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

Baseline assessment of macrolitter on the coastline of Algeria: Fit-for-purpose data for tailor-made measures to navigate the Plasticene Age

Curbing the growing threat of marine litter requires reliable, coherent and fit-for-purpose data. The present study reports the findings of beach macrolitter surveys carried out in seventeen sites along the coastline of Algeria. The median litter density recorded along these sites amounted to 578 items per 100 m of coastline (range: 317-2684 items/100 m). Every surveyed beach exceeded the European threshold value of 20 items per 100 m of coastline by a significant margin. In addition, the evaluation conducted employing the Mediterranean threshold value of 130 items per 100 m of coastline indicated that each of the seventeen surveyed beaches resides within the non-Good Environmental Status spectrum. A significant proportion of the litter, accounting for 43 %, is attributed to food and beverage consumption-related items, highlighting the impact of single-use food packaging, including food and beverage containers resulting from unsustainable practices mainly by beach users and inadequate waste management.

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.

The first assessment of marine litter on somalian coast: The case of Liido Beach, mogadishu

This paper presents the first assessment of marine litter in the Mogadishu coastal area of Somalia. Samples were collected monthly using 100 m × 40 m transect and classified following OSPAR Marine Litter Survey Guide while litter sources were identified using Ocean Conservancy Marine Debris Index. The results showed a total of 119873 items consisting of plastics (89.47%), clothing items (7.53%), and others (3.00%) recovered from Liido Beach. Litter density ranged from 2.19 items/m2 to 14.18 items/m2 with a mean of 6.25 items/m2 and Clean Coast Index (CCI) suggesting that Liido Beach is extremely dirty (>20 items/m2). In addition, the primary sources of marine litter at the beach are local recreational and shoreline activities (54.12%), and dumping (36.61%). The dominance of plastic litter on the beach poses potential threats to marine biodiversity in the Somalia coastal area and the West Indian Ocean. It is recommended that effective strategies and solutions to mitigate litter on the beach and other coastal areas in Somalia should be developed and compensated with public education and awareness campaigns across the country.

Closing the gap between existing large-area imaging research and marine conservation needs

Emerging technology has immense potential to increase the scale and efficiency of marine conservation. One such technology is large-area imaging (LAI), which relies on structure-from-motion photogrammetry to create composite products, including 3-dimensional (3-D) environmental models, that are larger in spatial extent than the individual images used to create them. Use of LAI has become widespread in certain fields of marine science, primarily to measure the 3D structure of benthic ecosystems and track change over time. However, the use of LAI in the field of marine conservation appears limited. We conducted a review of the coral reef literature on the use of LAI to identify research themes and regional trends in applications of this technology. We also surveyed 135 coral reef scientists and conservation practitioners to determine community familiarity with LAI, evaluate barriers practitioners face in using LAI, and identify applications of LAI believed to be most exciting or relevant to coral conservation. Adoption of LAI was limited primarily to researchers at institutions based in advanced economies and was applied infrequently to conservation, although conservation practitioners and survey respondents from emerging economies indicated they expect to use LAI in the future. Our results revealed disconnect between current LAI research topics and conservation priorities identified by practitioners, highlighting the need for more diverse, conservation-relevant research using LAI. We provide recommendations for how early adopters of LAI (typically Global North scientists from well-resourced institutions) can facilitate access to this conservation technology. These recommendations include developing training resources, creating partnerships for data storage and analysis, publishing standard operating procedures for LAI workflows, standardizing methods, developing tools for efficient data extraction from LAI products, and conducting conservation-relevant research using LAI.

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.

Feasibility Study for the Development of a Low-Cost, Compact, and Fast Sensor for the Detection and Classification of Microplastics in the Marine Environment

The detection and classification of microplastics in the marine environment is a complex task that implies the use of delicate and expensive instrumentation. In this paper, we present a preliminary feasibility study for the development of a low-cost, compact microplastics sensor that could be mounted, in principle, on a float of drifters, for the monitoring of large marine surfaces. The preliminary results of the study indicate that a simple sensor equipped with three infrared-sensitive photodiodes can reach classification accuracies around 90% for the most-diffused floating microplastics in the marine environment (polyethylene and polypropylene).

