Understanding spatiotemporal changes in landscape ecological risk is essential for regional ecological security and sustainable land management, particularly in environmentally sensitive areas. Using land-use data from five time points (2000, 2005, 2010, 2015, and 2020) together with topographic, climatic, vegetation, and socioeconomic data, we assessed landscape ecological risk dynamics in Xinzhou City, a typical ecologically sensitive region in the middle Yellow River Basin. We integrated a landscape ecological risk assessment model, spatial autocorrelation analysis, and an optimal-parameter geographical detector (OPGD) to identify the spatiotemporal patterns and key drivers of ecological risk. Built-up land expanded by 131.47% over the study period, while cropland and grassland both contracted. Land-use change was dominated by bidirectional conversion between cropland and grassland, alongside a net shift of both to built-up land. Overall, ecological risk declined: high- and relatively high-risk areas decreased by 1354.15 km². Spatially, the pattern remained stable, with higher risk in basins and lower risk in mountainous regions, although the trend varied across phases. Risk exhibited significant spatial clustering: low–low clusters remained stable in mountainous ecological source areas, whereas high–high clusters persisted in basin agricultural areas. Elevation was the dominant factor in spatial risk differentiation. Combined natural and anthropogenic factors shaped the overall risk pattern. Despite the regional decline, urbanization-induced disturbance remains the principal driver of local risk increases. Our findings demonstrate the value of coupling long-term land-use data with OPGD to evaluate the relative and interactive contributions of terrain constraints and anthropogenic drivers in complex mountain–basin systems, with implications for spatially targeted ecological restoration and sustainable land management in ecologically sensitive transition zones.

Wetlands sustain biodiversity and regulate hydrology, yet their internal functional heterogeneity is still poorly understood. Using a two-stage approach, we surveyed ten plots along the upper, middle, and lower reaches for plant communities, aboveground biomass, and environmental variables, and then monitored a selected site monthly with GF-1 and Sentinel-2 imagery (June 2023–June 2025). Ward clustering grouped the plots into three clusters; Dongzhangwu Wetland (LF10) was separated as a provisional singleton in the six-variable clustering and had the highest cluster-mean aboveground biomass (816.5 ± 426.2 g/m²; reed-dominated quadrats exceeded 900 g/m²) and a distinct soil–water profile. At LF10, NDVI peaked at 0.55 in summer and fell below 0.2 in winter, while NDWI-derived exposed water surfaces varied by more than 1 km², peaking in winter and reaching their lowest point in spring and summer; these extrema may partly reflect masking by emergent vegetation and, in winter, reduced evapotranspiration and possible water supplementation. Air temperature was most strongly correlated with NDVI and negatively correlated with water extent; both relationships persisted after deseasonalization (r = 0.45 for NDVI and −0.42 for water extent), while precipitation was unrelated to water extent. Given the short record, these associations should not be read as causal. The relatively high productivity of LF10 appears associated with its dominant reed community, seasonal hydrological regime, and favorable downstream hydrothermal conditions. The two-stage approach could provide a practical framework for basin-scale wetland monitoring, pending validation at additional sites.