New tools for old problems — comparing drone- and field-based assessments of a problematic plant species

Abstract

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.

A Review of Unoccupied Aerial Vehicle Use in Wetland Applications: Emerging Opportunities in Approach, Technology, and Data

“…One of the most common methods used throughout the reviewed papers is Structure from Motion (SfM) using RGB imagery. This photogrammetric technique can be employed to estimate the volume or height of vegetation in the study area [29,48,[53][54][55][56][57][58]. SfM is similar to LiDAR in that it generates point clouds for volumetric estimations, although sensors are cheaper and lighter than current LiDAR counterparts [59].…”

“…Image classification techniques often involved using Esri or AgiSoft software, with occasional classifications made in Python [56], Google Earth Engine [36]), eCognition [10,14,24,27,29,42,63,[69][70][71][72], and ENVI [13,14,73] software. Vegetation classification often used Random Forest algorithms [23,27,55,69,[74][75][76], and there was a significant cluster of studies using object-based image analysis (OBIA) [23,24,29,69,70,77,78]. A recent study used deep learning algorithms such as convolutional neural network architectures to classify coastal wetland land cover [79].…”

“…In our review, there were nine studies involving invasive plant identification, consisting of six mapping, one modeling, and two novel method papers (Figure 3). Issues caused by invasive species involve reduced habitat for native populations [74,112,113], modified nutrient cycling [113], and negative impacts on livestock [55,116]. Although some invasive aquatic species have beneficial traits, such as increased carbon capture and food for fish [55], aquatic managers typically aim to remove these species from the environment.…”

“…Issues caused by invasive species involve reduced habitat for native populations [74,112,113], modified nutrient cycling [113], and negative impacts on livestock [55,116]. Although some invasive aquatic species have beneficial traits, such as increased carbon capture and food for fish [55], aquatic managers typically aim to remove these species from the environment. Management goals pertaining to invasive species largely involve plant identification and remediation, and UAVs allow for on-the-ground immediate assessment and response.…”

“…Specific objectives for these studies include plant identification [55,68,74,112], biomass and plant height estimates [116], and herbicide application assessments [116]. Invasive plants that are often targeted for removal include species such as wild rice (Zizania latifolia) [116], hogweed (Heracleum mantegazzianum), Himalayan balsam (Impatiens glandulifera), Japanese knotweed (Fallopia japonica and F. sachalinensis) [25], sea couch grass (Elymus athericus) [55], blueberry hybrids (Vaccinium corymbosum x angustifolium) [114], saltmarsh cordgrass (Spartina alterniflora) [74], water hyacinth (Eichornia crassipes), water primrose (Ludwigia spp.) [13], glossy buckthorn (Frangula alnus) [115], and common reed (Phragmites sp.)…”