ITS2.7/ESSI2

Detecting and Monitoring Plastic Pollution in Rivers, Lakes, and Oceans.

Global plastic production has increased exponentially since the fifties, with 359 million metric tons manufactured in 2018 alone. Nearly 20% of this production took place within Europe, where at least half of discarded plastics collected for ‘recycling’ were instead exported to China and SE Asia. Every year, an increasing proportion of these plastics (in the order of millions of tons) enter and accumulate in our waterways and oceans. In riverine and marine systems, the presence of micro to macroplastic debris has generated a growing and persistent threat to the environment and ecosystems, as well as an urgent and multi-dimensional challenge for our society.

Methods for resource-efficient and large-scale detection and monitoring of plastic litter are still relatively new. However, in the last few years, they have blossomed across technologies and environments - from mounted cameras to drones to satellites, and from lakes and rivers to coastal waters and open oceans. These new technologies can be crucial to fill in the gaps between limited in situ observations and global models, allowing coverage across fine as well as large spatial scales, and over long time periods. We invite abstracts describing the use of cameras, drones, satellites and other remote sensing techniques to observe and monitor riverine and marine plastics. We also welcome work describing or demonstrating new approaches, methods and algorithms to improve the use of cameras and sensors for plastic detection on (and in) water.

Co-organized by EOS7/GI4/HS12/OS4
Convener: Lauren BiermannECSECS | Co-conveners: Katerina KikakiECSECS, Cecilia MartinECSECS, Irene RuizECSECS, Tim van EmmerikECSECS
vPICO presentations
| Thu, 29 Apr, 13:30–14:15 (CEST)

vPICO presentations: Thu, 29 Apr

Chairpersons: Lauren Biermann, Katerina Kikaki, Irene Ruiz
13:30–13:35
13:35–13:37
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EGU21-4017
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ECS
Louise Schreyers, Tim van Emmerik, Thanh-Khiet L. Bui, Lauren Biermann, Dung Le Quang, Niels Janssens, Emily Strady, Nguyen Hong Quan, Dung Duc Tran, and Martine van der Ploeg

Our recent field-based study undertaken at the Saigon river, Vietnam, shows that water hyacinths are responsible for entraining and transporting a majority of floating macroplastic litter. These invasive, free-floating water plants can form patches of several meters in length and width and tend to aggregate large amounts of plastic litter. Over the course of a six-week study, we demonstrated that 78% of the floating macroplastic observed were carried downstream accumulated within these floating plant patches.

The strong seasonality of water hyacinths, coupled with the temporal variability in macroplastic flux, calls for a longer monitoring effort. To this end, a one-year monitoring campaign is currently being undertaken at the Saigon river, which will apply satellite imagery, drone, camera imagery analysis and visual counting from bridges. Combined, these methods can help to characterize the contribution of hyacinths to macroplastic transport and accumulation at different temporal (from hours/days to weeks/months) and spatial (from sample sites to the river system) scales.

We evaluate the selected monitoring techniques, and present the preliminary results of this large-scale monitoring effort. We provide the first scientific overview of the contribution of water hyacinths in plastic transport relative to the total plastic transport, and its spatiotemporal variability. In addition, we assess the monitoring techniques used and provide suggestions for similar long-term monitoring strategies.

How to cite: Schreyers, L., van Emmerik, T., L. Bui, T.-K., Biermann, L., Le Quang, D., Janssens, N., Strady, E., Hong Quan, N., Duc Tran, D., and van der Ploeg, M.: Plastic plants: Long-term monitoring of macroplastic entrapment by water hyacinths in the Saigon river , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4017, https://doi.org/10.5194/egusphere-egu21-4017, 2021.

13:37–13:39
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EGU21-9156
Konstantinos Topouzelis, Apostolos Papakonstantinou, Marios Batsaris, Ioannis Moutzouris, Spyros Spondylidis, and Argyris Moustakas

The presence of plastic litters in the coastal zone has been recognized as a significant problem. It can dramatically affect flora and fauna and lead to severe economic impacts on coastal communities, tourism and fishing industries. Traditional beach litter reports include individual transects on the beach, reporting on the litter's presence through a dedicated measuring protocol. In the new era of drone imagery, a new integrated coastal marine litter observatory is proposed. This observatory is based on aerial images acquired through citizen science using low cost self-owned drones and the automatic identification of litter accumulation zones through computer vision. The methodology consists of four steps: i) a dedicated protocol for acquiring drone imagery from non-experienced citizens using commercial drones, ii) image pre-processing (image tiling and geo-enrichment) and crowdsourced annotation, iii) data classification to litter and no litter though an artificial intelligence classification approach and iv) marine litter density maps creation and reporting. The resulted density maps currently are produced calculating the tiles containing litter at areas of hundred square meters on the beach and the entire process requires some minutes to run once the aerial data is uploaded online. The density maps automatically are reported to a spatial data infrastructure, ideal for time series analysis. Classification accuracy calculated against manual identification of 77.6%. The coastal marine litter observatory presents several benefits against traditional reporting methods, i.e. improved measurement of the policies against plastic pollution, validating marine litter transportation models, monitoring the SDG Indicator 14.1.1, and most important, guiding the cleaning efforts towards areas with a significant amount of litter.

