Journal
URBAN CLIMATE
Volume 41, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.uclim.2021.101054
Keywords
Extreme heat; Remote sensing; Principal component analysis (PCA); Heat vulnerability index; Bivariate local indicator of spatial association; (bivariate LISA)
Funding
- Ministry of Science and Technology of ROC
- [MOST 109-2410-H-845-036-is]
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Global climate change has intensified extreme climate events, and this study used remote sensing and principal component analysis to analyze the spatial distribution of extreme heat and heat vulnerability in the Taipei metropolitan area. The findings indicate that the most vulnerable villages are clustered in downtown Taipei.
Global climate change has intensified extreme climate events, such as torrential rain, heavy snow, heatwaves, and droughts. Among all extreme disaster events, extreme heat might lead to long and intensified heatwaves and seriously impact human living environments and ecological habitats. This study applies remote sensing to identify the spatial variation in extreme heat and principal component analysis (PCA) to explore the spatial distribution of the heat vulnerability index in the Taipei metropolitan area. Finally, a bivariate local indicator of spatial association (bivariate LISA) is used to explore the relationship between extreme heat and the heat vulnerability index. The results show that most vulnerable villages are clustered in downtown Taipei. In addition, the bivariate LISA results show an aggregation of 346 high-high clusters, indicating extreme heat and relatively high vulnerability clustered in particular areas. The findings of this study could provide the basis for proposing adequate resilience strategies for areas suffering from extreme heat based on the heat vulnerability composition.
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