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

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.

Walkability and Greenness Do Not Walk Together: Investigating Associations between Greenness and Walkability in a Large Metropolitan City Context

“…First, while NDVI is a validated metric of greenness [ 48 ], recent research suggests that tree canopy cover or Google Street View-based indicators may be better indicators of perceived greenness [ 23 , 49 ]. This is because eye-level greenness visibility showed a better correlation with human perception, and top-down measures of greenness are distinct from eye-level measures [ 50 , 51 ]. Second, the NDVI values in this study varied within a narrow band of between 0.2 and 0.5.…”

Social media and deep learning capture the aesthetic quality of the landscape

“…Theoretically, these could capture more of the aesthetic quality of the landscape by providing a 3D perspective using the location of Flickr images. However, the challenge with visibility modelling at very large scales is the computational resources needed for the geo-spatial calculations 60 . For example, in our case, the sightlines from 9.8 million images would need to be calculated using a m Digital Elevation Model (DEM) for a 210,000 area.…”

Residential Green and Blue Spaces and Type 2 Diabetes Mellitus: A Population-Based Health Study in China

“…Fourth, NDVI and EVI were obtained based on the coded residential location from remote sensing; the top-down satellite image was different from the human eye level scene. In future research, we will try to use Google Street View (GSV) and deep learning to calculate the Green Landscape Index (GVI), which refers to greenness from the visual perspective of pedestrians [ 62 , 63 ]. Furthermore, since we did not have information on the traffic and noise, their potential confounding effects were not accounted for in the study [ 64 ].…”