How to cite: Topouzelis, K., Papakonstantinou, A., Batsaris, M., Moutzouris, I., Spondylidis, S., and Moustakas, A.: Towards a coastal marine litter observatory with combination of drone imagery, artificial intelligence, and citizen science, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9156, https://doi.org/10.5194/egusphere-egu21-9156, 2021.

13:39–13:41
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EGU21-10730
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ECS
Paolo Tasseron, Tim van Emmerik, Joseph Peller, Louise Schreyers, and Lauren Biermann

Airborne and spaceborne remote sensing (RS) collecting hyperspectral imagery provides a promising way forward for detection and monitoring of riverine and marine plastic pollution. However, a major challenge in the application of RS techniques is the lack of fundamental understanding of spectral signatures of floating plastic debris at multiple scales. Recent work emphasised the case for open-access hyperspectral reflectance reference libraries of commonly used polymer items. In this paper, we present a high-resolution hyperspectral image database of a unique mix of (i) 40 virgin macroplastic items, (ii) organic material of plant leaves, tree leaves and riparian vegetation, and (iii) 50 items of riverbank-harvested macrolitter including plastics and other anthropogenic debris. We used a double camera setup that covered the VIS-SWIR range from 400-1700 nm in a dark room experiment with controlled illumination. The cameras scanned the samples floating in water and captured high-resolution images in 336 spectral bands. From these images we identified diagnostic absorption features for different materials, item categories, and plastic polymers. The identification was done by applying a linear discriminant analysis to the spectra, allowing the creation of combined band indices distinguishing between the different item types and polymer categories. We present reflectance spectra of all items in our image dataset, complemented by easy-to-interpret visual representations of derived indices. We demonstrate the importance of high-resolution reference reflectance libraries, to (i) further optimise existing remote sensing monitoring techniques, and (ii) contribute towards the development of future plastic monitoring and classification missions.

How to cite: Tasseron, P., van Emmerik, T., Peller, J., Schreyers, L., and Biermann, L.: I spy with my hyperspectral eye: unique reflectance database of plastics and riverbank-harvested litter, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10730, https://doi.org/10.5194/egusphere-egu21-10730, 2021.

13:41–13:43
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EGU21-12085
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ECS
Niels Janssens, Lauren Biermann, Louise Schreyers, Martin Herold, and Tim van Emmerik

While efforts to quantify plastic waste accumulation in the marine environment are rapidly increasing, the data on plastic transport in rivers are relatively scarce. Rivers are a major source of plastic waste into the oceans and understanding seasonal dynamics of macroplastic transport is necessary to develop effective mitigation measures. Macroplastic transport in rivers varies significantly throughout the year. Research shows that in the case of the Saigon river, Vietnam, these plastic transport fluxes are mainly correlated to the amount of organic debris (mostly water hyacinths). Since large water hyacinths patches can be monitored from space, this gives the opportunity for large scale monitoring using freely available remote sensing products. Remote sensing products, such as Sentinel-2, can be applied to areas where water hyacinths occur and plastic emissions are estimated to be high. In this study, we present a first method to detect and monitor water hyacinths using optical remote sensing. This was done by developing an algorithm to automatically detect and quantify water hyacinth coverage for a large section of the Saigon river in Vietnam, for the year 2018. Spectral signatures of water,  infrastructure in the river, and water hyacinths were used to classify the water hyacinths coverage and dynamics using a Naive Bayes algorithm. Water hyacinths were promisingly identified with 95% accuracy by the Naive Bayes classifier. The comparison between the seasonal dynamics of classified water hyacinth and seasonal dynamics of the field measurements resulted in an overall Pearson correlation of 0.72. The comparison we attempted between seasonal dynamics of plastics from satellite and field measurements yielded a Pearson correlation of 0.48. With the next field campaign collecting in-situ data matched to satellite overpasses, we aim to improve this. In conclusion, we were able to successfully map seasonal dynamics of water hyacinth in an automated way using Sentinel-2 data. Our study provides the first step in exploring the possibilities of mapping water hyacinth from satellite as a proxy for river plastics.

