Floating marine litter detection algorithms and techniques using optical remote sensing data: A review

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

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

Beached Plastic Debris Index; a modern index for detecting plastics on beaches

Plastic pollution on shorelines poses a significant threat to coastal ecosystems, underscoring the urgent need for scalable detection methods to facilitate debris removal. In this study, the Beached Plastic Debris Index (BPDI) was developed to detect plastic accumulation on beaches using shortwave infrared spectral features. To validate the BPDI, plastic targets with varying sub-pixel covers were placed on a sand spit and captured using WorldView-3 satellite imagery. The performance of the BPDI was analysed in comparison with the Normalized Difference Plastic Index (NDPI), the Plastic Index (PI), and two hydrocarbon indices (HI, HC). The BPDI successfully detected the plastic targets from sand, water, and vegetation, outperforming the other indices and identifying pixels with <30 % plastic cover. The robustness of the BPDI suggests its potential as an effective tool for mapping plastic debris accumulations along coastlines.

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.

Marine litter along the Peruvian coast: spatiotemporal composition, sources, hazard, and human modification relations

Marine litter (ML) represents an escalating environmental issue, particularly in Latin America, where comprehensive studies are scarce despite critical solid waste management challenges and continuous human modification occurring on the coasts. To contribute to the knowledge of ML in the southeast Pacific, this study examined contamination across 10 beaches on Peru's extensive coast. Overall, ML contamination was categorized as moderate (with an ML concentration of 0.49 ± 0.64 items∙m-2), while significantly differing between summer (dirty with an ML concentration of 0.56 ± 0.66 items∙m-2) and winter (moderate with an ML concentration of 0.47 ± 0.60 items∙m-2). Three beaches were extremely dirty (concentrations of ML exceeded 1.0 items∙m-2). Predominant materials, items, and sources were plastic, cigarette butts (CBs), and mixed packaging. The Peruvian coast faced CB leachate impact (CBPI = 3.5 ± 3.5), reaching severe levels on two beaches, with considerable hazardous litter (HALI = 3.0 ± 2.9). Additionally, a higher degree of human modification was associated with higher ML levels along the coast.

Marine debris induced by the Great East Japan Earthquake and Tsunami: A multi-sensor remote sensing assessment

Using satellite remote sensing, we show the distribution, dominant type, and amounts of marine debris off the northeast coast of Japan after the Great East Japan Earthquake on 11 March 2011 and subsequent tsunami. Extensive marine debris was found on March 12, with the maximal amount found on March 13. The debris was found to be mainly wood (possibly lumber wood), with an estimated 1.5 million metric tons in an elongated water area of 6800 km2 (18 km E-W and 380 km N-S) near parallel to the coast between 36.75°N and 40.25°N. The amount decreased rapidly with time, with scattered debris patches captured in high-resolution satellite images up to April 6. These results provide new insights on the initial distribution of the Japanese Tsunami Marine Debris, which may be used to help find bottom deposition of debris and help refine numerical models to predict the debris trajectory and fate. SYNOPSIS: Marine debris induced by the 2011 Great East Japan Earthquake and Tsunami is found to be mainly composed of wood and possibly lumber wood from constructions, with maximum amount on 13 March 2011 distributed within a narrow band of ∼18 km near parallel to the northeast coast of Japan between 36.75°N and 40.25°N.

