Riverine Plastic Litter Monitoring Using Unmanned Aerial Vehicles (UAVs)

The use of artificial intelligence algorithms to detect macroplastics in aquatic environments: A critical review

The presence of macroplastic (MP) is having serious consequences on natural ecosystems, directly affecting biota and human wellbeing. Given this scenario, estimating MPs' abundance is crucial for assessing the issue and formulating effective waste management strategies. In this context, the main objective of this critical review is to analyze the use of machine learning (ML) techniques, with a particular interest in deep learning (DL) approaches, to detect, classify and quantify MPs in aquatic environments, supported by datasets such as satellite or aerial images and video recordings taken by unmanned aerial vehicles. This article provides a concise overview of artificial intelligence concepts, followed by a bibliometric analysis and a critical review. The search methodology aimed to categorize the scientific contributions through temporal and spatial criteria for bibliometric analysis, whereas the critical review was based on generating homogeneous groups according to the complexity of ML and DL methods, as well as the type of dataset. In light of the review carried out, classical ML techniques, such as random forest or support vector machines, showed robustness in MPs detection. However, it seems that achieving optimal efficiencies in multiclass classification is a limitation for these methods. Consequently, more advanced techniques such as DL approaches are taking the lead for the detection and multiclass classification of MPs. A series of architectures based on convolutional neural networks, and the use of complex pre-trained models through the transfer learning, are currently being explored (e.g., VGG16 and YOLO models), although currently the computational expense is high due to the need for processing large volumes of data. Additionally, there seems to be a trend towards detecting smaller plastic, which need higher resolution images. Finally, it is important to stress that since 2020 there has been a significant increase in scientific research focusing on transformer-based architectures for object detection. Although this can be considered the current state of the art, no studies have been identified that utilize these architectures for MP detection.

Experimental detection of marine plastic litter in surface waters by 405 nm LD-based fluorescence lidar

Plastic pollution has become a global challenge, affecting water quality and health. Plastics including polystyrene (PS), polyvinyl chloride (PVC), polypropylene (PP), polyethylene terephthalate (PET), and high-density polyethylene (HDPE), are significant contributors to environmental pollution. With the growing need for investigation and detection of plastics found in natural waters, we propose the use of a portable laser diode (LD)-based fluorescence lidar system for in-situ detection of plastic litters in surface waters based on excitation-emission fluorescence spectroscopic data. The experiments were carried out in a controlled environment using a fluorescence lidar system with 405 nm excitation wavelength to determine the fluorescence signals of several plastics at 470 nm emission wavelength. Simultaneous detection of PET plastic and Chlorella vulgaris were also observed to determine the fluorescence influence of chlorophyll in surface waters. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy was employed to study the chemical composition of the plastics used before and after being submerged in the water. Scanning electron microscopy (SEM) and high-resolution camera microscopy were used to analyze the morphology of the submerged PET samples. This study provides a basis for a new in-situ technique using a fluorescence lidar system for submerged or transparent plastics in surface waters.

On the vertical structure of non-buoyant plastics in turbulent transport

Plastic pollution is overflowing in rivers. A limited understanding of the physics of plastic transport in rivers hinders monitoring, the prediction of plastic fate and restricts the implementation of effective mitigation strategies. This study investigates two unexplored aspects of plastic transport dynamics across the near-surface, suspended and bed load layers: (i) the complex settling behaviour of plastics and (ii) their influence on plastic transport in river-like flows. Through hundreds of settling tests and thousands of 3D reconstructed plastic transport experiments, our findings show that plastics exhibit unique settling patterns and orientations, due to their geometric anisotropy, revealing a multimodal distribution of settling velocities. In the transport experiments, particle-bed interactions enhanced mixing beyond what established turbulent transport theories (Rouse profile) could predict in low-turbulence conditions, which extends the bed load layer beyond the classic definition of the bed load layer thickness for natural sediments. We propose a new vertical structure of turbulent transport equation that considers the stochastic nature of heterogeneous negatively buoyant plastics and their singularities.

