Enabling a large-scale assessment of litter along Saudi Arabian red sea shores by combining drones and machine learning

Assessment of coastal litter trends in tourist vs. non-tourist beaches: A case study from Indian coastal smart city

Coastal ecosystems are increasingly threatened by the accumulation of marine litter globally. Limited data availability along India's eastern coast hinders targeted mitigation efforts. This study assesses coastal litter along Visakhapatnam, a smart city on India's eastern coast, using the NOAA shoreline debris protocol. Litter assessments at 12 sites before and after the monsoon season revealed high mean litter densities (2.66 ± 0.31 items m-2 before monsoon, 2.03 ± 0.29 items m-2 after monsoon), exceeding the global average by twofold and the national average by five-fold. The tourist beaches saw a 63 % litter reduction after monsoon due to the implementation of better waste management practices, while non-tourist beaches saw a 16 % increase, highlighting disparities in waste management practices. Plastic comprised 86 % of litter, exceeding the global mean proportion (85 %) in marine litter. Alarmingly, 50 % of tourist beaches and all non-tourist beaches were classified as "extremely dirty" by the Clean Coast Index. Land-based influx through stormwater drains was identified as the primary source of litter. This study provides critical baseline data for India's eastern coast, emphasizing the urgent need for targeted interventions, including improved stormwater management and community engagement, to mitigate the escalating marine litter crisis. Further, the findings and recommendations provide valuable insights for managing plastic pollution in coastal cities with similar characteristics, particularly those influenced by monsoons and tourism.

Monitoring macroplastics in aquatic and terrestrial ecosystems: Expert survey reveals visual and drone-based census as most effective techniques

Anthropogenic litter, such as plastic, is investigated by the global scientific community from various fields employing diverse techniques. The goal is to assess and finally mitigate the pollutants' impacts on the natural environment. Plastic litter can accumulate in different matrices of aquatic and terrestrial ecosystems, impacting both biota and ecosystem functioning. Detection and quantification of macroplastics, and other litter, can be realized by jointly using visual census and remote sensing techniques. The primary objective of this research was to identify the most effective approach for monitoring macroplastic litter in riverine and marine environments through a comprehensive survey based on the experiences of the scientific community. Researchers involved in plastic pollution evaluated four litter occurrence and flux investigation methods (visual census, drone-based surveys, satellite imagery, and GPS/GNSS trackers) through a questionnaire. Traditional visual census and drone deployment were deemed as the most popular approaches among the 46 surveyed researchers, while satellite imagery and GPS/GNSS trackers received lower scores due to limited field validation and short performance ranges, respectively. On a scale from 0 to 5, visual census and drone-based surveys obtained 3.5 and 2.0, respectively, whereas satellite imagery and alternative solutions received scores lower than 1.2. Visual and drone censuses were used in high, medium and low-income countries, while satellite census and GPS/GNSS trackers were mostly used in high-income countries. This work provides an overview of the advantages and drawbacks of litter investigation techniques, contributing i) to the global harmonization of macroplastic litter monitoring and ii) providing a starting point for researchers and water managers approaching this topic. This work supports the selection and design of reliable and cost-effective monitoring approaches to mitigate the ambiguity in macroplastic data collection, contributing to the global harmonization of macroplastic litter monitoring protocols.

