Remotely Sensing the Source and Transport of Marine Plastic Debris in Bay Islands of Honduras (Caribbean Sea)

Macrolitter and microplastics along the East Pacific coasts - A homemade problem needing local solutions

The East Pacific (EP) region, especially the central and southern EP, has been fairly less studied than other world's regions with respect to marine litter pollution. This comprehensive literature review (257 peer-reviewed publications) showed that both macrolitter (mostly plastics) and microplastics tend to accumulate on EP shorelines. Moreover, they were also reported in all the other compartments investigated: sea surface, water column, seafloor and 'others'. Mostly local, land-based sources (e.g., tourism, poor waste management) were identified across the region, especially at continental sites from low and mid latitudes. Some sea-based sources (e.g., fisheries, long-distance drifting) were also identified at high latitudes and on oceanic islands, likely enhanced by the oceanographic dynamics of the EP that affect transport of floating litter. Our results suggest that effective solutions to the problem require local and preventive strategies to significantly reduce the levels of litter along the EP coasts.

A comparative study on source of water masses and nutrient supply in Zhanjiang Bay during the normal summer, rainstorm, and typhoon periods: Insights from dual water isotopes

Typhoons and rainstorms (rainfall >250 mm day-1) are extreme weather events that seriously impact coastal oceanography and biogeochemical cycles. However, changes in the mixing of water masses and nutrient supply induced by typhoons and rainstorms can hardly be identified and quantified by traditional methods owing to the complex hydrological conditions in coastal waters. In this study, we analysed a comparative data set of dual water isotopes (δD and δ18O), hydrological parameters, nutrients, and chlorophyll-a from three periods (normal summer, rainstorm, and typhoon periods) in Zhanjiang Bay, a typical semi-enclosed mariculture bay in South China, to address this issue. The results revealed a significant increase in contributions from freshwater during rainstorms and typhoons. Correspondingly, nutrient supplies from freshwater during these periods remarkably increased compared to the normal summer, indicating that heavy rainfall can transport substantial amounts of terrestrial nutrients into the bay. Furthermore, disparities in hydrodynamic processes between typhoon and rainstorm periods were notable due to inconsistencies in freshwater diffusion paths. During rainstorms, freshwater primarily diffuses towards the outer bay in the upper layer due to strong stratification and cannot form an ocean front. However, under intense external forces caused by the typhoon, high-salinity water intruded into the bay, and enhancement of vertical mixing disrupted stratification. The massive influx of freshwater column during the typhoon mixed with higher salinity seawater column in the bay led to the formation of an ocean front, which could retain contaminants. This study suggests that although both rainstorms and typhoons can discharge large quantities of terrestrial nutrients into Zhanjiang Bay, the front formed during the typhoon period impedes the contaminant transportation to open sea thereby deteriorating water quality and affecting mariculture activities within the bay.

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.

Trophic status of a coastal lagoon - marine harbor system: Potential outwelling rates to the Mesoamerican Barrier Reef southern region

Eutrophication is still a serious problem in many coastal areas, including the tropics, where river discharges of nutrients is usually high. The ecological stability and ecosystem services of the Mesoamerican Barrier Reef System (MBRS), the world's second-largest coral reef system, suffer a generalized impact by riverine discharge of sediment and organic and inorganic nutrients, which may lead to coastal eutrophication and a coral-macroalgal phase shift. However, few data exist on the MRBS coastal zone status, particularly in Honduras. Here, two in situ sampling campaigns were carried out (May 2017 and January 2018) in the Alvarado Lagoon and Puerto Cortés Bay (Honduras). Measurements included water column nutrients, chlorophyll-a (Chla), particulate organic and inorganic matter and net community metabolism, completed with satellite images analysis. The lagoon and bay environments are ecologically different systems and present different sensitivities to seasonal changes in precipitation as shown by the multivariate analysis. Nonetheless, net community production and respiration rates were neither different spatially, nor seasonally. In addition, both environments were highly eutrophic as shown by the TRIX index. Thus, the Puerto Cortés system represents an important source of dissolved nutrients and particulate matter to the coastal zone. Even though offshore, water quality, based on estimated outwelling rates from the Puerto Cortés system to the coastal waters of the southern MRBS region, improved considerably, concentrations of Chla and nutrients remained higher than those typically measured in non-polluted coral reefs in the Caribbean region and the suggested threshold values. In situ monitoring and assessment of these aspects are crucial to evaluate the ecological functioning of and threats on the MBRS, and elaborate and implement adequate policies for integrated management given its regional and global importance.

High-precision density mapping of marine debris and floating plastics via satellite imagery

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.

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.

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.

Marine Litter Detection by Sentinel-2: A Case Study in North Adriatic (Summer 2020)

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 status of marine debris/litter and plastic pollution in the Caribbean Large Marine Ecosystem (CLME): 1980-2020

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.

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

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

MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data

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.

