Drone remote sensing in urban forest management: A case study

Abstract

We applied drone remote sensing to identify relationships between key forest health indicators collected in the field and four Vegetative Indices (VI) to improve conservation management of urban forests. Key indicators of urban forest health revealed several areas of conservation concern including a majority of overstory trees in moderate to severe decline, canopy gaps, anthropogenic dumping, vines overtaking the forest canopy, and invasion by non-native plant species. We found plot-level vegetation index (VI) values of NDVI, NDRE, GNDVI, and GRVI calculated from drone imagery are significantly related to the impact of several of these ecological concerns as well as metrics of forest composition and equitability. Despite the small number of plots, too few to provide a general predictive framework, these findings indicate a substantial potential for drone remote sensing as a low-cost, efficient tool for urban forest management. We discuss how our findings can advance urban forest management and discuss challenges and opportunities for future drone VI research in urban natural areas.