Efficient plastic detection in coastal areas with selected spectral bands

Marine plastic pollution poses significant ecological, economic, and social challenges, necessitating innovative detection, management, and mitigation solutions. Spectral imaging and optical remote sensing have proven valuable tools in detecting and characterizing macroplastics in aquatic environments. Despite numerous studies focusing on bands of interest in the shortwave infrared spectrum, the high cost of sensors in this range makes it difficult to mass-produce them for long-term and large-scale applications. Therefore, we present the assessment and transfer of various machine learning models across four datasets to identify the key bands for detecting and classifying the most prevalent plastics in the marine environment within the visible and near-infrared (VNIR) range. Our study uses four different databases ranging from virgin plastics under laboratory conditions to weather plastics under field conditions. We used Sequential Feature Selection (SFS) and Random Forest (RF) models for the optimal band selection. The significance of homogeneous backgrounds for accurate detection is highlighted by a 97 % accuracy, and successful band transfers between datasets (87 %-91 %) suggest the feasibility of a sensor applicable across various scenarios. However, the model transfer requires further training for each specific dataset to achieve optimal accuracy. The results underscore the potential for broader application with continued refinement and expanded training datasets. Our findings provide valuable information for developing compelling and affordable detection sensors to address plastic pollution in coastal areas. This work paves the way towards enhancing the accuracy of marine litter detection and reduction globally, contributing to a sustainable future for our oceans.

Development of a ship-based camera monitoring system for floating marine debris

This study developed an automatic monitoring system for Floating Marine Debris (FMD) aimed at reducing the labor-intensiveness of traditional visual surveys. It involved creating a comprehensive FMD database using 55.6 h of video footage and numerous annotated images, which facilitated the training of a deep learning model based on the YOLOv8 architecture. Additionally, the study implemented the BoT-SORT algorithm for FMD tracking, significantly enhancing detection accuracy by effectively filtering out disturbances such as sea waves and seabirds, based on the movement patterns observed in FMD trajectories. Tested across 16 voyages in various marine environments, the system demonstrated high accuracy in recognizing different types of FMD, achieving a mean Average Precision (mAP@0.5) of 0.97. In terms of detecting FMD from video footage, the system reached an F1 score of 83.63 %. It showed potential as a viable substitute for manual methods for FMD larger than 20 cm.

The collateral effects of COVID-19 on marine pollution

The COVID-19 pandemic has gained significant attention to the intersection of public health crises and environmental challenges, particularly in the context of marine pollution. This paper examines the various impacts of the pandemic on marine environments, focusing on the pollution attributed to single-use plastics (SUPs) and personal protective equipment (PPE). Drawing on a comprehensive analysis of literature and case studies, the paper highlights the detrimental effects of increased plastic waste on marine ecosystems, biodiversity, and human health. Statistical data and graphical representations reveal the scale of plastic pollution during the pandemic, emphasizing the urgent need for mitigation strategies. The study evaluates innovative monitoring techniques and future recommendations, emphasizing stakeholder collaboration in sustainable waste management. By broadening geographic examples and comparative analyses, it provides a global perspective on the pandemic's impact, highlighting the importance of international cooperation for safeguarding marine ecosystems.

Unveiling reflectance spectral characteristics of floating plastics across varying coverages: insights and retrieval model

Marine floating debris, particularly chemically stable plastics, poses a significant global environmental concern. These materials, due to their prevalence and durability, linger on the ocean surface for extended durations, inflicting considerable harm on marine ecosystems, life, and the food chain. The traditional methodology for investigating marine floating debris mainly uses field observations, which are time-consuming, laborious, and constrained in observational scope. Consequently, there is an urgent need for more effective methodologies, such as remote sensing, to monitor marine floating debris, which will be of great significance for enhancing the management of their pollution. In this study, we employ controlled experiments and theoretical model simulations to investigate the spectral characteristics of remote sensing reflectance (Rrs(λ)) of two common types of floating plastic debris, specifically polyvinyl chloride (PVC) buoys and polypropylene (PP) bottles. Our analysis reveals distinct Rrs(λ) spectral characteristics for each type of plastic debris, differing significantly from that of the background water. Furthermore, both PVC buoys and PP bottles exhibit a similar absorption valley in the short-wave infrared region, with its depth increasing alongside the plastic coverage. Based on these findings, we develop a novel floating plastic index (FPI) and a corresponding retrieval model for estimating the coverage of floating plastic debris. Validation with simulated data and measurements from control experiments shows good performance of the retrieval model with high inversion accuracy, demonstrated by the values of the coefficient of determination, mean percentage error, mean absolute percentage error, and root mean square error of 0.97, -0.3%, 17.5%, and 3.98%, respectively, for the experimentally measured dataset. Our research provides a theoretical and methodological foundation for remote sensing retrieval of the coverages of floating PVC and PP plastics, as well as offers valuable insights for the analysis of other floating debris types in future studies.

