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In the context of marine litter monitoring, reporting the weight of beached litter can contribute to a better understanding of pollution sources and support clean-up activities. However, the litter scaling task requires considerable effort and specific equipment. This experimental study proposes and evaluates three methods to estimate beached litter weight from aerial images, employing different levels of litter categorization. The most promising approach (accuracy of 80 %) combined the outcomes of manual image screening with a generalized litter mean weight (14 g) derived from studies in the literature. Although the other two methods returned values of the same magnitude as the ground-truth, they were found less feasible for the aim. This study represents the first attempt to assess marine litter weight using remote sensing technology. Considering the exploratory nature of this study, further research is needed to enhance the reliability and robustness of the methods. • Three methods to estimate macro-litter weight from drone aerial images. • The methods are applicable on both manual and automated litter detection. • Weight estimations were of the same magnitude of the ground-truth values. • Best achievements obtained adopting a generalized mean litter weight of 14 g. • Improvement of drone-based litter surveys in coastal and riverine environments. [ABSTRACT FROM AUTHOR]

The abundance of litter pollution in the marine environment has been increasing globally. Remote sensing techniques are valuable tools to advance knowledge on litter abundance, distribution and dynamics. Images collected by Unmanned Aerial Vehicles (UAV, aka drones) are highly efficient to map and monitor local beached (BL) and floating (FL) marine litter items. In this work, the operational insights to carry out both BL and FL surveys using UAVs are detailly described. In particular, flight planning and deployment, along with image products processing and analysis, are reported and compared. Furthermore, analogies and differences between UAV-based BL and FL mapping are discussed, with focus on the challenges related to BL and FL item detection and recognition. Given the efficiency of UAV to map BL and FL, this remote sensing technique can replace traditional methods for litter monitoring, further improving the knowledge of marine litter dynamics in the marine environment. This communication aims at helping researchers in planning and performing optimized drone-based BL and FL surveys. [ABSTRACT FROM AUTHOR]

The use of Unmanned Aerial Systems (UAS, aka drones) has shown to be feasible to perform marine litter surveys. We operationally tested the use of multispectral images (5 bands) to classify litter type and material on a beach-dune system. For litter categorization by their multispectral characteristics, the Spectral Angle Mapping (SAM) technique was adopted. The SAM-based categorization of litter agreed with the visual classification, thus multispectral images can be used to fasten and/or making more robust the manual RGB image screening. Fully automated detection returned an F-score of 0.64, and a reasonable categorization of litter. Overall, the image-based litter density maps were in line with the manual detection. Assessments were promising given the complexity of the study area, where different dunes plants and partially-buried items challenged the UAS-based litter detection. The method can be easily implemented for both floating and beached litter, to advance litter survey in the environment. • Multispectral images from drone to categorize stranded litter materials and type • Sample Angle Mapper classifier adopted to process multispectral orthophoto • 42 litter items collected among the litter bulk and used as spectral references • Adequate litter materials and types categorization, despite the complex background • Multispectral images from drone can improve and advance litter survey in the environment. [ABSTRACT FROM AUTHOR]

This baseline reports scientific evidence of marine litter items embedded in the dune volume at two study sites on the North Atlantic Portuguese coast. We described how stranded litter participate in the sand dune growth/erosion processes on a natural beach-dune system. From the storm-eroded foredunes on the urbanized beach, we documented exhumed plastics with age up to 38 years. Whether litter burial was due to beach-dune morphodynamic processes, or to irresponsible and/or illegal dumping in the past, this work emphasises the need of improving buried litter census and monitoring on coastal dunes. Coastal erosion processes may further exhume litter buried in dune volumes and on other coastal environments over short- and long-term, re-exposing items into the marine environment. Thus, coastal erosion can be accounted as a secondary diffuse source of littering pollution, beside the multiple sources already identified in the environment. • Litter embedded in dune volume both on urbanized and natural beach-dune systems. • Stranded litter participate in the sand dune development processes. • Buried litter exhumed by storm impact and erosional processes • Exhumed plastics with age up to 38 years • Coastal erosion can be considered a secondary diffuse source of litter. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAV, aka drones) are being used for mapping macro-litter in the environment. As drone images require a manual processing task for detecting marine litter, it is of interest to evaluate the accuracy of non-expert citizen science operators (CSO) in performing this task. Students from Italian secondary schools (in this work, the CSO) were invited to identify, mark, and classify stranded litter items on a UAV orthophoto collected on an Italian beach. A specific training program and working tools were developed for the aim. The comparison with the standard in situ visual census survey returned a general underestimation (50%) of items. However, marine litter bulk categorisation was fairly in agreement with the in situ survey, especially for sources classification. The concordance level among CSO ranged between 60% and 91%, depending on the item properties considered (type, material, and colour). As the assessment accuracy was in line with previous works developed by experts, remote detection of marine litter on UAV images can be improved through citizen science programs, upon an appropriate training plan and provision of specific tools. [ABSTRACT FROM AUTHOR]

Unmanned aerial systems (UAS, aka drones) are being used to map macro-litter on the environment. Sixteen qualified researchers (operators), with different expertise and nationalities, were invited to identify, mark and categorize the litter items (manual image screening, MS) on three UAS images collected at two beaches. The coefficient of concordance (W) among operators varied between 0.5 and 0.7, depending on the litter parameter (type, material and colour) considered. Highest agreement was obtained for the type of items marked on the highest resolution image, among experts in litter surveys (W = 0.86), and within territorial subgroups (W = 0.85). Therefore, for a detailed categorization of litter on the environment, the MS should be performed by experienced and local operators, familiar with the most common type of litter present in the target area. This work provides insights for future operational improvements and optimizations of UAS-based images analysis to survey environmental pollution. • Unmanned aerial systems allow categorization of macro-litter on the environment. • Sixteen researchers with different expertise marked macro-litter on drone images. • Best agreement in marking and classifying litter items among experts in litter survey • Familiarity with most common litter items (i.e., plastic) was essential in the test. • Zoom factor in screening the image is fundamental for results accuracy. [ABSTRACT FROM AUTHOR]

