The timely and accurate mapping of the spatial distribution of grasslands is crucial for maintaining grassland habitats and ensuring the sustainable utilization of resources. We used Google Earth Engine (GEE) and Sentinel-2 data for mountain grassland extraction in Yunnan, China. The differences in the normalized vegetation index in the time-series data of different ground objects were compared. February to March, during grassland senescence, was the optimum phenological stage for grassland extraction. The spectral, textural of Sentinel-2, and topographic features of the Shuttle Radar Topography Mission (SRTM) were used for the classification. The features were optimized using the recursive feature elimination (RFE) feature importance selection algorithm. The overall accuracy of the random forest (RF) classification algorithm was 91.2%, the producer’s accuracy of grassland was 96.7%, and the user’s accuracy of grassland was 89.4%, exceeding that of the cart classification (Cart), support vector machine (SVM), and minimum distance classification (MDC). The SWIR1 and elevation were the most important features. The results show that Yunnan has abundant grassland resources, accounting for 18.99% of the land area; most grasslands are located in the northwest at altitudes above 3200 m and in the Yuanjiang River regions. This study provides a new approach for feature optimization and grassland extraction in mountainous areas, as well as essential data for the further investigation, evaluation, protection, and utilization of grassland resources.

Ancient Pu’er tea trees (Camellia sinensis var. assamica (J. W. Mast.) Kitam.) are an important ecological resource with high economic value. Knowledge of the environmental variables shaping the original distribution and the effects of climate change on the future potential distribution of these trees, as well as the identification of sustainable management approaches, is essential for ensuring their future health and production. Here, we used 28 current environmental variables and the future climate data to model the suitable areas for ancient Pu’er tea trees. We also compared the health of these ancient trees in areas under different local management strategies. The results suggested the general distribution is likely to remain stable, but there are environmentally suitable areas outside its current habitats. To achieve more sustainable management, the main areas in which the management of poorly-managed trees can be improved include learning from managers of well-managed trees and following the common technical management regulations stipulated by the local government. The suitable value ranges for environmental factors, potentially suitable areas under climate change, and assessment of management approaches will aid the future cultivation and transplantation of ancient Pu’er tea trees. The methodology includes management-level analysis and provides practical insights that could be applied to regions outside the most suitable areas identified.