Assessing Normalized Difference Vegetation Index as a proxy of urban greenspace exposure

Abstract

The Normalized Difference Vegetation Index (NDVI) is a popular proxy of urban greenspace (UGS). However, it’s unclear how NDVI approximates physical characteristics of UGS in the context of urban health studies, causing ambiguities in translating research findings to UGS management. Therefore, we collected data from Landsat and MODIS satellites and Lidar 3D scans in New York City as of circa 2013, and we evaluated linear and non-linear relationships between NDVI and UGS characteristics. We found that: (1) % UGS was the best predicted UGS characteristic by NDVI (R2: 0.35–0.90, varies by data source and unit of analysis), whereas average tree height was the worst (R2: 0.09–0.46). The predictive power on % canopy cover, tree density, and crown volume density was in a similar range (R2: 0.10–0.67). Prediction improved with finer-resolution NDVI sources and larger units of analysis at the cost of losing useful variations; (2) There was a saturation effect where a linear relationship underestimated UGS characteristics in areas of high NDVI. These areas typically had NAIP-NDVI greater than the range of 0.08–0.25, Landsat-NDVI greater than the range of 0.42–0.65, and MODIS-NDVI greater than the range of 0.49–0.75; (3) Smaller absolute errors from a linear NDVI-UGS relationship were often found in more developed locations. We therefore recommend NDVI as a reliable predictor of UGS coverage and its use in longitudinal studies. Future studies should also consider fine resolution land cover maps and Lidar, which are increasingly available to derive detailed UGS characteristics.