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Monitoring of marine litter at the sea surface, the beaches and the seafloor is essential to understanding their sources, pathways and sinks and design effective clean-up programs or increase public awareness for reducing litter waste. Up until today, seafloor litter is the least exploited component of marine litter. Although the protocols for recording and assessing seafloor litter in the deep-sea environments are currently being actively defined and practiced, shallow seafloor litter survey protocols are still notably under-developed. Moreover, trawling for fishing, which is the main means for collecting seafloor litter data, needs to be phased out in the coming years due to its high environmental footprint and be replaced by less destructive ways based on underwater imagery. In this paper we propose an integrated approach for assessing in detail the spatiotemporal distribution and composition of seafloor litter in shallow coastal environments, using common towed underwater cameras. Effort has been put to correctly estimating spatial litter densities regarding the true coverage of the visualized area, which was efficiently extracted through photogrammetric reconstruction of the seafloor. Interpretation of the spatial distribution of litter was aided by auxiliary bathymetric and swath sonar backscatter datasets, to determine the seabed geomorphological features that control their dispersion and composition. Local geo-morphology, along with any reported coastal anthropogenic activity, are correlated to seafloor litter densities to investigate the temporal and spatial dynamics that control their distribution and temporal trends in Syros Island, Cyclades, Greece. There, in the context of LIFE DEBAG project, monitoring of an urbanized shallow bay for 3 consecutive years has been performed to assess the impact of an intensive local awareness raising campaign to the local environment. A significant reduction of litter densities under the impact of this campaign has been documented, while links between the seafloor litter transport dynamics and the seabed micro- and macro-topography were made evident. Monitoring litter densities on the seafloor of urbanized shallow bays proved to be a prospective way of tracking marine litter pressures on the local marine environment., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Aerial and underwater imaging is being widely used for monitoring litter objects found at the sea surface, beaches and seafloor. However, litter monitoring requires a considerable amount of human effort, indicating the need for automatic and cost-effective approaches. Here we present an object detection approach that automatically detects seafloor marine litter in a real-world environment using a Region-based Convolution Neural Network. The neural network is trained on an imagery with 11 manually annotated litter categories and then evaluated on an independent part of the dataset, attaining a mean average precision score of 62%. The presence of other background features in the imagery (e.g., algae, seagrass, scattered boulders) resulted to higher number of predicted litter items compare to the observed ones. The results of the study are encouraging and suggest that deep learning has the potential to become a significant tool for automatically recognizing seafloor litter in surveys, accomplishing continuous and precise litter monitoring., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The abundance of marine debris was quantified for a total of sixty-two inaccessible beaches in the western Saronikos Gulf, Greece. High resolution images were obtained through vessel-based photography survey, merged into seamless photomosaics, and manually processed to quantify beach litter abundance. A sample of four selected beaches were subjected to detailed photography followed by beach macro-litter (≥ 2.5 cm) in-situ sampling surveys over a period of one year, to calibrate and validate the proposed method. Regression analysis between photographic and in-situ data showed a significant correlation, hence providing a highly accurate regression model to assess the real number of beach litter stranded on the rest of the investigated beaches, exhibiting clear correlations to the hydrodynamic status of the area and, provide an indication of the main litter sources. The proposed method is an easily applicable and useful tool for fast and low-cost macro-litter monitoring in extended, remote coastlines, when only photographic data are available., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

We assessed amounts, composition and net accumulation rates every ~15days of beach macro litter (≥2.5cm) on 4 Mediterranean beaches, on Corfu island, N. Ionian Sea, taking into account natural and anthropogenic drivers. Average net accumulation rate on all beaches was found 142±115N/100m/15d. By applying a Generalized Linear Model (GzLM) it was shown that sea transport is the dominant pathway affecting the amount and variability in beach litter loadings. Principal Component Analysis (PCA) on compositional data and indicator items discerned two more pathways of beach litter, i.e. in situ litter from beach goers and wind and/or runoff transport of litter from land. By comparing the PCA results to those from a simple item to source attribution, it is shown that regardless their source litter items arrive at beaches from various pathways. Our data provide baseline knowledge for designing monitoring strategies and for setting management targets., (Copyright © 2017 Elsevier Ltd. All rights reserved.)