Exploring the diurnal variations of the driving factors affecting block-based LST in a “Furnace city” using ECOSTRESS thermal imaging

•Diurnal variations of urban thermal environment were explored using ECOSTRESS LST data.•High-density buildings tend to have higher LST regardless of day or nighttime.•Correlation coefficients between urban structure metrics and LST varied diurnally.•Main driving factors affecting block-based LST va...

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Veröffentlicht in:Sustainable cities and society 2023-11, Vol.98, p.104841, Article 104841
Hauptverfasser: Yao, Xiong, Zeng, Xianjun, Zhu, Zhipeng, Lan, Yuxiang, Shen, Yuanping, Liu, Qunyue, Yang, Feng
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Sprache:eng
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Zusammenfassung:•Diurnal variations of urban thermal environment were explored using ECOSTRESS LST data.•High-density buildings tend to have higher LST regardless of day or nighttime.•Correlation coefficients between urban structure metrics and LST varied diurnally.•Main driving factors affecting block-based LST vary throughout the diurnal cycle.•Day-night trade-offs should be considered when selecting alleviating measures. Numerous studies have explored the spatial variations of land surface temperature (LST). However, few have analyzed the diurnal variations of LST and its driving factors at the block scale, nor have they utilized the Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LST products. To fill the gap, this study divided 1865 urban blocks into nine block categories and explored the diurnal variations of the driving factors influencing LST in a “Furnace city” using ECOSTRESS LST data. Our results revealed that the spatial patterns of LST varied diurnally; high LST tended to be distributed in blocks with low-rise high-density buildings in the daytime, whereas in high-rise high-density blocks at nighttime. The diurnal variations of the driving factors were explored according to correlation and stepwise regression analyses. The correlation coefficients between urban structures and LST varied diurnally, and this variance was most apparent in different block types. The stepwise regression analyses indicated that the driving factors considered in this study could explain over 35% of the variations in LST in the nighttime and daytime. Meanwhile, the adaptive planning strategies for different block categories were developed. Our findings provide a new perspective for block-level mitigation of the urban thermal environment.
ISSN:2210-6707
2210-6715
DOI:10.1016/j.scs.2023.104841