The Hexi Corridor plays a pivotal role in safeguarding China’s ecological security, functioning as a crucial conduit between economic and ecological systems. This study employs a multidisciplinary approach, integrating climate, evapotranspiration, and other variables, to analyze the trend in factors influencing ecological function and to evaluate the import of ecological service functions in the Hexi Corridor from 2000 to 2020. The findings reveal a distinctive spatial distribution pattern for regulating functions, with higher concentrations observed in the southern regions and lower concentrations in the northern regions. These functions include the storage of carbon, quality of habitat, the conservation of water, and soil and water conservation. It can be observed that the areas of general importance for ecosystem services are predominantly distributed across the northern and western sections of the Hexi Corridor, collectively representing 76.96% of the total area. Conversely, areas of general importance for ecosystem services are situated in regions characterized by a high altitude, intricate topography, and extensive glaciers as well as permafrost.

Rock glaciers have been recognized as key indicators of geomorphic and climatic processes in high mountain environments. In this study, Sentinel-2 MSI imagery and topographic data were integrated to construct enhanced feature sets for rock glacier identification. Three state-of-the-art deep learning models (U-Net, DeepLabV3+, and HRnet) were employed to perform semantic segmentation for extracting rock glacier boundaries in the Hunza River Basin, located in the eastern Karakoram Mountains. The combination of spectral and terrain features significantly improved the differentiation of rock glaciers from surrounding landforms, establishing a robust basis for model training. A series of comparative experiments were conducted to evaluate the performance of each model. The HRnet model achieved the highest overall accuracy, exhibiting superior capabilities in high-resolution feature representations and generalization. Using the HRnet framework, a total of 597 rock glaciers were identified, covering an area of 183.59 km². Spatial analysis revealed that these rock glaciers are concentrated between elevations of 4000 m and 6000 m, with maximum density near 5000 m, and a predominant south and southwest orientation. These spatial patterns reflect the combined influences of topography, thermal conditions, and snow accumulation on the formation and preservation of rock glaciers. The results confirm the effectiveness of deep learning-based semantic segmentation for large-scale rock glacier mapping. The proposed framework establishes a technical foundation for automated monitoring of alpine landforms and supports future assessments of rock glacier dynamics under climate variability.