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Marine plastic pollution detection and identification by using remote sensing-meta analysis
Abstract
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.
... Computational analysis of spectroscopic data and computer-assisted visual inspection are two majorly used methods for extracting key features, as well as compare between samples for presence of microscale plastics. These methods have been improved by incorporation of predictive modelling methods like machine learning and deep learning through instance-based and model-based arguments (Abdurahman et al., 2020;Phan & Luscombe, 2023;Waqas et al., 2023). Recently, Lin and colleagues had explored the ML methods and their applications for analysis of spectroscopic datasets (Lin et al., 2022). ...
... Continued and can endure unalike transport progressions such as tempestuous transport and settling velocity (Phan & Luscombe, 2023). Studies have also highlighted the importance of remote sensing for monitoring environmental distribution of MNPs (Waqas et al., 2023). The development of new imagery systems like hyperspectral imaging, flow control imaging, confocal laser microscopy, electron microscopy etc. have enabled monitoring and detection of MNPs (W. ...
... This have been enhanced by availability of new-age imaging equipment such as hyperspectral imaging, spatial imaging, flow imaging etc. Some of these have been reviewed in Waqas et al. (2023). For illustration, studies by Gnann et al. (2022), Mukonza and Chiang (2022), and Maximenko et al. (2019) have used in-situ data and findings from spectral indices for training a diverse set of supervised and unsupervised image classification models. ...
The dissemination of miniaturized plastics, both micro- and nano-plastics, athwart diverse ecosystems is an argument of global apprehension. The accretion of these plastics is due to their chemical steadiness. In arrears to their trivial size, frequently identification of miniaturized plastics is very problematic. The foremost approaches for identification of micro- and-nano plastics rely upon their visual inspection through microscopy and chemical analysis. The advent of high-throughput computing has eased the detection of miniaturized plastic pollution. Machine learning and computer vision methods are being readily applied for analyzing microscopy images to identify and classify microplastics. Molecular simulation methods are also being applied for studying the interaction between environment and microplastics. Additionally, remote sensing methods have also been used to collect and analyze suspected locations of microplastic pollution.
... They involve manually collecting, categorizing, and analyzing litter items. This process demands significant human effort and resources over long periods (Suaria and Aliani, 2014;Waqas et al., 2023;Pasquier et al., 2022). This labor-intensive approach highlights the need for a more rapid and efficient automatic method. ...
... Hence, in recent years, there has been a significant increase in the number of researchers focusing on marine litter detection. This has led to advancements in both methodologies and technologies (Topouzelis et al., 2021;Waqas et al., 2023;Goddijn-Murphy et al., 2024). One of the most potential technologies is hyperspectral imaging (HSI), which offers high-quality data for identifying and analyzing various materials based on spectral signatures (Bhargava et al., 2024). ...
... Remote sensing methods can be integrated with data-driven algorithms such as machine learning (ML) and deep learning (DL), hold promise in unsupervised identification of floating macroplastics types, and can enhance reliability in detection and quantification over time (Jakovljevic et al. 2020;Wendt-Potthoff et al. 2020;Wolf et al. 2020;Iordache et al. 2022;Solé Gómez et al. 2022;Tasseron et al. 2022;Jia et al. 2023;Mohsen et al. 2023;Sakti et al. 2023). However, despite these advantages, such methods are relatively unexplored in river systems (Geraeds et al. 2019;Solé Gómez et al. 2022) compared to their use in marine environments (Waqas et al. 2023). ...
... This also supports informed, timely management and mitigation strategies. Further, ML and DL approaches for imagery detection and classification could be essential for overcoming image quality challenges and improving the accuracy and efficiency of continuous river plastic monitoring (Waqas et al. 2023). Latest advancements in ML and DL techniques, have indeed demonstrated promising outcomes in detecting and categorizing macroplastics within river environments. ...
Given the exponential rise in global plastic production and its significant ecological and socioeconomic impacts, monitoring macroplastics in rivers has become a central focus of water management efforts. However, standardized monitoring method-ologies are lagging behind the rate of plastic waste currently entering aquatic systems on a global scale. This translates into a shortage of spatially and temporally refined data on the macroplastic pollution circulating in inland waters. Recent advancements in remote sensing techniques, primarily satellites, UASs, fixed and handheld cameras combined with crowd-sourced data and automated macroplastic detection using machine and deep learning, offer promising opportunities for versatile monitoring solutions. Thus, this paper reviews state-of-the-art approaches and emerging methods for macroplastic identification in rivers to provide researchers with a comprehensive inventory of techniques and to encourage the scientific community to harmonize monitoring methods and define standard protocols. According to our investigation, addressing the challenges of remote sensing-based river macroplastics monitoring mandates further efforts to enhance and integrate multiple platforms with an emphasis on long-term monitoring.
... Recent examples include detecting suspected plastics in the Pacific Ocean using WorldView-3, identifying plastic-sargassum accumulations near Hawai'i with Sentinel-2 and using SuperDoves to detect debriscontaining pixels in a riverine environment (Park et al., 2021;Ciappa, 2021;Garaba and Park, 2024). The limited number of studies is partly due to difficulties in collecting in-situ ground truth data to validate the presence of debris (Waqas et al., 2023). Open data initiatives like the MARIDA database are attempting to address this issue (Kikaki et al., 2022). ...
... Further challenges for detecting MPD, both floating and beached, include the impact of environmental factors like water turbidity and ocean colour on spectral reflectance (Waqas et al., 2023;Schmidt et al., 2023). These factors can influence the accuracy of spectral indices, particularly in regions with sparse accumulations of MPD (Veettil et al., 2022). ...
... Recent examples include detecting suspected plastics in the Pacific Ocean using WorldView-3, identifying plastic-sargassum accumulations near Hawai'i with Sentinel-2 and using SuperDoves to detect debriscontaining pixels in a riverine environment (Park et al., 2021;Ciappa, 2021;Garaba and Park, 2024). The limited number of studies is partly due to difficulties in collecting in-situ ground truth data to validate the presence of debris (Waqas et al., 2023). Open data initiatives like the MARIDA database are attempting to address this issue (Kikaki et al., 2022). ...
... Further challenges for detecting MPD, both floating and beached, include the impact of environmental factors like water turbidity and ocean colour on spectral reflectance (Waqas et al., 2023;Schmidt et al., 2023). These factors can influence the accuracy of spectral indices, particularly in regions with sparse accumulations of MPD (Veettil et al., 2022). ...
... According to the marine organisms' survey data of the water-intake area of coastal power plants in China and the relevant experience feedback of power plants at home and abroad [23,24], the main marine organisms' classifications affecting the water-intake safety of coastal power plants are shown in Table 2. Table 2. Classification of main marine organisms. ...
With the transformation of the world’s energy structure and the promotion of clean energy, nuclear power has come to the fore as an efficient and environmentally friendly form of energy. As critical components in the operation of nuclear power plants (NPPs), suction and transportation devices for thermal traps—designed to prevent and control floating bodies—are directly linked to the safety of power generation, the stability of operations, and the reliability of the power grid in NPPs. Technical research and advancements in these devices play a crucial role in enhancing their effectiveness. In view of the intrusion and blockage of floating bodies in the sewage network pocket of the water intake with the cold-source system for coastal NPPs, the key technologies to prevent and control floating bodies that may impair cooling resources in NPPs’ thermal traps is discussed. This paper introduces the safety risks and social impacts associated with water intake, highlights the importance of the water-intake system, and discusses the interception and transportation mechanisms in NPPs. The progress of explosive floating bodies’ interception, cleaning, suction, transportation, and intelligent monitoring technology is introduced through the technical progress in the types of floating bodies, as well as cutting, crushing, suction, and transportation devices. The technical progress of efficient treatment is realized, which provides reference for the research and practice of the key technologies for suction and transportation devices to prevent and control floating bodies’ suction and transportation devices in NPPs’ thermal traps.
