Spatiotemporal analysis of long-term vegetation dynamics using KNDVI and machine learning-based multifactor analysis

In the context of accelerated climate change and intensified human activities, exploring vegetation responses to diverse environmental stressors is essential for understanding ecosystem resilience and long-term sustainability. This study focuses on the Jialing River Basin in China to evaluate vegetation changes and their driving mechanisms during the growing season from 2000 to 2024. The Kernel Normalized Difference Vegetation Index (KNDVI), computed using the Google Earth Engine platform, was used to characterize vegetation dynamics. A Random Forest model, coupled with SHapley Additive exPlanations (SHAP), was then employed to quantify the effects of topography, climate, soil texture, air pollutants, and land-use attributes on vegetation dynamics. The Random Forest model demonstrated robust performance (training R 2 = 0.774–0.871; testing R 2 = 0.690–0.826; RMSE = 0.038), with minimal overfitting (4.45–8.43 %), indicating strong generalization in capturing KNDVI–environment relationships. Results indicated that: (1) the mean annual KNDVI was 0.263, exhibiting a north–south gradient with higher values in the north. Vegetation degradation occurred in 52.53 % of the basin (85,371.56 km 2 ), mainly in urban and agricultural areas; (2) the Hurst exponent suggested unstable future trends, with both improvement and degradation; and (3) SHAP analysis revealed that vegetation dynamics in the Jialing River Basin were mainly governed by elevation and land use, while climatic factors, particularly temperature, solar radiation, and precipitation, have increasingly influenced vegetation patterns under accelerated warming. The impacts of these factors were characterized by significant nonlinear responses, interactive effects, and threshold behaviors. These findings deepen our understanding of vegetation dynamics in topographically complex basins and highlight the intertwined roles of climate, topography, and anthropogenic factors in shaping ecosystem changes. • KNDVI improves vegetation monitoring by overcoming NDVI limitations. • Mean annual KNDVI was 0.263, higher in the north, lower in the south. • Vegetation degradation affected 52.53% of the basin, mainly urban and agricultural. • Elevation, land use, and climate were key drivers of vegetation change. • Findings support ecological conservation and sustainable land management.