A spatially explicit surface urban heat island database for the United States: Characterization, uncertainties, and possible applications
A spatially explicit surface urban heat island database for the United States: Characterization, uncertainties, and possible applications
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DOI：
10.1016/j.isprsjprs.2020.07.021
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发表时间：
2020-10-01
影响因子：
12.7
通讯作者：
Sheriff, G.
中科院分区：
文献类型：
--
作者：
Chakraborty, T.;Hsu, A.;Sheriff, G.
城市热岛效应是由城市尺度上地表空气动力学、热力学和辐射特性的变化所强烈调制的。从气候学和公共卫生的角度来看，对这一现象的兴趣导致了数百项城市热岛研究，其中大多数是在城市基础上进行的。然而，这些研究并没有提供一个完整的图片的城市热岛的行政单位使用一致的方法。为了解决这一差距，我们的特点晴空表面热岛（SUHI）强度在美国所有城市化地区使用修改后的简化城市范围（SUE）的方法相结合的融合遥感数据产品与多个美国人口普查定义的行政城市划定。我们发现，在497个城市化地区中，418个地区的夏季白天SUHI强度最高（1.91 +/- 0.97摄氏度），而冬季白天SUHI强度（0.87 +/- 0.45摄氏度）在439个地区最低。由于城市植被已被频繁引用作为一种有效的方式来减轻城市热岛，我们使用NDVI，卫星派生的代理活绿色植被，和美国人口普查区划定的特点，植被密度如何调制城市间，城市内，和季节间的变化SUHI强度。此外，我们还探讨了海拔和距离海岸混淆SUHI估计。为了进一步量化我们估计的不确定性，我们分析和讨论了这些卫星衍生产品在气候带的一些局限性，特别是使用遥感辐射温度和植被指数作为城市热量和植被覆盖的代理问题。我们展示了这个空间上明确的数据集的应用程序，表明对于大多数城市化地区，SUHI强度较低，在人口普查区具有较高的中位收入和较高比例的白色人。我们的分析还表明，贫困和非白人城市居民可能会遭受夏季SUHI可能的不利影响，而不会获得潜在的好处（例如，冬季气温升高），但要确定这一结果需要未来使用更全面的热应激指标进行研究。这项研究开发了新的方法进步，以表征SUHI及其城市内的变异性，与其他社会经济信息的来源相一致，这可能是相关的，在未来的跨学科研究，并作为一个可能的筛选工具，决策的聚合水平。本研究中开发的数据集在https://datadrivenlab.users.earthengine.app/view/usuhiapp上可视化。
The urban heat island (UHI) effect is strongly modulated by urban-scale changes to the aerodynamic, thermal, and radiative properties of the Earth's land surfaces. Interest in this phenomenon, both from the climatological and public health perspectives, has led to hundreds of UHI studies, mostly conducted on a city-by-city basis. These studies, however, do not provide a complete picture of the UHI for administrative units using a consistent methodology. To address this gap, we characterize clear-sky surface UHI (SUHI) intensities for all urbanized areas in the United States using a modified Simplified Urban-Extent (SUE) approach by combining a fusion of remotely-sensed data products with multiple US census-defined administrative urban delineations. We find the highest daytime SUHI intensities during summer (1.91 +/- 0.97 degrees C) for 418 of the 497 urbanized areas, while the winter daytime SUHI intensity (0.87 +/- 0.45 degrees C) is the lowest in 439 cases. Since urban vegetation has been frequently cited as an effective way to mitigate UHI, we use NDVI, a satellite-derived proxy for live green vegetation, and US census tract delineations to characterize how vegetation density modulates inter-urban, intra-urban, and inter-seasonal variability in SUHI intensity. In addition, we also explore how elevation and distance from the coast confound SUHI estimates. To further quantify the uncertainties in our estimates, we analyze and discuss some limitations of these satellite-derived products across climate zones, particularly issues with using remotely sensed radiometric temperature and vegetation indices as proxies for urban heat and vegetation cover. We demonstrate an application of this spatially explicit dataset, showing that for the majority of the urbanized areas, SUHI intensity is lower in census tracts with higher median income and higher proportion of white people. Our analysis also suggests that poor and non-white urban residents may suffer the possible adverse effects of summer SUHI without reaping the potential benefits (e.g., warmer temperatures) during winter, though establishing this result requires future research using more comprehensive heat stress metrics. This study develops new methodological advancements to characterize SUHI and its intra-urban variability at levels of aggregation consistent with sources of other socioeconomic information, which can be relevant in future inter-disciplinary research and as a possible screening tool for policy-making. The dataset developed in this study is visualized at: https://datadrivenlab.users.earthengine.app/view/usuhiapp.