Riparian vegetation plastic monitoring: A harmonized protocol for sampling macrolitter in vegetated riverine habitats

Many studies highlighted that rivers transported land-based plastics to the sea. However, most of the litter remains stuck in the fluvial ecosystem, also blocked by vegetation. To date, research on riverine macrolitter focused on floating and riverbank monitoring, thus methods to sample riverbank and floating litter have been developed. Concerning rivers, few recent studies highlighted the role of riparian vegetation in entrapping plastics. Given that vegetation represents a large part of riverine ecosystems and that the dynamics of plastics entrapped by vegetation are neglected, it appears pivotal to study in more detail how vegetation contributes to plastic retention. However, as current protocols and guidelines considered only floating and riverbank plastics without providing standardized and updated strategies to monitor litter in vegetation, here we aimed to develop a new standardized protocol and tools to assess plastics in vegetation. Specifically, we focused on unveiling the three-tridimensional structure of vegetation in relation to plastic occurrence, while considering seasonal and hydromorphological aspects. To investigate the trapping effect of vegetation, we developed a three-dimensional vegetation structure index (3DVI) related to plastics. The 3DVI index considers plant structure (i.e., number of branches) and diversity (i.e., species). To test the 3DVI, we conducted an in-situ case study in central Italy. We found that both primary and secondary riparian vegetation blocked plastic litter. In detail, 3DVI correlated with the number of plastics, highlighting that the densest and most diverse communities trap more plastics. Furthermore, we provided for the first time the assessment of seasonality for the macroplastic entrapment by riparian vegetation and a preliminary quantification of wind-blown plastics. Our results should be of interest to promote the development of standardized and harmonized monitoring strategies for riparian habitat management and conservation.

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.

Measuring riverine macroplastic: Methods, harmonisation, and quality control

River systems are a key environmental recipient of macroplastic pollution. Understanding the sources of macroplastic to rivers and the mechanisms controlling fate and transport is essential to identify and tailor measures that can effectively reduce global plastic pollution. Several guidelines exist for monitoring macroplastic in rivers; yet, no single method has emerged representing the standard approach. This reflects the substantial variability in river systems globally and the need to adapt methods to the local environmental context and monitoring goals. Here we present a critical review of methods used to measure macroplastic flows in rivers, with a specific focus on opportunities for methods testing, harmonisation, and quality assurance and quality control (QA/QC). Several studies have already revealed important findings; however, there is significant disparity in the reporting of methodologies and data. There is a need to converge methods, and their adaptations, towards greater comparability. This can be achieved through: i) methods testing to better understand what each method effectively measures and how it can be applied in different contexts; ii) incorporating QA/QC procedures during sampling and analysis; and iii) reporting methodological details and data in a more harmonised way to facilitate comparability and the utilisation of data by several end users, including policy makers. Setting this as a priority now will facilitate the collection of rigorous and comparable monitoring data to help frame solutions to limit plastic pollution, including the forthcoming global treaty on plastic pollution.

Machine learning-based detection and mapping of riverine litter utilizing Sentinel-2 imagery

Despite the substantial impact of rivers on the global marine litter problem, riverine litter has been accorded inadequate consideration. Therefore, our objective was to detect riverine litter by utilizing middle-scale multispectral satellite images and machine learning (ML), with the Tisza River (Hungary) as a study area. The Very High Resolution (VHR) images obtained from the Google Earth database were employed to recognize some riverine litter spots (a blend of anthropogenic and natural substances). These litter spots served as the basis for training and validating five supervised machine-learning algorithms based on Sentinel-2 images [Artificial Neural Network (ANN), Support Vector Classifier (SVC), Random Forest (RF), Naïve Bays (NB) and Decision Tree (DT)]. To evaluate the generalization capability of the developed models, they were tested on larger unseen data under varying hydrological conditions and with different litter sizes. Besides the best-performing model was used to investigate the spatio-temporal variations of riverine litter in the Middel Tisza. According to the results, almost all the developed models showed favorable metrics based on the validation dataset (e.g., F1-score; SVC: 0.94, ANN: 0.93, RF: 0.91, DT: 0.90, and NB: 0.83); however, during the testing process, they showed medium (e.g., F1-score; RF:0.69, SVC: 0.62; ANN: 0.62) to poor performance (e.g., F1-score; NB: 0.48; DT: 0.45). The capability of all models to detect litter was bounded to the pixel size of the Sentinel-2 images. Based on the spatio-temporal investigation, hydraulic structures (e.g., Kisköre Dam) are the greatest litter accumulation spots. Although the highest transport rate of litter occurs during floods, the largest litter spot area upstream of the Kisköre Dam was observed at low stages in summer. This study represents a preliminary step in the automatic detection of riverine litter; therefore, additional research incorporating a larger dataset with more representative small litter spots, as well as finer spatial resolution images is necessary.