... The detection of plastic litter in the environment is necessary to prevent its scattering and eventual clustering in the oceans and on the coasts and to plan and execute clean-up action. To this purpose, remote sensing, at all scales, plays an essential role [3][4][5]. Satellite data may be used to detect large-scale clusters of plastic debris [6][7][8][9], but higher-detail data [10][11][12][13][14] are necessary to detect low-density litter in relatively small areas, e.g., on the coasts and in rivers, and to guide collection teams and vessels effectively to polluted areas. ...
Plastic waste has become a critical environmental issue, necessitating effective methods for detection and monitoring. This article presents a machine-learning-based methodology and embedded solution to detect plastic waste in the environment using an airborne hyperspectral sensor operating in the short-wave infrared (SWIR) band. Experimental data were obtained from drone flights in several case studies in natural and controlled environments. Data were preprocessed to simply equalize the spectra across the whole band and across different environmental conditions, and machine learning techniques were applied to detect plastics even in real-time. Several algorithms for spectrum calibration, feature selection, and classification were optimized and compared to obtain an optimal solution that has high-quality results under cross-validation. This way, deploying the system in different environments without requiring complicated manual adjustments or re-learning is possible. The results of this work prove the feasibility of the proposed plastic litter detection approach using high-definition aerial remote sensing, with high specificity to plastic polymers that are not obtained using visible and NIR data.
... Emerging contaminants are categorized via environmental destiny and attributes, chemical constituents, sources and possible consequences. In order to effectively reduce the release of these emerging contaminants, preserve human health, conserve ecological integrity and monitor their environmental availability, the knowledge of their classification and description are imperative (Waqas et al., 2023). Also, continual investigation and collaboration by policymakers, scientists and stakeholders is essential on these novel toxins for sustainable environmental management and efficient pollution mitigation. ...
Aim: To address novel environmental remediation techniques for enhancing public health and ecosystem resilience. Problem Statement: Anthropogenic activities such as deforestation, unplanned urbanization, unsafe agricultural practices and use of wasteful energy reservoirs together with population explosion and rapid industrialization have caused serious environmental pollution affecting public health and disrupting the ecosystem. Identified Prominent pollutants causing environmental worries have also been identified. Significance of Study: The cleaning-up of hazardous contaminants using novel environmental remediation techniques to enhance public health and ecosystem resilience is imperative.
... Given that Sentinel-2 data pixels have resolutions ranging from 10 to 20 meters, it is unlikely for a pixel to be entirely covered by plastic or floating agglomerates. Combining the FDI with the NDVI aids in detecting these materials at the sub-pixel level [21,22,23,24,25]. FDI = I8 -I'8 (2) I' 8 ...
Typically, the detection of marine debris relies on in-situ campaigns that are characterized by huge human effort and limited spatial coverage. Following the need of a rapid solution for the detection of floating plastic, methods based on remote sensing data have been proposed recently. Their main limitation is represented by the lack of a general reference for evaluating performance. Recently, the Marine Debris Archive (MARIDA) has been released as a standard dataset to develop and evaluate Machine Learning (ML) algorithms for detection of Marine Plastic Debris. The MARIDA dataset has been created for simplifying the comparison between detection solutions with the aim of stimulating the research in the field of marine environment preservation. In this work, an assessment of spectral based solutions is proposed by evaluating performance on MARIDA dataset. The outcome highlights the need of precise reference for fair evaluation.
... Since the 1970s, environmental remote sensing (RS) has been gaining importance in geospatial studies and has been strengthened with the advent of NASA's Landsat satellites [1,2], providing the longest time series (1972-today) of worldwide satellite data [3]. These new technologies have been a fundamental support for studies of the earth's surface serving different scientific disciplines [4][5][6]. ...
While open-pit mining activities represent one of the human-derived most impactful land cover changes, these changes and the linked restoration processes can be challenging to assess. This article presents a reproducible methodology carried out with cloud processing of satellite images (Google Earth Engine (GEE)) to evaluate the evolution of open-pit mining activities and their restoration in a Mediterranean landscape. For this purpose, the calculation of the normalized differential vegetation index (NDVI) was used to obtain a quantitative parameter to monitor vegetation presence in each extractive area. To validate these results, confusion matrices were performed between the classification obtained in the study and the official land cover mapping, using randomly selected mining areas as test points, with an average accuracy of 88%. According to the methodology used, the surface of areas denuded by mining in the period 1984–2023 has fluctuated over time, with a maximum in 2005 coinciding with the peak of the Spanish construction boom, and a subsequent decrease towards the present. From these results, it can be concluded that Landsat-type data processed using GEE provide a quick and useful tool for monitoring the evolution of mining activity, including restoration trends, becoming particularly valuable for public bodies’ inspections or decision making.
Floating plastic debris on water surfaces poses both immediate and long-term threats to the environment. Therefore, identifying and monitoring plastic pollution is crucial for understanding its location and scale. This article presents a framework for detecting and tracking floating plastic debris in inland waters using optical satellite image time series, leveraging the advantages of multitemporal Earth observation data. The detection process begins with a rule-based approach that analyzes variations in signal intensity, temporal patterns, spectral characteristics, and information fusion to identify potential plastic candidates. Once sensitive areas are detected, they can be continuously monitored, and the extent of plastic coverage at a subpixel level is estimated using spectral unmixing. The method requires only a specified time frame and area of interest as input parameters, eliminating the need for manually selecting specific images or outlining regions of interest. Several case studies demonstrate the successful application of this workflow, developed as a Google Earth Engine application, to identify highly affected areas in full Sentinel-2 scenes. These examples span different continents and environmental contexts, capturing floating plastic debris of varying types and dynamics.
Litter pollution, particularly from plastic waste, has escalated into a global crisis that poses substantial risks to aquatic ecosystems and human health. This comprehensive review systematically explores litter transport dynamics from terrestrial sources to marine environments, focusing on key objectives that include examining the intricate hydrodynamic processes involved in the movement of litter from land to sea. It identifies significant inland contributors to marine litter, such as rivers, urban runoff, and stormwater drainage outflows, as critical sources often overlooked in current studies. Natural factors, such as rainfall and riverine flow, and anthropogenic ones, such as urbanization and waste management practices, make litter mobilization and transport processes very complex. A key finding is that current techniques and data cannot grasp this complexity, pointing to the need for an improved holistic modeling framework. By synthesizing insights from diverse studies, the need for cross-disciplinary collaboration and robust data-sharing frameworks is highlighted to enhance model reliability and predictive capacity. Ultimately, these findings provide critical insights and guidance for environmental policymakers, researchers, and community organizers, empowering them to implement effective measures to combat litter pollution and safeguard marine habitats for future generations.
The marine environment is crucial to Earth's ecosystem, but anthropogenic and natural sources of radiation pose significant risks to both ocean ecosystems and human health. Radionuclides, while powerful tools as tracers, can be transported in seawater, and contaminate water, sediment, and marine life through various pathways including ingestion. This chapter outlines the various sampling techniques and instruments used for radiation detection in marine environments, along with their advantages and limitations. It also covers monitoring technologies, quality assurance, and quality control procedures. Radiation measurements for marine environments employ a variety of methodologies depending on the sampling method. Some methods directly detect radiation levels, while others focus on identifying the presence of long-lived radionuclides at trace and ultra-trace concentrations. Radiation detection plays a vital role in generating a baseline of radiation levels in marine environments, the impact of radioactive discharge from nuclear reactors and other industrial bodies in biota and marine ecosystems and finding the abnormal deviation in a level of radioactive substance.