This work analyses the cross-shore (80 m) and long-shore (200 m) spatial and size distribution of macro-litter on coastal dunes, employing a mapping framework based on an Unmanned Aerial System (UAS, aka drone) and a GIS mobile application. Over the cross-shore, plastic percentage increased from 60% to 90% landwards. The largest items (processed wood) were found on the embryo dune. Plastic bottles and paper napkins were trapped by the foredune grass, while the largest fishing-related items were intercepted by the low scrub plant community on the backdune. Over the long-shore, plastic percentage and items size increased from the urbanized area towards the natural dunes. This work assessed the abundance of marine litter on coastal dune sectors, underlining the role of distinct vegetation types in trapping items of different size. The mapping framework can promote further marine litter monitoring programs and support specific strategies for protecting the dune ecosystems. [Display omitted] • Marine macro-litter survey on coastal dunes by drone and mobile application • Cross-shore and long-shore distribution of marine litter on dunes • A novel semi-automatic technique estimated the size of items. • Smallest litter items were trapped by the foredune grass. • Largest items mostly found intercepted by low scrubs on the backdune. [ABSTRACT FROM AUTHOR]

Marine litter pollution on coastal dunes has received limited scientific attention when compared with sandy shores. This paper proposes a new framework based on the combined use of Unmanned Aerial Systems (UAS) and a mobile application to map and quantify marine macro-litter (>2.5 cm) accumulation on coastal dunes. The first application on a dune area of 200 m × 80 m at the north-east Atlantic Portuguese coast is shown. Nine different marine litter categories were found, with styrofoam fragments (23% of the total amount) and plastic bottles (20%) being the most abundant items. Plastic was the most common material (76%). The highest number of items (272) was found on the backdune, mostly related with fishing activities (octopus pots and Styrofoam fragments). In contrast, the highest density (0.031 items/m2) was found on the foredune, with the most abundant items associated with human recreational activities (for example, plastic bottles, bags, papers and napkins). Three major marine litter hotspots (~0.1 items/m2) were identified in correspondence of dune blowouts. The recognition of the primary marine litter pathways highlighted the main role that wind and overwash events play on dune contamination, and suggests that the dune ridge restoration can act as a mitigation measure for preventing marine litter accumulation on the backdune. This study shows how UAS offer the possibility of a detailed non-intrusive survey, and gives a new impulse to coastal dune litter monitoring, where the long residence time of marine debris may threaten the bio-ecological equilibrium of these ecosystems. Unlabelled Image • UAV- and mobile-based novel framework to map marine macro-litter on coastal dunes • Non-intrusive characterization of marine litter type and categories • Spatial analysis distribution of marine litter in relation to dune sectors and features • Highest density of litter found on the foredune, highest abundance on the backdune • Marine debris hotspots found in correspondence of dune blowouts and trails [ABSTRACT FROM AUTHOR]

Unmanned aerial systems (UASs) have recently been proven to be valuable remote sensing tools for detecting marine macro litter (MML), with the potential of supporting pollution monitoring programs on coasts. Very low altitude images, acquired with a low-cost RGB camera onboard a UAS on a sandy beach, were used to characterize the abundance of stranded macro litter. We developed an object-oriented classification strategy for automatically identifying the marine macro litter items on a UAS-based orthomosaic. A comparison is presented among three automated object-oriented machine learning (OOML) techniques, namely random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). Overall, the detection was satisfactory for the three techniques, with mean F-scores of 65% for KNN, 68% for SVM, and 72% for RF. A comparison with manual detection showed that the RF technique was the most accurate OOML macro litter detector, as it returned the best overall detection quality (F-score) with the lowest number of false positives. Because the number of tuning parameters varied among the three automated machine learning techniques and considering that the three generated abundance maps correlated similarly with the abundance map produced manually, the simplest KNN classifier was preferred to the more complex RF. This work contributes to advances in remote sensing marine litter surveys on coasts, optimizing the automated detection on UAS-derived orthomosaics. MML abundance maps, produced by UAS surveys, assist coastal managers and authorities through environmental pollution monitoring programs. In addition, they contribute to search and evaluation of the mitigation measures and improve clean-up operations on coastal environments. [ABSTRACT FROM AUTHOR]

Recent works have shown the feasibility of Unmanned Aerial Systems (UAS) for monitoring marine pollution. We provide a comparison among techniques to detect and map marine litter objects on an UAS-derived orthophoto of a sandy beach-dune system. Manual image screening technique allowed a detailed description of marine litter categories. Random forest classifier returned the best-automated detection rate (F-score 70%), while convolutional neural network performed slightly worse (F-score 60%) due to a higher number of false positive detections. We show that automatic methods allow faster and more frequent surveys, while still providing a reliable density map of the marine litter load. Image manual screening should be preferred when the characterization of marine litter type and material is required. Our analysis suggests that the use of UAS-derived orthophoto is appropriate to obtain a detailed geolocation of marine litter items, requires much less human effort and allows a wider area coverage. Unlabelled Image • Unmanned Aerial Systems effective for mapping marine litter on a beach-dune system • Orthophoto manual screening able to detect and discretize type of marine litter • Random forest and convolutional neural network reliable for automated density mapping • Strengths and weaknesses of UAS-based techniques versus visual census [ABSTRACT FROM AUTHOR]