Variations of Urban Thermal Risk with Local Climate Zones

Jiaxing Xin, Jun Yang (), Yipeng Jiang (), Zhipeng Shi, Cui Jin, Xiangming Xiao, Jianhong (Cecilia) Xia and Ruxin Yang
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Jiaxing Xin: Human Settlements Research Center, Liaoning Normal University, Dalian 116029, China
Jun Yang: Human Settlements Research Center, Liaoning Normal University, Dalian 116029, China
Yipeng Jiang: School of Marine Law and Humanities, Dalian Ocean University, Dalian 116023, China
Zhipeng Shi: Human Settlements Research Center, Liaoning Normal University, Dalian 116029, China
Cui Jin: Human Settlements Research Center, Liaoning Normal University, Dalian 116029, China
Xiangming Xiao: Department of Microbiology and Plant Biology, Center for Earth Observation and Modeling, University of Oklahoma, Norman, OK 73019, USA
Jianhong (Cecilia) Xia: School of Earth and Planetary Sciences (EPS), Curtin University, Perth, WA 6845, Australia
Ruxin Yang: Jangho Architecture College, Northeastern University, Shenyang 110016, China

IJERPH, 2023, vol. 20, issue 4, 1-14

Abstract: Due to the differences in land cover and natural surroundings within cities, residents in various regions face different thermal risks. Therefore, this study combined multi-source data to analyze the relationship between urban heat risk and local climate zones (LCZ). We found that in downtown Shenyang, the building-type LCZ was mainly found in urban centers, while the natural- type LCZ was mainly found in suburbs. Heat risk was highest in urban centers, gradually decreasing along the suburban direction. The thermal risk indices of the building-type LCZs were significantly higher than those of the natural types. Among the building types of LCZs, LCZ 8 (open middle high-rise) had the highest average thermal risk index (0.48), followed by LCZ 3 (0.46). Among the natural types of LCZs, LCZ E (bare rock and paved) and LCZ F (bare soil and sand) had the highest thermal risk indices, reaching 0.31 and 0.29, respectively. This study evaluated the thermal risk of the Shenyang central urban area from the perspective of LCZs and combined it with high-resolution remote sensing data to provide a reference for thermal risk mitigation in future urban planning.

Keywords: local climate zones; land surface temperature; single-window algorithm; heat risk index; Shenyang (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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