Identification of illegally dumped plastic waste in a highly polluted river in Indonesia using Sentinel-2 satellite imagery

Plastic waste monitoring technology based on Earth observation satellites is one approach that is currently under development in various studies. The complexity of land cover and the high human activity around rivers necessitate the development of studies that can improve the accuracy of monitoring plastic waste in river areas. This study aims to identify illegal dumping in a river area using the adjusted plastic index (API) and Sentinel-2 satellite imagery data. Rancamanyar River has been selected as the research area; it is one of the tributaries of Citarum Indonesia and is an open lotic-simple form, oxbow lake type river. Our study is the first attempt to construct an API and random forest machine learning using Sentinel-2 to identify the illegal dumping of plastic waste. The algorithm development integrated the plastic index algorithm with the normalized difference vegetation index (NDVI) and normalized buildup indices. For the validation process, the results of plastic waste image classification based on Pleiades satellite imagery and Unmanned Aerial Vehicle (UAV) photogrammetry was used. The validation results show that the API succeeded in improving the accuracy of identifying plastic waste, which gave a better correlation in the r-value and p-value by + 0.287014 and + 3.76 × 10^-26 with Pleiades, and + 0.143131 and + 3.17 × 10^-10 with UAV.

Gaining new insights into macroplastic transport 'hotlines' and fine-scale retention-remobilisation using small floating high-resolution satellite drifters in the Chao Phraya River estuary of Bangkok

In river plastic pollution research little is known about the detailed pathways and interruptions that occur during the journey of macroplastic debris (>5 cm) from land to sea. Data on fine-scale and high-accuracy transport trajectories and cycles of retention (when macroplastics are trapped, e.g. at a pier) and remobilisation is needed to inform global river plastic transport models as well as mechanical cleanup efforts. Though well established in the marine environment, the use of floating satellite drifters to understand macroplastic debris transport in tidal rivers and estuaries is in its infancy. Exploring the capacity to investigate fine-scale macroplastic debris-estuary interactions, this study brings together, on the one hand, a small, sensitive, floating satellite drifter with, on the other hand, the major riverine-marine habitat of the Chao Phraya River estuary at Bangkok, Thailand. The used grapefruit-sized drifters (n = 5) with minimal drogue (ρ ≈ 0.67 g/cm³) sent their positions at up to 4 m and 5 min spatiotemporal resolution via cellular GSM network for up to 48 days. This study indicates that river macroplastic debris transport 'hotlines' (positions where floating debris will likely pass by in a river) as well as retention-remobilisation cycles can be studied at fine scale. On their way through the river and gulf, covering between 9 and 696 km, drifters got stuck up to 23 times, spending 80% of their river lifetime in retention. Furthermore, it is outlined that the trajectories can be linked with environmental factors such as bathymetry and tides to more accurately model macroplastic debris behaviour in rivers. Finally, it is shown that trajectories crossing the riverine-marine continuum at the estuary can be accurately traced to support future investigations on the so far scarcely evidenced river mouth emissions of macroplastic debris.

From city to sea: Spatiotemporal dynamics of floating macrolitter in the Tiber River

Rivers are undoubtedly the main pathway of waste dispersed in the environment that from land reaches oceans and seas increasing the amount of marine litter. Major cities are a great source of riverine litter as large urbanization can originate pressure on the integrated waste management resulting in litter entering the rivers. Within this study, we aim to investigate the dynamic of floating riverine macrolitter (items >2.5 cm) in the city of Rome before it reaches the sea by assessing the composition, amount, and seasonal trends of litter transported from the urban centre to the main river mouth of Tiber River. Visual surveys for a whole year (March 2021-February 2022) were conducted from two bridges, Scienza Bridge (in the city) and Scafa Bridge (at the main river mouth) and followed JRC/RIMMEL protocol for riverine litter monitoring. Overall, similar litter composition was observed from the city centre to the mouth with a prevalence of plastic material, mainly related to fragmentation process (i.e. plastic pieces) and single use items, mainly in food and beverage sectors. An extrapolated annual loading of 4 × 10⁵ items/year was estimated at the main mouth of Tiber River. The litter flux seems to be influenced by the seasonal variability and hydrometeorological parameters. The frequency of size classes decreases with increasing size in both sites, and more than half of the recorded items were below 10 cm. Specific categories belonging to "other plastics" have been reported related to anti-Covid-19 behaviour such as face masks and beverage sector, e.g. bottle lids and rings. The main colour of plastics was white, suggesting weathering process of floating riverine litter. This study contributes to increasing knowledge of the origin, composition and spatiotemporal dynamics of riverine floating litter from the city and entering the sea.

