Assessment of Urban Heat Risk in Mountain Environments: A Case Study of Chongqing Metropolitan Area, China

Dechao Chen, Xinliang Xu, Zongyao Sun, Luo Liu, Zhi Qiao and Tai Huang
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Dechao Chen: National and Local Joint Engineering Laboratory of Municipal Sewage Resource Utilization Technology, Jiangsu Key Laboratory of Environmental Science and Engineering, School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
Xinliang Xu: State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Zongyao Sun: School of Architecture, Tianjin University, Tianjin 300272, China
Luo Liu: Guangdong Province Key Laboratory for Land Use and Consolidation, The College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Zhi Qiao: School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
Tai Huang: Department of Tourism Management, Soochow University, Suzhou 215123, China

Sustainability, 2019, vol. 12, issue 1, 1-15

Abstract: For urban climatic environments, the urban heat island (UHI) effect resulting from land use and land cover change (LUCC) caused by human activities is rapidly becoming one of the most notable characteristics of urban climate change due to urban expansion. UHI effects have become a significant barrier to the process of urbanization and sustainable development of the urban ecological environment. Predicting the spatial and temporal patterns of the urban heat environment from the spatial relationship between land use and land surface temperature (LST) is key to predicting urban heat environment risk. This study established an Urban Heat Environment Risk Model (UHERM) as follows. First, the urban LST was normalized and classified during three different periods. Second, a Markov model was constructed based on spatio-temporal change in the urban heat environment between the initial year (2005) and middle year (2010), and then a cellular automata (CA) model was used to reveal spatial relationships between the urban heat environments of the two periods and land use in the initial year. The spatio-temporal pattern in a future year (2015) was predicted and the accuracy of the simulation was verified. Finally, the spatio-temporal pattern of urban heat environment risk was quantitatively forecasted based on the decision rule for the urban heat environment risk considering both the present and future status of the spatial characteristics of the urban heat environment. The MODIS LST product and LUCC dataset retrieved from remote sensing images were used to verify the accuracy of UHERM and to forecast the spatio-temporal pattern of urban heat environment risk during the period of 2015–2020. The results showed that the risk of urban heat environment is increasing in the Chongqing metropolitan area. This method for quantitatively evaluating the spatio-temporal pattern of urban heat environment risk could guide sustainable growth and provide effective theoretical and technical support for the regulation of urban spatial structure to minimize urban heat environment risk.

Keywords: urban heat environment risk; spatio-temporal pattern; land surface temperature; land use; CA-Markov model; Chongqing metropolitan area (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
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