Modelling and mapping eye-level greenness visibility exposure using multi-source data at high spatial resolutions

被引:78
作者
Labib, S. M. [1 ,2 ]
Huck, Jonny J. [1 ]
Lindley, Sarah [1 ]
机构
[1] Univ Manchester, Sch Environm Educ & Dev SEED, Dept Geog, Arthur Lewis Bldg,1st Floor,Oxford Rd, Manchester M13 9PL, Lancs, England
[2] Univ Cambridge, Ctr Diet & Act Res CEDAR, MRC Epidemiol Unit, Clifford Allbutt Bldg, Cambridge CB2 0AH, England
基金
英国艺术与人文研究理事会; 英国自然环境研究理事会; 英国经济与社会研究理事会;
关键词
Greenspace; Eye level greenness visibility; Environmental exposure; Geographic Information Systems; Urban health; Street view; GOOGLE STREET VIEW; VISUAL IMPACT; BLUE SPACE; HEALTH; INFRASTRUCTURE; ASSOCIATIONS; GREENERY; RECOVERY; ENVIRONMENT; GREENSPACE;
D O I
10.1016/j.scitotenv.2020.143050
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The visibility of natural greenness is associated with several health benefits along multiple pathways, including stress recovery and attention restoration mechanisms. However, existing methodologies are inadequate for capturing eye-level greenness visibility exposure at high spatial resolutions for observers located on the ground. As a response, we developed an innovative methodological approach to model and map eye-level greenness visibility exposure for 5 m interval locations within a large study area. We used multi-source spatial data and applied viewshed analysis in conjunction with a distance decay model to compute a novel Viewshed Greenness Visibility Index (VGVI) at more than 86 million observer locations. We compared our eye-level visibility exposure map with traditional top-down greenness exposure metrics such as Normalised Differential Vegetation Index (NDVI) and a Street view based Green View Index (SGVI). Furthermore, we compared greenness visibility at street-only locations with total neighbourhood greenness visibility. We found strong to moderate correlations (r = 0.65-0.42, p < 0.05) between greenness visibility and mean NDVI, with a decreasing trend in correlation strength at increasing buffer distances from observer locations. Our findings suggest that top-down and eye-level measurements of greenness are two distinct metrics for assessing greenness exposure. Additionally, VGVI showed a strong correlation (r = 0.481, p < 0.01) with SGVI. Although the new VGVI has good agreement with existing street view based measures, we found that street-only greenness visibility values are not wholly representative of total neighbourhood visibility due to the under-representation of visible greenness in locations such as backyards and community parks. Our new methodology overcomes such underestimations, is easily transferable, and offers a computationally efficient approach to assessing eye-level greenness exposure. (C) 2020 The Authors. Published by Elsevier B.V.
引用
收藏
页数:14
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