Aerial and underwater drones for marine litter monitoring in shallow coastal waters: factors influencing item detection and cost-efficiency

Although marine litter monitoring has increased over the years, the pollution of coastal waters is still understudied and there is a need for spatial and temporal data. Aerial (UAV) and underwater (ROV) drones have demonstrated their potential as monitoring tools at coastal sites; however, suitable conditions for use and cost-efficiency of the methods still need attention. This study tested UAVs and ROVs for the monitoring of floating, submerged, and seafloor items using artificial plastic plates and assessed the influence of water conditions (water transparency, color, depth, bottom substrate), item characteristics (color and size), and method settings (flight/dive height) on detection accuracy. A cost-efficiency analysis suggests that both UAV and ROV methods lie within the same cost and efficiency category as current on-boat observation and scuba diving methods and shall be considered for further testing in real scenarios for official marine litter monitoring methods.

Fate identification and management strategies of non-recyclable plastic waste through the integration of material flow analysis and leakage hotspot modeling

Low priority on waste management has impacted the complex environmental issue of plastic waste pollution, as evident by results of this study where it was found that 24.3% of waste generation in Jakarta and Bandung is emitted into the waterway due to the high intensity of human activity in the urban area. In this study, we investigated the viable integration between material flow analysis and leakage hotspot modeling to improve management strategies for plastic pollution in water systems and open environments. Using a multi-criteria assessment of plastic leakage from current waste management, a material flow analysis was developed on a city-wide scale defining the fate of plastic waste. Geospatial analysis was assigned to develop a calculation for identification and hydrological analysis while identifying the potential amount of plastic leakage to the river system. The results show that 2603 tons of plastic accumulated along the mainstream of the Ciliwung River on an annual basis, and a high-density population like that in Bandung discarded 1547 tons in a one-year period to the Cikapundung River. The methods and results of this study are applicable towards improving the control mechanisms of river rejuvenation from plastic leakage by addressing proper management in concentrated locations.

UAV remote sensing applications in marine monitoring: Knowledge visualization and review

With the booming development of information technology and the growing demand for remote sensing data, unmanned aerial vehicle (UAV) remote sensing technology has emerged. In recent years, UAV remote sensing technology has developed rapidly and has been widely used in the fields of military defense, agricultural monitoring, surveying and mapping management, and disaster and emergency response and management. Currently, increasingly serious marine biological and environmental problems are raising the need for effective and timely monitoring. Compared with traditional marine monitoring technologies, UAV remote sensing is becoming an important means for marine monitoring thanks to its flexibility, efficiency and low cost, while still producing systematic data with high spatial and temporal resolutions. This study visualizes the knowledge domain of the application and research advances of UAV remote sensing in marine monitoring by analyzing 1130 articles (from 1993 to early 2022) using a bibliometric approach and provides a review of the application of UAVs in marine management mapping, marine disaster and environmental monitoring, and marine wildlife monitoring. It aims to promote the extensive application of UAV remote sensing in the field of marine research.

Detection and Classification of Floating Plastic Litter Using a Vessel-Mounted Video Camera and Deep Learning

Marine plastic pollution is a major environmental concern, with significant ecological, economic, public health and aesthetic consequences. Despite this, the quantity and distribution of marine plastics is poorly understood. Better understanding of the global abundance and distribution of marine plastic debris is vital for global mitigation and policy. Remote sensing methods could provide substantial data to overcome this issue. However, developments have been hampered by the limited availability of in situ data, which are necessary for development and validation of remote sensing methods. Current in situ methods of floating macroplastics (size greater than 1 cm) are usually conducted through human visual surveys, often being costly, time-intensive and limited in coverage. To overcome this issue, we present a novel approach to collecting in situ data using a trained object-detection algorithm to detect and quantify marine macroplastics from video footage taken from vessel-mounted general consumer cameras. Our model was able to successfully detect the presence or absence of plastics from real-world footage with an accuracy of 95.2% without the need to pre-screen the images for horizon or other landscape features, making it highly portable to other environmental conditions. Additionally, the model was able to differentiate between plastic object types with a Mean Average Precision of 68% and an F1-Score of 0.64. Further analysis suggests that a way to improve the separation among object types using only object detection might be through increasing the proportion of the image area covered by the plastic object. Overall, these results demonstrate how low-cost vessel-mounted cameras combined with machine learning have the potential to provide substantial harmonised in situ data of global macroplastic abundance and distribution.

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.

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.

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Fighting Earth's degradation and safeguarding the environment are subjects of topical interest and sources of hot debate in today's society. According to the United Nations, there is a compelling need to take immediate actions worldwide and to implement large-scale monitoring policies aimed at counteracting the unprecedented levels of air, land, and water pollution. This requires going beyond the legacy technologies currently employed by government authorities and adopting more advanced systems that guarantee a continuous and pervasive monitoring of the environment in all its different aspects. In this paper, we take the research on integrated and large-scale environmental monitoring a step further by providing a comprehensive review that covers transversally all the main applications of wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs), and crowdsensing monitoring technologies. By outlining the available solutions and current limitations, we identify in the cooperation among terrestrial (WSN/crowdsensing) and aerial (UAVs) sensing, coupled with the adoption of advanced signal processing techniques, the major pillars at the basis of future integrated (air, land, and water) and large-scale environmental monitoring systems. This review not only consolidates the progresses achieved in the field of environmental monitoring, but also sheds new lights on potential future research directions and synergies among different research areas.