Floating plastic debris on water surfaces imposes both short- and long-term burdens on nature. Hence, identifying and monitoring plastic is important to document the location and scale of the phenomenon. Evaluating the opportunities provided by multi-temporal Earth observation data, this paper proposes a framework to detect and monitor plastic debris floating on inland waters using optical satellite image time series. First, the detection of plastic candidates is conducted with a rule-based approach relying on variations in signal intensity, temporal patterns, spectral features, and information fusion. Second, identified sensitive areas can be monitored over time, and the extent of plastic cover at sub-pixel level estimated using spectral unmixing. The method needs as main input parameters only a temporal time frame and an area of interest, therefore no specific image must be selected nor region of interest manually outlined. Several examples are reported in which the same workflow, developed as a Google Earth Engine application, is successfully applied to identify critical affected areas in full Sentinel-2 scenes, across different continents and contexts, and exhibiting floating plastic debris varying both in type and dynamics.
Plastic debris pollution transported by river systems to lakes and oceans has emerged as a significant environmental concern with adverse impacts on ecosystems, food webs, and human health. Remote sensing presents a cost-effective approach to bolster interception and removal efforts. However, unlike marine environments, the optical properties of plastic debris in fresh waters remain poorly understood. This study aims to address this gap by providing an open-access hyperspectral reflectance database of floating weathered and virgin plastic debris found in river systems under controlled laboratory experiments. Utilizing natural waters from the Mississippi River, the database was assembled using a remote sensing data acquisition system deployed over a hydraulic flume operating under subcritical flow conditions and varying suspended sediment concentrations. The measurements encompass hyperspectral diffused light reflectance from ultraviolet (UV, 350 nm) to shortwave infrared (SWIR, 2500 nm) wavelengths. The database archived in Network Common Data Form (NetCDF) and Comma-separated values (CSV), offers valuable insights for better understanding key spectral signatures indicative of floating plastic debris, with different fractional abundance, in freshwater ecosystems.
Typically, the detection of marine debris relies on in-situ campaign that are characterized by huge human effort and limited spatial coverage. Following the need of a rapid solution for the detection of floating plastic, methods based on remote sensing data have been proposed recently. Their main limitation is represented by the lack of a general reference for evaluating the performance. Recently, the Marine Debris Archive (MARIDA) has been released as a standard dataset to develop and evaluate Machine Learning (ML) algorithms for detection of Marine Plastic Debris. The MARIDA dataset has been created for simplifying the comparison between detection solutions with the aim of stimulating the research in the field of marine environment preservation. In this work, an assessment of spectral based solutions is proposed by evaluating performance on MARIDA dataset. The outcome highlights the need of precise reference for fair evaluation.
Microplastics (MPs) have grown to be a common environmental issue because they are frequently found in both aquatic and terrestrial ecosystems. Their resistance to degradation allows them to endure for decades or even centuries, posing a threat to aquatic life and human health. Therefore, designing effective strategies to remove them from the environment is one of the most urgent environmental concerns. We has focused on the current abundance of MPs in different water bodies in this review article, and investigated the developments of catalysts, including eletrocatalysts, photocatalysts, and biocatalysts, for removing MPs. Among them, photocatalysis and microbial degradation technologies are widely investigated, showing promise at laboratory scale for the transformation of MPs to water-soluble hydrocarbons and carbon dioxide, offering the potential for long-term water security and ecological stability, and deserving further attention. Additionally, the challenges involved in the MP treatment and future prospective have been emphasized.
The increasing issue of microplastic pollution in Asian countries requires the application of Geographic Information System (GIS) and Remote Sensing (RS) approaches. This integrated approach empowers researchers to proficiently map, monitor, and comprehend the dispersion of MPs in diverse Asian landscapes, spanning water bodies, soils, and urban areas. This chapter emphasizes the pivotal role of GIS and RS in pinpointing MPs pollution hotspots and assessing associated environmental and health hazards. The importance of tailored approaches is underscored, considering challenges like limited data access and regional variations. Leveraging GIS and RS facilitates the formulation of effective policies and promotes sustainable practices to prevent environmental degradation and mitigate potential health risks linked to MPs pollution in Asia. Good policies will standardize methodologies for data collection, encourage global cooperation, and integrate spatial insights into overarching policy frameworks. Beyond strengthening monitoring capabilities, this holistic approach empowers local communities to actively participate in the fight against MPs contamination.
Hurricane Katrina (category 5 with maximum wind of 280 km/h when the eye is in the central Gulf of Mexico) made landfall near New Orleans on August 29, 2005, causing millions of cubic meters of disaster debris, severe flooding, and US$125 billion in damage. Yet, despite numerous reports on its environmental and economic impacts, little is known about how much debris has entered the marine environment. Here, using satellite images (MODIS, MERIS, and Landsat), airborne photographs, and imaging spectroscopy, we show the distribution, possible types, and amount of Katrina-induced debris in the northern Gulf of Mexico. Satellite images collected between August 30 and September 19 show elongated image features around the Mississippi River Delta in a region bounded by 92.5°W-87.5°W and 27.8°N-30.25°N. Image spectroscopy and color appearance of these image features indicate that they are likely dominated by driftwood (including construction lumber) and dead plants (e.g., uprooted marsh) and possibly mixed with plastics and other materials. The image sequence shows that if aggregated together to completely cover the water surface, the maximal debris area reached 21.7 km2 on August 31 to the east of the delta, which drifted to the west following the ocean currents. When measured by area in satellite images, this perhaps represents a historical record of all previously reported floating debris due to natural disasters such as hurricanes, floodings, and tsunamis.
Floating plastic debris represent an environmental threat to the maritime environment as they drift the oceans. Developing tools to detect and remove them from our oceans is critical. We present an approach to detect and distinguish suspect plastic debris from other floating materials (driftwood, seaweed, sea snot, sea foam and pumice) using Sentinel-2 data. We use Extreme Gradient Boosting trained with data compiled from published works complemented by manual interpretation of satellite images. The method is trained with two spectral bands and seven spectral indices computed from the Sentinel-2 spectral bands. We consider three application scenarios. The first uses the database created under the scope of this work. While the classification achieved a 98% accuracy rate for suspect plastic debris, we acknowledge the need for ground truth validation. The second, to enlarge the training data set, uses synthetic data generated through a Wasserstein Generative Adversarial Network. A supervised model trained exclusively with synthetic data successfully classified suspect plastic pixels with an accuracy of 83%. The third comprises an ensemble model that quantifies uncertainty about the predictions obtained with the classifier. We correctly classified 75% of the suspect plastic pixels. However, while the classification accuracy decreased, with the integration of uncertainty in the predictions the number of misclassifications also significantly decreased when compared to the model with the highest accuracy of the previous scenarios. Due to the mixed band nature of the sensor and sub-pixel coverage of debris within a pixel, the application to other data sets might not be straightforward.
The last couple of years has been ground-breaking for marine pollution monitoring purposes. It has been suggested that combining multi-spectral satellite information and machine learning approaches are effective to monitor plastic pollutants in the ocean environment. Recent research has made theoretical progress in identifying marine debris and suspected plastic (MD&SP) through machine learning whereas no study has fully explored the application of these methods for mapping and monitoring marine debris density. Therefore, this article consists of three main components: (1) the development and validation of a supervised machine learning marine debris detection model, (2) to map the MD&SP density into an automated tool called MAP-Mapper and finally (3) evaluation of the entire system for out-of-distribution (OOD) test locations. Developed MAP-Mapper architectures provide users with options to achieve high precision (abbv. -HP) or optimum precision-recall (abbv. -Opt) values in terms of training/test dataset. Our MAP-Mapper-HP model greatly increases the MD&SP detection precision to 95%, while the MAP-Mapper-Opt achieves 87–88% precision–recall pair. To efficiently measure density mapping findings at OOD test locations, we propose the Marine Debris Map (MDM) index, which combines the average probability of a pixel belonging to the MD&SP class and the number of detections in a given time frame. The high MDM findings of the proposed approach are found to be consistent with existing marine litter and plastic pollution areas, and these are presented with available evidence citing literature and field studies.