Density and distribution patterns of seafloor macrolitter in the eastern Red Sea

The constant production of plastic and incessant growth of waste pollution continues to alter the marine environment from the coasts and surface waters to the deep sea. The quantification and investigation of macrolitter on the vast seabed of the ocean are challenging tasks that must be undertaken to elucidate the impact of anthropogenic activity on the marine environment and facilitate subsequent implementation of legally binding waste management regulations. In this study, we analyzed >60,000 images collected during 84 dives surveying 62.1 km of seabed in the eastern Red Sea to quantify the abundance and density of seafloor macrolitter. The surveyed depth of the seabed varied between 35 and 2415 m, and litter was observed at depths ranging from 93 to 2415 m. The litter density varied between 0 and 73,798 items km-2, with the mean (± SE) and median densities of 4069 ± 1188 and 1371 items km-2, respectively. Plastic was the main litter category, comprising 46 % of all litter. The density of litter was higher at deeper depths (>1400 m) and increased significantly at distances farther from the shore. The results of this study suggest that maritime traffic and the possible direct litter discharge from vessels are the main anthropogenic sources of seafloor litter in the eastern Red Sea. Thus, we emphasize the urgency of conservation efforts and strict waste regulations to preserve the marine ecosystem of the Red Sea.

How much does marine litter weigh? A literature review to improve monitoring, support modelling and optimize clean-up activities

The weight of marine litter has been marginally considered in comparison to counting and categorizing items. However, weight determines litter dynamics on water and coasts, and it is an essential parameter for planning and optimizing clean-up activities. This work reviewed 80 publications that reported both the number and weight of beached macro-litter worldwide. On average, a litter item weighed 19.5 ± 20.3 g, with a median weight of 13.4 g. Plastics composed 80% by number and 51% by weight of the global litter bulk. A plastic item weighed 12.9 ± 13.8 g on average, with a median weight of 9 g. The analysis based on continents and on water bodies returned similar values, which can be used to estimate litter weight on beaches from past and future visual census surveys, and from remote sensing imagery. Overall, this work can improve litter monitoring reports and support dynamics modelling, thereby contributing for environmental protection and mitigation efforts.

Quantification of litter in cities using a smartphone application and citizen science in conjunction with deep learning-based image processing

Cities are a major source of litter pollution. Determination of the abundance and composition of plastic litter in cities is imperative for effective pollution management, environmental protection, and sustainable urban development. Therefore, here, a multidisciplinary approach to quantify and classify the abundance of litter in urban environments is proposed. In the present study, litter data collection was integrated via the Pirika smartphone application and conducted image analysis based on deep learning. Pirika was launched in May 2018 and, to date, has collected approximately one million images. Visual classification revealed that the most common types of litter were cans, plastic bags, plastic bottles, cigarette butts, cigarette boxes, and sanitary masks, in that order. The top six categories accounted for approximately 80 % of the total, whereas the top three categories accounted for more than 60 % of the total imaged litter. A deep-learning image processing algorithm was developed to automatically identify the top six litter categories. Both precision and recall derived from the model were higher than 75 %, enabling proper litter categorization. The quantity of litter derived from automated image processing was also plotted on a map using location data acquired concurrently with the images by the smartphone application. Conclusively, this study demonstrates that citizen science supported by smartphone applications and deep learning-based image processing can enable the visualization, quantification, and characterization of street litter in cities.

Application of improved machine learning in large-scale investigation of plastic waste distribution in tourism Intensive artificial coastlines

Oceans are ultimately a sink of plastic waste. Complex artificial coastlines pose remarkable challenges for coastal plastic waste monitoring. With the development of machine learning methods, high detection accuracy can be achieved; however, many false positives have been noted in various network models used for plastic waste investigation. In this study, extensive surveys of artificial coastlines were conducted using drones along the Dongjiang Port artificial coastline in the Binhai District, Tianjin, China. The deep learning model YOLOv8 was enhanced by integrating the InceptionNeXt and LSK modules into the network to improve its detection accuracy for plastic waste and reduce instances of tourists being misidentified as plastic. In total, 553 high-resolution coastline images with 3488 items of detected plastic waste were compared using the original and improved YOLOv8 models. The improved YOLOv8s-IL model achieved a detection rate of 64.9%, a notable increase of 11.5% compared with that of the original model. The number of false positives in the improved YOLOv8s-IL model was reduced to 32.3%, the multi-class F-score reached 76.5%, and the average detection time per image was only 2.7 s. The findings of this study provide technical support for future large-scale monitoring of plastic waste on artificial coastlines.

