Modelling and mapping eye-level greenness visibility exposure using multi-source data at high spatial resolutions
Author
Labib, S.M.
Huck, Jonny J.
Lindley, Sarah
Institutions
Abstract
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.