Work

UAV-based fine-scale daily relationship characterization between 3D urban surface thermal dynamics and landscape patterns

Year: 2025

Type: article

Abstract: The urban landscape has altered regional thermal circulation, induced the Urban Heat Island (UHI) effect, and exposed over 50% of the global population to heat stress. This study employed an Unmanned Aerial Vehicle (UAV) thermal imager to capture fine-scale 24-hour urban 3D surface thermal dynamics and characterize its daily fine-scale relationship to landscape patterns. The results demonstrated that over 50% of the temperature drift/variations were normalized by the Digital Number Probability Density Function Fitting and Discrete Wavelet Transform-based (DDWT) method, horizontal UHI shifted from built-up areas during high-solar-altitude periods to waters at sunset, while façade insignificantly interacted with the horizontal thermal process. The machine learning model CatBoost correlating 3D landscape metrics and active-pixel-defined temperature process explained 86.0% of temperature variance and 63.5% of temperature change rate variance. The interpretations of Shapley additive explanations (SHAP) and Accumulated Local Effects (ALE) identified albedo and distance thresholds for mitigating UHI as 5 m to trees, 20 m to waters, and 20–50 m to buildings. The framework of this study provides actionable insights for neighborhood-scale UHI mitigation strategies. • 24-h 3D surface thermal dynamics at 0.3 m scale in 2 h interval was identified. • The DDWT efficiently eliminates temperature drift and temporal variation. • CatBoost models 3D interaction of temperature processes and landscape patterns. • SHAP and ALE characterize the contribution of main landscape metrics.

Source: Sustainable Cities and Society

DOI: 10.1016/j.scs.2025.106704