An Object-Based Image Analysis Workflow for Monitoring Shallow-Water Aquatic Vegetation in Multispectral Drone Imagery
2018
Dominique Chabot | Christopher Dillon | Adam Shemrock | Nicholas Weissflog | Eric P. S. Sager
High-resolution drone aerial surveys combined with object-based image analysis are transforming our capacity to monitor and manage aquatic vegetation in an era of invasive species. To better exploit the potential of these technologies, there is a need to develop more efficient and accessible analysis workflows and focus more efforts on the distinct challenge of mapping submerged vegetation. We present a straightforward workflow developed to monitor emergent and submerged invasive water soldier (Stratiotes aloides) in shallow waters of the Trent-Severn Waterway in Ontario, Canada. The main elements of the workflow are: (1) collection of radiometrically calibrated multispectral imagery including a near-infrared band: (2) multistage segmentation of the imagery involving an initial separation of above-water from submerged features: and (3) automated classification of features with a supervised machine-learning classifier. The approach yielded excellent classification accuracy for emergent features (overall accuracy = 92%: kappa = 88%: water soldier producer&rsquo:s accuracy = 92%: user&rsquo:s accuracy = 91%) and good accuracy for submerged features (overall accuracy = 84%: kappa = 75%: water soldier producer&rsquo:s accuracy = 71%: user&rsquo:s accuracy = 84%). The workflow employs off-the-shelf graphical software tools requiring no programming or coding, and could therefore be used by anyone with basic GIS and image analysis skills for a potentially wide variety of aquatic vegetation monitoring operations.
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