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.

Quantifying Floating Plastic Debris at Sea Using Vessel-Based Optical Data and Artificial Intelligence

Despite recent advances in remote sensing of large accumulations of floating plastic debris, mainly in coastal regions, the quantification of individual macroplastic objects (>50 cm) remains challenging. Here, we have trained an object-detection algorithm by selecting and labeling footage of floating plastic debris recorded offshore with GPS-enabled action cameras aboard vessels of opportunity. Macroplastic numerical concentrations are estimated by combining the object detection solution with bulk processing of the optical data. Our results are consistent with macroplastic densities predicted by global plastic dispersal models, and reveal first insights into how camera recorded offshore macroplastic densities compare to micro- and mesoplastic concentrations collected with neuston trawls.

Drones for litter mapping: An inter-operator concordance test in marking beached items on aerial images

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.

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.

Detecting stranded macro-litter categories on drone orthophoto by a multi-class Neural Network

The use of Unmanned Aerial Systems (UAS, aka drones) images for mapping macro-litter in the environment have been exponentially increasing in the recent years. In this work, we developed a multi-class Neural Network (NN) to automatically identify stranded plastic litter categories on an UAS-derived orthophoto. The best results were assessed for items that did not have substantial intra-class colour variability, such as octopus pots and fishing ropes (F-score = 61%, on average). Instead, performance was poor (37%) for plastic bottles and fragments, due to their changing intra-class colours. On average, the performance improved 24% when the binary detection (litter/non-litter, F-Score = 73%) was considered, however this approach did not discriminate the litter categories. This work gives a new perspective for the automated litter detection on drone images, suggesting that colour-based approach can be used to improve the categorization of stranded litter on UAS orthophoto.

Transfer dynamics of macroplastics in estuaries - New insights from the Seine estuary: Part 3. What fate for macroplastics?

Macroplastic emissions from the Seine estuary to the English Channel were estimated using institutional cleaning of riverbanks, combined with a tagged litter experiment. Cleaning were performed between March 2018 and April 2019 by the non-profit company Naturaul'un over 19 sites covering 20 km of riverbanks. A total of 365 tagged litter (90% macroplastics) was released in the estuary in March (n = 200), at the end of the winter/spring flood 2018, in July (n = 58), August (n = 56) and September 2018 (n = 51) during low river flow periods. Over the total tagged litter, 102 (28%) were recovered by Naturaul'un. Relative to the total amount of macroplastics (>5 cm) collected and the estimated amount of smaller/hidden macroplastics (>5 mm) not collected, the maximum macroplastic emission to the English Channel was estimated to be ~100-200 metric tons per year.

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

Beach litter assessments rely on time inefficient and high human cost protocols, mining the attainment of global beach litter estimates. Here we show the application of an emerging technique, the use of drones for acquisition of high-resolution beach images coupled with machine learning for their automatic processing, aimed at achieving the first national-scale beach litter survey completed by only one operator. The aerial survey had a time efficiency of 570 ± 40 m² min^-1 and the machine learning reached a mean (±SE) detection sensitivity of 59 ± 3% with high resolution images. The resulting mean (±SE) litter density on Saudi Arabian shores of the Red Sea is of 0.12 ± 0.02 litter items m^-2, distributed independently of the population density in the area around the sampling station. Instead, accumulation of litter depended on the exposure of the beach to the prevailing wind and litter composition differed between islands and the main shore, where recreational activities are the major source of anthropogenic debris.

Macroplastics in rivers: present knowledge, issues and challenges

Macroplastics are the primary contributor to riverine plastic pollution by mass, posing a wide range of serious threats for riverine systems, from adversely affecting various life forms within the riverine system, to potentially increasing flood risk, and generally resulting in adverse effects on any livelihoods. Compared to other river-related research disciplines, research into riverine macroplastics and their effects has not yet featured prominently. Various quantification methods are presently used to assess the presence of macroplastics at different locations within river systems; however, overcoming limitations and unifying methods remain an essential need. Macroplastic dynamics in rivers are subject to various factors, including both material and river characteristics. We review the diverse factors that potentially influence macroplastic dynamics in rivers, and highlight our knowledge limits. We advocate for future research that enables synergies between improved field quantification techniques, use of global protocols and data sharing, and laboratory experiments. This is needed to obtain a riverine macroplastic budget model, required for the implementation of targeted management practices. Finally, a multilayer potential management strategy is presented: (i) reducing the macroplastic supply into rivers; (ii) removing effectively and safely macroplastics from within rivers; and (iii) treating macroplastics once removed from the riverine system.

Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review

In less than two decades, UASs (unmanned aerial systems) have revolutionized the field of hydrology, bridging the gap between traditional satellite observations and ground-based measurements and allowing the limitations of manned aircraft to be overcome. With unparalleled spatial and temporal resolutions and product-tailoring possibilities, UAS are contributing to the acquisition of large volumes of data on water bodies, submerged parameters and their interactions in different hydrological contexts and in inaccessible or hazardous locations. This paper provides a comprehensive review of 122 works on the applications of UASs in surface water and groundwater research with a purpose-oriented approach. Concretely, the review addresses: (i) the current applications of UAS in surface and groundwater studies, (ii) the type of platforms and sensors mainly used in these tasks, (iii) types of products generated from UAS-borne data, (iv) the associated advantages and limitations, and (v) knowledge gaps and future prospects of UASs application in hydrology. The first aim of this review is to serve as a reference or introductory document for all researchers and water managers who are interested in embracing this novel technology. The second aim is to unify in a single document all the possibilities, potential approaches and results obtained by different authors through the implementation of UASs.

The Drone Revolution of Shark Science: A Review

Over the past decade, drones have become a popular tool for wildlife management and research. Drones have shown significant value for animals that were often difficult or dangerous to study using traditional survey methods. In the past five years drone technology has become commonplace for shark research with their use above, and more recently, below the water helping to minimise knowledge gaps about these cryptic species. Drones have enhanced our understanding of shark behaviour and are critically important tools, not only due to the importance and conservation of the animals in the ecosystem, but to also help minimise dangerous encounters with humans. To provide some guidance for their future use in relation to sharks, this review provides an overview of how drones are currently used with critical context for shark monitoring. We show how drones have been used to fill knowledge gaps around fundamental shark behaviours or movements, social interactions, and predation across multiple species and scenarios. We further detail the advancement in technology across sensors, automation, and artificial intelligence that are improving our abilities in data collection and analysis and opening opportunities for shark-related beach safety. An investigation of the shark-based research potential for underwater drones (ROV/AUV) is also provided. Finally, this review provides baseline observations that have been pioneered for shark research and recommendations for how drones might be used to enhance our knowledge in the future.

A Citizen Science Unmanned Aerial System Data Acquisition Protocol and Deep Learning Techniques for the Automatic Detection and Mapping of Marine Litter Concentrations in the Coastal Zone

Marine litter (ML) accumulation in the coastal zone has been recognized as a major problem in our time, as it can dramatically affect the environment, marine ecosystems, and coastal communities. Existing monitoring methods fail to respond to the spatiotemporal changes and dynamics of ML concentrations. Recent works showed that unmanned aerial systems (UAS), along with computer vision methods, provide a feasible alternative for ML monitoring. In this context, we proposed a citizen science UAS data acquisition and annotation protocol combined with deep learning techniques for the automatic detection and mapping of ML concentrations in the coastal zone. Five convolutional neural networks (CNNs) were trained to classify UAS image tiles into two classes: (a) litter and (b) no litter. Testing the CCNs’ generalization ability to an unseen dataset, we found that the VVG19 CNN returned an overall accuracy of 77.6% and an f-score of 77.42%. ML density maps were created using the automated classification results. They were compared with those produced by a manual screening classification proving our approach’s geographical transferability to new and unknown beaches. Although ML recognition is still a challenging task, this study provides evidence about the feasibility of using a citizen science UAS-based monitoring method in combination with deep learning techniques for the quantification of the ML load in the coastal zone using density maps.

Transfer dynamics of macroplastics in estuaries - New insights from the Seine estuary: Part 2. Short-term dynamics based on GPS-trackers

The dynamics of plastic debris were assessed in the Seine River, especially in the estuary, using plastic bottles equipped with GPS-trackers. In one year, 50 trajectories were recorded, covering a wide range of hydrometeorological conditions. Results show a succession of stranding/remobilization episodes in combination with alternating upstream and downstream transport in the estuary. In the end, 100% of the tracked bottles stranded somewhere, for hours or weeks, from one to several times at different sites. The overall picture shows that different physical phenomena interact with various time scales ranging from hours/days (high/low tides) to weeks/months (spring/neap tides and highest tides) and years (seasonal river flow). Thus, the fate of plastic debris is highly unpredictable, but the consequence of those interactions is that the transfer of debris is chaotic and not straightforward, and its residence time is much longer than the transit time of water.

