Detection of macro marine litter along a coastal spit using UAV images

Marine litter (ML) abundance has been increasing globally, and is recognized as one of the main socio-economic and environmental concerns and a risk for human health. Despite society have adopting solutions to reduce the production of plastic and has discovered many recycling solutions, much of it ends up discarded in the natural ecosystem. Monitoring operations have been realized worldwide in order to collect information on the types, quantities and distribution of marine debris or to assess the efficiency of mitigation measures. The traditional beach monitoring using 100 m stretch is generally used but such approach is generally considered as time and costs consuming. In addition, the results generally present a high spatio-temporal variability. Within the IT-HR Interreg project NET4mPLASTIC, we used UAV images to map (visual screening) the presence of marine litter on a coastal spit in the northern part of the Po Delta. We used two classes of items, which are well identified items and non-identified items. In addition, the UAV images provided also geomorphological, vegetation and habitat information. Our results show that macro-litter items are present all over the system (from the beach to the lagoon) and suggest different scenarios of plastic debris accumulation. Indeed, ML are mainly concentrated in the beach wrack, dunes, and saltmarshes highlighting the role of the vegetation in trapping ML. Moreover, ML will most probably remain trapped by the saltmarsh vegetation, while cleaning operations may remove the ML present in the beach wrack. In addition, the presence of ML all over the spit profile suggests the importance of the tides, wave and wind actions in transporting and distributing ML. The accumulation of floating debris in coastal dunes is mostly driven by alongshore currents and wave set up in the nearshore area and successively by the wind, while tidal currents may be responsible for the presence of ML in the salt marsh. Finally, considering that coastal management scenarios and decisions rely on numerical models that can predict best practices for coastal sustainability, our results might help local agencies and stakeholders develop good practices for manage coastal environments.