解析
地理
环境规划
环境资源管理
计算机科学
环境科学
人工智能
作者
Tao Sun,Liding Chen,Ranhao Sun
标识
DOI:10.1016/j.ufug.2024.128264
摘要
The thermal environment in urban communities significantly impacts the comfort and well-being of residents. Reducing the surface temperature in these areas is critical for improving the quality of life of residents and mitigating the urban heat island effect. Various factors, including buildings, roads, and green spaces, can influence the cooling effects in community areas. However, there is currently a lack of research focusing on examining the relationship between the surface temperature and community characteristics in urban settings. In this paper, using highly urbanized Beijing as an example, 1-m spatial resolution imagery (GaoFen-2) was used to extract 241 communities with different vegetation and building characteristics. High-resolution surface temperature data, calculated using the Google Earth Engine (GEE), facilitated the investigation of the cooling efficiency of surfaces in communities. We then evaluated the contributions of the green space ratio, building shadows, and green space pattern to the surface temperature. This study revealed the following: (1) an inverse nonlinear relationship was found between the green space ratio and surface temperature. The cooling efficiency decreased by 2/3 when the green space ratio exceeded 0.43-0.46. (2) The proportions of green spaces and building shadows were the primary drivers of cooling, accounting for more than 85% of the overall cooling effect. (3) In communities with high-rise buildings, the cooling effect of building shadows was greater than that of vegetation patterns. This study could provide a reference for renovating existing communities and planning green spaces in newly developed urban areas.
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