In recent years, marine plastic pollution has seen increased coverage in the public interest and research due to a greater understanding of the scale and impact of plastic pollution within the marine environment. Considering the hazard that plastic waste poses on the environment, marine life, and on humans, remote-sensing techniques could provide timely information on their detection and dynamics. The remote sensing of marine plastic is a relatively new field and research into the capabilities of radar for detecting and monitoring marine plastic pollution is generally limited, with several interactions and mechanisms being largely unknown. Here, we exploit the use of a C- and X-band radar to understand the capabilities of monitoring marine plastics. Our results show that backscattering differences in the C- and X-band between the reference water (called here as “clean”) and the test water filled with plastic can be detected in some conditions (based on statistical analysis). Overall, the results indicate that the X-band frequency performs significantly better than the C-band frequency, with X-band detecting significant differences in backscattering in 48/68 test cases compared with C-band detecting differences in 20/67 test cases. We also find that the difference in backscattering is dependent on the size and shape of the plastic object, as well as the wave conditions which the plastic is moving on. This study provides new insights on the radar capabilities for detecting marine plastic litter and new information which can be used in the planning of future missions and studies on the remote sensing of marine plastic pollution.
Plastic waste is a significant environmental pollutant that is difficult to monitor. We created a system of neural networks to analyze spectral, spatial, and temporal components of Sentinel-2 satellite data to identify terrestrial aggregations of waste. The system works at wide geographic scale, finding waste sites in twelve countries across Southeast Asia. We evaluated performance in Indonesia and detected 374 waste aggregations, more than double the number of sites found in public databases. The same system deployed in Southeast Asia identifies 996 subsequently confirmed waste sites. For each detected site, we algorithmically monitor waste site footprints through time and cross-reference other datasets to generate physical and social metadata. 19% of detected waste sites are located within 200 m of a waterway. Numerous sites sit directly on riverbanks, with high risk of ocean leakage.
Monitoring marine contamination by floating litter can be particularly challenging since debris are continuously moving over a large spatial extent pushed by currents, waves, and winds. Floating litter contamination have mostly relied on opportunistic surveys from vessels, modeling and, more recently, remote sensing with spectral analysis. This study explores how a low-cost commercial unmanned aircraft system equipped with a high-resolution RGB camera can be used as an alternative to conduct floating litter surveys in coastal waters or from vessels. The study compares different processing and analytical strategies and discusses operational constraints. Collected UAS images were analyzed using three different approaches: (i) manual counting (MC), using visual inspection and image annotation with object counts as a baseline; (ii) pixel-based detection, an automated color analysis process to assess overall contamination; and (iii) machine learning (ML), automated object detection and identification using state-of-the-art convolutional neural network (CNNs). Our findings illustrate that MC still remains the most precise method for classifying different floating objects. ML still has a heterogeneous performance in correctly identifying different classes of floating litter; however, it demonstrates promising results in detecting floating items, which can be leveraged to scale up monitoring efforts and be used in automated analysis of large sets of imagery to assess relative floating litter contamination.
This article investigates the use of deep knockoff, a modern statistical variable selection methodology, to uncover the spectral signatures of marine debris. This method uses a generative model by leveraging deep neural networks to learn the high dimensional distribution of reflectance in visible to near infrared wavelengths. To that end, a public dataset obtained from ground-based labeling of the observations by the Multispectral Instrument (MSI) on-board Sentinel-2 satellite is used. Through controlling the false discovery rate, consistent with the known physical causalities, the results indicate that the near infrared (band 8, 833 nm) and red (band 4, 665 nm) are the most important bands, respectively for discrimination of marine (plastic) debris from the background water. In the presence of dense
Sargassum
macroalgae, the deep knockoff isolates the green (band 3, 560 nm) and the narrow NIR (band 8a, 864.7 nm) as another important band.
The spatial and temporal coverage of spaceborne optical imaging systems are well suited for automated marine litter monitoring. However, developing machine learning-based detection and identification algorithms requires large amounts of data. Indeed, when it comes to marine debris, ground validated data is scarce. In this study, we propose a general methodology that leverages synthetic data in order to avoid overfitting and generalizes well. The idea is to utilize realistic models of spaceborne optical image acquisition and marine litter to generate large amounts of data to train the machine learning algorithms. These can then be used to detect marine pollution automatically on real satellite images. The main contribution of our study is showing that algorithms trained on simulated data can be successfully transferred to real-life situations. We present the general components of our framework, our modeling of satellites and marine debris and a proof of concept implementation for macro-plastic detection with Sentinel-2 images. In this case study, we generated a large dataset (more than 16,000 pixels of marine debris) composed of seawater, plastic, and wood and trained a Random Forest classifier on it. This classifier, when tested on real satellite images, successfully discriminates marine litter from seawater, thus proving the effectiveness of our approach and paving the way for machine learning-based marine litter detection with even more representative simulation models.
Large-area, artificial floating marine litter (FML) targets were deployed during a controlled field experiment and data acquisition campaign: the Plastic Litter Project 2021. A set of 22 Sentinel-2 images, along with UAS data and ancillary measurements were acquired. Spectral analysis of the FML and natural debris (wooden planks) targets was performed, along with spectral comparison and separability analysis between FML and other floating materials such as marine mucilage and pollen. The effects of biofouling and submersion on the spectral signal of FML were also investigated under realistic field conditions. Detection of FML is performed through a partial unmixing methodology. Floating substances such as pollen exhibit similar spectral characteristics to FML, and are difficult to differentiate. Biofouling is shown to affect the magnitude and shape of the FML signal mainly in the RGB bands, with less significant effect on the infrared part of the spectrum. Submersion affects the FML signal throughout the range of the Sentinel-2 satellite, with the most significant effect in the NIR part of the spectrum. Sentinel-2 detection of FML can be successfully performed through a partial unmixing methodology for FML concentrations with abundance fractions of 20%, under reasonable conditions.
This paper addresses the development of a novel zero-shot learning method for remote marine litter hyperspectral imaging data classification. The work consisted of using an airborne acquired marine litter hyperspectral imaging dataset that contains data about different plastic targets and other materials and assessing the viability of detecting and classifying plastic materials without knowing their exact spectral response in an unsupervised manner. The classification of the marine litter samples was divided into known and unknown classes, i.e., classes that were hidden from the dataset during the training phase. The obtained results show a marine litter automated detection for all the classes, including (in the worst case of an unknown class) a precision rate over 56% and an overall accuracy of 98.71%
Review literature on how satellite sensors can be exploited to monitor contaminants of emerging concerns (CECs) like microplastics and nanoplastics
A fast and precise detection of floating plastics debris is necessary for monitoring and saving the sea ecosystem. Recent studies have demonstrated how remote sensing (and in particular satellites) can be helpful in such detection. In particular, data provided by satellite sensors allow to continuously monitoring wide areas of our planet interested by plastic litters. In this work, the possibility of exploiting different optical remote sensing satellite methods is investigated: the analysis is conducted starting from multi-spectral data and moving tom hyperspectral one. Data acquired from Sentinel 2 and from PRISMA sensors are considered showing the added value of these systems in the detection of marine litter within marine areas.