Marine Litter Tracking System: A Case Study with Open-Source Technology and a Citizen Science-Based Approach

It is well established that most of the plastic pollution found in the oceans is transported via rivers. Unfortunately, the main processes contributing to plastic and debris displacement through riparian systems is still poorly understood. The Marine Litter Drifter project from the Arno River aims at using modern consumer software and hardware technologies to track the movements of real anthropogenic marine debris (AMD) from rivers. The innovative "Marine Litter Trackers" (MLT) were utilized as they are reliable, robust, self-powered and they present almost no maintenance costs. Furthermore, they can be built not only by those trained in the field but also by those with no specific expertise, including high school students, simply by following the instructions. Five dispersion experiments were successfully conducted from April 2021 to December 2021, using different types of trackers in different seasons and weather conditions. The maximum distance tracked was 2845 km for a period of 94 days. The activity at sea was integrated by use of Lagrangian numerical models that also assisted in planning the deployments and the recovery of drifters. The observed tracking data in turn were used for calibration and validation, recursively improving their quality. The dynamics of marine litter (ML) dispersion in the Tyrrhenian Sea is also discussed, along with the potential for open-source approaches including the "citizen science" perspective for both improving big data collection and educating/awareness-raising on AMD issues.

Marine beach litter monitoring strategies along Mediterranean coasts. A methodological review

Marine beach litter (MBL) represents a serious issue for marine life, coastal ecosystems, human health and several economical activities. The Mediterranean Sea is a semi enclosed basin particularly vulnerable to this problem. Its coasts are threatened by critical anthropogenic pressures that sum up with intensive fishing and shipping, and the slow turnover of its waters. In the last decades, several scientific and participative initiatives have been conducted to study, monitor and clean-up shorelines. These studies were generally characterized by differences in timing and frequency of the surveys, as well as in litter sampling, classification and analysis. This paper presents a systematic review of current literature concerning MBL monitoring strategies along the Mediterranean coasts. Scopus indexed studies are analysed to identify discrepancies and similarities among the applied protocols, understand where current gaps lie, and point out what would be needed to develop a basin-scale efficient monitoring for the Mediterranean Sea.

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.

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 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.

Monitoring Light Pollution with an Unmanned Aerial Vehicle: A Case Study Comparing RGB Images and Night Ground Brightness

There are several tools and methods to quantify light pollution due to direct or reflected light emitted towards the sky. Unmanned aerial vehicles (UAV) are still rarely used in light pollution studies. In this study, a digital camera and a sky quality meter mounted on a UAV have been used to study the relationship between indices computed on night images and night ground brightness (NGB) measured by an optical device pointed downward towards the ground. Both measurements were taken simultaneously during flights at an altitude of 70 and 100 m, and with varying exposure time. NGB correlated significantly both with the brightness index (−0.49 ÷ −0.56) and with red (−0.52 ÷ −0.58) and green band indices (−0.42 ÷ −0.58). A linear regression model based on the luminous intensity index was able to estimate observed NGB with an RMSE varying between 0.21 and 0.46 mpsas. Multispectral analysis applied to images taken at 70 m showed that increasing exposure time might cause a saturation of the colors of the image, especially in the red band, that worsens the correlation between image indices and NGB. Our study suggests that the combined use of low cost devices such as UAV and a sky quality meter can be used for assessing hotspot areas of light pollution originating from the surface.

Numerical Simulation of the Trajectory of Garbage Falling into the Sea at the Coastal Landfill in Northeast Taiwan

This study used a numerical model to simulate the floating trajectory of garbage falling into the sea from the landfill near the coast of Wanghaixiang Bay in northeast Taiwan to understand its impact on the local environment. The Regional Ocean Model System was used to simulate the probability densities of the distribution of garbage drifting trajectories under scenarios of no-wind, northeast monsoon, and typhoons. The results show that, in the no-wind scenario, garbage was mainly affected by tidal currents. It moved in the northwest–southeast direction outside the bay. In the northeast monsoon scenario, garbage was forced toward the shore due to the windage effect. In typhoon scenarios, strong winds forced the garbage to the shore, as typhoons continued to advance and the wind direction kept changing, the garbage trajectory was also changed. After typhoons moved away, the drifting trajectory of the garbage was again affected by tidal currents. When the garbage falling into the sea was located in the bay or the mouth of the bay, the garbage had a higher probability of being forced into the bay by typhoons.

