Assessing urban greenery by harvesting street view data: A review

被引:34
作者
Lu, Yanzhi [1 ,2 ]
Ferranti, Emma Jayne Sakamoto [2 ,3 ,5 ]
Chapman, Lee [1 ]
Pfrang, Christian [1 ,4 ]
机构
[1] Univ Birmingham, Sch Geog Earth & Environm Sci, Birmingham B15 2TT, England
[2] Univ Birmingham, Birmingham Inst Forest Res BIFoR, Edgbaston B15 2TT, England
[3] Univ Birmingham, Sch Engn, Edgbaston B15 2TT, England
[4] Univ Reading, Dept Meteorol, Whiteknights Rd, Reading RG6 6BG, England
[5] Univ Birmingham, Sch Engn, Edgbaston B15 2TT, Birmingham, England
基金
英国工程与自然科学研究理事会;
关键词
Street -level image; Urban greening; Street trees; Ecosystem services; SHADE PROVISION; TREES; NEIGHBORHOOD; SKY; LANDSCAPE; SPACE; ASSOCIATIONS; VEGETATION; BENEFITS; REMOVAL;
D O I
10.1016/j.ufug.2023.127917
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Urban greenery is of great significance for sustainable urban development due to the diverse ecosystem services it provides. Assessing urban greenery can reveal its impact on urban areas and provide the evidence base for strategic urban forest management and planning, thereby contributing to sustainable urban development. Street View (SV) images are being used more frequently and widely for assessing urban greenery due to the advantages of providing new perspective and saving workload and research costs. In this paper, 135 peer-reviewed publi-cations that employed SV to assess urban greenery between 2010 and 2022 are reviewed. Presently, the most widely applied area of SV-based urban greenery research is to extract the green view index. Although this has many potential applications for assessing ecosystem services, it has most often been used to date to identify the impact of street greenery on residents' physical and mental health, activities, and well-being (i.e., cultural ser-vices). In contrast, fewer studies have explored the other ecosystem services related to the greening. Overall, as an emerging urban environment research method, this review shows that there are still challenges in the uti-lisation of SV images for assessing urban greenery applications. These include the insufficient spatial and tem-poral coverage of SV images, low data collection accuracy and immaturity of suitable deep learning techniques on object identification. However, there is clear potential for these approaches to be developed to support a broader range of urban greenery studies that consider different ecosystem services and/or specific types of green infrastructure, for example, street trees.
引用
收藏
页数:14
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