Mountain disturbances, such as insect infestation, wildfire, timber harvesting and building construction, often cumulatively and interactively affect ecosystem services (ESs). There is a lack of studies exploring the impacts of natural and human disturbances on ESs and the trade-offs/synergies among ESs, particularly in mountain ecosystem with diverse landscapes. Using the Qilian Nature Reserve as a case study area, this study quantified mountain disturbances through Landsat time-series data and BEAST algorithm, evaluated three critical ESs of carbon sequestration, water yield and habitat quality through CASA model and InVEST model, using Bayesian Networks (BN) model to simulate ES, and quantified the impacts of disturbances on ESs and their trade-offs under different environment conditions through multi-scenario analysis. The results suggest that BEAST could effectively capture the patterns and dynamics of small-scale disturbances, which were difficult to be reflected by normal land use/cover products. The established BN model could simulate the spatio-temporal dynamics of carbon sequestration, water yield and habitat quality, with average classification error of 17.81%, 12.72% and 4.49% for each ES, respectively. Significant synergy exists between carbon sequestration and habitat quality at regional scale, while trade-off existed between water yield and the other two ESs. Specifically, these trade-offs/synergies among ESs tend to be weak at medium state of ESs, but stronger at higher and lower states. Thus, significant differences existed in the “win-lose” solutions between water yield and the other two ESs, further resulting the limited space to improve three ESs simultaneously. Disturbances at medium frequency and low-medium intensity are beneficial for the maintenance and improvement of three ESs. BN model has been demonstrated to be a promising decision support tool to integrate small-scale disturbances into ES evaluation and identify the most suitable management solutions for mountain ecosystems, which could provide critical information for decision-makers and guiding sustainable development.

Accurate, comprehensive, and spatially continuous monitoring of mining-induced surface subsidence is essential for geohazard prevention, ecological restoration, and safe mining. Conventional approaches, however, are limited by the sparse spatial distribution of GNSS observations, the difficulty of InSAR in resolving large and rapidly evolving deformation, and the reliance of probability integral method (PIM) calibration on sparse observations. Here, we evaluate an integrated UAV-LiDAR–PIM workflow for basin-wide characterization of mining-induced subsidence and improved spatial constraint of PIM parameters. We use the 3206 working face in an Ordos mining area as a case study. DEM differencing of multi-temporal UAV-LiDAR observations characterizes the spatial distribution of the subsidence basin. Uniform sampling within the affected zone then provides a high-density dataset for PIM calibration, which we compare with conventional profile-based monitoring. The two UAV-LiDAR DEM epochs yielded vertical quality-control RMSEs of 0.052 and 0.050 m, respectively, with a mean of 0.051 m. These results support their use for basin-scale deformation analysis. The inverted angular parameters indicate a larger mining influence extent along the dip direction than along the strike. Surface movement followed initial, active, and declining stages over 423 days, with a maximum subsidence rate of 87.39 mm d⁻¹ during the active stage. The integrated workflow reduces the spatial-sampling limitations of profile-based monitoring and adds two-dimensional constraints on basin geometry and directional variation. Combining basin-wide UAV-LiDAR observations with conventional profiles may improve site-specific PIM calibration and support mining-hazard assessment and ecological-restoration planning.