Marine litter weight estimation from UAV imagery: Three potential methodologies to advance macrolitter reports

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.

Discernible decline in macroplastic litter inputs to the central eastern Red Sea shoreline during the COVID-19 lockdown

Plastic debris accumulating on beaches pose a major threat to marine ecosystems. Unexpected events affecting human operations, such as the COVID-19 pandemic, which prompted governments to implement safety measures and restrictions, can serve as an unplanned investigation of anthropogenic pressure on the marine environment. This study aimed to explore deviations in macroplastic delivery rates to the central eastern Red Sea shoreline during three distinct population mobility periods: before, during, and after COVID-19 restrictions, spanning from January 2019 to June 2022. We observed a 50 % reduction in the estimated macroplastic delivery rates during the lockdown, followed by a 25 % increase after restrictions were eased. Seasonal variations in delivery rates were also observed, with higher values during the winter monsoon. Reduced shoreline litter delivery during the pandemic highlights human operations as a cause of macroplastic litter and suggests the potential of temporary measures to reduce plastic pollution in the coastal environment.

Lost and found: Patterns of marine litter accumulation on the remote Island of Santa Luzia, Cabo Verde

Santa Luzia, an uninhabited island in the archipelago of Cabo Verde, serves as a natural laboratory and important nesting site for loggerhead turtles Carettacaretta. The island constitutes an Integral Natural Reserve and a Marine Protected Area. We assessed marine litter accumulation on sandy beaches of the island and analysed their spatial patterns using two sampling methods: at a fine scale, sand samples from 1 × 1 m squares were collected, identifying debris larger than 1 mm; at a coarse scale, drone surveys were conducted to identify visible marine debris (>25 mm) in aerial images. We sampled six points on three beaches of the island: Achados (three points), Francisca (two points) and Palmo Tostão (one point). Then, we modelled the abundance of marine debris using topographical variables as explanatory factors, derived from digital surface models (DSM). Our findings reveal that the island is a significant repository for marine litter (>84% composed of plastics), with up to 917 plastic items per m2 in the sand samples and a maximum of 38 macro-debris items per m2 in the drone surveys. Plastic fragments dominate, followed by plastic pellets (at the fine-scale approach) and fishing materials (at the coarse-scale approach). We observed that north-facing, higher-elevation beaches accumulate more large marine litter, while slope and elevation affect their spatial distribution within the beach. Achados Beach faces severe marine debris pollution challenges, and the upcoming climate changes could exacerbate this problem.

Drones for litter monitoring on coasts and rivers: suitable flight altitude and image resolution

Multirotor drones can be efficiently used to monitor macro-litter in coastal and riverine environments. Litter on beaches, dunes and riverbanks, along with floating litter on coastal and river waters, can be spotted and mapped from aerial drone images. Items detection and classification are prone to image resolution, which is expressed in terms of Ground Sampling Distance (GSD). The GSD is determined by drone flight altitude and camera properties. This paper investigates what is a suitable GSD value for litter survey. Drone flight altitude and camera setup should be chosen to obtain a GSD between 0.5 cm/px and 1.25 cm/px. Within this range, the lowest GSD allows litter categorization and classification, whereas the highest value should be adopted for a coarser litter census. In the vision of drawing up a global protocol for drone-based litter surveys, this work sets the ground for homogenizing data collection and litter assessments.

The octopus pot on the North Atlantic Iberian coast: A plague of plastic on beaches and dunes

This baseline focuses on the octopus pot, a litter item found on the North Atlantic Iberian coast. Octopus pots are deployed from vessels in ropes, with several hundred units, and placed on the seabed, to capture mostly Octopus Vulgaris. The loss of gears due to extreme seas state, bad weather and/or fishing-related unforeseen circumstances, cause the octopus pots contaminating beaches and dunes, where they are transported by sea current, waves and wind actions. This work i) gives an overview of the use of octopus pot on fisheries, ii) analyses the spatial distribution of this item on the coast, and iii) discusses the potential measures for tackling the octopus pot plague on the North Atlantic Iberian coast. Overall, it is urgent to promote conducive policies and strategies for a sustainable waste management of octopus pots, based on Reduce, Reuse and Recycle hierarchical framework.

