Abstract
Marine litter is a global problem that requires soon management and design of mitigation strategies. Marine litter monitoring is an essential step to assess the abundances, distributions, sinks and hotspots of pollution as well as the effectiveness of mitigation measures. However, these need to be time and cost-efficient, fit for purpose and context, as well as provide a standardized methodology suitable for comparison among surveys. In Europe, the Marine Strategy Framework Directive (MSFD) provides a structure for the effective implementation of long-term monitoring. For beaches, the well-established 100 m OSPAR macrolitter monitoring exists. However, this method requires a high staff effort and suffers from a high spatio-temporal variability of the results. In this study, we test the potential of aerial drones or Unmanned Aerial Vehicles (UAVs) together with a Geographic Information System approach for semi-automatic classification of meso- (1–25 mm) and macrolitter (>25 mm) at four beaches of the southern Baltic Sea. Visual screening of drone images in recovery experiments (50 m2 areas) at 10 m height revealed an accuracy of 99%. The total accuracy of classification using object-based classification was 45–90% for the classification with four classes and 50–66% for the classification with six classes, depending on the algorithm and flight height used. On 100 m beach monitoring transects the accuracy was between 39–74% (4 classes) and 25–74% (6 classes), with very low kappa values, indicating that the GIS classification method cannot be regarded as a reliable method for the detection of litter in the Southern Baltic. In terms of cost-efficiency, the drone method showed high reproducibility and moderate accuracy, with much lower flexibility and quality of data than a comparable spatial-OSPAR method. Consequently, our results suggest that drone based monitoring cannot be recommended as a replacement or complement existing methods in southern Baltic beaches. However, drone monitoring could be useful at other sites and other methods for image analysis should be tested to explore this tool for fast-screening of non-accessible sites, fragile ecosystems, floating litter or heavily polluted beaches.
Highlights
The pollution of seas and coasts with marine litter, especially plastics, is a growing global problem (United Nations Environment Programme, 2019)
The drone gives good positional relative accuracy- that is how points on a map are placed relative to each other- which we suggest is sufficient for image classification, as we are not overlaying different orthomosaics but rather making a comparison of the classification results between different flight altitudes and algorithms
The results of this test classification showed that images at 10 m height gave a closer and sharper look into smaller objects than images obtained at 15 and 18 m height (Supplementary Figure S4), which reduced the noise of the background but smaller objects were more difficult to identify and classify
Summary
The pollution of seas and coasts with marine litter, especially plastics, is a growing global problem (United Nations Environment Programme, 2019). The state of pollution of beaches with macrolitter (>25 mm), and its associated problems are well known and documented for many regions worldwide (Abu-Hilal and Al-Najjar, 2009; Jayasiri et al, 2013; Rosevelt et al, 2013; Topçu et al, 2013; Duhec et al, 2015; Hidalgo-Ruz et al, 2018). Marine litter is addressed as one of the UN Sustainable Development Goals (SDG 14.1) aiming at preventing and significantly reducing pollution in the world oceans by 2025 (United Nations, 2019). In-situ beach litter monitoring is a commonly applied survey worldwide, until today there is no clear consensus on the monitoring strategy to be used and units are difficult to compare (Serra-Gonçalves et al, 2019)
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