A significant amount of the produced solid waste reaching the oceans is made of plastics. The amount of plastic debris in the ocean and coastal areas is steadily increasing and is now a major global environmental issue. The monitoring of marine plastic litter, ground-based monitoring systems and/or field campaigns are time-consuming, expensive, require great organisational efforts, and provide very limited information in terms of the spatial and temporal dynamics of marine debris. Earth Observation (EO) by satellite can contribute significantly to marine plastic litter detection. In 2019, a new hyperspectral satellite, called PRISMA, was launched by the Italian Space Agency. The high spectral resolution of PRISMA may allow for better detection of floating plastic materials. At the same time, Machine Learning (ML) algorithms have the potential to find hidden patterns and identify complex relations among data and are increasingly employed in EO. This paper presents the development of a new method of identifying floating plastic objects in coastal areas by exploiting pan-sharpened hyperspectral PRISMA data, based on the combination of unsupervised and supervised ML algorithms. The study consisted of a configuration phase, during which the algorithms were trained in a fully controlled test, and a validation phase, in which the pre-trained algorithms were applied to satellite data collected at different sites and in different periods of the year. Despite the limited input data, results suggest that the tested ML approach, applied to pan-sharpened PRISMA data, can effectively recognise floating objects and plastic targets. The study indicates that increasing input datasets can help achieve higher-quality results.
Marine debris is a serious problem for marine ecosystems and related coastal activities. We carry out a study using in-situ debris clean-up data (collected by a local Japanese company) together with high spatial resolution satellite images to determine how well the satellite images can be used to estimate the amount and type of debris deposited on the beaches of the island in southern Japan. We use machine learning techniques to analyze the satellite images and find that Shannon's entropy computed from World-View 2 and 3 imagery from Maxar Corporation yields a useful detection and mapping of the coastal debris when compared with the in-situ clean-up data. We also assign a debris concentration to each satellite image pixel to visualize the distribution of the debris. The algorithm linking the satellite images to the ground truth clean-up data can now be used in areas, where no ground truth data are available.
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.
In recent years, the remote sensing of marine plastic litter has been rapidly evolving and the technology is most advanced in the visible (VIS), near-infrared (NIR), and short-wave infrared (SWIR) wavelengths. It has become clear that sensing using VIS-SWIR bands, based on the surface reflectance of sunlight, would benefit from complementary measurements using different technologies. Thermal infrared (TIR) sensing shows potential as a novel method for monitoring macro plastic litter floating on the water surface, as the physics behind surface-leaving TIR is different. We assessed a thermal
radiance model for floating plastic litter using a small UAV-grade FLIR Vue Pro R 640 thermal camera by flying it over controlled floating plastic litter targets during the day and night and in different seasons. Experiments in the laboratory supported the field measurements. We investigated the effects of environmental conditions, such as temperatures, light intensity, the presence of clouds, and biofouling. TIR sensing could complement observations from VIS, NIR, and SWIR in several valuable ways. For example, TIR sensing could be used for monitoring during the night, to detect plastics invisible to VIS-SWIR, to discriminate whitecaps from marine litter, and to detect litter pollution over
clear, shallow waters. In this study, we have shown the previously unconfirmed potential of using TIR sensing for monitoring floating plastic litter.
Aggregates of floating materials detected in North Adriatic in six Sentinel-2 scenes of August 2020 have been investigated. Most of the floating materials were identified by the chlorophyll red edge and consisted of vegetal materials, probably conveyed by rivers and exchanged with the lagoons. Traces of marine litter were looked for in the spectral anomalies of the Red Edge bands, assuming changes of the red edge in pixels where marine litter was mixed with vegetal materials. About half of the detected patches were unclassified due to the weakness of the useful signal (pixel filling percentage < 25%). The classification produced 59% of vegetal materials, 16% of marine litter mixed with vegetal materials and 22% of intermediate cases. A small percentage (2%) was attributed to submerged vegetal materials, found in isolated patches. The previous percentages were obtained with a separation criterion based on arbitrary thresholds. The patches were more concentrated at the mouths of the northern rivers, less off the Venice lagoon, and very few outside the Po River, with the minimal river outflow during the period. Sentinel-2 is a valid tool for the discrimination of marine litter in aggregates of floating matter. The proposed method requires validation, and the North Adriatic is an excellent site for field work, as in summer many patches of floating matter form in proximity to the coast.
The increasing level of marine plastic pollution poses severe threats to the marine ecosystem and biodiversity. Open remote sensing data and advanced machine learning (ML) algorithms could be a cost-effective solution for identifying large plastic patches across the scale. The potential application of such resources in detecting and discriminating marine floating plastics (MFP) are not fully explored. Therefore, the present study attempted to explore the full functionality of open Sentinel satellite data and ML models for detecting and classifying the MFP in Mytilene (Greece), Limassol (Cyprus), Skala Loutron, Greece, Calabria (Italy), and Beirut (Lebanon). Two ML models, i.e. Support Vector Machine (SVM) and Random Forest (RF), were utilized to perform the classification analysis. In-situ plastic location data was collected from the control experiments conducted in Mytilene, Greece (in 2018 and 2019), Skala Loutron, Greece (2021), and Limassol, Cyprus (2018), and the same was considered for training the models. The accuracy and performances of the trained models were further tested on unseen new data collected from Calabria, Italy and Beirut, Lebanon. Both remote sensing bands and spectral indices were used for developing the ML models. A spectral signature profile for marine plastic was created for discriminating the floating plastic from other marine debris. A newly developed index, kernel Normalized Difference Vegetation Index (kNDVI), was incorporated into the modelling to examine its contribution to model performances. Both SVM and RF were performed well in five models and test case combinations. Among the two ML models, the highest performance was measured for the RF. The inclusion of kNDVI was found effective and increased the model performances, reflected by high balanced accuracy measured for model 2 (~89% to ~100% for SVM and ~92% to ~98% for RF). An automated floating plastic detection system was developed and tested in Calabria and Beirut using the best-performed model. The trained model had detected the floating plastic for both sites with ~80%–90%% accuracy. Among the six predictors, the Floating Debris Index (FDI) was the most important variable for detecting marine floating plastic. These findings collectively suggest that high-resolution remote sensing imagery and the automated ML models can be an effective alternative for the cost-effective detection of MFP. Future research will be directed toward collecting quality training data to develop robust automated models and prepare a spectral library for different plastic objects for discriminating plastic from other marine floating debris and advancing the marine plastic pollution research by taking full advantage of open-source data and technologies.
Plastic pollution in the marine environment is a pervasive, global problem that threatens wildlife and human health. Routine monitoring is required to determine pollution hotspots, focus clean-up efforts, and assess the efficacy of legislation implemented to reduce environmental contamination. The shoreline represents an accessible area, relative to open water, from which to monitor this. Unmanned aerial vehicles (UAVs) offer a low-cost platform for remote sensing that operates below cloud coverage, which can interfere with satellite imagery. Detection of plastic using visible light is limited however, and results may be improved by using short-wave infrared (SWIR) imagery to collect chemical information. Within the commercial recycling industry, plastic items are sorted successfully based on their composition using SWIR instrumentation that measures the chemical spectra of waste items under controlled illumination. Here, proof of concept is established for aerial detection of domestic and shoreline-harvested plastic items on a beach under natural sunlight with a lightweight (800 g), hyperspectral SWIR camera deployed at an altitude of ∼ 5 m over ∼ 30-m transects. The results of spectral correlation mapping to compare imagery spectra to polyethylene and polypropylene reference spectra demonstrate that these two polymers can be successfully detected with this novel method.
Currently, a significant amount of research is focused on detecting Marine Debris and assessing its spectral behaviour via remote sensing, ultimately aiming at new operational monitoring solutions. Here, we introduce a Marine Debris Archive (MARIDA), as a benchmark dataset for developing and evaluating Machine Learning (ML) algorithms capable of detecting Marine Debris. MARIDA is the first dataset based on the multispectral Sentinel-2 (S2) satellite data, which distinguishes Marine Debris from various marine features that co-exist, including Sargassum macroalgae, Ships, Natural Organic Material, Waves, Wakes, Foam, dissimilar water types (i.e., Clear, Turbid Water, Sediment-Laden Water, Shallow Water), and Clouds. We provide annotations (georeferenced polygons/ pixels) from verified plastic debris events in several geographical regions globally, during different seasons, years and sea state conditions. A detailed spectral and statistical analysis of the MARIDA dataset is presented along with well-established ML baselines for weakly supervised semantic segmentation and multi-label classification tasks. MARIDA is an open-access dataset which enables the research community to explore the spectral behaviour of certain floating materials, sea state features and water types, to develop and evaluate Marine Debris detection solutions based on artificial intelligence and deep learning architectures, as well as satellite pre-processing pipelines.
