Phenology-based classification of invasive annual grasses to the species level

The ability to detect and map invasive plants to the species level, both at high resolution and over large extents, is essential for their targeted management. Yet development of such remote sensing methodology is challenged by the spectral and structural similarities among many invasive and native plant species. We developed a multi-temporal classification approach that uses unmanned aerial vehicle (UAV) imagery to map two invasive annual grasses to the species level, and to distinguish these from key functional types of native vegetation, based upon differences in plant phenology. For a case study area in the western Great Basin, USA, we intentionally over-sampled with frequent (n=9) UAV flights over the growing season. Using this information we compared the importance of spectral variation at a given point in time (i.e., with and without near-infrared wavelengths), with spectral variation across multiple time periods. We found that differences in species phenology allowed for accurat...