Spectral variability in fine-scale drone-based imaging spectroscopy does not impede detection of target invasive plant species - Huelsman, Kelsey; Epstein, Howard; Yang, Xi; Mullori, Lydia; Červená, Lucie; Walker, Roderick (Frontiers in Remote Sensing, 2023)
Land managers are making concerted efforts to control the spread of invasive plants, a task that demands extensive ecosystem monitoring, for which unoccupied aerial vehicles (UAVs or drones) are becoming increasingly popular. The high spatial resolution of UAV imagery may positively or negatively affect plant species differentiation, as reflectance spectra of pixels may be highly variable when finely resolved. We assessed this impact on detection of invasive plant species Ailanthus altissima (tree of heaven) and Elaeagnus umbellata (autumn olive) using fine-resolution images collected in northwestern Virginia in June 2020 by a UAV with a Headwall Hyperspec visible and near-infrared hyperspectral imager. Though E. umbellata had greater intraspecific variability relative to interspecific variability over more wavelengths than A. altissima, the classification accuracy was greater for E. umbellata (95%) than for A. altissima (66%). This suggests that spectral differences between species of interest and others are not necessarily obscured by intraspecific variability. Therefore, the use of UAV-based spectroscopy for species identification may overcome reflectance variability in fine resolution imagery.