Extreme storms, such as tropical cyclones, are responsible for a significant portion of the plastic debris transported from land to sea yet little is known about the storm response of microplastics and other debris in offshore and open waters. To investigate this, we conducted floating plastic surveys in the center of Sagami Bay, Japan approximately 30 km from the coastline, before and after the passage of a typhoon. The concentrations (number of particles/km²) of micro- and mesoplastics were two orders of magnitude higher 1-day after the typhoon than the values recorded pre-typhoon and the mass (g/km²) of plastic particles (sum of micro- and mesoplastics) increased 1,300 times immediately after the storm. However, the remarkably high abundance of micro- and mesoplastics found at 1-day after the typhoon returned to the pre-typhoon levels in just 2 days. Model simulations also suggested that during an extreme storm a significant amount of micro- and mesoplastics can be rapidly swept away from coastal to open waters over a short period of time. To better estimate the annual load of plastics from land to sea it is important to consider the increase in leakages of plastic debris into the ocean associated with extreme storm events.
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.
With the Philippines ranking as the third largest source of plastics that end up in the oceans, there is a need to further explore methodologies that will become an aid in plastic waste removal from the ocean. Manila Bay is a natural harbor in the Philippines that serves as the center of different economic activities. However, the bay is also threatened with plastic pollution due to increasing population and industrial activities. BASECO is one of the areas in Manila Bay where clean-up activities are focused as this is where trash accumulates. Sentinel-2 images are provided free of charge by the European Commission's Copernicus Programme. Satellite images from June 2019 to May 2020 were inspected, then cloud-free images were downloaded. After downloading and pre-processing, spectral data of different types of plastic such as shipping pouch, bubble wrap, styrofoam, PET bottle, sando bag and snack packaging that were measured by a spectrometer during a fieldwork by the Development of Integrated Mapping, Monitoring, and Analytical Network System for Manila Bay and Linked Environments (project MapABLE) were utilized in the selection of training data. Then, indices such as the Normalized Vegetation Index (NDVI), Floating Debris Index (FDI) and Plastic Index (PI) from previous studies were analyzed for further separation of classes used as training data. These training data served as an input to the two supervised classification methods, Naive Bayes and Mixture Tuned Matched Filtering (MTMF). Both methods were validated by reports and articles from Philippine agencies indicating the spots where trash frequently accumulates.
Marine plastic debris (MPD) is a globally relevant environmental challenge, with an estimated 8 million tons of synthetic debris entering the marine environment each year. Plastic has been found in all parts of the marine environment, including the surface layers of the ocean, within the water column, in coastal waters, on the benthic layer and on beaches. While research on detecting MPD using remote sensing is increasing, most of it focuses on detecting floating debris in open waters, rather than detecting MPD on beaches. However, beaches present challenges that are unique from other parts of the marine environment. In order to better understand the spectral properties of beached MPD, we present the SWIR reflectance of weathered MPD and virgin plastics over a sandy substrate. We conducted spectral feature analysis on the different plastic groups to better understand the impact that polymers have on our ability to detect synthetic debris at sub-pixel surface covers that occur on beaches. Our results show that the minimum surface cover required to detect MPD on a sandy surface varies between 2–8% for different polymer types. Furthermore, plastic composition affects the magnitude of spectral absorption. This suggests that variation in both surface cover and polymer type will inform the efficacy of beach litter detection methods.
We present a direct and proxy-based approach to qualitatively and semi-quantitatively observe floating plastic litter in the Great Pacific Garbage Patch (GPGP) based on a survey in 2018 using very high geo-spatial resolution 8-waveband WorldView-3 imagery. A proxy for the plastics was defined as a waveband difference for anomalies in the top-of-the-atmosphere spectra. The anomalies were computed by subtracting spatially varying reflectance of the surrounding ocean water as background from the top-of-the-atmosphere reflectance. Spectral shapes and magnitude were also evaluated using a reference target of known plastics, The Ocean Cleanup System 001 Wilson. Presence of ‘suspected plastics’ was confirmed by the similarity in derived anomalies and spectral shapes with respect to the known plastics in the image as well as direct observations in the true color composites. The proposed proxy-based approach is a step towards future mapping techniques of suspected floating plastics with potential operational monitoring applications from the Sentinel-2 that recently started regular imaging over the GPGP that will be supported or validated by numerical solutions and net trawling survey.
Marine debris is considered a threat to the inhabitants, as well as the marine environments. Accumulation of marine debris, besides climate change factors, including warming water, sea-level rise, and changes in oceans’ chemistry, are causing the potential collapse of the marine environment’s health. Due to the increase of marine debris, including plastics in coastlines, ocean and sea surfaces, and even in deep ocean layers, there is a need for developing new advanced technology for the detection of large-sized marine pollution (with sizes larger than 1 m) using state-of-the-art remote sensing and machine learning tools. Therefore, we developed a cloud-based framework for large-scale marine pollution detection with the integration of Sentinel-2 satellite imagery and advanced machine learning tools on the Sentinel Hub cloud application programming interface (API). Moreover, we evaluated the performance of two shallow machine learning algorithms of random forest (RF) and support vector machine (SVM), as well as the deep learning method of the generative adversarial network-random forest (GAN-RF) for the detection of ocean plastics in the pilot site of Mytilene Island, Greece. Based on the obtained results, the shallow algorithms of RF and SVM achieved an overall accuracy of 88% and 84%, respectively, with available training data of plastic debris. The GAN-RF classifier improved the detection of ocean plastics of the RF method by 8%, achieving an overall accuracy of 96% by generating several synthetic ocean plastic samples.
The aim of this work is to verify and demonstrate the possibility of using X-band radars to identify, discriminate, characterize and follow small floating aggregations of marine litter (Small Garbage Islands—SGIs) made up mainly of plastic debris. To this end, a radar measurement campaign was carried out on a series of controlled releases into the sea of SGI modules assembled in the lab using the waste collected along a beach near the port of Livorno, in Tuscany, where the X-band radar of the Institute of Bioeconomy (IBE) of the National Research Council (CNR) is installed. The results of this first measurement campaign, which are illustrated in this preliminary work, are of interest to the entire scientific community that operates in the field of macroplastics analysis and monitoring, opening a new experimental avenue for the use of X-band radars also to monitor plastic waste at sea. Furthermore, the results obtained suggest good prospects for the use of X-band radars also for the study of coastal hydrodynamics on a local scale as well as in areas where it would be difficult to carry out measurements employing other technologies.
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.
As a significant contributor of plastic waste to the marine environment, Indonesia is striving to construct a national strategy for reducing plastic debris. Hence, the primary aim of this study is to create a model for plastic waste quantity originating from the mainland, accumulated in estuaries. This was achieved by compiling baseline data of marine plastic disposal from the mainland via comprehensive contextualisation of data generated by remote sensing technology and spatial analysis. The parameters used in this study cover plastic waste generation, land cover, population distribution, and human activity identification. These parameters were then used to generate the plastic waste disposal index; that is, the distribution of waste from the mainland, flowing through the river, and ultimately accumulating in the estuary. The plastic waste distribution is calculated based on the weighting method and overlap analysis between land and coastal areas. The results indicate that 0.6% of Indonesia, including metropolitan cities, account for the highest generation of plastic waste. Indicating of plastic releases to the ocean applied by of developing three different scenarios with the highest estimation 11.94 tonnes on a daily basis in an urban area, intended as the baseline study for setting priority zone for plastic waste management.