Marine litter in the Red Sea: Status and policy implications

The Red Sea's unique ecosystem is home to >1500 species. However, the presence of anthropogenic litter, whether from land-based or sea-based sources, may pose a potential risk to the Red Sea fauna and flora. This work analyzes marine litter in the Red Sea, utilizing the Drivers-Pressure-State-Impact-Response (DPSIR) framework to group findings in a survey of peer-reviewed studies. The review is further augmented with a survey of the current response, covering regional and national instruments. Although research addressing marine litter in the Red Sea is not as rich as for other seas, studies suggest marine litter is abundant and that the influx of litter is driven by recreational activity, fishing, and shipping. Moreover, the COVID-19 pandemic has exacerbated the influx of marine litter to the Red Sea due to improper disposal of personal protective equipment (PPE). The response has intensified in recent years, with regional and national frameworks established and initiatives driven by Non-Governmental Organizations (NGOs). We discuss whether the regional action plan addresses the specific concerns uncovered in marine litter studies while providing a comparison with plans of other regional seas.

'Eye in the sky': Off-the-shelf unmanned aerial vehicle (UAV) highlights exposure of marine turtles to floating litter (FML) in nearshore waters of Mayo Bay, Philippines

Litter is a serious threat to the marine environment, with detrimental effects on wildlife and marine biodiversity. Limited data as a result of funding and logistical challenges in developing countries hamper our understanding of the problem. Here, we employed commercial unmanned aerial vehicle (UAV) as a cost-effective tool to study the exposure of marine turtles to floating marine litter (FML) in waters of Mayo Bay, Philippines. A quadcopter UAV was flown autonomously with on-board camera capturing videos during the flight. Still frames were extracted when either turtle or litter were detected in post-flight processing. The extracted frames were georeferenced and mapped using QGIS software. Results showed that turtles are highly exposed to FML in nearshore waters. Moreover, spatial dependence between FML and turtles was also observed. The study highlights the effectiveness of UAVs in marine litter research and underscores the threat of FML to turtles in nearshore waters.

Beach litter survey by drones: Mini-review and discussion of a potential standardization

The abundance of beach litter has been increasing globally during the last decades, and it is an issue of global concern. A new survey strategy, based on uncrewed aerial vehicles (UAV, aka drones), has been recently adopted to improve the monitoring of beach macro-litter items abundance and distribution. This work identified and analysed the 15 studies that used drone for beach litter surveys on an operational basis. The analysis of technical parameters for drone flight deployment revealed that flight altitude varied between 5 and 40 m. The analysis of final assessments showed that, through manual and/or automated items detection on images, most of studies provided litter bulk characteristics (type, material and size), along with litter distribution maps. The potential standardization of drone-based litter survey would allow a comparison among surveys, however it seems difficult to propose a standard set of flight parameters, given the wide variety of coastal environments, the different devices available, and the diverse objectives of drone-based litter surveys. On the other hand, in our view, a set of common outcomes can be proposed, based on the grid mapping process, which can be easily generated following the procedure indicated in the paper. This work sets the ground for the development of a standardized protocol for drone litter data collection, analysis and assessments. This would allow the provision of broad scale comparative studies to support coastal management at both national and international scales.

