Drone-based monitoring of intertidal blue mussel beds in the Wadden Sea – comparison of a threshold and two machine learning approaches

Abstract

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In the Wadden Sea, intertidal mussel beds of the blue mussel (Mytilus edulis) and the Pacific oyster (Magallana gigas) form distinct epibenthic communities and local hotspots of high biomass and biodiversity. To detect and evaluate natural and anthropogenic processes, a ground-based monitoring program started over 25 years ago in the German Wadden Sea. In this study, we describe the potential of drones and machine learning approaches for a remote sensing-based integration into an existing monitoring program of intertidal mussel beds. A fixed wing drone was used to cover an area of up to 39ha in a single flight, with an overall time saving potential of 50%. Applying machine learning approaches, a detailed extraction of the intertidal blue mussel bed coverage with an overall accuracy (OA) up to 95.6% was reached, applying a Support Vector Machine (SVM). The application of a multispectral sensor improved the classification performance. Compared to ground-based monitoring, the drone-based method provided significantly more information on the area extension, coverage, and associated algae of the mussel beds. The results show that drones can provide a non-invasive way to survey large and difficult to access areas providing detailed maps of mussel beds and their internal structures.

Keywords

• blue mussel (Mytilus edulis)
• remote sensing
• unmanned aerial vehicles (UAV)
• coastal monitoring
• support vector machine
• machine learning