Correlation Analysis between Urban Green Space and Land Surface Temperature from the Perspective of Spatial Heterogeneity: A Case Study within the Sixth Ring Road of Beijing

Wenrui Liu, Baoquan Jia (), Tong Li, Qiumeng Zhang and Jie Ma
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Wenrui Liu: Research Institute of Forestry, Chinese Academy of Forestry, Key Laboratory of Tree Breeding and Cultivation, National Forestry and Grassland Administration, Beijing 100091, China
Baoquan Jia: Research Institute of Forestry, Chinese Academy of Forestry, Key Laboratory of Tree Breeding and Cultivation, National Forestry and Grassland Administration, Beijing 100091, China
Tong Li: Research Institute of Forestry, Chinese Academy of Forestry, Key Laboratory of Tree Breeding and Cultivation, National Forestry and Grassland Administration, Beijing 100091, China
Qiumeng Zhang: Research Institute of Forestry, Chinese Academy of Forestry, Key Laboratory of Tree Breeding and Cultivation, National Forestry and Grassland Administration, Beijing 100091, China
Jie Ma: Henan Institute of Science and Technology, Xinxiang 453003, China

Sustainability, 2022, vol. 14, issue 20, 1-20

Abstract: Urban greening has been widely regarded as the most effective, lasting, and economical strategy for alleviating the effects of urban heat islands (UHIs). Previous studies on the cooling effect of urban green spaces (UGSs) tend to analyze the correlation between landscape metrics and land-surface temperature (LST) based on a global parameter estimation, while ignoring urban heterogeneity and autocorrelation. This study focuses on the sixth ring road of Beijing and uses Landsat 8 imagery to retrieve the LST and extract the position of UGSs. We propose a new approach to optimize the selection of landscape metrics, to identify the least and most effective metrics to establish a geographically weighted regression (GWR) model, and to plot the distribution of local regression coefficients to investigate the spatially heterogeneous cooling effects of greenspaces. The effect of UGS landscape metrics on the LST differs according to spatial location; the method enhances our understanding of the effects of UGS spatial configuration on UHIs and better guides the planning and construction of future UGSs.

Keywords: land surface temperature; Landsat 8; urban heterogeneity; geographically weighted regression; urban green space (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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