Priorities to inform research on marine plastic pollution in Southeast Asia

Southeast Asia is considered to have some of the highest levels of marine plastic pollution in the world. It is therefore vitally important to increase our understanding of the impacts and risks of plastic pollution to marine ecosystems and the essential services they provide to support the development of mitigation measures in the region. An interdisciplinary, international network of experts (Australia, Indonesia, Ireland, Malaysia, the Philippines, Singapore, Thailand, the United Kingdom, and Vietnam) set a research agenda for marine plastic pollution in the region, synthesizing current knowledge and highlighting areas for further research in Southeast Asia. Using an inductive method, 21 research questions emerged under five non-predefined key themes, grouping them according to which: (1) characterise marine plastic pollution in Southeast Asia; (2) explore its movement and fate across the region; (3) describe the biological and chemical modifications marine plastic pollution undergoes; (4) detail its environmental, social, and economic impacts; and, finally, (5) target regional policies and possible solutions. Questions relating to these research priority areas highlight the importance of better understanding the fate of marine plastic pollution, its degradation, and the impacts and risks it can generate across communities and different ecosystem services. Knowledge of these aspects will help support actions which currently suffer from transboundary problems, lack of responsibility, and inaction to tackle the issue from its point source in the region. Being profoundly affected by marine plastic pollution, Southeast Asian countries provide an opportunity to test the effectiveness of innovative and socially inclusive changes in marine plastic governance, as well as both high and low-tech solutions, which can offer insights and actionable models to the rest of the world.

Detection and assessment of marine litter in an uninhabited island, Arabian Gulf: A case study with conventional and machine learning approaches

In 2018, the Ministry of Municipality and Environment, Qatar removed 90 t of marine litter (ML) from the Ras Rakan Island (RRI), a remote uninhabited island in the Arabian Gulf (hereinafter referred to as Gulf). To identify the sources of ML and understand the post-cleaning ML accumulation rate, a ML survey was conducted around RRI in 2019. A total of 1341 ML items were found around RRI with an average abundance of 3.4 items/m². In addition, a machine learning approach was applied to extract the quantity and types of ML from 10,400 images from the sampling sites (beaches) to make the ML clean-up process and monitoring effort more efficient. The image coordinates of ML objects were used to train an object detection algorithm 'You Only Look Once (YOLO-v5)' to automatically detect ML from video data. An image enhancement technique was performed to improve the quality of unclear images. The best performing YOLO-v5 model had 90% of mean Average Precision (mAP) while maintaining near real-time processing speeds at 2 ms/image. The abundance of ML around RRI was higher than that found on the coast of mainland Qatar. 61.5% of the sampling locations are considered as 'extremely dirty' based on Clean Coast Index. Windward beaches had higher ML concentrations (derived from neighbouring countries) than the leeward beaches. Like RRI, most of the uninhabited islands in the Arabian Gulf are home to many seabirds and sea turtles, and could act as major sinks for ML deposition. Therefore, implementation of this machine learning technique to all islands allows estimating and mitigating the load of ML for achieving a sustaining and a cleaner ocean.

Temporal trends and spatial distribution of research topics in anthropogenic marine debris study: Topic modelling using latent Dirichlet allocation

The release of anthropogenic marine debris (AMD) is one of the major environmental challenges of our time. In this study, a topic model called latent Dirichlet allocation (LDA) was used to infer the research topics about AMD to provide the whole picture of the research area. The results of the LDA showed that the AMD research topics are mostly applied topics and belong to interdisciplinary or transdisciplinary research areas. Furthermore, the analysis of the temporal trends of the topics showed that topics related to such as plastic pollution exhibit an upward trend, whereas those dealing with the spatiotemporal dynamics and distribution patterns of marine debris showed a downward trend. The analysis of topic distribution over countries showed that research is scarce in landlocked countries. The findings of this study can be used as a map for the area of AMD study by various stakeholders related to marine debris issues.