Floating marine macro-litter in the North Western Mediterranean Sea: Results from a combined monitoring approach

The aim of the present study was twofold: (i) to validate the drone methodology for floating marine macro-litter (FMML) monitoring, by comparing the results obtained through concurrent drone surveys and visual observations from vessels, and (ii) to assess FMML densities along the North Western Mediterranean Sea using the validated drone surveys. The comparison between monitoring techniques was performed based on 18 concurrent drone/vessel transects. Similar densities of FMML were detected through the two methods (16 items km^-2 from the drone method vs 19 items km^-2 from the vessel-based visual method). The assessment of FMML densities was done using 40 additional drone transects performed over the waters off the Catalan coast. The densities of FMML observed ranged 0-200 items km^-2. These results provide a validation of the use of drones to monitor FMML and contribute to increasing the knowledge about the density of FMML in the North Western Mediterranean Sea.

Operational Protocols for the Use of Drones in Marine Animal Research

The use of drones to study marine animals shows promise for the examination of numerous aspects of their ecology, behaviour, health and movement patterns. However, the responses of some marine phyla to the presence of drones varies broadly, as do the general operational protocols used to study them. Inconsistent methodological approaches could lead to difficulties comparing studies and can call into question the repeatability of research. This review draws on current literature and researchers with a wealth of practical experience to outline the idiosyncrasies of studying various marine taxa with drones. We also outline current best practice for drone operation in marine environments based on the literature and our practical experience in the field. The protocols outlined herein will be of use to researchers interested in incorporating drones as a tool into their research on marine animals and will help form consistent approaches for drone-based studies in the future.

Plastic Hotspot Mapping in Urban Water Systems

Reducing plastic pollution in rivers, lakes, and oceans is beneficial to aquatic animals and human livelihood. To achieve this, reliable observations of the abundance, spatiotemporal variation, and composition of plastics in aquatic ecosystems are crucial. Current efforts mainly focus on collecting data on the open ocean, on beaches and coastlines, and in river systems. Urban areas are the main source of plastic leakage into the natural environment, yet data on plastic pollution in urban water systems are scarce. In this paper, we present a simple method for plastic hotspot mapping in urban water systems. Through visual observations, macroplastic abundance and polymer categories are determined. Due to its simplicity, this method is suitable for citizen science data collection. A first application in the Dutch cities of Leiden and Wageningen showed similar mean plastic densities (111–133 items/km canal) and composition (75–80% soft plastics), but different spatial distributions. These observations emphasize the importance of long-term data collection to further understand and quantify spatiotemporal variations of plastics in urban water systems. In turn, this will support improved estimates of the contribution of urban areas to the plastic pollution of rivers and oceans.

Macroplastic Storage and Remobilization in Rivers

The paper presents a conceptual model of the route of macroplastic debris (>5 mm) through a fluvial system, which can support future works on the overlooked processes of macroplastic storage and remobilization in rivers. We divided the macroplastic route into (1) input, (2) transport, (3) storage, (4) remobilization and (5) output phases. Phase 1 is mainly controlled by humans, phases 2–4 by fluvial processes, and phase 5 by both types of controls. We hypothesize that the natural characteristics of fluvial systems and their modification by dam reservoirs and flood embankments construction are key controls on macroplastic storage and remobilization in rivers. The zone of macroplastic storage can be defined as a river floodplain inundated since the beginning of widespread disposal of plastic waste to the environment in the 1960s and the remobilization zone as a part of the storage zone influenced by floodwaters and bank erosion. The amount of macroplastic in both zones can be estimated using data on the abundance of surface- and subsurface-stored macroplastic and the lateral and vertical extent of the zones. Our model creates the framework for estimation of how much plastic has accumulated in rivers and will be present in future riverscapes.

Remote Sensing of Sea Surface Artificial Floating Plastic Targets with Sentinel-2 and Unmanned Aerial Systems (Plastic Litter Project 2019)

