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

S.M Labib,Jonny J Huck,Sarah Lindley

doi:10.1016/j.scitotenv.2020.143050

S.M Labib, Jonny J Huck + Show 1 more

Open Access

https://doi.org/10.1016/j.scitotenv.2020.143050

Copy DOI

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.

Highlights

The visibility of the natural environment, exposure to visible greenness, is associated with psychological mechanisms of positive health outcomes, as elucidated by attention restoration theory (Kaplan and Kaplan, 1989; Kaplan, 1995; Kaplan, 2001) and stress recovery theory (Ulrich, 1984; Ulrich et al, 1991; Brown et al, 2013)
The new Viewshed Greenness Visibility Index (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
We modelled and mapped greenness visibility at 5 m spatial resolution; it is possible that our land use and land cover (LULC) map has misclassified small greenspaces sized less than 25 m2, and that the digital surface model (DSM) has underestimated the height of smaller objects

Summary

Introduction

The visibility of the natural environment, exposure to visible greenness, is associated with psychological mechanisms of positive health outcomes, as elucidated by attention restoration theory (Kaplan and Kaplan, 1989; Kaplan, 1995; Kaplan, 2001) and stress recovery theory (Ulrich, 1984; Ulrich et al, 1991; Brown et al, 2013). Analyses of the health benefits of the natural environment tend to rely on measures of blue and greenspace availability and/or accessibility This reliance has emerged partly due to the relative ease of developing associated exposure metrics based on satellite images (e.g., Normalised Differential Vegetation Index - NDVI) or other spatially explicit data (e.g., land cover) (Zhan et al, 2020; Huang et al, 2020; Browning and Lee, 2017). These availability and accessibility metrics usually do not fully capture all of the pathways through which humans experience nature (Labib et al, 2020a; Lindley et al, 2019; Díaz et al, 2018), in part due to such metrics being derived from a top-down ‘bird's eye’ view in 2D space (x, y) (Labib et al, 2020a; Wang et al, 2020). Omission of explicit greenness visibility metrics may be important in urban environments where there is limited green infrastructure supporting nature's contributions to people (Tzoulas et al, 2007; Díaz et al, 2018; Helbich et al, 2019)

Methods

Results

Discussion

Conclusion