Insitute of Plant Sciences and Microbiology, University of Hamburg, Ohnhorststr. 18, 22609, Hamburg, Germany.
Netzwerk für Angewandte Ökologie, Hamburg, Germany.
Environ Monit Assess. 2021 Jan 27;193(2):90. doi: 10.1007/s10661-021-08852-2.
Plant species that negatively affect their environment by encroachment require constant management and monitoring through field surveys. Drones have been suggested to support field surveyors allowing more accurate mapping with just-in-time aerial imagery. Furthermore, object-based image analysis tools could increase the accuracy of species maps. However, only few studies compare species distribution maps resulting from traditional field surveys and object-based image analysis using drone imagery. We acquired drone imagery for a saltmarsh area (18 ha) on the Hallig Nordstrandischmoor (Germany) with patches of Elymus athericus, a tall grass which encroaches higher parts of saltmarshes. A field survey was conducted afterwards using the drone orthoimagery as a baseline. We used object-based image analysis (OBIA) to segment CIR imagery into polygons which were classified into eight land cover classes. Finally, we compared polygons of the field-based and OBIA-based maps visually and for location, area, and overlap before and after post-processing. OBIA-based classification yielded good results (kappa = 0.937) and agreed in general with the field-based maps (field = 6.29 ha, drone = 6.22 ha with E. athericus dominance). Post-processing revealed 0.31 ha of misclassified polygons, which were often related to water runnels or shadows, leaving 5.91 ha of E. athericus cover. Overlap of both polygon maps was only 70% resulting from many small patches identified where E. athericus was absent. In sum, drones can greatly support field surveys in monitoring of plant species by allowing for accurate species maps and just-in-time captured very-high-resolution imagery.
通过实地调查，需要不断管理和监测那些通过侵占而对环境产生负面影响的植物物种。有人建议使用无人机来支持实地调查员，以便通过即时航拍图像进行更准确的测绘。此外，基于对象的图像分析工具可以提高物种地图的准确性。然而，只有少数研究比较了传统实地调查和基于无人机图像的基于对象的图像分析产生的物种分布地图。我们为德国 Hallig Nordstrandischmoor 的一个盐沼地区（18 公顷）获取了无人机图像，该地区有一片 Elymus athericus 高草，它侵占了盐沼的较高部分。随后，使用无人机正射影像作为基准进行了实地调查。我们使用基于对象的图像分析（OBIA）将 CIR 图像分割成多边形，然后将这些多边形分类为八个土地覆盖类别。最后，我们在视觉上以及在处理前后的位置、面积和重叠方面比较了基于实地和基于 OBIA 的地图的多边形。基于 OBIA 的分类产生了很好的结果（kappa=0.937），并且总体上与基于实地的地图一致（实地=6.29 公顷，无人机=6.22 公顷，E. athericus 占主导地位）。后处理揭示了 0.31 公顷的分类错误多边形，这些多边形通常与水流或阴影有关，留下了 5.91 公顷的 E. athericus 覆盖。由于识别出许多 E. athericus 不存在的小斑块，两个多边形地图的重叠仅为 70%。总的来说，无人机可以通过允许准确的物种地图和即时捕获的超高分辨率图像，极大地支持实地调查在监测植物物种方面的工作。