How to cite: Janssens, N., Biermann, L., Schreyers, L., Herold, M., and van Emmerik, T.: Monitoring plastic accumulation in water hyacinths using remote sensing, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12085, https://doi.org/10.5194/egusphere-egu21-12085, 2021.

13:43–13:45
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EGU21-12168
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ECS
Umberto Andriolo, Gil Gonçalves, Filipa Bessa, Paula Sobral, Luis Pinto, Diogo Duarte, Angela Fontán-Bouzas, and Luisa Gonçalves

Unmanned Aerial Systems (UAS, aka drones) are being used to map marine macro-litter on the coast. Within the UAS4Litter project, the application of UAS has been applied on three sandy beach-dune systems on the wave-dominated North Atlantic Portuguese coast. Several technical solutions have been tested in terms of drone mapping performance, manual image screening and marine litter map analysis. The conceptualization and implementation of a multidisciplinary framework allowed to improve and making more efficient the mapping of marine litter items with UAS on coastal environment. 

The location of major marine litter loads within the monitored areas were found associated to beach slope and water level dynamics on the beach profiles. Moreover, the abundance of marine pollution was related to the geographical location and level of urbanization of the study sites. The testing of machine learning techniques underlined that automated technique returned reliable abundance map of marine litter, while manual image screening was required for a detailed categorization of the items. 

As marine litter pollution on coastal dunes has received limited scientific attention when compared with sandy shores, a novel non-intrusive UAS-based marine litter survey have been also applied to quantify the level of contamination on coastal dunes. The results showed the influence of the different dune plant communities in trapping distinct type of marine litter, and the role played by wind and overwash events in defining the items pathways through the dune blowouts. 

The experiences on the Portuguese coast show that UAS allows an integrated approach for marine litter mapping, beach morphodynamic and nearshore hydrodynamic, setting the ground for marine litter dynamic modelling on the shore. Besides, UAS can give a new impulse to coastal dune litter monitoring, where the long residence time of marine debris threat the bio-ecological equilibrium of these ecosystems.

How to cite: Andriolo, U., Gonçalves, G., Bessa, F., Sobral, P., Pinto, L., Duarte, D., Fontán-Bouzas, A., and Gonçalves, L.: On the use of drones to detect and map marine macro-litter on the North Atlantic Portuguese beach-dune systems: the experiences of UAS4Litter project, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12168, https://doi.org/10.5194/egusphere-egu21-12168, 2021.

13:45–13:47
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EGU21-12272
Frans Buschman and Sophie Broere

An Acoustic Doppler Current Profiler (ADCP) is commonly used to monitor flow velocity. An accurate method to obtain discharge in a river or a channel is to mount an ADCP to a boat and sail transects across the channel. Additionally, these surveys may also be used to obtain the amount of plastic items in the water column. The transport of plastic items suspended in the water column may be substantial and is more challenging to monitor than the transport of floating items. We carried out a feasibility test in a harbour of a river. We deployed the ADCP horizontally at 1.0 m depth and released plastic items (and similarly shaped organic items for comparison) 5 times at 1.0, 3.0 and 5.0 m from the ADCP. We compared the signal strength in a 5 s period after release with the background signal strength.

The item was steady within the detection volume for the majority of the 5 s periods. Three out of five plastic items had signal strengths a least 5 dB higher than the background strength (at several distances). We conclude that at least these items were detected. The similarly shaped organic items generally had a lower signal strength. Although the response of each item as a function of orientation, distance along and across the beam should be investigated further, the feasibility study shows the potential to additionally determine the amount of plastic items in the water column from ADCP observations.  

How to cite: Buschman, F. and Broere, S.: Detecting plastic items in the water column using an Acoustic Doppler Current Profiler (ADCP), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12272, https://doi.org/10.5194/egusphere-egu21-12272, 2021.

13:47–13:49
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EGU21-14802
Omjyoti Dutta, Beatriz Revilla-Romero, Adrian Sanz-Díaz, Fernando Martin-Rodriguez, Orentino Mojon-Ojea, Ana Mancho, Guillermo García-Sánchez, and Gerard Margarit Martín

Marine litter is a growing problem that advances parallel to economic and industrial development and seriously affects ecosystems. One of the most abundant pollutants are plastics. The BEWATS project focuses on innovative tools for remote marine litter control and management through satellite and UAV’s. The areas of study are currently at the Vigo coast in Galicia (North-West of Spain). In this area, there are many high natural value beaches including Nature Reserve and part of a National Park. These beaches are receiving an increasing amount of marine litter, mainly plastic, helped by strong currents in the area. Every few months, these beaches are clean and the collected litter information tracked. In this context, the BEWATS project concentrates on tracking the possible path through which marine litter reaches the area of interest. In this presentation, we will discuss how this is achieved by data fusion from UAV imagery, marine dynamics model simulations and Earth-observation satellite data (Sentinel-2). To detect possible marine litter, we have developed a novel synthetic data-based approach to marine litter detection using Sentinel-2 images and machine learning techniques. Within this approach, one can classify and quantify according to pixel-level litter fraction present. We have validated our approach with existing open-sourced available datasets.  

