Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns
Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns
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DOI：
10.1007/s10980-009-9402-4
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发表时间：
2010-01-01
影响因子：
5.2
通讯作者：
Wu, Jianguo
中科院分区：
文献类型：
--
作者：
Buyantuyev, Alexander;Wu, Jianguo
城市热岛（UHI）现象是城市景观中常见的环境问题，影响气候和生态过程。在这里，我们研究了位于美国亚利桑那州索诺兰沙漠北部的凤凰城大都市区与土地覆盖特性相关的地表城市热岛的昼夜和季节特征。对来自先进星载热发射和反射辐射计的夏季（六月）和秋季（十月）两季昼夜图像的表面温度模式进行了分析。尽管城市核心区普遍比其他地区温暖（尤其是在夜间），但沿城市化梯度没有发现一致的趋势。 10月白天数据显示，大部分城市化地区起到了散热器的作用。温度模式还揭示了城市内部的温度差异与城乡差异一样大，甚至更大。回归分析证实了植被（白天）和路面（夜间）在解释地表温度时空变化方面的重要作用。虽然这些变量似乎是地表温度的主要驱动因素，但它们对地表温度的影响在很大程度上是由人类介导的，正如白天温度与家庭收入中位数之间的高度相关性所表明的那样。然而，到了晚上，附近的社会经济地位对地表温度的控制作用要小得多。最后，本研究利用地理加权回归来解释空间变化的关系，因此它是一个更适合进行涉及具有自相关结构的多个空间数据层的研究的分析框架。
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.