Close-range remote sensing-based detection and identification of macroplastics on water assisted by artificial intelligence: A review

Detection and identification of macroplastic debris in aquatic environments is crucial to understand and counter the growing emergence and current developments in distribution and deposition of macroplastics. In this context, close-range remote sensing approaches revealing spatial and spectral properties of macroplastics are very beneficial. To date, field surveys and visual census approaches are broadly acknowledged methods to acquire information, but since 2018 techniques based on remote sensing and artificial intelligence are advancing. Despite their proven efficiency, speed and wide applicability, there are still obstacles to overcome, especially when looking at the availability and accessibility of data. Thus, our review summarizes state-of-the-art research about the visual recognition and identification of different sorts of macroplastics. The focus is on both data acquisition techniques and evaluation methods, including Machine Learning and Deep Learning, but resulting products and published data will also be taken into account. Our aim is to provide a critical overview and outlook in a time where this research direction is thriving fast. This study shows that most Machine Learning and Deep Learning approaches are still in an infancy state regarding accuracy and detail when compared to visual monitoring, even though their results look very promising.

Detection of River Plastic Using UAV Sensor Data and Deep Learning

Plastic pollution is a critical global issue. Increases in plastic consumption have triggered increased production, which in turn has led to increased plastic disposal. In situ observation of plastic litter is tedious and cumbersome, especially in rural areas and around transboundary rivers. We therefore propose automatic mapping of plastic in rivers using unmanned aerial vehicles (UAVs) and deep learning (DL) models that require modest compute resources. We evaluate the method at two different sites: the Houay Mak Hiao River, a tributary of the Mekong River in Vientiane, Laos, and Khlong Nueng canal in Talad Thai, Khlong Luang, Pathum Thani, Thailand. Detection models in the You Only Look Once (YOLO) family are evaluated in terms of runtime resources and mean average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.5. YOLOv5s is found to be the most effective model, with low computational cost and a very high mAP of 0.81 without transfer learning for the Houay Mak Hiao dataset. The performance of all models is improved by transfer learning from Talad Thai to Houay Mak Hiao. Pre-trained YOLOv4 with transfer learning obtains the overall highest accuracy, with a 3.0% increase in mAP to 0.83, compared to the marginal increase of 2% in mAP for pre-trained YOLOv5s. YOLOv3, when trained from scratch, shows the greatest benefit from transfer learning, with an increase in mAP from 0.59 to 0.81 after transfer learning from Talad Thai to Houay Mak Hiao. The pre-trained YOLOv5s model using the Houay Mak Hiao dataset is found to provide the best tradeoff between accuracy and computational complexity, requiring model resources yet providing reliable plastic detection with or without transfer learning. Various stakeholders in the effort to monitor and reduce plastic waste in our waterways can utilize the resulting deep learning approach irrespective of location.

Spatio-temporal variations of litter on Qingdao tourist beaches in China

Beaches are an integral part of coastal tourism, but they are deteriorated by the beachgoers and recreational activities due to lack of adequate beach environmental awareness and management. Litter is widely distributed in marine and coastal environment and has been considered a severe concern. In China investigations to determine the beach litter abundance and pollution level are limited. The aim of this study is to estimate spatio-temporal distribution and composition of litter on 10 well-known Qingdao tourist beaches, involving pollution level by beach quality indexes. Beach litter was collected within an area of 25 × 25 m² in both summer (May, June and July) and winter (Nov, Dec and Jan) seasons, and was classified into eight categories. The abundance of beach litter was found higher in summer (0.13 ± 0.04 items/m²) than in winter (0.04 ± 0.01 items/m²). Overall, the percentage of plastics were higher in both summer (23.48%) and winter (24.04%) than that of other litter categories. Based on Clean Coast Index, 70% of beaches were very clean, 25% clean, and 5% moderately clean. Beach Grade Index showed that 15% beaches were very good, 5% good, 55% fair, and 25% poor. 85% beaches constituted some quantity of hazardous litter and 15% had no hazardous litter for Hazardous Items index. The findings suggest that the sources of beach litter along Qingdao beaches mainly come from the recreational and tourist activities. The substantial quantity of litter is also being transported by ocean (tides or current), which are finally deposited along beachfront. Despite regular cleaning operation along most of Qingdao beaches, suggested management practices involve mitigation measures, source reduction, change in littering behavior to improve further quality of beaches.

