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 unoccupied aerial vehicles (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 = 8) 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 accurate classification of nine cover types, including the two annual grass species of interest, using just three dates of imagery that captured species-specific differences in the timing of active growth, seed head production, and senescence. Availability of near-infrared imagery proved less important than true-color RGB imagery collected at appropriate time periods. Thus, multi-temporal information provides a substitute for more extensive spectral information obtained from a single point in time. The substitution of temporal for spectral information is particularly well suited to UAV remote sensing, where the timing of image collection can be flexible. The datasets arising from our multi-temporal classification approach provide high-resolution information for modeling patterns of invasive plant spread, for quantifying plant invasion risk, and for early detection of novel plant invasions when patch sizes are still small. Widespread application and up-scaling of our approach requires advances in our ability to model the variability in phenology that occurs across years and over fine spatial scales, even within a single species.
