4.7 Article

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

期刊

LANDSCAPE ECOLOGY
卷 25, 期 1, 页码 17-33

出版社

SPRINGER
DOI: 10.1007/s10980-009-9402-4

关键词

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

资金

  1. National Science Foundation [DEB-0423704]
  2. Central Arizona-Phoenix Long-Term Ecological Research [BCS-0508002]
  3. Direct For Biological Sciences [1026865] Funding Source: National Science Foundation

向作者/读者索取更多资源

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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