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.
引用
收藏
页数:18
相关论文
共 50 条
  • [31] Diagnosis and Planning Strategies for Quality of Urban Street Space Based on Street View Images
    Wang, Jiwu
    Hu, Yali
    Duolihong, Wuxihong
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2023, 12 (01)
  • [32] Exploring associations between streetscape factors and crime behaviors using Google Street View images
    Deng, Mingyu
    Yang, Wei
    Chen, Chao
    Liu, Chenxi
    FRONTIERS OF COMPUTER SCIENCE, 2022, 16 (04)
  • [33] Exploring associations between streetscape factors and crime behaviors using Google Street View images
    Mingyu Deng
    Wei Yang
    Chao Chen
    Chenxi Liu
    Frontiers of Computer Science, 2022, 16
  • [34] Exploring associations between streetscape factors and crime behaviors using Google Street View images
    Mingyu DENG
    Wei YANG
    Chao CHEN
    Chenxi LIU
    Frontiers of Computer Science, 2022, 16 (04) : 47 - 60
  • [35] Coverage and bias of street view imagery in mapping the urban environment
    Fan, Zicheng
    Feng, Chen-Chieh
    Biljecki, Filip
    COMPUTERS ENVIRONMENT AND URBAN SYSTEMS, 2025, 117
  • [36] Spatial Effects of Landscape Patterns of Urban Patches with Different Vegetation Fractions on Urban Thermal Environment
    Zhang, Yu
    Wang, Yuchen
    Ding, Nan
    REMOTE SENSING, 2022, 14 (22)
  • [37] Development of a system for assessing the quality of urban street-level greenery using street view images and deep learning
    Xia, Yixi
    Yabuki, Nobuyoshi
    Fukuda, Tomohiro
    URBAN FORESTRY & URBAN GREENING, 2021, 59
  • [38] Integrating restorative perception into urban street planning: A framework using street view images, deep learning, and space syntax
    Wu, Yunfei
    Liu, Qiqi
    Hang, Tian
    Yang, Yihong
    Wang, Yijun
    Cao, Lei
    CITIES, 2024, 147
  • [39] Effects of tree plantings with varying street aspect ratios on the thermal environment using a mechanistic urban canopy model
    Chen, Taihan
    Meili, Naika
    Fatichi, Simone
    Hang, Jian
    Tan, Puay Yok
    Yuan, Chao
    BUILDING AND ENVIRONMENT, 2023, 246
  • [40] Vectorized dataset of roadside noise barriers in China using street view imagery
    Qian, Zhen
    Chen, Min
    Yang, Yue
    Zhong, Teng
    Zhang, Fan
    Zhu, Rui
    Zhang, Kai
    Zhang, Zhixin
    Sun, Zhuo
    Ma, Peilong
    Lu, Guonian
    Ye, Yu
    Yan, Jinyue
    EARTH SYSTEM SCIENCE DATA, 2022, 14 (09) : 4057 - 4076