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.

Real-Time UAV Trash Monitoring System

This study proposes a marine trash detection system based on unmanned aerial vehicles (UAVs) and aims to replace manpower with UAVs to detect marine trash efficiently and provide information to government agencies regarding real-time trash pollution. Internet technology and computer–machine interaction were applied in this study, which involves the deployment of a marine trash detection system on a drone’s onboard computer for real-time calculations. Images of marine trash were provided to train a modified YOLO model (You Look Only Once networks). The UAV was shown to be able to fly along a predefined path and detect trash in coastal areas. The detection results were sent to a data streaming platform for data processing and analysis. The Kafka message queuing system and the Mongo database were used for data transmission and analysis. It was shown that a real-time drone map monitoring station can be built up at any place where mobile communication is accessible. While a UAV is automatically controlled by an onboard computer, it can also be controlled through a remote station. It was shown that the proposed system can perform data analysis and transmit heatmaps of coastal trash information to a remote site. From the heatmaps, government agencies can use trash categories and locations to take further action.

A Water Surface Contaminants Monitoring Method Based on Airborne Depth Reasoning

Water surface plastic pollution turns out to be a global issue, having aroused rising attention worldwide. How to monitor water surface plastic waste in real time and accurately collect and analyze the relevant numerical data has become a hotspot in water environment research. (1) Background: Over the past few years, unmanned aerial vehicles (UAVs) have been progressively adopted to conduct studies on the monitoring of water surface plastic waste. On the whole, the monitored data are stored in the UAVS to be subsequently retrieved and analyzed, thereby probably causing the loss of real-time information and hindering the whole monitoring process from being fully automated. (2) Methods: An investigation was conducted on the relationship, function and relevant mechanism between various types of plastic waste in the water surface system. On that basis, this study built a deep learning-based lightweight water surface plastic waste detection model, which was capable of automatically detecting and locating different water surface plastic waste. Moreover, a UAV platform-based edge computing architecture was built. (3) Results: The delay of return task data and UAV energy consumption were effectively reduced, and computing and network resources were optimally allocated. (4) Conclusions: The UAV platform based on airborne depth reasoning is expected to be the mainstream means of water environment monitoring in the future.

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.

Replicate analyses of OSPAR beach litter data

Replicate surveys of beach litter have seldom been performed in the past. In this study, replicate surveys of beach litter were conducted on the beach north of Hörnum (Sylt, Germany), from 2015 to 2019, applying a slightly modified OSPAR protocol of beach litter monitoring. Descriptive statistics and power analyses were calculated on data resulting from these replicate surveys, to find out whether the scatter of replicate beach litter data decreases and the statistical power increases with increasing numbers of replicate surveys. From 2015 to 2019, mean total abundances, given as numbers of litter items, ranged from 19 to 185 litter items on a 50 m section of beach. With increasing numbers of replicate surveys, the scatter given by the coefficient of variation (CV) significantly increased up to 113%. Statistical power considerably increased with increasing numbers of replicate beach sections, e.g. from 82% (two beach sections) to nearly 100% (five beach sections) for a given reduction of beach litter of 50%. Based on these results from a morphologically straight coastline, the use of replicate surveys would be sensible for the future monitoring of beach litter. However, there is high need for studies, which consider coastlines with varying morphology.

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.

Assessment of marine litter through remote sensing: recent approaches and future goals

This bibliographic review provides an overview of techniques used to detect marine litter using remote sensing. The review classified studies in terms of platform (satellite, aircrafts, drones), sensors (passive or active), spectral (visible, infrared, microwaves), spatial resolution (<1 to >30 m), type and size (macroplastics, microplastics), or classification methodology (sighting, photointerpretation, supervised). Most studies applied satellite information to address marine litter using multi- and hyper- spectral optical sensors. The correspondence analysis on analyzed variables exhibited that aircrafts with high spatial resolution (<3 m) with optical sensors (λ = 400 to 2500 nm) seem to be the most optimum combination to target marine litter, while satellites carrying Synthetic Aperture Radar (SAR) sensors (λ = 3.1 to 5.6 cm) may detect sea-slicks associated to surfactants that might contain high concentration of microplastics. Gaps indicate that future goals in marine litter detection should be addressed with platforms including optical and SAR sensors.

