Most nature and health research use the normalized difference vegetation index (NDVI) for measuring greenness exposure. However, little is known about what NDVI measures in terms of vegetation types (e.g., canopy, grass coverage) within certain analysis zones (e.g., 500 m buffer). Additionally, exploration is needed to understand how to interpret changes in average NDVI (e.g., per 0.1 increments) exposure in relation to changes in vegetation amount and types. In this study, we aimed to explore what vegetation types and amounts best explain the average NDVI and how changes in average NDVI values indicate changes in different vegetation coverages. We used spatial modeling to sample mean NDVI and percentages of vegetation for sample locations within the Greater Manchester case study area. We fitted linear, nonlinear, and mixed multivariate and univariate generalized additive models (GAMs) for multiple spatial scales to identify the relationships between NDVI and vegetation amount and types. Our results showed that the relationships between NDVI and individual vegetation types mostly follow nonlinear trends. We found that canopy and shrubs coverage exhibited a greater influence on mean NDVI exposure values than grass coverage at 300 and 500 m indicating that NDVI values are sensitive to certain types and amounts of vegetation within various buffer zones. We also identified increment in mean NDVI exposure values at lower, mid, and high ranges might be associated with varying changes in total greenspace percentage and individual vegetation types. For instance, at 300 m buffer, an increment of mean NDVI in the lower range (e.g., from 0.2 to 0.3) is associated with an about 17% increase in greenspace percentage. Overall, interpreting changes in NDVI values for urban greening interventions would require careful evaluation of the relative changes in types and quantities of vegetation for different buffer zones.

While COVID-19 lockdowns have slowed coronavirus transmission, such structural measures also have unintended consequences on mental and physical health. Growing evidence shows that exposure to the natural environment (e.g., blue-green spaces) can improve human health and wellbeing. In this narrative review, we synthesized the evidence about nature's contributions to health and wellbeing during the first two years of the COVID-19 pandemic. We found that during the pandemic, people experienced multiple types of nature, including both outdoors and indoors. Frequency of visits to outdoor natural areas (i.e., public parks) depended on lockdown severity and socio-cultural contexts. Other forms of nature exposure, such as spending time in private gardens and viewing outdoor greenery from windows, may have increased. The majority of the evidence suggests nature exposure during COVID-19 pandemic was associated with less depression, anxiety, stress, and more happiness and life satisfaction. Additionally, nature exposure was correlated with less physical inactivity and fewer sleep disturbances. Evidence was mixed regarding associations between nature exposure and COVID-related health outcomes, while nature visits might be associated with greater rates of COVID-19 transmission and mortality when proper social distancing measures were not maintained. Findings on whether nature exposure during lockdowns helped ameliorate health inequities by impacting the health of lower-socioeconomic populations more than their higher-socioeconomic counterparts for example were mixed. Based on these findings, we argue that nature exposure may have buffered the negative mental and behavioral impacts of lockdowns during the COVID-19 pandemic. Recovery and resilience during the current crises and future public health crises might be improved with nature-based infrastructure, interventions, designs, and governance.

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.