The COVID-19 pandemic has obliged Governments all around the world to implement confinement and social distancing measures. Leisure and business activities on beaches and in ports have restricted direct and indirect contamination from, for example, plastics, hydrocarbon spillage, microbiological loads, and noise levels. This has led to temporarily improved environmental conditions, and the beaches having conditions closer to Marine Protected Areas. Here we report some impacts that have been studied using local surveys and qualitative observations in Ecuador at the popular beaches and ports of Salinas, Manta, and Galapagos. Satellite data support this information. Online surveys were carried out at critical moments of the pandemic: May (15th) and just after when measures were relaxed a little, but within lockdown in July (21st) 2020. Respondents were asked to compare conditions before and during the pandemic lockdown. Most (97-99%) suggested that beaches had significantly improved from visual observations during confinement. On a scale from 1 (worst) to 5 (best), the beaches of Salinas and Manta respectively were rated 2.2 and 2.8 (less than acceptable) before quarantine, and 4.5 and 4.3 after; results from the second survey (after 18 weeks of restrictions) were much the same. Replies from Galapagos showed a similar trend but with less marked differences. In addition to the beaches having less plastic and garbage, more fish, and large marine organisms, including humpback whales (Megaptera novaeangliae), dolphin (bottlenose, Tursiops truncatus), and manta ray (Manta sp.) were observed near to shore. At Galapagos beaches, turtles, sea lions, and sharks were observed many more times than pre COVID. Quantitative satellite data on Chlorophyl and attenuation coefficient (Kd, 490 nm) support the qualitative survey data that there is an improvement in coastal environment quality. Here we recommend that this unique opportunity resulting from the COVID-19 pandemic is used locally, regionally and globally to construct baseline data sets that include information on physical, chemical, biological, and microbiological factors in coastal zones. These parameters can then help establish
Litter is a serious threat to the marine environment, with detrimental effects on wildlife and marine biodiversity. Limited data as a result of funding and logistical challenges in developing countries hamper our understanding of the problem. Here, we employed commercial unmanned aerial vehicle (UAV) as a cost-effective tool to study the exposure of marine turtles to floating marine litter (FML) in waters of Mayo Bay, Philippines. A quadcopter UAV was flown autonomously with on-board camera capturing videos during the flight. Still frames were extracted when either turtle or litter were detected in post-flight processing. The extracted frames were georeferenced and mapped using QGIS software. Results showed that turtles are highly exposed to FML in nearshore waters. Moreover, spatial dependence between FML and turtles was also observed. The study highlights the effectiveness of UAVs in marine litter research and underscores the threat of FML to turtles in nearshore waters.
Plastic pollution in coastal and marine areas is an ongoing environmental concern in the world. Despite its growing concern worldwide, there is a knowledge gap in terms of its detection and quantification of spatial distribution patterns. In this review paper, we investigated the existing trends in monitoring marine and beached (macro and meso) plastic debris using remote sensing techniques. Marine plastic debris monitoring using remote sensing data is highly complex, partially due to the constraints in spatial and spectral resolutions as well as due to the differences in surface reflectance properties of different polymers. Modern remote sensing with high-quality data, including airborne, spaceborne, and data taken from an unmanned aerial vehicle (UAV), have revolutionized marine litter mapping. UAV platform offers a promising and cost-effective way for mapping both floating and beached marine plastic debris compared to data from other platforms. Automatic and manual delineation for mapping marine plastic litters can be applied depending on the objective of the study. For assisting beach clean-up processes, automatic detection of marine plastic debris is enough for locating the worksite. For detailed characterization of debris, including type, size and material, manual delineation of high resolution images is necessary. More accurate mapping of marine plastics can be achieved by developing detailed spectral libraries with material level details and launching dedicated sensors for marine litter mapping in the future. Challenges exist in mapping submerged marine plastics, plastic materials conglomerated with biomass and unaccounted for beached marine litter buried along the coasts due to accretion and erosion.
Basic monitoring of the marine environment is crucial for the early warning and assessment of marine hydrometeorological conditions, climate change, and ecosystem disasters. In recent years, many marine environmental monitoring platforms have been established, such as offshore platforms, ships, or sensors placed on specially designed buoys or submerged marine structures. These platforms typically use a variety of sensors to provide high-quality observations, while they are limited by low spatial resolution and high cost during data acquisition. Satellite remote sensing allows monitoring over a larger ocean area; however, it is susceptible to cloud contamination and atmospheric effects that subject the results to large uncertainties. Unmanned vehicles have become more widely used as platforms in marine science and ocean engineering in recent years due to their ease of deployment, mobility, and the low cost involved in data acquisition. Researchers can acquire data according to their schedules and convenience, offering significant improvements over those obtained by traditional platforms. This study presents the state-of-the-art research on available unmanned vehicle observation platforms, including unmanned aerial vehicles (UAVs), underwater gliders (UGs), unmanned surface vehicles (USVs), and unmanned ships (USs), for marine environmental monitoring, and compares them with satellite remote sensing. The recent applications in marine environments have focused on marine biochemical and ecosystem features, marine physical features, marine pollution, and marine aerosols monitoring, and their integration with other products are also analysed. Additionally, the prospects of future ocean observation systems combining unmanned vehicle platforms (UVPs), global and regional autonomous platform networks, and remote sensing data are discussed.
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.
The insatiable desire of society for plastic goods has led to synthetic materials becoming omnipresent in the marine environment. In attempting to address the problem of plastic pollution, we propose an image classifier based on the YOLOv5 deep learning tool that is able to classify and localize marine debris and marine life in images and video recordings. Utilizing the region of interest line and the centroid tracking counting methods, the image classifier was able to count marine debris and fish displayed in video footage. Results revealed that, with a counting accuracy of 79 %, the centroid tracking method proved more efficient thanks to its ability to trace the geometric center of the bounding box of detected marine litter. Remarkably, the proposed method achieved a mean average precision of 89.4 % when validated on nine categories of objects. Finally, its impact can be enhanced substantially if integrated into other surveying methods or applications.
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.
Detection and identification of macroplastic debris in aquatic environments is crucial to understand and counter the growing emergence and current developments in distribution and deposition of macroplastics. In this context, close-range remote sensing approaches revealing spatial and spectral properties of macroplastics are very beneficial. To date, field surveys and visual census approaches are broadly acknowledged methods to acquire information, but since 2018, techniques based on remote sensing and artificial intelligence are on the rise. Despite their proven efficiency, speed and wide applicability, there are still obstacles to overcome, especially when looking at the availability and accessibility of data. Thus, our review paper looks into state-of-the-art research about the visual recognition and identification of different sorts of macroplastics. The focus is on both data acquisition techniques and evaluation methods, including Machine Learning and Deep Learning, but resulting products and published data will also be taken into account. Our aim is to provide a critical overview and outlook in a time where this research direction is thriving fast. This study shows that most Machine Learning and Deep Learning approaches are still in an infancy state regarding accuracy and detail when compared to visual monitoring, even though their results look very promising.
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.
As an efficient mode of modern agriculture, plastic greenhouse (PG) has significantly increased crop yields, but it is also criticized for changing the agriculture landscape and posing a threat to the environment. Accurate and timely information on PG distribution is essential for the strategic planning of modern agriculture as well as the projection of the environmental impacts. However, PG mapping over a large area has been a long-term challenge. Compared with classifier-based methods, index-based methods have the advantages of fast speed and convenience, which are very suitable for rapid large-scale mapping. The existing PG indices face the diversity of PG types and background environments, and the seasonal variation of PG spectra. To solve these problems, this study proposes a novel spectral index using Sentinel-2 images, namely the Advanced Plastic Greenhouse Index (APGI), to map PGs at a large scale. Four typical PG planting regions in the world, including Almería (Spain), Anamur (Turkey), Weifang (China), and Nantong (China), were selected as study areas. Based on the spectral analysis, some common spectral characteristics of PGs (i.e., high reflectance in NIR wavelengths and strong absorption in red and SWIR2 wavelengths) were observed and used in the APGI for highlighting PG areas. Besides, the coastal aerosol band and the red band were selected as optimal indicators to distinguish PG from other land covers which share similar spectral characteristics with PG. The experimental results indicate that the APGI has obvious advantages in enhancing PG information and suppressing non-PG backgrounds in various scenes compared with the existing indices. The APGI achieved the PG mapping accuracy with an OA of 90.63% ~ 97.50% and an F1 score of 80.56% ~ 96.20% in all study cases. Furthermore, the APGI also showed its robustness in seasonal variations and different datasets.