Editorial for the Special Issue “Remote Sensing of the Oceans: Blue Economy and Marine Pollution”

Oceans represent an extraordinary source of resources that needs to be preserved while being exploited [...]

Unmanned Aerial Vehicle (UAV) applications in coastal zone management-a review

Climate change and intense anthropogenic activities have heightened the vulnerability of coastal areas globally. The intensification in the dynamism and uncertainty of coastal processes and change in the past few decades have led researchers and coastal managers to explore new tools with the capability of undertaking a rapid assessment of coastal resources at a relatively lower cost compared with the conventional in situ data collection. The latest advances in unmanned aerial vehicle (UAV) platforms and sensor technologies have made them useful environmental remote sensing tools due to the high temporal and spatial resolution and relatively inexpensive operating costs. This study reviews literature that explored UAV applications in five different areas of the coastal zone comprising the intertidal, coastal organisms and habitats, marine litter, coastal zone disaster management, and coastal zone land use and land cover mapping. The review provides evidence of the potentials and effectiveness of UAVs for coastal zone management (CZM). However, factors such as difficulty in imaging water, setting out ground control points (GCPs) for geolocation of images, and processing large volumes of data can pose a challenge to coastal managers. Extensive review shows the capabilities of current UAV technologies for monitoring and tracking changes in the coastal environment at high spatial and temporal resolution.

Towards the Development of Portable and In Situ Optical Devices for Detection of Micro and Nanoplastics in Water: A Review on the Current Status

The prevalent nature of micro and nanoplastics (MP/NPs) on environmental pollution and health-related issues has led to the development of various methods, usually based on Fourier-transform infrared (FTIR) and Raman spectroscopies, for their detection. Unfortunately, most of the developed techniques are laboratory-based with little focus on in situ detection of MPs. In this review, we aim to give an up-to-date report on the different optical measurement methods that have been exploited in the screening of MPs isolated from their natural environments, such as water. The progress and the potential of portable optical sensors for field studies of MPs are described, including remote sensing methods. We also propose other optical methods to be considered for the development of potential in situ integrated optical devices for continuous detection of MPs and NPs. Integrated optical solutions are especially necessary for the development of robust portable and in situ optical sensors for the quantitative detection and classification of water-based MPs.

Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R

The threats posed by floating marine macro-litter (FMML) of anthropogenic origin to the marine fauna, and marine ecosystems in general, are universally recognized. Dedicated monitoring programmes and mitigation measures are in place to address this issue worldwide, with the increasing support of new technologies and the automation of analytical processes. In the current study, we developed algorithms capable of detecting and quantifying FMML in aerial images, and a web-oriented application that allows users to identify FMML within images of the sea surface. The proposed algorithm is based on a deep learning approach that uses convolutional neural networks (CNNs) capable of learning from unstructured or unlabelled data. The CNN-based deep learning model was trained and tested using 3723 aerial images (50% containing FMML, 50% without FMML) taken by drones and aircraft over the waters of the NW Mediterranean Sea. The accuracies of image classification (performed using all the images for training and testing the model) and cross-validation (performed using 90% of images for training and 10% for testing) were 0.85 and 0.81, respectively. The Shiny package of R was then used to develop a user-friendly application to identify and quantify FMML within the aerial images. The implementation of this, and similar algorithms, allows streamlining substantially the detection and quantification of FMML, providing support to the monitoring and assessment of this environmental threat. However, the automated monitoring of FMML in the open sea still represents a technological challenge, and further research is needed to improve the accuracy of current algorithms.

Beach-dune morphodynamics and marine macro-litter abundance: An integrated approach with Unmanned Aerial System

This work shows an integrated approach for coastal environmental monitoring, which aimed to understand the relation between beach-dune morphodynamics, marine litter abundance and environmental forcing. Three unmanned aerial system (UAS) flights were deployed on a beach-dune system at the Atlantic Portuguese coast to assess two main goals: (i) quantifying the morphological changes that occurred among flights, with focus on dune erosion, and (ii) mapping the changes of marine macro-litter abundance on the shore. Two most vulnerable-to-erosion sectors of the beach were identified. In the northern sector, the groin affected the downdrift shoreline, with dune erosion of about 1 m. In the central part of the beach, the dunes recessed about 4 m during the winter, being more exposed to environmental forcing due to the absence of dune vegetation. Marine litter occupation area on the beach decreased from 25% to 20% over the winter, with octopus pots (13%) and fragments (69%) being the most abundant items on average. Litter distribution varied in relation to swash elevation, wind speed and direction. With low swash elevation, the wind played a predominant role in moving the stranded items northwards, whereas high swash elevation concentrated the items at the dune foot. This study emphasizes the potential of UAS in allowing an integrated approach for coastal erosion monitoring and marine litter mapping, and set the ground for marine litter dynamic modelling on the shore.

