A Simplified Framework for High-Resolution Urban Vegetation Classification with Optical Imagery in the Los Angeles Megacity

Abstract

High spatial resolution maps of Los Angeles, California are needed to capture the heterogeneity of urban land cover while spanning the regional domain used in carbon and water cycle models. We present a simplified framework for developing a high spatial resolution map of urban vegetation cover in the Southern California Air Basin (SoCAB) with publicly available satellite imagery. This method uses Sentinel-2 (10–60 × 10–60 m) and National Agriculture Imagery Program (NAIP) (0.6 × 0.6 m) optical imagery to classify urban and non-urban areas of impervious surface, tree, grass, shrub, bare soil/non-photosynthetic vegetation, and water. Our approach was designed for Los Angeles, a geographically complex megacity characterized by diverse Mediterranean land cover and a mix of high-rise buildings and topographic features that produce strong shadow effects. We show that a combined NAIP and Sentinel-2 classification reduces misclassified shadow pixels and resolves spatially heterogeneous vegetation gradients across urban and non-urban regions in SoCAB at 0.6–10 m resolution with 85% overall accuracy and 88% weighted overall accuracy. Results from this study will enable the long-term monitoring of land cover change associated with urbanization and quantification of biospheric contributions to carbon and water cycling in cities.