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.

Exploring seasonal, spatial and pathways of marine litter pollution along the Southeastern Black Sea Cost of Türkiye

Spatial and temporal variations in marine litter density and composition along the Southeastern Black Sea Coast were investigated. A total of 156,371 litter items weighing 327,258.3 kg were collected. The highest frequency of litter material by number was 15,869 ± 103.88 items/m2 16 and 74.466 ± 7.23 by weight. The highest litter concentrations (77,768 items; 81,737.1 kg) were observed in autumn, mainly comprising single-use items, with plastic being the most abundant (54.05 %), followed by metal (15.69 %), and paper (10.45 %). The subcategories of plastic litter items bags, caps/lids, cigarette lighters, cosmetic packages, gloves, and plastics pieces were found to be the most abundant litter in number. According to Principal Component Analysis (PCA) and Kruskal-Wallis statistical tests (p < 0.005), significant differences in marine litter were identified among the stations and seasons. These findings offer insights for modeling studies, advocating restrictions on single-use products, and enacting legal regulations for local governance.

High levels of anthropogenic litter trapped in a mangrove area under the influence of different uses

The contamination of mangroves by anthropogenic litter has increased in recent decades. Notably, Brazil occupies a prominent status within Latin America, boasting the second-largest mangrove areas globally. In Santos-São Vicente Estuarine System (SESS), mangroves coexist with a preeminent port complex and substantial urbanization rates. Nevertheless, the anthropogenic litter occurrence and distribution in this ecosystem remains unknown. This study aimed to comprehensively assess anthropogenic litter across 13 strategically positioned sites in the SESS. The total litter density (Mean ± SD) was 22.84 ± 36.47 (0.00-142.00) items·m-2, putting the SESS among the top four most contaminated mangrove ecosystems worldwide. Residential zones accumulated more litter than uninhabited areas and significant correlation was seen with human modification index. Plastic was the prevalent material (70.4 %), measuring mostly between 2.5 and 30 cm (41.1 %). It is imperative that local authorities adopt comprehensive strategies to mitigate contamination, while also curtailing the litter inputs to the SSES mangrove ecosystem.

Large-scale detection of marine debris in coastal areas with Sentinel-2

Detecting and quantifying marine pollution and macroplastics is an increasingly pressing ecological issue that directly impacts ecology and human health. Here, remote sensing can provide reliable estimates of plastic pollution by regularly monitoring and detecting marine debris in coastal areas. In this work, we present a detector for marine debris built on a deep segmentation model that outputs a probability for marine debris at the pixel level. We train this detector with a combination of annotated datasets of marine debris and evaluate it on specifically selected test sites where it is highly probable that plastic pollution is present in the detected marine debris. We integrate data-centric artificial intelligence principles by devising a training strategy with extensive sampling of negative examples and an automated label refinement of coarse hand labels. This yields a deep learning model that achieves higher accuracies on benchmark comparisons than existing detection models trained on previous datasets.

Marine plastic pollution detection and identification by using remote sensing-meta analysis

