Exploring the Effects of Roadside Vegetation on the Urban Thermal Environment Using Street View Images

被引:8
|
作者
Li, Bin [1 ]
Xing, Hanfa [1 ,2 ,3 ]
Cao, Duanguang [1 ]
Yang, Guang [2 ,3 ]
Zhang, Huanxue [1 ]
机构
[1] Shandong Normal Univ, Coll Geog & Environm, Jinan 250300, Peoples R China
[2] South China Normal Univ, Fac Engn, Beidou Res Inst, Foshan 528000, Peoples R China
[3] South China Normal Univ, Sch Geog, Guangzhou 510631, Peoples R China
基金
中国国家自然科学基金;
关键词
spatial relationship; grass-shrub-tree; land surface temperature; geographically weighted regression model; GEOGRAPHICALLY WEIGHTED REGRESSION; LAND-SURFACE TEMPERATURE; HEAT-ISLAND; CITY; GREENERY; IMPACTS; DAYTIME;
D O I
10.3390/ijerph19031272
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Roadsides are important urban public spaces where residents are in direct contact with the thermal environment. Understanding the effects of different vegetation types on the roadside thermal environment has been an important aspect of recent urban research. Although previous studies have shown that the thermal environment is related to the type and configuration of vegetation, remote sensing-based technology is not applicable for extracting different vegetation types at the roadside scale. The rapid development and usage of street view data provide a way to solve this problem, as street view data have a unique pedestrian perspective. In this study, we explored the effects of different roadside vegetation types on land surface temperatures (LSTs) using street view images. First, the grasses-shrubs-trees (GST) ratios were extracted from 19,596 street view images using semantic segmentation technology, while LST and normalized difference vegetation index (NDVI) values were extracted from Landsat-8 images using the radiation transfer equation algorithm. Second, the effects of different vegetation types on roadside LSTs were explored based on geographically weighted regression (GWR), and the different performances of the analyses using remotely sensed images and street view images were discussed. The results indicate that GST vegetation has different cooling effects in different spaces, with a fitting value of 0.835 determined using GWR. Among these spaces, the areas with a significant cooling effect provided by grass are mainly located in the core commercial area of Futian District, which is densely populated by people and vehicles; the areas with a significant cooling effect provided by shrubs are mainly located in the industrial park in the south, which has the highest industrial heat emissions; the areas with a significant cooling effect provided by trees are mainly located in the core area of Futian, which is densely populated by roads and buildings. These are also the areas with the most severe heat island effect in Futian. This study expands our understanding of the relationship between roadside vegetation and the urban thermal environment, and has scientific significance for the planning and guiding of urban thermal environment regulation.
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页数:18
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