Knowledge on the spatio-temporal distribution of invasive plant species is vital to maintain biodiversity in grasslands which are threatened by the invasion of such plants and to evaluate the effect of control activities conducted. Manual digitising of aerial images with field verification is the standard method to create maps of the invasive Lupinus polyphyllus Lindl. (Lupine) in semi-natural grasslands of the UNESCO biosphere reserve “Rhön”. As the standard method is labour-intensive, a workflow was developed to map lupine coverage using an unmanned aerial vehicle (UAV)-borne remote sensing (RS) along with object-based image analysis (OBIA). UAV-borne red, green, blue and thermal imaging, as well as photogrammetric canopy height modelling (CHM) were applied. Images were segmented by unsupervised parameter optimisation into image objects representing lupine plants and grass vegetation. Image objects obtained were classified using random forest classification modelling based on objects’ attributes. The classification model was employed to create lupine distribution maps of test areas, and predicted data were compared with manually digitised lupine coverage maps. The classification models yielded a mean prediction accuracy of 89%. The maximum difference in lupine area between classified and digitised lupine maps was 5%. Moreover, the pixel-wise map comparison showed that 88% of all pixels matched between classified and digitised maps. Our results indicated that lupine coverage mapping using UAV-borne RS data and OBIA provides similar results as the standard manual digitising method and, thus, offers a valuable tool to map invasive lupine on grasslands.

关于入侵植物物种的时空分布的知识对于维护受到此类植物入侵威胁的草原生物多样性以及评估所进行的控制活动的影响至关重要。通过现场验证手动数字化航空影像是创建侵入性羽扇豆地图的标准方法林德 （羽扇豆）在联合国教科文组织生物圈的半天然草原中，保留为“罗恩”。由于标准方法是劳动密集型的，因此开发了工作流以使用无人飞行器（UAV）的遥感（RS）以及基于对象的图像分析（OBIA）绘制羽扇豆覆盖图。应用了无人机的红色，绿色，蓝色和热成像以及摄影测量的树冠高度模型（CHM）。通过无监督参数优化将图像分割成代表羽扇豆植物和草木植被的图像对象。使用基于对象属性的随机森林分类模型对获得的图像对象进行分类。使用分类模型创建测试区域的羽扇豆分布图，并将预测数据与手动数字化的羽扇豆覆盖图进行比较。分类模型的平均预测准确性为89％。分类和数字化的羽扇豆地图之间的羽扇豆面积最大差异为5％。此外，逐像素地图比较显示，所有像素的88％在分类地图和数字化地图之间匹配。我们的结果表明，使用无人机传播的RS数据和OBIA进行的羽扇豆覆盖度制图可提供与标准手动数字化方法相似的结果，从而提供了在草地上绘制侵入性羽扇豆的有价值的工具。