The persistent plastic litter, originating from different sources and transported from rivers to oceans, has posed serious biological, ecological, and chemical effects on the marine ecosystem, and is considered a global issue. In the past decade, many studies have identified, monitored, and tracked marine plastic debris in coastal and open ocean areas using remote sensing technologies. Compared to traditional surveying methods, high-resolution (spatial and temporal) multispectral or hyperspectral remote sensing data have been substantially used to monitor floating marine macro litter (FMML). In this systematic review, we present an overview of remote sensing data and techniques for detecting FMML, as well as their challenges and opportunities. We reviewed the studies based on different sensors and platforms, spatial and spectral resolution, ground sampling data, plastic detection methods, and accuracy obtained in detecting marine litter. In addition, this study elaborates the usefulness of high-resolution remote sensing data in Visible (VIS), Near-infrared (NIR), and Short-Wave InfraRed (SWIR) range, along with spectral signatures of plastic, in-situ samples, and spectral indices for automatic detection of FMML. Moreover, the Thermal Infrared (TIR), Synthetic aperture radar (SAR), and Light Detection and Ranging (LiDAR) data were introduced and these were demonstrated that could be used as a supplement dataset for the identification and quantification of FMML.

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.

Full stage networks with auxiliary focal loss and multi-attention module for submarine garbage object detection

Submarine garbage is constantly destroying the marine ecological environment and polluting the ocean. It is critical to use detection methods to quickly locate and identify submarine garbage. The background of submarine garbage images is much more complex than that of natural scene images, with object deformation and missing contours putting higher demands on the detection network. To solve the problem of low accuracy under complex backgrounds, full stage networks with auxiliary focal loss and multi-attention module are proposed for submarine garbage object detection based on YOLO. To maximize the gradient combination, a hierarchical fusion feature mechanism and a segmentation and merging strategy are used in this paper to optimize the difference in gradient combination to obtain full-stage features. Then the criss-cross attention module is used to precisely extract multi-scale features of small object dense regions while removing noise information from complex backgrounds. Finally, the auxiliary focal loss function addresses the issue of unbalanced positive and negative samples, focusing on the learning of difficult samples while improving overall detection precision. Based on comparative experiments and ablation experiments, the FSA networks achieved state-of-the-art performance, and is applicable to the real-time object detection of submarine garbage in complex backgrounds.

Improving sea surface floating matter identification from Sentinel-2 MSI imagery using optical radiative simulation of neighborhood difference

The reflectance difference (ΔR) between a floating matter pixel and a nearby water reference pixel is a method of atmospheric radiation unmixing. This technique unveils target signals by referencing the background within the horizontal neighborhood. ΔR is effective for removing the mixed-pixel effect and partial atmospheric path radiance. However, other atmospheric interference sources in the difference pixel, including atmospheric extinction and sunglint, need to be clarified. To address these challenges, we combined in situ floating matter endmember spectra for simulation and Sentinel-2 Multispectral Instrument (MSI) sensors for validation. We focused on radiative transfer simulation of horizontal neighborhood and vertical atmospheric column, investigating the bilateral conversion of ΔR between bottom-of-atmosphere (BOA) and top-of-atmosphere (TOA) signals, and clarifying how the atmosphere affects the difference pixel (ΔR) and floating matter identification. Results showed that direct use of TOA ΔR works in discriminating algae from non-algae floating matters under weak sunglint, and is a suitable candidate for no bother with atmospheric correction, least uncertain, and wider coverage. And then, sunglint interference is also inevitable, whether serious or not.

An advanced remote sensing retrieval method for urban non-optically active water quality parameters: An example from Shanghai

The optical insensitivity of non-optically active water quality parameters (NAWQPs) presents a significant challenge for remote sensing-based quantitative monitoring, which is an important tool for water quality assessment and management. Based on the analysis of the samples from Shanghai, China, it was found that the spectral morphological characteristics of the water body were obviously different under the combined effect of multiple NAWQPs. In view of this, in this paper, a machine learning method was proposed for the retrieval of urban NAWQPs by using multi-spectral scale morphological combined feature (MSMCF). The proposed method integrates both local and global spectral morphological features, and employs a multi-scale approach to enhance its applicability and stability, providing a more accurate and robust solution. To explore the applicability of the MSMCF method in retrieving urban NAWQPs, different methods were tested in terms of the retrieval accuracy and stability on the measured data and three different hyperspectral data. As can be seen from the results, the proposed method has good retrieval performance, which can be applied to hyperspectral data with different spectral resolutions with certain ability to suppress noise. Further analysis indicates that the sensitivity of each NAWQP to spectral morphological features varies. The research methods and findings in this paper can promote the development of hyperspectral and remote sensing technology in the prevention and treatment of urban water quality deterioration, and provide reference for related research.

