Mapping the occurrence patterns of invasive plant species and understanding their invasion dynamics is a crucial requirement for preventing further spread to so far unaffected regions. An established approach to map invasive species across large areas is based on the combination of satellite or aerial remote sensing data with ground truth data from fieldwork. Unmanned aerial vehicles (UAV, also referred to as unmanned aerial systems (UAS)) may represent an interesting and low-cost alternative to labor-intensive fieldwork. Despite the increasing use of UAVs in the field of remote sensing in the last years, operational methods to combine UAV and satellite data are still sparse. Here, we present a new methodological framework to estimate the fractional coverage (FC%) of the invasive shrub species Ulex europaeus (common gorse) on Chiloé Island (south-central Chile), based on ultra-high-resolution UAV images and a medium resolution intra-annual time-series of Sentinel-2. Our framework is based on three steps: 1) Land cover classification of the UAV orthoimages, 2) reduce the spatial shift between UAV-based land cover classification maps and Sentinel-2 imagery and 3) identify optimal satellite acquisition dates for estimating the actual distribution of Ulex europaeus.

In Step 2 we translate the challenging co-registration task between two datasets with very different spatial resolutions into an (machine learning) optimization problem where the UAV-based land cover classification maps obtained in Step 1 are systematically shifted against the satellite images. Based on several Random Forest (RF) models, an optimal fit between varying land cover fractions and the spectral information of Sentinel-2 is identified to correct the spatial offset between both datasets.

Considering the spatial shifts of the UAV orthoimages and using optimally timed Sentinel-2 acquisitions led to a significant improvement for the estimation of the current distribution of Ulex europaeus. Furthermore, we found that the Sentinel-2 acquisition from November (flowering time of Ulex europaeus) was particularly important in distinguishing Ulex europaeus from other plant species. Our mapping results could support local efforts in controlling Ulex europaeus. Furthermore, the proposed workflow should be transferable to other use cases where individual target species that are visually detectable in UAV imagery are considered. These findings confirm and underline the great potential of UAV-based groundtruth data for detecting invasive species.

绘制入侵植物物种的发生模式并了解其入侵动态，是防止进一步传播到迄今未受影响地区的关键要求。在大范围内绘制入侵物种图谱的既定方法是基于卫星或空中遥感数据与实地调查的地面真相数据的组合。无人机（UAV，也称为无人机系统（UAS））可能是劳动力密集型野外作业的一种有趣且低成本的替代方案。尽管近些年来无人机在遥感领域的使用越来越多，但结合无人机和卫星数据的操作方法仍然很少。这里，我们提供了一种新的方法框架，基于超高分辨率的无人机图像和中等分辨率的内部影像，估算了智利南部中部奇洛埃岛上入侵灌木树种Ulex europaeus（普通金雀鱼）的覆盖率（FC％） -Sentinel-2的年度时间序列。我们的框架基于以下三个步骤：1）无人机正射影像的土地覆盖分类，2）减少基于无人机的土地覆盖分类图和Sentinel-2影像之间的空间偏移，以及3）确定最佳卫星采集日期以估算实际分布Ulex europaeus。

在第2步中，我们将具有非常不同的空间分辨率的两个数据集之间具有挑战性的共注册任务转换为一个（机器学习）优化问题，在该问题中，第1步中获得的基于UAV的土地覆盖分类图相对于卫星图像进行了系统地平移。基于几个随机森林（RF）模型，确定了不同土地覆盖率与Sentinel-2光谱信息之间的最佳拟合，以校正两个数据集之间的空间偏移。

考虑到无人机正射影像的空间偏移，并使用最佳定时的Sentinel-2采集技术，可以极大地改善欧洲euro虫当前分布的估计。此外，我们发现从11月开始收购Sentinel-2（Ulex europaeus的开花时间）对于区分Ulex europaeus和其他植物物种特别重要。我们的地图绘制结果可以支持当地控制欧洲油松的工作。此外，建议的工作流程应可转移到其他用例中，其中考虑了在无人机图像中可视检测到的单个目标物种。这些发现证实并强调了基于无人机的地面真相数据在检测入侵物种方面的巨大潜力。