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It is crucial to clarify the nonlinear effects of urban multidimensional characteristics on land surface temperature (LST). However, the combined consideration of the urban green space (UGS), water bodies, buildings, and socio-economic factors is limited. And the diurnal differences in their thermal effects have been less considered. In this study, central Beijing was taken as study area. Local climate zones (LCZ) were firstly applied to reveal spatiotemporal heterogeneity of LST. Then, the interpretable machine learning methods were utilized to quantitatively reveal nonlinear thermal effects of urban multidimensional characteristics, i.e., the UGS, water bodies, and building landscape features, and socio-economic features. The results indicated that built type LCZs have a higher average LST compared to natural type LCZs. And the LST of built type LCZs is simultaneously influenced by buildings’ density and height characteristics. Daytime LST is mainly affected by the landscape proportions of UGS, buildings, and trees, while nighttime LST is more influenced by socio-economic and building characteristics. The thermal effects of key factors exhibit nonlinear characteristics. Whether during the day or night, the impact of building coverage on LST is greater than that of building height, consistently exhibiting a warming effect. While, the building height and water body edge density factors both exhibited a reversal trend in their thermal impact between day and night. Our study also emphasized the importance of trees type in UGS and provided recommendations for UGS planning based on sensitivity and contribution considerations. These findings can help to regulate urban LST and promote sustainable urban development. |
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