To reveal the changes and future states of ecosystem service value (ESV) and landscape ecological risk index (LERI) in typical dry valley ecosystems in the 21st century, this study utilized high-precision land use data combined with ESV assessment, LERI assessment, bivariate spatial autocorrelation, Geodetector, CA-Markov model, and standard deviation ellipse methods. The spatio - temporal evolution, spatial associations and differentiation characteristics of ESV and LERI in the Anning River Basin from 2000 to 2020 were analyzed, and made predictions for 2030. The results indicated: (1) The ESV in the study area exhibited a decreasing trend from 2000 to 2030, with a cumulative reduction of 1.444 billion CNY, where forest land contributed over 78.46% of the total ESV. The basin presented a spatial pattern of lower ESV in the valley and higher in mountainous areas, dominated by high-value areas (accounting for approximately 41.54% of the total area). (2) LERI showed an overall declining trend during the study period, with a spatial pattern similar to ESV, characterized by low values in valleys and higher values in mountainous regions. The extremely low-risk areas were predominant (approximately 37.00%). (3) The spatial differentiation of ESV and LERI was jointly influenced by natural and economic factors, with agricultural production potential and annual evaporation identified as the dominant factors ( q values of 0.18 and 0.28, respectively). The center of gravity of ESV distribution moved towards the northeast, whereas LERI shifted to the southeast. (4) ESV and LERI exhibited positive spatial correlations (Moran’s I > 0), with high ESV—low LERI as the main LISA cluster (approximately 20.92%). These findings highlight the necessity for adopting tailored ecological protection measures based on the spatiotemporal distribution and evolution characteristics of ESV and LERI to promote sustainable regional ecological development.

This study investigated the spatiotemporal evolution characteristics of cultivated land in the Hehuang Valley based on terrain gradient analysis, aiming to provide a scientific reference for the rational utilization of cultivated land in this region. By integrating long-term high-resolution land use data with methods including terrain gradient factor analysis, kernel density analysis, PLUS model and standard deviation ellipse theory. we analyzed the spatiotemporal changes, topographic gradient differentiation patterns, and evolution trends of cultivated land in the Hehuang Valley from 1980 to 2020. The results showed that: (1) Cultivated land in the Hehuang Valley exhibited a net loss of 119.24 km² from 1980 to 2020, predominantly converted to grassland, water area, and built-up area. (2) Higher cultivated land density in river valleys contrasted with sparse distribution in mountainous zones. The spatial centroid shifted 2.277 km northwestward, accompanied by progressive spatial contraction. (3) Cultivated land was concentrated on sunlit slopes of hills and plains below 2,772 m altitude and 17.42° slope gradient, where human activities induced significant land use transitions. (4) Population density was the dominant factor influencing cultivated land changes, with a contribution of 0.12. The increase in cultivated land was primarily distributed along the Datong River and the Yellow River, while cultivated land decreased rapidly in the Xining section of the Huangshui River. Location-specific management strategies are imperative to optimize resource allocation and ensure sustainable agricultural development under heterogeneous environmental constraints. Given the significant terrain gradient differentiation in the distribution and change of cultivated land in the Hehuang Valley, sustainable management practices should adhere to local conditions to promote the sustainable development of cultivated land resources.

Landslides pose significant threats to sustainable development by causing infrastructure damage and ecosystem degradation, particularly in densely vegetated mountainous regions. To support sustainable land-use planning and disaster-resilient development, this study integrates three advanced vegetation metrics—Vegetation Formation Group (VFG), aboveground biomass (AGB), and forest canopy height (FCH)—into landslide susceptibility modeling. Using Yuanling County, a subtropical vegetated region in China, as a case study, we developed a novel ensemble model, AdaBoost-CB (AdaBoost-CatBoost), and compared its performance with mainstream machine learning models including RF, XGBoost, and LGB. The results show that AdaBoost-CB achieved the highest Area Under the Curve (AUC) value of 0.915. Furthermore, it yielded the highest landslide frequency ratio of 6.51 in the very-high-susceptibility zones. The dominant landslide-controlling factors—NDVI, elevation, slope gradient, slope aspect, and rainfall—were consistently identified across six models. These findings provide a scientific basis for sustainable land-use planning and disaster risk reduction strategies, contributing directly to the goals of sustainable development in vulnerable mountainous regions.

Against the background of global warming, snow cover, as an extremely sensitive and active component of the cryosphere, plays an indispensable role in regulating regional water circulation, energy balance mechanisms and the climate system. To explore the dynamic variation characteristics and driving mechanisms of snow depth in southeastern Xizang, this study took Chamdo City as the research area. Based on multi-source datasets including snow depth, meteorology, vegetation, topography, and population density from 2000 to 2020, methods such as the coefficient of variation, Theil–Sen trend analysis, Mann–Kendall test, Hurst index, and geographical detector were adopted to systematically analyze the spatiotemporal patterns of snow depth variations and their influencing factors. The results indicate that, temporally, the overall snow depth in Chamdo City showed a fluctuating increasing trend over the past 20 years, with an annual growth rate of 0.03 cm. It exhibited distinct characteristics across three stages: snow depth increased at a rate of 0.12 cm·a⁻¹ from 2000 to 2005, decreased at 0.05 cm·a⁻¹ during 2005–2015, and rose rapidly from 2015 to 2020 at a growth rate of 0.52 cm·a⁻¹. Spatially, the distribution of snow depth varied significantly. The extremely shallow snow cover area (≤2 cm) accounted for 51.71% of the total area, primarily concentrated in low-altitude regions with intensive human activities. In contrast, the relatively deep (6–10 cm) and extremely deep (>10 cm) snow cover areas together constituted 14.34% of the total, mainly distributed in high-altitude regions with sparse populations. Hurst index analysis revealed that 61.71% of the study area exhibited persistent changes in snow depth, with a trend toward deepening snow cover in the future. The results from the geographical detector show that air temperature (X9, q = 0.90) was the core driving factor dominating the static spatial differentiation of multi-year average snow depth. Furthermore, the interactions between slope (X4) and air temperature (X9), vegetation type (X6) and air temperature (X9), and population density (X5) and aspect (X8) all demonstrated bivariate enhancement effects, with explanatory power significantly higher than that of individual factors. This study provides a scientific reference for water resource management, snowmelt runoff prediction and snow disaster prevention in Chamdo City.