Beach debris quantity and composition around the world: A bibliometric and systematic review

Anthropogenic pollution of marine ecosystems caused by beach debris has become a serious environmental concern in the last few decades. Regarding the raising production of beach debris, the present work aimed to summarize the quantity and quality of beach debris reported from different beach areas of the world. Also, a bibliometric analysis was used to analyze research trends and upgrade knowledge in this research area. Using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), the eligible papers reviewed regarding beach debris abundance along with their composition from 2010, which were extracted from the Scopus database. The results of the study showed that plastic items represented the dominant material (61.25%), followed by food (5.88%), wood (5.78%), metals (5.2%), and glass (5%). Further, the beaches studied were classified into three degrees, including highly polluted (31.5 items/m2), moderate polluted (3.47 items/m2), and low polluted (0.37 items/m2), based on the average abundance of debris.

Beached and Floating Litter Surveys by Unmanned Aerial Vehicles: Operational Analogies and Differences

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.

Operational use of multispectral images for macro-litter mapping and categorization by Unmanned Aerial Vehicle

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.

Pixel-level image classification for detecting beach litter using a deep learning approach

Mitigating and preventing beach litter from entering the ocean is urgently required. Monitoring beach litter solely through human effort is cumbersome, with respect to both time and cost. To address this problem, an artificial intelligence technique that can automatically identify different-sized beach litter is proposed. The technique was established by training a deep learning model that enables pixel-wise classification (semantic segmentation) using beach images taken by an observer on the beach. Eight segmentation classes that include two beach litter classes were defined, and the results were qualitatively and quantitatively verified. Segmentation performance was adequately high based on three metrics: Intersection over Union (IoU), precision, and recall, although there is room for further improvement. The potency of the method was demonstrated when it was applied to images taken in different places from training data images, and the coverage of artificial litter calculated and discussed using drone images provided ground truth.

Litter Detection with Deep Learning: A Comparative Study

Pollution in the form of litter in the natural environment is one of the great challenges of our times. Automated litter detection can help assess waste occurrences in the environment. Different machine learning solutions have been explored to develop litter detection tools, thereby supporting research, citizen science, and volunteer clean-up initiatives. However, to the best of our knowledge, no work has investigated the performance of state-of-the-art deep learning object detection approaches in the context of litter detection. In particular, no studies have focused on the assessment of those methods aiming their use in devices with low processing capabilities, e.g., mobile phones, typically employed in citizen science activities. In this paper, we fill this literature gap. We performed a comparative study involving state-of-the-art CNN architectures (e.g., Faster RCNN, Mask-RCNN, EfficientDet, RetinaNet and YOLO-v5), two litter image datasets and a smartphone. We also introduce a new dataset for litter detection, named PlastOPol, composed of 2418 images and 5300 annotations. The experimental results demonstrate that object detectors based on the YOLO family are promising for the construction of litter detection solutions, with superior performance in terms of detection accuracy, processing time, and memory footprint.

Is coastal erosion a source of marine litter pollution? Evidence of coastal dunes being a reservoir of plastics

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.

Citizen Science for Marine Litter Detection and Classification on Unmanned Aerial Vehicle Images

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.

