Using phenology to improve invasive plant detection in fine‐scale hyperspectral drone‐based images

Abstract Mapping and managing invasive plants are top priorities for land managers, but traditional approaches are time and labor‐intensive. To improve detection efforts, we explored the effectiveness of hyperspectral, drone‐based detection algorithms that incorporate phenology. We collected fine‐resolution (3 cm) hyperspectral images using a drone equipped with a Nano‐Hyperspec imager on seven dates from April to November, 2020 and then used a subsample of pixels from the images to develop multitemporal detection algorithms for three invasive plant species within heterogeneous vegetation communities. The three species are invasive in much of the U.S. and in Virginia, where the data were collected: Ailanthus altissima (tree of heaven), Elaeagnus umbellata (autumn olive), and Rhamnus davurica (Dahurian buckthorn). We determined when each species could be accurately detected, what spectral features allowed for detection, and the consistency of those features over a growing season. All three species could be detected in June. Only E. umbellata had consistently accurate algorithms and used consistent features in the visible and red edge across the growing season. Its most accurate detection algorithms in the summer included features in the yellow‐orange spectral region. A. altissima and R. davurica were both detectable in the mid‐ and late‐growing seasons, with little overlap in key spectral features across dates. Our results indicate that even a small subset of data from hyperspectral imagery can be used to accurately detect invasive plants in heterogeneous plant communities, and that incorporating species‐specific phenological traits into detection algorithms improves detection, laying methodological and theoretical groundwork for the future of invasive species management.