Urban heat islands (UHIs) are intensifying under rapid urbanization and climate change, creating a need for interpretable tools that support climate-sensitive urban planning. This study develops a high-resolution urban climate assessment framework to quantify the effects of urban morphology, vegetation, and topography on land surface temperature (LST) in Clermont-Ferrand, France. Using 1 m thermal imagery and geospatial indicators aggregated across 8286 one-hectare cells, a multiple linear regression model explained 72% of LST variability. Statistical comparison of more than 106,000 grid cells confirmed significantly higher temperatures in urban areas, validating the UHI effect. Vegetation (NDVI) emerged as the dominant cooling factor, whereas road surfaces and Gross Floor Area (GFA) contributed to thermal amplification. Interaction analysis showed that vegetation provides greater cooling benefits in dense urban environments, while topography modifies the thermal influence of urban density. Mediation analysis further demonstrated that NDVI indirectly regulates LST through land-use composition, highlighting both direct and mediated thermal pathways. Sensitivity and scenario analyses illustrated how estimated LST varies across combined configurations of vegetation, development density, road allocation, and topographic conditions. To facilitate practical application, the regression framework was implemented as an interactive planning-support platform integrating scenario exploration, nomogram-based estimation, mediation visualization, and sensitivity analysis. The proposed approach bridges urban climate research and planning practice by providing an explainable framework for rapid urban heat assessment and climate-resilient urban design.
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Mansour et al. (2026) studied this question.
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