Optimizing Urban Thermal Environments Through 2D/3D Landscape Pattern Analysis: A Machine Learning-Driven Approach for the Yangtze River Delta Urban Agglomeration

To address the escalating urban heat stress driven by global warming and rapid urbanization, this study integrates multi-source remote sensing data to assess the spatiotemporal dynamics of summer thermal comfort across the Yangtze River Delta Urban Agglomeration (YRDUA) from 2000 to 2020. By combining 2D landscape pattern metrics with 3D building morphological features, this study employs an XGBoost model enhanced with SHAP and PDP techniques to reveal the nonlinear and threshold effects of landscape configurations on the Universal Thermal Climate Index (UTCI). The results show the following: (1) during the study period, over 90% of the region experienced strong or extreme heat stress, and 76.8% of the area exhibited a rising UTCI trend, with an average increase of 0.09 °C per year; (2) forest coverage exceeding 50% reduced the UTCI by approximately 2.5 °C, and an increased water area lowered the UTCI by around 1.5 °C, while highly clustered cropland intensified the UTCI by about 1.5 °C; and (3) a moderate increase in building height and shape complexity improved ventilation and shading, reducing the UTCI by roughly 0.5 °C. These findings highlight that optimizing the blue–green infrastructure and 3D urban form are effective strategies to mitigate urban heat stress, offering scientific guidance for sustainable urban planning.