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城市气候是否遵循城市形态?分析具有不同背景气候的 3 个城市的城市内 LST 轨迹与城市形态趋势。

Does urban climate follow urban form? Analysing intraurban LST trajectories versus urban form trends in 3 cities with different background climates.

机构信息

Institute of Geography, Ruhr University Bochum, 44801 Bochum, Germany.

Institute of Geography, Ruhr University Bochum, 44801 Bochum, Germany; Universidad Autónoma de Chile, 7500912 Santiago, Chile.

出版信息

Sci Total Environ. 2022 Jul 15;830:154570. doi: 10.1016/j.scitotenv.2022.154570. Epub 2022 Mar 14.

DOI:10.1016/j.scitotenv.2022.154570
PMID:35302019
Abstract

The current climate change trend urges the application of efficient spatial planning to mitigate the effects of urbanization on local urban warming. Nevertheless, how urban temperatures respond to urban form changes inside cities is still insufficiently understood. In this paper, we explored the relationship between urban form and diurnal space-time land surface temperature (LST) trends (2003-2019) in Beijing (continental climate), Cairo (arid) and Santiago (temperate). We analysed changes in land cover, white sky albedo (WSA), night-time lights (NL) and the enhanced vegetation index (EVI) inside areas representing clustered thermal performance (steady cold and hot spots and warming cold and hot spots). The structure of local climate zones (LCZs) was assessed for each LST trend. To test the relevance of other urban form dimensions, we analysed the hierarchical influence of the employed 2D metrics (i.e., built-up cover, WSA, NL and EVI) and additional 3D indicators (i.e., height and volume) on LST, applying machine learning classification and regression trees (CARTs) to Beijing's data. Despite diverse patterns of urban form change, cities in our sample present common LST trends, with thermal differences as a consequence of local climate. LCZs are composed of highly heterogeneous built-up areas inside LST trend categories. In the case of Beijing, LST is hierarchically driven by footprint, WSA and EVI. Moreover, by adding height and volume, urban form differences between LST trend classes that are not evident with 2D data were found. Our findings suggest that a compact green urban tissue is necessary to cope with the current trends of urban warming, taking into account city-specific measures based on the local background climate.

摘要

当前的气候变化趋势促使人们应用高效的空间规划来减轻城市化对当地城市热的影响。然而,城市形态变化如何影响城市内部的日地时空地表温度(LST)趋势仍了解不足。本文探讨了城市形态与北京(大陆性气候)、开罗(干旱)和圣地亚哥(温带)的昼夜时空土地表面温度(LST)趋势(2003-2019)之间的关系。我们分析了代表聚类热性能(稳定冷热点和暖冷热点)区域内的土地覆盖、白天空反照率(WSA)、夜间灯光(NL)和增强植被指数(EVI)的变化。评估了每个 LST 趋势的局部气候区(LCZ)结构。为了测试其他城市形态维度的相关性,我们分析了所采用的 2D 指标(即建筑覆盖、WSA、NL 和 EVI)和其他 3D 指标(即高度和体积)对 LST 的层次影响,将机器学习分类和回归树(CART)应用于北京的数据。尽管城市形态变化模式不同,但我们样本中的城市呈现出共同的 LST 趋势,这是由于当地气候造成的热差异。LCZ 由 LST 趋势类别内高度异质的建成区组成。就北京而言,LST 主要由足迹、WSA 和 EVI 驱动。此外,通过添加高度和体积,可以发现 2D 数据中不明显的 LST 趋势类别之间的城市形态差异。我们的研究结果表明,为了应对当前的城市变暖趋势,有必要建立一个紧凑的绿色城市组织,同时考虑到基于当地背景气候的特定城市措施。

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