Urban Heat Island Differentiation and Influencing Factors: A Local Climate Zone Perspective

With the acceleration of urbanization, the urban heat island (UHI) effect has become a major environmental challenge, severely affecting the quality of life of residents and the ecological environment. Quantitative analysis of the factors influencing urban heat island intensity (UHII) is crucial for precise urban planning. Although extensive research has investigated the causes of UHI effects and their spatial variability, most studies focus on macro-scale analyses, overlooking the spatial heterogeneity of thermal characteristics within local climate zones (LCZs) under rapid urbanization. To address this gap, this study took the central urban area of Chengdu, constructing a LCZ map using multisource remote sensing data. Moran’s Index was employed to analyze the spatial clustering effects of UHI across different LCZs. By constructing Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models, the study further explored the influencing factors within these climate zones. The results showed that: (1) Chengdu’s built and natural environments had comparable proportions, with the scattered building zone comprising the highest proportion at 22.12% in the built environment, and the low vegetation zone accounting for 21.8% in the natural environment. The UHII values in this study ranged from 10.2 °C to −1.58 °C, based on specific measurement conditions. Since UHII varied with meteorological conditions, time, seasons, and the selection of rural reference points, these values represented dynamic results during the study period and were not constant. (2) Chengdu’s urban spatial morphology and UHII exhibited significant spatial heterogeneity, with a global Moran’s I index of 0.734, indicating a high degree of spatial correlation. The highest local Moran’s I value was found in the proportion of impervious surfaces (0.776), while the lowest is in the floor area ratio (0.176). (3) The GWR model demonstrated greater explanatory power compared to the OLS model, with a fit of 0.827. The impact of spatial morphological factors on UHII varied significantly across different environments, with the most substantial difference observed in the sky view factor, which has a standard deviation of 13.639. The findings provide precise recommendations for ecological spatial planning, aiming to mitigate the UHI effect and enhance the quality of life for urban residents.