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

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...

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Autores principales: Labib, S.M., Huck, Jonny J., Lindley, Sarah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Authors. Published by Elsevier B.V. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7562921/
https://www.ncbi.nlm.nih.gov/pubmed/33129523
http://dx.doi.org/10.1016/j.scitotenv.2020.143050
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author Labib, S.M.
Huck, Jonny J.
Lindley, Sarah
author_facet Labib, S.M.
Huck, Jonny J.
Lindley, Sarah
author_sort Labib, S.M.
collection PubMed
description 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.
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spelling pubmed-75629212020-10-16 Modelling and mapping eye-level greenness visibility exposure using multi-source data at high spatial resolutions Labib, S.M. Huck, Jonny J. Lindley, Sarah Sci Total Environ Article 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. The Authors. Published by Elsevier B.V. 2021-02-10 2020-10-16 /pmc/articles/PMC7562921/ /pubmed/33129523 http://dx.doi.org/10.1016/j.scitotenv.2020.143050 Text en © 2020 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Labib, S.M.
Huck, Jonny J.
Lindley, Sarah
Modelling and mapping eye-level greenness visibility exposure using multi-source data at high spatial resolutions
title Modelling and mapping eye-level greenness visibility exposure using multi-source data at high spatial resolutions
title_full Modelling and mapping eye-level greenness visibility exposure using multi-source data at high spatial resolutions
title_fullStr Modelling and mapping eye-level greenness visibility exposure using multi-source data at high spatial resolutions
title_full_unstemmed Modelling and mapping eye-level greenness visibility exposure using multi-source data at high spatial resolutions
title_short Modelling and mapping eye-level greenness visibility exposure using multi-source data at high spatial resolutions
title_sort modelling and mapping eye-level greenness visibility exposure using multi-source data at high spatial resolutions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7562921/
https://www.ncbi.nlm.nih.gov/pubmed/33129523
http://dx.doi.org/10.1016/j.scitotenv.2020.143050
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