Characterizing the impact of urban morphology heterogeneity on land surface temperature in Guangzhou, China. (October 2016)

• Record Type: Journal Article
• Authors: Guo, Guanhua; Zhou, Xiaoqing; Wu, Zhifeng; Xiao, Rongbo; Chen, Yingbiao
• Is Part Of: Environmental modelling & software. Volume 84(2016:Oct.)
• Page Start: 427
• Page End: 439
• Publication Date: 2016-10
• Subjects: Urban heat island (UHI) -- Land surface temperature (LST) -- Urban morphology -- Building height -- Building density -- Sky view factor (SVF); Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.06.021
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The urban morphology is regarded as one of the main reasons for urban heat island (UHI). However, its effect on UHI in city-scale urban areas has seldom been examined. In this paper, we presented a rule-based regression model for investigating the nonlinear relationship between land surface temperature (LST) and urban morphology represented by building height, building density and sky view factor (SVF) across different dates in 2005. Results found that an urban morphology of medium building height and lower density significantly yielded higher LST variation levels, whereas the lowest LST variation levels occurred in high-rise and high-dense building arrays. Compared to building height, building density had a stronger influence on LST. Medium SVF values produced the lowest LST, whereas the largest and smallest SVF values produced the highest LST. Results also showed how rule-based regression model offer great performance in detecting the nonlinear mechanisms of LST as well. Highlights: Urban morphology of medium building height and a lower building density significantly cause higher LST. Compared to building height, building density has a stronger influence on the LST. A rule-based regression is superior for detecting the nonlinear mechanisms of urban morphology for LST. Medium SVF values produce the coolest LST, whereas the maximal and minimal SVF values yield the highest LST.