Quantitative assessment and driving factors analysis of surface urban heat island of urban agglomerations in China based on GEE

被引：1
作者：
Zhang H. [1 ]
Cen X. [1 ]
An H. [1 ]
Yin Y. [1 ]
机构：
[1] College of Geography and Environmental Science, Northwest Normal University, Lanzhou
来源：
Environmental Science and Pollution Research | 2024年 / 31卷 / 34期
基金：
中国国家自然科学基金;
关键词：
Driving factors; Geo-detector model; Google Earth Engine; Surface urban heat island intensity; Urban agglomerations in China;
D O I：
10.1007/s11356-024-34205-w
中图分类号：
学科分类号：
摘要：
The urban heat island (UHI) effect generated by the development of high-speed urbanization has become one of the major problems affecting the urban ecological environment. As the main body of urbanization in China, China’s urban agglomerations are the core areas of urban heat island effect. The purpose of this study is to study the spatial–temporal characteristics and driving factors of surface urban heat island in 19 urban agglomerations in China, with a view to providing theoretical references for the prevention of urban thermal environmental risks. Based on Google Earth Engine (GEE), this paper estimated the surface urban heat island intensity (SUHII) of 19 urban agglomerations in China from 2003 to 2019 using MODIS land surface temperature (LST) data. Correlation analysis and regression analysis were used to explore the correlation between the change of SUHII and driving factors. Finally, the driving factors of SUHII were detected by the geo-detector model. Results showed that (1) the SUHII of 19 urban agglomerations in arid and semi-arid areas of northwestern China is higher than that in humid areas of eastern and southeastern China. (2) The SUHII of 19 urban agglomerations in China generally shows a decreasing trend, and the spatial variation of the change trend is significant. (3) There are positive correlations between SUHII and reference evapotranspiration (ET0), population density (POP), gross domestic product (GDP), nitrogen dioxide (NO2), ozone (O3), and ultraviolet aerosol index (UVAI); negative correlations with normalized difference vegetation index (NDVI), DEM, sulfur dioxide (SO2), carbon monoxide (CO), and formaldehyde (HCHO); the correlations all pass the significance test of P < 0.05 and are statistically significant. (4) The factor detection results showed that NDVI, land cover type (LC), and UVAI were the main driving factors of SUHII. The interaction detection results showed that the interaction between O3 and UVAI had the most significant impact on SUHII. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
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页码：47350 / 47364
页数：14
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