dc.creator | Labib, S.M. | |
dc.creator | Huck, Jonny J. | |
dc.creator | Lindley, Sarah | |
dc.date.accessioned | 2020-10-21T20:30:39Z | |
dc.date.accessioned | 2022-09-23T18:39:37Z | |
dc.date.available | 2020-10-21T20:30:39Z | |
dc.date.available | 2022-09-23T18:39:37Z | |
dc.date.created | 2020-10-21T20:30:39Z | |
dc.identifier | 0048-9697 | |
dc.identifier | https://doi.org/10.1016/j.scitotenv.2020.143050 | |
dc.identifier | http://hdl.handle.net/20.500.12010/14675 | |
dc.identifier | https://doi.org/10.1016/j.scitotenv.2020.143050 | |
dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/3505050 | |
dc.description.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. | |
dc.language | eng | |
dc.publisher | Science of the Total Environment | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.rights | Abierto (Texto Completo) | |
dc.source | reponame:Expeditio Repositorio Institucional UJTL | |
dc.source | instname:Universidad de Bogotá Jorge Tadeo Lozano | |
dc.subject | Greenspace | |
dc.subject | Eye level greenness visibility | |
dc.subject | Environmental exposure | |
dc.subject | Geographic Information Systems | |
dc.subject | Urban health | |
dc.title | Modelling and mapping eye-level greenness visibility exposure using multi-source data at high spatial resolutions | |