A new paradigm for estimating the prevalence of plastic litter in the marine environment

The intelligent method proposed herein is formulated on a deep learning technique which can identify, localise and map the shape of plastic debris in the marine environment. Utilising images depicting plastic litter from six beaches in Cyprus, the developed tool pointed to a plastic litter density of 0.035 items/m². Extrapolated to the entire shorelines of the island, the intelligent approach estimated about 66,000 plastic articles weighting a total of ≈1000 kg. Besides deducing the plastic litter density, the dimensions of all documented plastic litter were determined with the aid of the OpenCV Contours image processing tool. Results revealed that the dominant object length ranged between 10 and 30 cm which is in agreement with the length of common plastic litter often spoiling these coastlines. Concluding, only in-situ visual scan sample surveys and no manual collection means were used to predict the density and the dimensions of the plastic litter.

Source, sea and sink-A holistic approach to understanding plastic pollution in the Southern Caribbean

Marine plastics are considered to be a major threat to the sustainable use of marine and coastal resources of the Caribbean, on which the region relies heavily for tourism and fishing. To date, little work has quantified plastics within the Caribbean marine environment or examined their potential sources. This study aimed to address this by holistically integrating marine (surface water, subsurface water and sediment) and terrestrial sampling and Lagrangian particle tracking to examine the potential origins, flows and quantities of plastics within the Southern Caribbean. Terrestrial litter and the microplastics identified in marine samples may arise from the maritime and tourism industries, both of which are major contributors to the economies of the Caribbean region. The San Blas islands, Panama had the highest abundance of microplastics at a depth of 25 m, and significantly greater quantities in surface water than recorded in the other countries. Modelling indicated the microplastics likely arose from mainland Panama, which has some of the highest levels of mismanaged waste. Antigua had among the lowest quantities of terrestrial and marine plastics, yet the greatest diversity of polymers. Modelling indicated the majority of the microplastics in Antiguan coastal surface were likely to have originated from the wider North Atlantic Ocean. Ocean currents influence the movements of plastics and thus the relative contributions arising from local and distant sources which become distributed within a country's territorial water. These transboundary movements can undermine local or national legislation aimed at reducing plastic pollution. While this study presents a snapshot of plastic pollution, it contributes towards the void of knowledge regarding marine plastic pollution in the Caribbean Sea and highlights the need for international and interdisciplinary collaborative research and solutions to plastic pollution.

A baseline analysis of marine debris on southern islands of Belize

Marine debris is a global issue with acute impacts. Using beach transect surveys, this study investigates debris prevalence on 7 islands in the Caribbean country of Belize. 1754 items were cataloged based on object size, form, material, condition, and economic use. Most of the litter was plastics (68.1%). Styrofoam was the second highest in abundance (9.46%), followed by foam/rubber items (8.04%), glass (3.82%), metal (2.57%), and aluminum (1.94%). Most litter was associated with an urban source (74.8%), while 4.2% and 2.1% were linked to industrial and fishing activities respectively. This study provides a novel baseline for future studies in the scarcely studied region, especially as Belize's economy continues in the conscious shift away from single-use plastic and styrofoam products.

A Cloud-Based Framework for Large-Scale Monitoring of Ocean Plastics Using Multi-Spectral Satellite Imagery and Generative Adversarial Network

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.

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

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.

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

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

Marine plastic litter detection offshore Hawai'i by Sentinel-2

Marine litter patches were detected from Sentinel-2 offshore Hawaii's Big Island (Hawaii) within 10 miles from the coast in the prevalent windward direction (NE), for a total sea surface of 3.0 km². The patches have a filament-like shape with different orientation, lengths of several kilometers and width from tens to hundreds of meters. A comparison with the typical spectra of "sargassum" and "seaweed" patches emphasized differences in the red edge portion of the spectrum for large part of the filaments. Frequency of plastic pollution on Hawaiian beaches and spectral characteristics of the filaments suggest these patches largely consist of plastic debris. A detection method of plastic litter for Sentinel-2 data resampled at 20 m resolution based on the analysis of the red edge bands is proposed.

Advancing Floating Macroplastic Detection from Space Using Experimental Hyperspectral Imagery

Airborne and spaceborne remote sensing (RS) collecting hyperspectral imagery provides unprecedented opportunities for the detection and monitoring of floating riverine and marine plastic debris. However, a major challenge in the application of RS techniques is the lack of a fundamental understanding of spectral signatures of water-borne plastic debris. Recent work has emphasised the case for open-access hyperspectral reflectance reference libraries of commonly used polymer items. In this paper, we present and analyse a high-resolution hyperspectral image database of a unique mix of 40 virgin macroplastic items and vegetation. Our double camera setup covered the visible to shortwave infrared (VIS-SWIR) range from 400 to 1700 nm in a darkroom experiment with controlled illumination. The cameras scanned the samples floating in water and captured high-resolution images in 336 spectral bands. Using the resulting reflectance spectra of 1.89 million pixels in linear discriminant analyses (LDA), we determined the importance of each spectral band for discriminating between water and mixed floating debris, and vegetation and plastics. The absorption peaks of plastics (1215 nm, 1410 nm) and vegetation (710 nm, 1450 nm) are associated with high LDA weights. We then compared Sentinel-2 and Worldview-3 satellite bands with these outcomes and identified 12 satellite bands to overlap with important wavelengths for discrimination between the classes. Lastly, the Normalised Vegetation Difference Index (NDVI) and Floating Debris Index (FDI) were calculated to determine why they work, and how they could potentially be improved. These findings could be used to enhance existing efforts in monitoring macroplastic pollution, as well as form a baseline for the design of future multispectral RS systems.

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

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