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Remote Sens. 2016, 8(9), 716; doi:10.3390/rs8090716

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

Science and Engineering Faculty, Queensland University of Technology (QUT), 2 George Street, 4000 Brisbane, Australia
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Academic Editors: Benjamin Bechtel, Iphigenia Keramitsoglou, Simone Kotthaus, James A. Voogt, Klemen Zakšek, Zhaoliang Li and Prasad S. Thenkabail
Received: 20 June 2016 / Revised: 17 August 2016 / Accepted: 24 August 2016 / Published: 31 August 2016
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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. View Full-Text
Keywords: urban heat island; land surface temperature; geographically weighted regression; landsat OLI; landsat TM; cross-sectional analysis; longitudinal analysis urban heat island; land surface temperature; geographically weighted regression; landsat OLI; landsat TM; cross-sectional analysis; longitudinal analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Deilami, K.; Kamruzzaman, M.; Hayes, J.F. Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia. Remote Sens. 2016, 8, 716.

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