UAV Approach for Detecting Plastic Marine Debris on the Beach: A Case Study in the Po River Delta (Italy)

Anthropogenic marine debris (AMD) represent a global threat for aquatic environments. It is important to locate and monitor the distribution and presence of macroplastics along beaches to prevent degradation into microplastics (MP), which are potentially more harmful and more difficult to remove. UAV imaging represents a quick method for acquiring pictures with a ground spatial resolution of a few centimeters. In this work, we investigate strategies for AMD mapping on beaches with different ground resolutions and with elevation and multispectral data in support of RGB orthomosaics. Operators with varying levels of expertise and knowledge of the coastal environment map the AMD on four to five transects manually, using a range of photogrammetric tools. The initial survey was repeated after one year; in both surveys, beach litter was collected and further analyzed in the laboratory. Operators assign three levels of confidence when recognizing and describing AMD. Preliminary validation of results shows that items identified with high confidence were almost always classified properly. Approaching the detected items in terms of surface instead of a simple count increased the percentage of mapped litter significantly when compared to those collected. Multispectral data in near-infrared (NIR) wavelengths and digital surface models (DSMs) did not significantly improve the efficiency of manual mapping, even if vegetation features were removed using NDVI maps. In conclusion, this research shows that a good solution for performing beach AMD mapping can be represented by using RGB imagery with a spatial resolution of about 200 pix/m for detecting macroplastics and, in particular, focusing on the largest items. From the point of view of assessing and monitoring potential sources of MP, this approach is not only feasible but also quick, practical, and sustainable.

Exploration of the Medicinal Flora of the Aljumum Region in Saudi Arabia

Understanding the natural resources of native flora in a particular area is essential to be able to identify, record, and update existing records concerning the flora of that area, especially medicinal plants. Until recently, there has been very little scientific documentation on the biological diversity of Aljumum flora. The current study aimed to document medicinal plants among the flora of this region and determine the traditional usages that are documented in the literature. In the flowering season from November 2019 to May 2020, we conducted more than 80 field trips to the study area. The results reported 90 species belonging to 79 genera and 34 families in the Aljumum region, which constitute 82 species of medicinal plants from a total of 2253 known species in Saudi Arabia. The most distributed species were Calotropis procera, Panicum turgidum, and Aerva javanica (5.31%); within four endemic families, we found Fabaceae (32.35%), Poaceae (20.58%), and Asteraceae and Brassicaceae (17.64%). The present study reviews a collection of medicinal plants in Aljumum used in ethnomedicine. Additionally, these natural resources should be preserved, and therefore, conservation programs should be established to protect the natural diversity of the plant species in this region with sustainable environmental management.

Spatial and size distribution of macro-litter on coastal dunes from drone images: A case study on the Atlantic coast

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.

Anthropogenic litter density and composition data acquired flying commercial drones on sandy beaches along the Saudi Arabian Red Sea

Anthropogenic litter density and composition data were obtained by conducting aerial surveys on 44 beaches along the Saudi Arabian Coast of the Red Sea [1]. The aerial surveys were completed with commercial drones of the DJI Phantom suite flown at a 10 m altitude. The stills have a resolution of less than 0.5 cm pixels-1, hence, litter objects of few centimetres like bottle caps are easily detectable in the drone images. We here provide a subsample of the drone images acquired. To spare the time needed to visually count the litter objects in the thousands of drone images acquired, these were automatically screened using an object detection algorithm, specifically a Faster R-CNN, able to perform a binary classification in litter and non-litter and to categorize the objects in classes. The multi-class classification, however, is a challenging problem and, hence, it was conducted only on the 15 beaches that showed the highest performance after the binary classification. The performance of the algorithm was calculated by visually screening a subsample of images and it was used to correct the output of the Faster R-CNN. The described steps allowed to obtain an estimate of the litter density in 44 beaches and the litter composition in 15 beaches. By multiplying the relative abundance of each litter class and the median weight of objects belonging to each class, we obtained an estimate of the total mass of plastic beached on 15 beaches. Possible predictors of litter density and mass are the population and marine traffic densities at the site, the exposure of the beach to the prevailing wind and the wind speed, the fetch length and the presence of vegetation where litter could get trapped. Making such raw data (i.e. litter density and composition and their predictors) available can help building the base for a robust global estimate of anthropogenic litter in coastal environments and it is particularly important if data regards an understudied region like the Arabian Peninsula. Moreover, we share a subsample of the original drone images to allow usage from stakeholders.