Urban heat islands and landscape heterogeneity: Linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns

Alexander Buyantuyev, Jianguo Wu

Research output: Contribution to journalArticle

328 Citations (Scopus)

Abstract

The urban heat island (UHI) phenomenon is a common environmental problem in urban landscapes which affects both climatic and ecological processes. Here we examined the diurnal and seasonal characteristics of the Surface UHI in relation to land-cover properties in the Phoenix metropolitan region, located in the northern Sonoran desert, Arizona, USA. Surface temperature patterns derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer for two day-night pairs of imagery from the summer (June) and the autumn (October) seasons were analyzed. Although the urban core was generally warmer than the rest of the area (especially at night), no consistent trends were found along the urbanization gradient. October daytime data showed that most of the urbanized area acted as a heat sink. Temperature patterns also revealed intra-urban temperature differences that were as large as, or even larger than, urban-rural differences. Regression analyses confirmed the important role of vegetation (daytime) and pavements (nighttime) in explaining spatio-temporal variation of surface temperatures. While these variables appear to be the main drivers of surface temperatures, their effects on surface temperatures are mediated considerably by humans as suggested by the high correlation between daytime temperatures and median family income. At night, however, the neighborhood socio-economic status was a much less controlling factor of surface temperatures. Finally, this study utilized geographically weighted regression which accounts for spatially varying relationships, and as such it is a more appropriate analytical framework for conducting research involving multiple spatial data layers with autocorrelated structures.

Original languageEnglish (US)
Pages (from-to)17-33
Number of pages17
JournalLandscape Ecology
Volume25
Issue number1
DOIs
StatePublished - Jan 2010

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heat island
heat
land cover
surface temperature
regression
metropolitan region
family income
desert
urbanization
environmental impact
driver
ASTER
temperature
analytical framework
pavement
temperature effect
spatial data
trend
temporal variation
imagery

Keywords

  • Geographically weighted regression
  • Land cover
  • Surface temperature
  • Surface urban heat island
  • Urbanization

ASJC Scopus subject areas

  • Nature and Landscape Conservation
  • Ecology
  • Geography, Planning and Development

Cite this

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abstract = "The urban heat island (UHI) phenomenon is a common environmental problem in urban landscapes which affects both climatic and ecological processes. Here we examined the diurnal and seasonal characteristics of the Surface UHI in relation to land-cover properties in the Phoenix metropolitan region, located in the northern Sonoran desert, Arizona, USA. Surface temperature patterns derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer for two day-night pairs of imagery from the summer (June) and the autumn (October) seasons were analyzed. Although the urban core was generally warmer than the rest of the area (especially at night), no consistent trends were found along the urbanization gradient. October daytime data showed that most of the urbanized area acted as a heat sink. Temperature patterns also revealed intra-urban temperature differences that were as large as, or even larger than, urban-rural differences. Regression analyses confirmed the important role of vegetation (daytime) and pavements (nighttime) in explaining spatio-temporal variation of surface temperatures. While these variables appear to be the main drivers of surface temperatures, their effects on surface temperatures are mediated considerably by humans as suggested by the high correlation between daytime temperatures and median family income. At night, however, the neighborhood socio-economic status was a much less controlling factor of surface temperatures. Finally, this study utilized geographically weighted regression which accounts for spatially varying relationships, and as such it is a more appropriate analytical framework for conducting research involving multiple spatial data layers with autocorrelated structures.",
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