Correlation or causality between land cover patterns and the urban heat island effect? Evidence from Brisbane, Australia

Kaveh Deilami, Md Kamruzzaman, John Francis Hayes

Research output: Contribution to journalArticleResearchpeer-review

19 Citations (Scopus)

Abstract

Numerous studies have identified associations between the surface urban heat island (SUHI) effect (i.e., SUHI, hereinafter is referred to as UHI) and urban growth, particularly changes in land cover patterns. This research questions their causal links to answer a key policy question: If cities restrict urban expansion and encourage people to live within existing urban areas, will that help in controlling UHI? The question has been answered by estimating four models using data from Brisbane, Australia: Model 1-cross-sectional ordinary least square (OLS) regression-to examine the association between the UHI effect and land cover patterns in 2013; Model 2-cross-sectional geographically weighted regression (GWR)-to examine whether the outputs generated from Model 1 possess significant spatial variations; Model 3-longitudinal OLS-to examine whether changes in land cover patterns led to changes in UHI effects between 2004 and 2013; and Model 4-longitudinal GWR-to examine whether the outputs generated from Model 3 vary significantly over space. All estimations were controlled for potential confounding effects (e.g., population, employment and dwelling densities). Results from the cross-sectional OLS and GWR models were consistent with previous findings and showed that porosity is negatively associated with the UHI effect in 2013. In contrast, population density has a positive association. Results from the longitudinal OLS and GWR models confirm their causal linkages and showed that an increase in porosity level reduced the UHI effect, whereas an increase in population density increased the UHI effect. The findings suggest that even a containment of population growth within existing urban areas will lead to the UHI effect. However, this can be significantly minimized through proper land use planning, by creating a balance between urban and non-urban uses of existing urban areas.

Original languageEnglish
Article number716
Number of pages28
JournalRemote Sensing
Volume8
DOIs
Publication statusPublished - 2016
Externally publishedYes

Keywords

  • Cross-sectional analysis
  • Geographically weighted regression
  • Land surface temperature
  • Landsat OLI
  • Landsat TM
  • Longitudinal analysis
  • Urban heat island

Cite this

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title = "Correlation or causality between land cover patterns and the urban heat island effect? Evidence from Brisbane, Australia",
abstract = "Numerous studies have identified associations between the surface urban heat island (SUHI) effect (i.e., SUHI, hereinafter is referred to as UHI) and urban growth, particularly changes in land cover patterns. This research questions their causal links to answer a key policy question: If cities restrict urban expansion and encourage people to live within existing urban areas, will that help in controlling UHI? The question has been answered by estimating four models using data from Brisbane, Australia: Model 1-cross-sectional ordinary least square (OLS) regression-to examine the association between the UHI effect and land cover patterns in 2013; Model 2-cross-sectional geographically weighted regression (GWR)-to examine whether the outputs generated from Model 1 possess significant spatial variations; Model 3-longitudinal OLS-to examine whether changes in land cover patterns led to changes in UHI effects between 2004 and 2013; and Model 4-longitudinal GWR-to examine whether the outputs generated from Model 3 vary significantly over space. All estimations were controlled for potential confounding effects (e.g., population, employment and dwelling densities). Results from the cross-sectional OLS and GWR models were consistent with previous findings and showed that porosity is negatively associated with the UHI effect in 2013. In contrast, population density has a positive association. Results from the longitudinal OLS and GWR models confirm their causal linkages and showed that an increase in porosity level reduced the UHI effect, whereas an increase in population density increased the UHI effect. The findings suggest that even a containment of population growth within existing urban areas will lead to the UHI effect. However, this can be significantly minimized through proper land use planning, by creating a balance between urban and non-urban uses of existing urban areas.",
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Correlation or causality between land cover patterns and the urban heat island effect? Evidence from Brisbane, Australia. / Deilami, Kaveh; Kamruzzaman, Md; Hayes, John Francis.

In: Remote Sensing, Vol. 8, 716, 2016.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Correlation or causality between land cover patterns and the urban heat island effect? Evidence from Brisbane, Australia

AU - Deilami, Kaveh

AU - Kamruzzaman, Md

AU - Hayes, John Francis

PY - 2016

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KW - Geographically weighted regression

KW - Land surface temperature

KW - Landsat OLI

KW - Landsat TM

KW - Longitudinal analysis

KW - Urban heat island

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JO - Remote Sensing

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