes were then calculated for different degradation grades of grassland to examine the correlation between roads and degraded grassland in spatial distance and the spatial distribution characteristics of different degradation grades of grassland. The results show that the grassland area in Shangri-La was 2207.94 km², of which the heavily degraded area reaches 824.03 km², exceeding the undegraded grassland area by 172.62 km², indicating that the grassland degradation is severe. From south to north, the proportion of heavily degraded and moderately degraded grassland in townships gradually decreased, while the proportion of lightly degraded and undegraded grassland gradually increased. The townships with high percentages of degraded grassland were predominantly located in the southern area, where there was a dense road network and well-developed transport networks, particularly along National Highway 214, which is the main road in Shangri-La. Conversely, townships with low percentages are generally located in the north with dispersed roads and sparse transport lines. The study’s outcomes are significant in providing a better understanding of the current status of grassland degradation and promoting the sustainable utilization of grassland resources in Shangri-La.

Accurate acquisition of tree species and forest age information is crucial for forest ecosystem protection and sustainable development. Although such information can be obtained through remote sensing technology, the accurate extraction of tree species and forest age still faces significant challenges due to the remote sensing data characteristics and the complex and variable mountainous natural geographical environment. This paper employs the Landsat-NDVI long-term series data from 1991 to 2021 to address this problem. A growth characteristic change model is established for the main dominant tree species in Shangri_La City, including Pinus yunnanensis, Pinus densata Mast, Picea & Abies, and Quercus acutissima, throughout the growth cycle in vertical zones at different altitudes. Besides, the study compared the NDVI time series data of the area to be identified with the growth characteristic model to match the appropriate growth range automatically, and their similarity is determined to complete the tree species classification. The forest age is then obtained based on the optimal matching point of the NDVI time series data in the growth model. This method can simultaneously obtain tree species and forest age information and acquire forest age when the image duration exceeds the remote sensing image record. Ultimately, the overall accuracy of tree species classification reached 83.42%, and the forest age fitting results also showed a high degree of correlation, with a coefficient of determination (R²) of 0.92. The root mean square error (RMSE) of forest age in Shangri-La, dominantly covered by mature forests/overmature forests, has dropped to 10.59. This study significantly improves remote sensing technology’s accuracy and application efficiency in multi-parameter inversion of forest resources while providing a new technical means for forest resource management and ecological protection.