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.

以高分辨率和大范围检测入侵植物并将其映射到物种水平的能力对于它们的目标管理至关重要。然而，这种遥感方法的发展受到许多入侵植物和本地植物物种之间光谱和结构相似性的挑战。我们开发了一种多时相分类方法，该方法使用无人飞行器 (UAV) 图像将两种入侵的一年生草制图绘制到物种级别，并根据植物物候学的差异将它们与本地植被的关键功能类型区分开来。对于美国西部大盆地的一个案例研究区，我们故意使用频繁的 ( n = 8) 生长季节无人机飞行。使用这些信息，我们比较了给定时间点（即，有和没有近红外波长）的光谱变化的重要性，以及跨多个时间段的光谱变化。我们发现，物种物候的差异允许对九种覆盖类型进行准确分类，包括两种感兴趣的一年生草种，仅使用三个日期的图像捕捉活跃生长、种子头部产生和衰老时间的物种特异性差异. 事实证明，近红外图像的可用性不如在适当时间段收集的真彩色 RGB 图像重要。因此，多时间信息提供了从单个时间点获得的更广泛的光谱信息的替代品。光谱信息的时间替代特别适合无人机遥感，其中图像收集的时间可以灵活。我们的多时态分类方法产生的数据集为入侵植物传播模式建模、植物入侵风险量化以及在斑块大小仍然较小时早期检测新植物入侵提供了高分辨率信息。我们方法的广泛应用和升级需要我们提高对物候变化进行建模的能力，这些变化发生在多年和精细空间尺度上，即使在单个物种内也是如此。我们的多时态分类方法产生的数据集为入侵植物传播模式建模、植物入侵风险量化以及在斑块大小仍然较小时早期检测新植物入侵提供了高分辨率信息。我们方法的广泛应用和升级需要我们提高对物候变化进行建模的能力，这些变化发生在多年和精细空间尺度上，即使在单个物种内也是如此。我们的多时态分类方法产生的数据集为入侵植物传播模式建模、植物入侵风险量化以及在斑块大小仍然较小时早期检测新植物入侵提供了高分辨率信息。我们方法的广泛应用和升级需要我们提高对物候变化进行建模的能力，这些变化发生在多年和精细空间尺度上，即使在单个物种内也是如此。