Instant deep sea debris detection for maneuverable underwater machines to build sustainable ocean using deep neural network

Deep sea debris is any persistent man-made material that ends up in the deep sea. The scale and rapidly increasing amount of sea debris are endangering the health of the ocean. So, many marine communities are struggling for the objective of a clean, healthy, resilient, safe, and sustainably harvested ocean. That includes deep sea debris removal with maneuverable underwater machines. Previous studies have demonstrated that deep learning methods can successfully extract features from seabed images or videos, and are capable of identifying and detecting debris to facilitate debris collection. In this paper, the lightweight neural network (termed DSDebrisNet), which can leverage the detection speed and identification performance to achieve instant detection with high accuracy, is proposed to implement compound-scaled deep sea debris detection. In DSDebrisNet, a hybrid loss function considering the illumination and detection problem was also introduced to improve performance. In addition, the DSDebris dataset is constructed by extracting images and video frames from the JAMSTEC dataset and labeled using a graphical image annotation tool. The experiments are implemented on the deep sea debris dataset, and the results indicate that the proposed methodology can achieve promising detection accuracy in real-time. The in-depth study also provides significant evidence for the successful extension branch of artificial intelligence to the deep sea research domain.

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.

Microplastics in Fish and Fishery Products and Risks for Human Health: A Review

In recent years, plastic waste has become a universally significant environmental problem. Ingestion of food and water contaminated with microplastics is the main route of human exposure. Fishery products are an important source of microplastics in the human diet. Once ingested, microplastics reach the gastrointestinal tract and can be absorbed causing oxidative stress, cytotoxicity, and translocation to other tissues. Furthermore, microplastics can release chemical substances (organic and inorganic) present in their matrix or previously absorbed from the environment and act as carriers of microorganisms. Additives present in microplastics such as polybrominated diphenyl ethers (PBDE), bisphenol A (BPA), nonylphenol (NP), octylphenol (OP), and potentially toxic elements can be harmful for humans. However, to date, the data we have are not sufficient to perform a reliable assessment of the risks to human health. Further studies on the toxicokinetics and toxicity of microplastics in humans are needed.

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.

Remote detection of marine debris using Sentinel-2 imagery: A cautious note on spectral interpretations

Remote detection of marine debris (also called marine litter) has received increased attention in the past decade, with the Multispectral Instruments (MSI) onboard the Sentinel-2A and Sentinel-2B satellites being the most used sensors. However, because of their mixed band resolutions and small sub-pixel coverage of debris within a pixel (e.g., <10 %), caution is required when interpreting the spectral shapes of MSI pixels. Otherwise, the spectrally distorted shapes may be misused as spectral endmembers (signatures) or interpreted as from certain types of floating matters. Here, using simulations and MSI data, I show the origin of the spectral distortions and emphasize why both pixel averaging and pixel subtraction are critical in algorithm design and spectral interpretation for the purpose of remote detection of marine debris using Sentinel-2 MSI sensors.

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.

Plastic waste cleanup priorities to reduce marine pollution: A spatiotemporal analysis for Accra and Lagos with satellite data