There are increasing concerns over the threat of nanoplastics to environmental and human health. However, multidisciplinary barriers persist between the communities assessing the risks to environmental and human health. As a result, the hazards and risks of nanoplastics remain uncertain. Here, we identify key knowledge gaps by evaluating the exposure of nanoplastics in the environment, assessing their bio-nano interactions, and examining their potential risks to humans and the environment. We suggest considering nanoplastics a complex and dynamic mixture of polymers, additives, and contaminants, with interconnected risks to environmental and human health. We call for comprehensive integration of One Health approach to produce robust multidisciplinary evidence to nanoplastics threats at the planetary level. Although there are many challenges, this holistic approach incorporates the relevance of environmental exposure and multi-sectoral responses, which provide the opportunity to identify the risk mitigation strategies of nanoplastics to build resilient health systems.
Plastic pollution is one of several anthropogenic stressors putting pressure on ecosystems of the Caribbean Large Marine Ecosystem (CLME). A ‘Clean Ocean’ is one of the ambitious goals of the United Nations (UN) Decade of Ocean Science for Sustainable Development. If this is to be realized, it is imperative to build upon the work of the previous decades (1980–2020). The objectives of the present study were to assess the state of knowledge about: (i) the distribution, quantification, sources, transport and fate of marine debris/litter and microplastics in the coastal/marine environment of the CLME and, (ii) the effects of plastics on biodiversity. Snapshots, i.e., peer-reviewed studies and multi-year (1991–2020) marine debris data from International Coastal Cleanup (ICC) events, indicated that plastic debris was a persistent issue in multiple ecosystems and environmental compartments of the CLME. Collectively, a suite of approaches (debris categorization, remote sensing, particle tracking) indicated that plastic debris originated from a combination of land and marine-based sources, with the former more significant than the latter. Rivers were identified as an important means of transporting mismanaged land-based waste to the marine environment. Oceanic currents were important to the transport of plastic debris into, within and out of the region. Plastic debris posed a threat to the biodiversity of the CLME, with specific biological, physical, ecological and chemical effects being identified. Existing data can be used to inform interventions to mitigate the leakage of plastic waste to the marine environment. Given the persistent and transboundary nature of the issue, further elucidation of the problem, its causes and effects must be prioritized, while simultaneously harmonizing regional and international approaches.
Microplastics pollution has received growing attention worldwide in recent years. However, data on microplastics in the freshwater environment are still limited, especially in high-latitude nature reserves in Northern China. The first study on microplastic pollution in the Liaohe River Reserve in Northern China is reported here, and mesoplastics were also incorporated. Surface water and sediment samples were collected from 32 sites along the nature reserve. The abundance, type, shape, color, and size of micro- and mesoplastics were measured using density extraction, optical microscopy, and FTIR spectroscopy. The data showed that diverse micro- and mesoplastics were found widespread in the 32 sites, and the average abundance of these plastics was 0.11±0.04 10−2 items/L in surface water and 62.29±54.30 items/kg in sediment. Moreover, 70% and 66% were smaller than 2000 µm in surface water and sediment, respectively. Fiber accounted for 91.86% in surface water and 43.48% in sediment, indicating that the major source of micro- and mesoplastics in the Liaohe River Reserve may be domestic sewage and aquaculture. A total of 16 and 27 polymers were identified in surface water and sediment, respectively, and mostly consisted of rayon, polyester, polystyrene, and poly(ethylene terephthalate). Moreover, both the risk index and the pollution load index demonstrated a low risk of micro- and mesoplastics in surface water and sediment in the Liaohe River Reserve.
In 2021, coastal communities in the Sea of Marmara were impacted by large sea snot events. Based on data collected by several satellite sensors, we analyze and present the spectral reflectance characteristics of sea snots in the visible and near-infrared wavelengths. Sea snots often form elongated image slicks in Red-Green-Blue (RGB) or false-colour RGB (FRGB) images. Most reflectance spectra of sea snots show largely featureless spectral shapes, with continuous reflectance increases from the blue to the red, which then become flat toward the near infrared wavelengths. Some reflectance spectra show a local trough around 670 nm, indicating presence of chlorophyll a pigment, and thereby live algae. A 20-year time series of satellite data also reveals large-scale sea snot events in the Sea of Marmara before 2021. These results suggest that it might be possible to develop algorithms to search and map sea snots at a global scale, as sea snot events also occur in other regions. The results also indicate that remote differentiation of sea snots and marine debris using multi-band sensors may be difficult because of the spectral similarity between them.
Floating Marine Litter (FML) are mainly plastics or synthetic polymers that float on the sea surface after being deliberately discarded or unintentionally lost along beaches, rivers or marine environments. In recent years, much focus has been placed on locating, tracking and removing plastic items in both coastal areas and in the open ocean. The use of high-resolution multispectral satellite images for such purpose is very promising, since satellite images can systematically monitor much larger areas in comparison to the traditional in situ observations. This paper contains a literature review of the published research regarding the optical remote detection of floating marine debris and the proposed associated methodologies. The main aim of this review is to compile all available information on detection methodologies, providing at the same time valuable insights into the different approaches used for floating marine litter monitoring. First, a brief introduction into the theoretical basis of a spaceborne floating marine litter detection system is given. Next, published articles, or relevant research work have been compartmentalised, for analysing the proposed procedures and assisting in a further assessment of their methodological frameworks. Lastly, conclusions and bottlenecks of the existing knowledge on marine litter detection from space are derived. Although the remote detection of floating marine litter is currently limited by inherent restrictions of the available satellite sensors specifications, we highlight how the methodological processing chain can significantly affect the future accuracy of plastic detection from space.
Sewer overflow (SO), which has attracted global attention, poses serious threat to public health and ecosystem. SO impacts public health via consumption of contaminated drinking water, aerosolization of pathogens, food-chain transmission, and direct contact with fecally-polluted rivers and beach sediments during recreation. However, no study has attempted to map the linkage between SO and public health including Covid-19 using scientometric analysis and systematic review of literature. Results showed that only few countries were actively involved in SO research in relation to public health. Furthermore, there are renewed calls to scale up environmental surveillance to safeguard public health. To safeguard public health, it is important for public health authorities to optimize water and wastewater treatment plants and improve building ventilation and plumbing systems to minimize pathogen transmission within buildings and transportation systems. In addition, health authorities should formulate appropriate policies that can enhance environmental surveillance and facilitate real-time monitoring of sewer overflow. Increased public awareness on strict personal hygiene and point-of-use-water-treatment such as boiling drinking water will go a long way to safeguard public health. Ecotoxicological studies and health risk assessment of exposure to pathogens via different transmission routes is also required to appropriately inform the use of lockdowns, minimize their socio-economic impact and guide evidence-based welfare/social policy interventions. Soft infrastructures, optimized sewer maintenance and prescreening of sewer overflow are recommended to reduce stormwater burden on wastewater treatment plant, curtail pathogen transmission and marine plastic pollution. Comprehensive, integrated surveillance and global collaborative efforts are important to curtail on-going Covid-19 pandemic and improve resilience against future pandemics.
