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Spatiotemporal Patterns and Drivers of the Surface Urban Heat Island in 36 Major Cities in China: A Comparison of Two Different Methods for Delineating Rural Areas

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  • Lu Niu

    (State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Ronglin Tang

    (State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Yazhen Jiang

    (State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Xiaoming Zhou

    (School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, China)

Abstract

Urban heat islands (UHIs) are an important issue in urban sustainability, and the standardized calculation of surface urban heat island (SUHI) intensity has been a common concern of researchers in the past. In this study, we used the administrative borders (AB) method and an optimized simplified urban-extent (OSUE) algorithm to calculate the surface urban heat island intensity from 2001 to 2017 for 36 major cities in mainland China by using Moderate Resolution Imaging Spectroradiometer (MODIS) images. The spatiotemporal differences between these two methods were analyzed from the perspectives of the regional and national patterns and the daily, monthly, and annual trends. Regardless of the spatial or temporal scale, the calculation results of these two methods showed extremely similar patterns, especially for the daytime. However, when the calculated SUHI intensities were investigated through a regression analysis with multiple driving factors, we found that, although natural conditions were the main drivers for both methods, the anthropogenic factors obtained from statistical data (population and gross domestic product) were more correlated with the SUHI intensity from the AB method. This trend was probably caused by the spatial extent of the statistical data, which aligned more closely with the rural extent in the AB method. This study not only explores the standardization of the calculation of urban heat intensity but also provides insights into the relationship between urban development and the SUHI.

Suggested Citation

  • Lu Niu & Ronglin Tang & Yazhen Jiang & Xiaoming Zhou, 2020. "Spatiotemporal Patterns and Drivers of the Surface Urban Heat Island in 36 Major Cities in China: A Comparison of Two Different Methods for Delineating Rural Areas," Sustainability, MDPI, vol. 12(2), pages 1-17, January.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:2:p:478-:d:306404
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    References listed on IDEAS

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    1. Chang Cao & Xuhui Lee & Shoudong Liu & Natalie Schultz & Wei Xiao & Mi Zhang & Lei Zhao, 2016. "Urban heat islands in China enhanced by haze pollution," Nature Communications, Nature, vol. 7(1), pages 1-7, November.
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    3. repec:wyi:journl:002258 is not listed on IDEAS
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    5. Gao, Xiang & Long, Cheryl Xiaoning, 2014. "Cultural border, administrative border, and regional economic development: Evidence from Chinese cities," China Economic Review, Elsevier, vol. 31(C), pages 247-264.
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    Cited by:

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    2. Yuji Murayama & Matamyo Simwanda & Manjula Ranagalage, 2021. "Spatiotemporal Analysis of Urbanization Using GIS and Remote Sensing in Developing Countries," Sustainability, MDPI, vol. 13(7), pages 1-5, March.
    3. Nadeem Ullah & Muhammad Amir Siddique & Mengyue Ding & Sara Grigoryan & Irshad Ahmad Khan & Zhihao Kang & Shangen Tsou & Tianlin Zhang & Yike Hu & Yazhuo Zhang, 2023. "The Impact of Urbanization on Urban Heat Island: Predictive Approach Using Google Earth Engine and CA-Markov Modelling (2005–2050) of Tianjin City, China," IJERPH, MDPI, vol. 20(3), pages 1-15, February.
    4. He, Bao-Jie & Wang, Junsong & Zhu, Jin & Qi, Jinda, 2022. "Beating the urban heat: Situation, background, impacts and the way forward in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    5. Muhammad Sajid Mehmood & Zeeshan Zafar & Muhammad Sajjad & Sadam Hussain & Shiyan Zhai & Yaochen Qin, 2022. "Time Series Analyses and Forecasting of Surface Urban Heat Island Intensity Using ARIMA Model in Punjab, Pakistan," Land, MDPI, vol. 12(1), pages 1-20, December.
    6. Paul Eduardo Vásquez-Álvarez & Carlos Flores-Vázquez & Juan-Carlos Cobos-Torres & Sandra Lucía Cobos-Mora, 2022. "Urban Heat Island Mitigation through Planned Simulation," Sustainability, MDPI, vol. 14(14), pages 1-15, July.

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