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Seasonal contrast of the dominant factors for spatial distribution of land surface temperature in urban areas
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2018-09-01 , DOI: 10.1016/j.rse.2018.06.010 Jian Peng , Jinglei Jia , Yanxu Liu , Huilei Li , Jiansheng Wu
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2018-09-01 , DOI: 10.1016/j.rse.2018.06.010 Jian Peng , Jinglei Jia , Yanxu Liu , Huilei Li , Jiansheng Wu
Abstract Urban heat island (UHI) has become an urban eco-environmental problem globally. Land surface temperature (LST) is widely used to quantify UHI. This study used Shenzhen, a southern coastal city in China, as an example to explore the relationship between spatial variation of LST in different seasons and the influencing factors in five dimensions, integrating the methods of ordinary least-squares regression, stepwise regression, all-subsets regression, and hierarchical partitioning analysis. The results showed that the most important factor affecting spatial heterogeneity of LST in summer was the normalized difference build-up index (53.62%, for contributing rate), whereas in the transition season the most important factor was the normalized difference vegetation index (NDVI) (47.84%). In winter the construction land percentage and NDVI (26.84% and 25.56%, respectively) were the most influential. Artificial surface and green space had a dominant effect on LST spatial differentiation. Landscape configuration and diversity were not the dominant influencing factors in summer or in the transition season. Furthermore, the independent contribution rate of the Shannon diversity index (SHDI) reached 8.79% in the transition season, while in winter, the independent contribution rates of SHDI and the landscape shape index were 8.52% and 3.45%, respectively. The influence of landscape diversity and configuration factors tended to increase as LST reduced, while the contribution rate of the important factors such as artificial surface and green space decreased significantly. These relationships indicate that the influence of landscape configuration and diversity factors on LST is relatively weak, and can be easily concealed by the influence of landscape components, especially when the spatial variation of LST is not strong. These findings can help to develop UHI adaptation strategies based on local conditions.
更新日期：2018-09-01
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