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Analysis of Surface Urban Heat Island in the Guangzhou-Foshan Metropolitan Area Based on Local Climate Zones

Author

Listed:
  • Xiaxuan He

    (School of Architecture, South China University of Technology, Guangzhou 510641, China
    School of Civil Engineering and Architecture, Guangxi Minzu University, Nanning 530008, China)

  • Qifeng Yuan

    (School of Architecture, South China University of Technology, Guangzhou 510641, China
    State Key Laboratory of Subtropical Building and Urban Science, Guangzhou 510640, China)

  • Yinghong Qin

    (School of Civil Engineering and Architecture, Guangxi Minzu University, Nanning 530008, China)

  • Junwen Lu

    (School of Architecture, South China University of Technology, Guangzhou 510641, China)

  • Gang Li

    (School of Architecture, South China University of Technology, Guangzhou 510641, China)

Abstract

Understanding the driving mechanisms behind surface urban heat island (SUHI) effects is essential for mitigating the degradation of urban thermal environments and enhancing urban livability. However, previous studies have primarily concentrated on central urban areas, lacking a comprehensive analysis of the entire metropolitan area over distinct time periods. Additionally, most studies have relied on regression analysis models such as ordinary least squares (OLS) or logistic regression, without adequately analyzing the spatial heterogeneity of factors influencing the surface urban heat island (SUHI) effects. Therefore, this study aims to explore the spatial heterogeneity and driving mechanisms of surface urban heat island (SUHI) effects in the Guangzhou-Foshan metropolitan area across different time periods. The Local Climate Zones (LCZs) method was employed to analyze the landscape characteristics and spatial structure of the Guangzhou-Foshan metropolis for the years 2013, 2018, and 2023. Furthermore, Geographically Weighted Regression (GWR), Multi-scale Geographically Weighted Regression (MGWR), and Geographical Detector (GD) models were utilized to investigate the interactions between influencing factors (land cover factors, urban environmental factors, socio-economic factors) and Surface Urban Heat Island Intensity (SUHII), maximizing the explanation of SUHII across all time periods. Three main findings emerged: First, the Local Climate Zones (LCZs) in the Guangzhou-Foshan metropolitan area exhibited significant spatial heterogeneity, with a non-linear relationship to SUHII. Second, the SUHI effects displayed a distinct core-periphery pattern, with Large lowrise (LCZ 8) and compact lowrise (LCZ 3) areas showing the highest SUHII levels in urban core zones. Third, land cover factors emerged as the most influential factors on SUHI effects in the Guangzhou-Foshan metropolis. These results indicate that SUHI effects exhibit notable spatial heterogeneity, and varying negative influencing factors can be leveraged to mitigate SUHI effects in different metropolitan locations. Such findings offer crucial insights for future urban policy-making.

Suggested Citation

  • Xiaxuan He & Qifeng Yuan & Yinghong Qin & Junwen Lu & Gang Li, 2024. "Analysis of Surface Urban Heat Island in the Guangzhou-Foshan Metropolitan Area Based on Local Climate Zones," Land, MDPI, vol. 13(10), pages 1-34, October.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:10:p:1626-:d:1493471
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    References listed on IDEAS

    as
    1. Lingfei Shi & Feng Ling & Giles M. Foody & Zhen Yang & Xixi Liu & Yun Du, 2021. "Seasonal SUHI Analysis Using Local Climate Zone Classification: A Case Study of Wuhan, China," IJERPH, MDPI, vol. 18(14), pages 1-13, July.
    2. Yan Huang & Wei Lang & Tingting Chen & Jiemin Wu, 2023. "Regional Coordinated Development in the Megacity Regions: Spatial Pattern and Driving Forces of the Guangzhou-Foshan Cross-Border Area in China," Land, MDPI, vol. 12(4), pages 1-27, March.
    3. Chenmei Liao & Yifan Zuo & Rob Law & Yingying Wang & Mu Zhang, 2022. "Spatial Differentiation, Influencing Factors, and Development Paths of Rural Tourism Resources in Guangdong Province," Land, MDPI, vol. 11(11), pages 1-18, November.
    4. Yinuo Xu & Wei Hou & Chunxiao Zhang, 2023. "Spatial Association Rules and Thermal Environment Differentiation Evaluation of Local Climate Zone and Urban Functional Zone," Land, MDPI, vol. 12(9), pages 1-18, August.
    5. Matthias Schonlau & Rosie Yuyan Zou, 2020. "The random forest algorithm for statistical learning," Stata Journal, StataCorp LLC, vol. 20(1), pages 3-29, March.
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