Surface Urban Heat Island Dynamics and Land Use Change in the Shillong Planning Area, India: A Geospatial and Machine Learning Approach

The escalating climate crisis presents a profound challenge to global environmental equilibrium, with rapid urbanization acting as a primary catalyst for land-use transformation. This study investigates the intricate relationship between land use and land cover changes (LULC) and the intensification of the Surface Urban Heat Island (SUHI) effect within the Shillong Planning Area (SPA). By integrating remote sensing data with advanced geospatial modeling and machine learning architectures which include Random Forest (RF), Support Vector Machine (SVM), and XGBoost, the research provides a comprehensive analysis of environmental shifts from 2000 to 2024, with predictive projections extending to 2034 and 2044. The analysis reveals a significant expansion in the built environment, with the Normalized Difference Built-up Index (NDBI) rising from 0.17 to 0.26. This urban growth has come at the expense of ecological health, as evidenced by a decline in the Normalized Difference Vegetation Index (NDVI) from a peak of 0.87 down to 0.74. A strong negative correlation between vegetative density and Land Surface Temperature (LST) underscores the critical role of green infrastructure in regional climate regulation. SUHI projections using the RF model, which achieved an Area Under the Curve (AUC) of 0.868, estimate SUHI values of 6.02 °C for 2034 and 6.66 °C for 2044. Predicted LULC scenarios for 2034 and 2044 suggest continued urban expansion, likely intensifying thermal stress. The application of predictive modeling through machine learning provides a robust framework to inform climate-resilient urban planning and sustainable land management.