Estimation of biogenic volatile organic compound (BVOC) emissions in forest ecosystems using drone-based lidar, photogrammetry, and image recognition technologies

Abstract

Biogenic volatile organic compounds (BVOCs), as a crucial component that impacts atmospheric chemistry and ecological interactions with various organisms, play a significant role in the atmosphere–ecosystem relationship. However, traditional field observation methods are challenging for accurately estimating BVOC emissions in forest ecosystems with high biodiversity, leading to significant uncertainty in quantifying these compounds. To address this issue, this research proposes a workflow utilizing drone-mounted lidar and photogrammetry technologies for identifying plant species to obtain accurate BVOC emission data. By applying this workflow to a typical subtropical forest plot, the following findings were made: the drone-mounted lidar and photogrammetry modules effectively segmented trees and acquired single wood structures and images of each tree. Image recognition technology enabled relatively accurate identification of tree species, with the highest-frequency family being Euphorbiaceae. The largest cumulative isoprene emissions in the study plot were from the Myrtaceae family, while those of monoterpenes were from the Rubiaceae family. To fully leverage the estimation results of BVOC emissions directly from individual tree levels, it may be necessary for communities to establish more comprehensive tree species emission databases and models.