Here we present a high-resolution landslide susceptibility model for Guiyang County, China, developed to support sustainable disaster risk management. Our approach couples optimized positive and negative training samples with an ensemble of machine-learning algorithms to maximize predictive fidelity. We compiled a georeferenced inventory of 146 landslides by integrating historical records with systematic field validation. Sample optimization was central to our methodology: landslide presence points were refined via buffer-based dilution, and four classifiers—SVM, LDA, RF, and ET—were trained with identical covariate sets to ensure comparability. Three strategies for selecting pseudo-absences—buffering, low-slope filtering, and coupling with the IOE—were benchmarked. The Slope-IOE-O model, which synergizes low-gradient screening with entropy-weighted sampling, yielded the highest predictive capacity (AUC = 0.965). SHAP-based interpretability revealed that slope, monthly maximum rainfall, surface roughness, and elevation collectively dominate susceptibility, with pronounced non-linearities and interactions. Slope contribution peaks at 20–30°, monthly maximum rainfall exhibits a critical threshold near 225 mm, and the synergy between high roughness and road density amplifies landslide risk. Spatially, susceptibility follows a pronounced north–south gradient, with high-hazard corridors aligned along northern and southern mountain belts and the urban core of southern Guiyang County. By integrating rigorously curated training data with robust machine-learning workflows, this study provides a transferable framework for proactive landslide risk assessment, offering scientific support for sustainable land-use planning and resilient development in mountainous regions.

The vegetation types on the Qinghai-Tibet Plateau are complex and diverse, and the ecological environment is fragile and sensitive. It is very important to study the dynamic changes in vegetation and the main factors related to these changes to grasp the present state of the regional ecosystem, maintain the balance of the ecosystem and promote the sustainable development of the ecosystem. Therefore, this paper is based on SPOT/VEG NDVI (normalized difference vegetation index) data, land use data, topographic data and meteorological data from 1999 to 2019. The spatiotemporal variation in the NDVI over the Tibetan Plateau in the last 21 years and its response to different driving factors were investigated by using the Sen slope method, Mann–Kendall mutation test, partial correlation analysis and geographical detector method. The results showed that (1) the vegetation coverage on the Qinghai-Tibet Plateau showed an increasing trend from 1999 to 2019, with increases in approximately 67.00% of the plateau area. (2) The spatial differences in vegetation coverage were large; notably, low-density vegetation areas decreased obviously, moderate-density vegetation areas accounted for approximately 50% of the total area, high-density vegetation areas were the least common, and the overall growth rate was significant. (3) The NDVI was positively correlated with temperature and precipitation, and a positive correlation was observed in more than 66% of the region. (4) The order of the influence of single driving factors on the NDVI was as follows: precipitation > soil type > altitude > temperature > gradient > slope > population density > GDP. (5) The effect of multiple factors was significantly higher than that of single driving factors, with a notable nonlinear influence. The interactions between meteorological factors, such as precipitation, and topographic factors, such as altitude, were important, with a q value over 0.79.