Scale effects in remotely sensed greenspace metrics and how to mitigate them for environmental health exposure assessment

Abstract

Metrics representing exposure to the natural environment are widely used in environmental health-related studies. They are calculated using a variety of different data sources representing greenspace and a range of buffer sizes representing human interaction with the environment. Previous studies have identified issues relating to buffer distance and scaling effects on greenspace exposure assessments when using satellite image-derived metrics. We evaluate the spatial scale sensitivity of three common greenspace metrics (i.e., Normalised Difference Vegetation Index- NDVI, Leaf Area Index- LAI, and Land Use and Land Cover-LULC), using lacunarity analysis, as a scale-dependent measure of heterogeneity based on the principles of fractals. By producing a ‘lacunarity curve’ across multiple spatial scales, we defined the scale-variances for specific greenspace metrics, including the upper scale limit at which the metrics become invariant, approximately 640 m for Sentinel-2 and 480 m for Landsat-8. Each of the greenspace metrics we considered exhibited scale sensitivities, meaning that each is expected to have a different influence on the strength and significance of the statistical associations found between greenspace exposure and health depending on the spatial scale of analysis (e.g., buffer distance). Using lacunarity curves, we produced a novel composite, multi-scale greenspace ‘exposure index’ in which each input scale is weighted according to its relative scale sensitivity. We also created a multi-scale, multi-metric map combining the different vegetation measures while accounting for scale. We found that cumulative exposure gradients across a large urban conurbation are even more marked when using our multi-scale ‘exposure index’ maps compared to traditional approaches. Our multi-scale, composite greenspace ‘exposure index’ mapping techniques are not as vulnerable to scale effects as traditional approaches and can be readily transferred to the analysis of other environmental exposure variables such as air pollution.