Remote sensing is a promising tool for the detection of floating marine plastics offering extensive area coverage and frequent observations. While floating plastics are reported in high concentrations in many places around the globe, no referencing dataset exists either for understanding the spectral behavior of floating plastics in a real environment, or for calibrating remote sensing algorithms and validating their results. To tackle this problem, we initiated the Plastic Litter Projects (PLPs), where large artificial plastic targets were constructed and deployed on the sea surface. The first such experiment was realised in the summer of 2018 (PLP2018) with three large targets of 10 × 10 m. Hereafter, we present the second Plastic Litter Project (PLP2019), where smaller 5 × 5 m targets were constructed to better simulate near-real conditions and examine the limitations of the detection with Sentinel-2 images. The smaller targets and the multiple acquisition dates allowed for several observations, with the targets being connected in a modular way to create different configurations of various sizes, material composition and coverage. A spectral signature for the PET (polyethylene terephthalate) targets was produced through modifying the U.S. Geological Survey PET signature using an inverse spectral unmixing calculation, and the resulting signature was used to perform a matched filtering processing on the Sentinel-2 images. The results provide evidence that under suitable conditions, pixels with a PET abundance fraction of at least as low as 25% can be successfully detected, while pinpointing several factors that significantly impact the detection capabilities. To the best of our knowledge, the 2018 and 2019 Plastic Litter Projects are to date the only large-scale field experiments on the remote detection of floating marine litter in a near-real environment and can be used as a reference for more extensive validation/calibration campaigns.

A Deep Learning Model for Automatic Plastic Mapping Using Unmanned Aerial Vehicle (UAV) Data

Although plastic pollution is one of the most noteworthy environmental issues nowadays, there is still a knowledge gap in terms of monitoring the spatial distribution of plastics, which is needed to prevent its negative effects and to plan mitigation actions. Unmanned Aerial Vehicles (UAVs) can provide suitable data for mapping floating plastic, but most of the methods require visual interpretation and manual labeling. The main goals of this paper are to determine the suitability of deep learning algorithms for automatic floating plastic extraction from UAV orthophotos, testing the possibility of differentiating plastic types, and exploring the relationship between spatial resolution and detectable plastic size, in order to define a methodology for UAV surveys to map floating plastic. Two study areas and three datasets were used to train and validate the models. An end-to-end semantic segmentation algorithm based on U-Net architecture using the ResUNet50 provided the highest accuracy to map different plastic materials (F1-score: Oriented Polystyrene (OPS): 0.86; Nylon: 0.88; Polyethylene terephthalate (PET): 0.92; plastic (in general): 0.78), showing its ability to identify plastic types. The classification accuracy decreased with the decrease in spatial resolution, performing best on 4 mm resolution images for all kinds of plastic. The model provided reliable estimates of the area and volume of the plastics, which is crucial information for a cleaning campaign.

Unmanned Aerial Vehicles for Debris Survey in Coastal Areas: Long-Term Monitoring Programme to Study Spatial and Temporal Accumulation of the Dynamics of Beached Marine Litter

Unmanned aerial vehicles (UAVs) are becoming increasingly accessible tools with widespread use as environmental monitoring systems. They can be used for anthropogenic marine debris survey, a recently growing research field. In fact, while the increasing efforts for offshore investigations lead to a considerable collection of data on this type of pollution in the open sea, there is still little knowledge of the materials deposited along the coasts and the mechanism that leads to their accumulation pattern. UAVs can be effective in bridging this gap by increasing the amount of data acquired to study coastal deposits, while also limiting the anthropogenic impact in protected areas. In this study, UAVs have been used to acquire geo-referenced RGB images in a selected zone of a protected marine area (the Migliarino, Massacciuccoli, and San Rossore park near Pisa, Italy), during a long-term (ten months) monitoring programme. A post processing system based on visual interpretation of the images allows the localization and identification of the anthropogenic marine debris within the scanned area, and the estimation of their spatial and temporal distribution in different zones of the beach. These results provide an opportunity to investigate the dynamics of accumulation over time, suggesting that our approach might be appropriate for monitoring and collecting such data in isolated, and especially in protected, areas with significant benefits for different types of stakeholders.

Seasonality of riverine macroplastic transport

Marine plastic pollution is an increasing environmental threat. Although it is assumed that most marine plastics are transported from land to the ocean through rivers, only limited data on riverine plastic transport exists. Recently, new methods have been introduced to characterize riverine plastics consistently through time and space. For example, combining visual counting observations and plastic debris sampling can provide order of magnitude estimations of plastic transport through a river. In this paper, we present findings from multi-season measurement campaign in the Saigon River, Vietnam. For the first time, we demonstrate that macroplastic transport exhibits strong temporal variation. The monthly averaged plastic transport changes up to a factor five within the measurement period. As it is unclear what drives the variation in plastic transport, relations between rainfall, river discharge, presence of organic material and plastic transport have been explored. Furthermore, we present new findings on the cross-sectional and vertical distribution of riverine plastic transport. With this paper we present new insights in the origin and fate of riverine plastic transport, emphasizing the severity of the emerging thread of plastic pollution on riverine ecosystems.