Influencing Factor Analysis of Vegetation Spatio-Temporal Variability in the Beijing–Tianjin–Hebei Region Based on Interpretable Machine Learning

To address the insufficient quantitative understanding of vegetation driving mechanisms across spatio-temporal scales, this study integrated multi-source data and machine learning methods to simulate and analyze Normalized Difference Vegetation Index (NDVI) changes in the Beijing–Tianjin–Hebei (BTH) region over the past two decades. Using the SHapley Additive exPlanations (SHAP) method, we identified the most important predictors of climate and human activities in the XGBoost model and quantified their spatial contributions. We further analyzed the spatio-temporal variation of the main predictors across different land use types The main findings were as follows: (1) The XGBoost model achieved excellent performance (R2 > 0.96, MEA < 0.02, RMSE < 0.027) on the datasets from 2000 to 2020, outperforming random forest (RF), support vector machines (SVM), and K-nearest neighbors (KNN) in prediction accuracy. (2) Vegetation showed an overall improving trend, with areas exhibiting significant improvement accounting for 47.96% of the total region. Precipitation, temperature, and human activities were identified as the most significant predictors of NDVI. Their relative importance varied over time, and NDVI responses to these factors exhibited clear spatial heterogeneity. (3) Primary predictors differed by land use type: NDVI in cropland and grassland was mainly driven by precipitation, forest NDVI by temperature, and urban/built-up areas by human activities. This study developed an analytical framework integrating nonlinearity and spatial heterogeneity, achieving a quantitative “overall-categorical” analysis of the important predictors behind NDVI changes. The approach provided a novel methodological reference for attributing vegetation dynamics. The findings contributed to the implementation of classified regulation in the BTH region, promoting the transition of human activities toward ecological restoration.