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Inner Mongolia's Hulunbuir region, a representative temperate grassland and vital ecological barrier in northern China, faces increasing risks from grassland degradation. However, most remote-sensing assessments rely on static vegetation conditions and lack a framework that captures vegetation state and long-term trends. To address this gap, this study proposes a novel risk assessment model integrating ‘state’ and ‘trend’ components to link vegetation dynamics with degradation processes. Using 2010~2024 remote sensing data (FVC, AGB, NPP, ET, and their trends) together with field-measured grassland degradation index (GDI), we employed machine learning to optimize multi-scale nonlinear risk quantification. The Mann-Kendall test and Sen's slope were further used to quantify long-term trends, and threshold analysis was applied to identify ecological stability conditions. The results indicate that (1) degradation generally eased over the 15-year period, with high-risk areas shifting westward; (2) high-risk grassland area decreased in 2024 compared with 2010; and (3) when vegetation height exceeds 60 cm, aboveground biomass (AGB) surpasses 350 g/m² or the number of plant species exceeds 24, grasslands tend to remain at low risk, indicating stable ecosystem structure and function. These findings provide process-based diagnostic thresholds and practical support for regional grassland management and long-term monitoring.

A multi-scale risk assessment of grassland degradation based on vegetation dynamics: a case study in the Hulunbuir Grassland, China