Estimating standing biomass of exotic macrophytes using sUAS

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

“…Invasive plants are some of the most widespread biological threats to wetland environments, causing rapid ecological and economic damage throughout these biomes [13,25,116]. In our review, there were nine studies involving invasive plant identification, consisting of six mapping, one modeling, and two novel method papers (Figure 3).…”

“…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.…”

“…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.)…”

“…Spectral indices such as the normalized difference vegetation index (NDVI) [55,112] and the normalized difference water index (NDWI) [55] are common for invasive species detection, while texture-based analysis [68], deep learning [114], and object-based image analysis (OBIA) are successful methods used for invasive species classification [55,74]. As many of the UAV-based invasive species studies, we reviewed used RGB (n = 7) and multispectral (n = 3) imagery, orthophotos [25,74,112], and digital surface models [55,116] were common data products. To overcome spectral limitations of such data, some studies developed sophisticated mapping approaches using Machine Learning algorithms; for example, Cohen and Lewis [115] proposed a monitoring system for two common invasive plants in the Great Lakes coastal ecosystems with the CNN-based software for automated recognition of these species.…”

“…A more recent study demonstrates that imaging spectroscopy through aerial hyperspectral sensors can increase vegetation classification accuracy [13]. While mapping invasive plants is relatively easy to learn and produces sharable outputs with managers and stakeholders [116], limitations of this method include difficulties identifying submerged or emergent aquatic plants due to visibility issues from turbid water and solar reflection on the water's surface [154]. With reduced sensor costs and increased data storage capacity, hyperspectral sensors on UAVs will pave the way for more accurate identification of invasive species [13].…”