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| China's surface urban heat island drivers and its spatial heterogeneity |
| NIU Lu1, ZHANG Zheng-feng1, PENG Zhong2,3, JIANG Ya-zhen2,3, LIU Meng4, ZHOU Xiao-min5, TANG Rong-lin2,3 |
1. School of Public Administration and Policy, Renmin University of China, Beijing 100872, China;
2. State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
3. University of Chinese Academy of Sciences, Beijing 100049, China;
4. Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China;
5. School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, China |
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Abstract Based on satellite remote sensing data acquired through Moderate Resolution Imaging Spectrometer (MODIS), not only was the annual mean surface urban heat island intensity of 284prefecture-level cities in 2018 figured out, but spatial distribution patterns and spatial agglomeration models of surface urban heat islands in China were analyzed. Combining multivariate remote sensing data, meteorological data and socioeconomic statistics, a geographically weighted regression model was utilized to analyze spatial heterogeneity in main drivers for surface urban heat island intensity during daytime and nighttime. As demonstrated by relevant results, an obvious spatial autocorrelation existed in spatial distribution of China's surface urban heat island intensity. Compared with the traditional global ordinary least squares (OLS) model, interpretation of the drivers was significantly improved according to the geographically weighted regression model. Moreover, determination coefficients for daytime and nighttime increased from 0.651 and 0.189 in the OLS model to 0.876 and 0.659 respectively. In addition, both the residual sum of squares and the Akaike information criterion were calculated to be lower by the geographically weighted regression model. In terms of the drivers, vegetation placed a significantly negative influence on surface urban heat island intensity during the daytime, while structural differences were proved to exist in directions of influence that was applied by other factors along with geographic position changes. On the whole, surface urban heat island intensity was most significantly affected by differences in urban and rural vegetation in daytime; but at night, it was susceptible to socio-economic factors.
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Received: 25 June 2021
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