文献类型：期刊文献

英文题名：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

作者：Liu, Wenrui[1,2,3] Jia, Baoquan[1,2] Li, Tong[1,2] Zhang, Qiumeng[1,2] Ma, Jie[4]

第一作者：Liu, Wenrui

通信作者：Jia, BQ[1];Jia, BQ[2]

机构：[1]Chinese Acad Forestry, Res Inst Forestry, Key Lab Tree Breeding & Cultivat, Natl Forestry & Grassland Adm, Beijing 100091, Peoples R China;[2]Natl Forestry & Grassland Adm, Res Ctr Urban Forestry, Beijing 100091, Peoples R China;[3]Pingdingshan Univ, Sch Tourism & Planning, Pingdingshan 467000, Peoples R China;[4]Henan Inst Sci & Technol, Xinxiang 453003, Henan, Peoples R China

年份：2022

卷号：14

期号：20

外文期刊名：SUSTAINABILITY

收录：;Scopus(收录号：2-s2.0-85140635480);WOS:【SSCI(收录号:WOS:000873522800001)，SCI-EXPANDED(收录号:WOS:000873522800001)】；

基金：This work was supported by the special fund for the basic research and development program in the Central Non-profit Research Institutes of China [No. CAFYBB2020ZB008].

语种：英文

外文关键词：land surface temperature; Landsat 8; urban heterogeneity; geographically weighted regression; urban green space

摘要：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.