Remote Sensing-Based Analysis of Urban Heat Island Driving Factors: A Local Climate Zone Perspective

Abstract

This study utilized multisource remote sensing data and advanced technology to investigate the potential driving factors of urban heat island (UHI) effects from the perspective of local climate zones (LCZs), including natural, social, and urban three-dimensional (3-D) structural factors. Using MODIS land surface temperature remote sensing data products and supplementary datasets, the simplified urban-extent algorithm was employed to identify UHI areas and quantify UHI Intensity (UHII). The stepwise multiple linear regression method and SHapley Additive exPlanations-explained eXtreme gradient boosting machine learning method were then applied to attribute UHII to 15 selected driving factors across 17 LCZ types in 369 Chinese cities. The findings indicate that large UHI areas are predominantly associated with low-rise LCZ types, where compact building arrangements intensify UHII, and increased building heights exacerbate this effect. During daytime, the UHI effects are largely driven by urban 3-D structures, particularly within LCZ 1-6 areas. Conversely, at night, the UHI effect is more significantly impacted by natural environmental factors. These insights offer a robust scientific foundation for urban planners to craft LCZ-specific strategies aimed at fostering the development of sustainable cities and communities.

Keywords

• Driving factors
• local climate zones
• MODIS land surface temperature (LST)
• remote sensing
• stepwise multiple linear regression (SMLR)
• urban heat island