ArticleThe Science of the total environment2021
Modelling and mapping eye-level greenness visibility exposure using multi-source data at high spatial resolutions.
Article in The Science of the total environment, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Urban greenspace and dementia: Measures, mechanisms, and methodological guidance.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Review
- Predicting dynamic individual out-of-hospital cardiac arrest risks using explainable machine learning: a multicenter study in China.NPJ digital medicine · 2026Article
- Using deep learning to explore the impacts of street-view green space on school myopia prevalence: a multicenter, cross-sectional study.Scientific reports · 2026Article
- Assessing human-scale green equity in the 15-minute city using street-view-based visual landscape indicators and explainable machine learning: a case study of Chengdu, China.Frontiers in public health · 2026Article
- Expert perspectives on exposure-response functions for urban health policy: Lessons from a UBDPolicy workshop.Environmental research · 2026Review
- Examining streetscape visuals and emotional responses through social media and street view image analysis.International journal of health geographics · 2025Article
- Visual exposure to buildings in Switzerland: spatial patterns and changes over six decades.Scientific reports · 2025Article
- Restoring nature, enhancing active mobility: The role of street greenery in the EU's 2024 restoration law.Ambio · 2025Article
- Harnessing Geospatial Artificial Intelligence (GeoAI) for Environmental Epidemiology: A Narrative Review.Current environmental health reports · 2025Review
- Unraveling nonlinear effects of environment features on green view index using multiple data sources and explainable machine learning.Scientific reports · 2024Article
- Measuring the 3-30-300 rule to help cities meet nature access thresholds.The Science of the total environment · 2024Review
- Measuring environmental exposures in people's activity space: The need to account for travel modes and exposure decay.Journal of exposure science & environmental epidemiology · 2023Article
- Effect Modifications of Overhead-View and Eye-Level Urban Greenery on Heat-Mortality Associations: Small-Area Analyses Using Case Time Series Design and Different Greenery Measurements.Environmental health perspectives · 2023Article
- Satisfaction with urban trees associates with tree canopy cover and tree visibility around the home.npj urban sustainability · 2023Article
- Social media and deep learning capture the aesthetic quality of the landscape.Scientific reports · 2021Article
- Walkability and Greenness Do Not Walk Together: Investigating Associations between Greenness and Walkability in a Large Metropolitan City Context.International journal of environmental research and public health · 2021Article
- Residential Green and Blue Spaces and Type 2 Diabetes Mellitus: A Population-Based Health Study in China.Toxics · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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