Land Use Change, Flood Exposure, and Vegetation Stress in the Chi River Basin, Northeast Thailand: A Multi-Temporal Remote Sensing and Machine Learning Assessment

Climate change has intensified hydrological extremes in Southeast Asia, yet the relationships between land use change, flood exposure, and vegetation stress remain poorly understood in tropical floodplain environments. This study assessed land use dynamics, flood exposure, and vegetation stress in the Upper Chi River Basin, Northeast Thailand, using multi-temporal remote sensing datasets spanning 2015–2024, official land use data from the Land Development Department (LDD), and machine learning approaches, including Random Forest classification of binary flood occurrence, SHAP analysis, and spatial statistics. Agricultural land dominated the landscape (84.2% in 2023), with 11.0% land use change occurring between 2015 and 2023, primarily agricultural conversion from forest (38.6% of Forest Area converted) and water body (30.0% of Water Body converted). Flood frequency was highest in areas classified as Water Bodies (0.38 events) and Agricultural Areas (0.55 events), while Forest Areas showed the greatest resilience (0.05 events). Random Forest classification of flood occurrence achieved an overall accuracy of 0.920 and AUC-ROC of 0.905, with DEM emerging as the dominant predictor in both Random Forest importance (0.435) and mean absolute SHAP (0.308). Vegetation stress affected 21.1% of the basin, with 59.2% of points showing an NDVI decline greater than 0.02 and 33.3% falling below the VCI < 0.4 drought threshold. Four sensitivity classes were identified: Low (66.4%), Moderate (19.2%), High (12.4%), and Very High (2.0%), with Very High Sensitivity concentrated along the Chi River corridor in low-lying areas (mean DEM = 146.0 m). Moran’s I confirmed strong spatial clustering of flood frequency (I = 0.787) and sensitivity class (I = 0.534), highlighting the need for spatial statistical approaches. The findings support elevation-based land use zoning, forest conservation, and climate-resilient agricultural practices to enhance flood resilience and contribute to Sustainable Development Goals 2 (Zero Hunger), 11 (Sustainable Cities), 13 (Climate Action), and 15 (Life on Land).