Plastic waste, with an estimated lifetime of centuries, accounts for the major share of marine litter. Each year, thousands of fish, sea birds, sea turtles, and other marine species are killed by ingesting or becoming entangled with plastic debris. Reducing marine plastic pollution is particularly challenging for developing countries owing to the wide dispersal of plastic waste disposal and scarce public cleanup resources. To costeffectively reduce marine pollution, resources should target "hotspot" areas, where large volumes of plastic litter have a high likelihood of ending up in the ocean. Using new public information, this study develops a hotspot targeting strategy for Accra and Lagos, which are major sources of marine plastic pollution in West Africa. The same global information sources can support hotspot analyses for many other coastal cities that generate marine plastic waste. The methodology combines georeferenced household survey data on plastic use, measures of seasonal variation in marine plastic pollution from satellite imagery, and a model of plastic waste transport to the ocean that uses information on topography, seasonal rainfall, drainage to rivers, and river transport to the ocean. For cleanup, the results for West Africa assign the highest locational priority to areas with heavy plastic-waste disposal along river channels or in steeply sloped locations with high rainfall runoff potential near rivers. They assign the highest temporal priority to just before the onset of the first-semester rainy season, when runoff from the first rains transports large volumes of plastic waste that have accumulated during the dry season.

Increasing the Sentinel-2 potential for marine plastic litter monitoring through image fusion techniques

Sentinel-2 (S2) images have been used in several projects to detect large accumulations of marine litter and plastic targets. Their limited spatial resolution though hinders the detection of relatively small floating accumulations of marine debris. Thus, this study aims at overcoming this limit through the exploration of fusion with very high-resolution WorldView-2/3 (WV-2/3) images. Various state-of-the-art approaches (component substitution, spectral unmixing, deep learning) were applied on data collected in synchronized acquisitions of plastic targets of various sizes and materials in seawater. The fused images were evaluated for spectral and spatial distortions, as well as their ability to spectrally discriminate plastics from water. Several WV-2/3 band combinations were investigated and five litter indexes were applied. Results showed that: a) the VNIR combination is the optimal one, b) the smallest observable plastic target is 0.6 × 0.6 m² and c) SWIR bands are important for marine litter detection.

Litter in coastal and marine environments

Litter is one of the most pervasive and fastest-growing anthropogenic alterations of the World's coasts and oceans. Along with climate change, litter has been identified as one of humankind's most critical environmental problems that demand urgent solutions. Litter magnitudes and distribution, and the related detrimental environmental effects, have been documented in all existing coastal and marine environments (e.g., beaches, dunes, abyssal plains and submarine canyons, among others). Litter's presence is now so ubiquitous in the environment that it serves as a geological indicator of the Anthropocene. As part of the solution to this out-of-hand problem, Marine Pollution Bulletin has produced this Special Issue entitled "Litter in Coastal and Marine Environments". This collection of 37 papers provides a focal point for such related current studies and, in part, seeks to discuss implementing specific management strategies under different scenarios. No single solution exists to cope with the litter issue. However, legally binding global governance that will effectively limit and control the magnitude of litter pollution is greatly needed. The topical range of this collection of papers includes case studies focussing on litter types (mainly dominated by plastics), sources, impacts and solutions.

Characteristics and Trends of Ocean Remote Sensing Research from 1990 to 2020: A Bibliometric Network Analysis and Its Implications

The ocean is of great significance in the climate system, global resources and strategic decision making. With the continuous improvement in remote sensing technology, ocean remote sensing research has increasingly become an important topic for resource development and environmental protection. This paper uses bibliometric analysis method and VOSviewer visual software to conduct analysis. The analysis focuses on the period from 1990 to 2020. The analysis results show that articles have been steadily increasing over the past two decades. Scholars and researchers form the United States, China and Europe (mainly Western European countries), as well as NASA, Chinese Academy of Sciences and NOAA have bigger influence in this field to some extent. Among them, the United States and NASA holds the core leading position. Moreover, global cooperation in this field presents certain characteristics of geographical distribution. This study also reveals journals that include the most publications and subject categories that are highly relevant to related fields. Cluster analysis shows that remote sensing, ocean color, MODIS (or Moderate Resolution Imaging Spectroradiometer), chlorophy, sea ice and climate change are main research hotspots. In addition, in the context of climate warming, researchers have improved monitoring technology for remote sensing to warn and protect ocean ecosystems in hotspots (the Arctic and Antarctica). The valuable results obtained from this study will help academic professionals keep informed of the latest developments and identify future research directions in the field related to ocean remote sensing.