Spatial and Temporal Distribution of Chemically Characterized Microplastics within the Protected Area of Pelagos Sanctuary (NW Mediterranean Sea): Focus on Natural and Urban Beaches

Data on the abundance and distribution of Anthropogenic Marine Debris (AMD) on the coastal areas of the northern Tyrrhenian coast are still scarce. The objective of this study is to characterize, in terms of size, color, morphology and polymeric nature, the Large Microplastics (LMPs), i.e., plastic objects within 1 and 5 mm, sampled on three beaches located within the coastal macro-area of the Pelagos Sanctuary, an international protected zone in the north-western Mediterranean. The beaches have similar morphological characteristics but different degrees of urbanization. LMPs were sampled seasonally for one year. The polymeric nature of a representative subsample of the collected LMPs was investigated using a portable Raman instrument, to assess the feasibility of in situ characterization. In this study, 26,486 items were sorted by typology (Expanded Polystyrene-EPS, fragments, and resin pellets), size, and for fragments and resin pellets, also by color and chemical nature. Statistical data on the quantity, density, type, spatial distribution, and seasonality of the sampled LMPs are presented. Differences in LMP abundance and composition were detected among sites. A seasonality trend emerges from our statistical analysis, depending on both LMP typology and urbanization degrees of the beaches. Our data do not show the existence of a relationship between the size of the investigated MPs and their color, while they suggest that the type of polymer influences the degree of fragmentation. This underlines the need to further investigate the mechanisms leading to the production and dispersion of MPs in coastal areas, taking into account both the urbanization of the beach, and therefore the possible sources of input, and the different types of MPs. Finally, a Raman portable instrument proved to be a valuable aid in performing in situ polymeric characterization of LMPs.

UAV-Derived Data Application for Environmental Monitoring of the Coastal Area of Lake Sevan, Armenia with a Changing Water Level

The paper presents the range and applications of thematic tasks for ultra-high spatial resolution data from small unmanned aerial vehicles (UAVs) in the integral system of environmental multi-platform and multi-scaled monitoring of Lake Sevan, which is one of the greatest freshwater lakes in Eurasia. From the 1930s, it had been subjected to human-driven changing of the water level with associated and currently exacerbated environmental issues. We elaborated the specific techniques of optical and thermal surveys for the different coastal sites and phenomena in study. UAV-derived optical imagery and thermal stream were processed by a Structure-from-Motion algorithm to create digital surface models (DSMs) and ortho-imagery for several key sites. UAV imagery were used as additional sources of detailed spatial data under large-scale mapping of current land-use and point sources of water pollution in the coastal zone, and a main data source on environmental violations, especially sewage discharge or illegal landfills. The revealed present-day coastal types were mapped at a large scale, and the net changes of shoreline position and rates of shore erosion were calculated on multi-temporal UAV data using modified Hausdorff’s distance. Based on highly-detailed DSMs, we revealed the areas and objects at risk of flooding under the projected water level rise to 1903.5 m along the west coasts of Minor Sevan being the most popular recreational area. We indicated that the structural and environmental state of marsh coasts and coastal wetlands as potential sources of lake eutrophication and associated algal blooms could be more efficiently studied under thermal UAV surveys than optical ones. We proposed to consider UAV surveys as a necessary intermediary between ground data and satellite imagery with different spatial resolutions for the complex environmental monitoring of the coastal area and water body of Lake Sevan as a whole.

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.

Mapping marine litter on coastal dunes with unmanned aerial systems: A showcase on the Atlantic Coast

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

Quantifying Marine Macro Litter Abundance on a Sandy Beach Using Unmanned Aerial Systems and Object-Oriented Machine Learning Methods

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

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.