城市热岛
地理
背景(考古学)
索引(排版)
城市规划
环境科学
地图学
自然地理学
环境资源管理
气象学
计算机科学
土木工程
考古
万维网
工程类
作者
Hana Bobáľová,Vladimír Falťan,Alexandra Benová,Miroslav Kožuch,Michaela Kotianová,František Petrovič
标识
DOI:10.1016/j.ufug.2024.128217
摘要
The importance of assessing urban greenery is growing, especially in the context of global warming and the emergence of urban heat islands. The paper proposes a set of measurements to evaluate the quality and accessibility of greenery based on freely available geographic data: Sentinel-2 satellite images, Urban Atlas, and Open Street Map. The quality (or availability) of urban greenery is derived from Sentinel-2 images by using the spectral Forest Index (FI). The proposed Neighborhood Green Quality Indexes (NGQI0/30, NGQI350) evaluate greenery in the near and far surroundings of the spatial unit by averaging the FI values. The Accessibility to Green Space Index (AGSI) measures accessibility to green spaces (GS) based on the number of theoretical functional levels within reach and the average normalised distance to the nearest GS across levels. We also propose to separately assess accessibility to parks with amenities through the Accessibility to Urban Parks Index (AUPI). Such areas attract specific groups of the population, such as elderly people and mothers with children, who are among the most vulnerable during heat waves. The proposed set of measurements can be applied to a square grid and to buildings. A mutual comparison of the proposed measurements in Bratislava, the capital of Slovakia, confirmed that each measurement captures different characteristics of urban greenery. The comparison of FI with two other widely used green quality indicators showed that FI achieves the highest correlation with the land surface temperature during hot summer days. When evaluating the precision of the NGQI0/30 calculation, Sentinel-2-derived FI proved to be a sufficient substitute for high-resolution urban greenery data sources. Since the proposed measurements do not require complex calculations and hard-to-obtain data, they are easily applicable to other cities.
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