The BEWATS project is led by Vigo University, which provides UAV’s imagery, and the Spanish Research Council (CSIC) provides marine dynamics models for tracking waste routes and delineation of waste concentration zones. In this context, GMV provides Earth observation based solution of detecting marine litter. BEWATS is founded by the Biodiversity Foundation of the Spanish Ministry for the Ecological Transition and the Demographic Challenge.

How to cite: Dutta, O., Revilla-Romero, B., Sanz-Díaz, A., Martin-Rodriguez, F., Mojon-Ojea, O., Mancho, A., García-Sánchez, G., and Margarit Martín, G.: BEWATS: BEACH WASTE TRACKING SYSTEM USING SATELLITE, UAV’s and MARINE DYNAMICS MODELS. , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14802, https://doi.org/10.5194/egusphere-egu21-14802, 2021.

13:49–13:51
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EGU21-15150
Juan Morales, Juan Gómez Barreiro, Aroa Marcos Pascual, and Santos Barrios Sánchez

Plastics stand out for being cheap, lightweight, easily moulded and highly endurance materials, hence used in a wide range of applications all around the planet.  One of the reasons of the amazing durability is that common plastics are not biodegradable, being able to remain intact several hundreds of years on the environment. However, an important phenomenon occurring on plastics is the degradation, which can be due to the UV radiation, either mechanical or biological activity, temperature degradation, or even hydrolysis processes, making the plastic materials weaker and fragile, fractioning the plastics into smaller and smaller pieces.
Because of their low density, plastic fragments can be affected by long distance transport on the water column. In addition, some plastics fractions could be incorporated into the sediments, which, in the long-term, could act as a secondary source of plastics. Due to the presence of chemicals, either as additives or sorbed contaminants on their surfaces, plastic materials have become a global environmental concern and need to be evaluated.
Despite the great amount of research done in marine water transport and debris of plastic, freshwater environments remain less known.  Microplastics (MP) have been observed in both sediments and water samples of lakes and rivers. Water reservoirs are critical sites in terms of water supply management and have to be monitored for MP at different scales both in water and sediments. There exist different sources of microplastics in continental waters like urban runoff, sewage sludge or agricultural wastes. In this sense, wastewater treatment plants have been identified as one of the main sources for the release of plastics into freshwater and terrestrial environments which may lead to further concern.
Here, we study the distribution of microplastics in sediments found in the Santa Teresa water reservoir, in Salamanca (Spain), in the area close to Guijuelo town. The aim of this work is to optimize a methodology to study the influence of the outputs from wastewater treatment plant and to evaluate how plastics distribution in sediments around the reservoir is related to the plant. Our work also deals with the seasonality by analysing spring and fall sediments for differences on microplastic densities and composition, along different stations downstream Tormes River. MP morphological analyses was use to categorize particles from different grain-size fractions. Raman microspectroscopy was used to characterize microplastics, while optical microscopy allowed us quantifying microplastics of each sediment sample and grain-size fraction.
Our results show that there is positive correlation between microplastics density in sediments and the proximity to the plant. Most of the microplastics found in sediments are related to fibers potentially from industrial, urban and agricultural origins, most likely coming from the wastewater treatment plant.

How to cite: Morales, J., Gómez Barreiro, J., Marcos Pascual, A., and Barrios Sánchez, S.: Microplastics in sediments of Santa Teresa water reservoir (Salamanca, Spain): methodology and sources. , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15150, https://doi.org/10.5194/egusphere-egu21-15150, 2021.

13:51–13:53
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EGU21-15204
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ECS
Robin de Vries, Matthias Egger, Thomas Mani, and Laurent Lebreton

Remote sensing of marine debris has seen recent successes in coastal regions. However, these approaches focus on the detection of large accumulations of marine debris, often mixed with organic waste and related to events. Individual large plastic items (macroplastics, > 50cm) in remote marine environments are a substantial part of the marine debris surface mass budget, yet  remain poorly quantified.

Current knowledge on the accumulation of macroplastic debris at the ocean surface is mostly limited due to methodological constraints. Macroplastics are typically too large for collection by neuston trawls. Furthermore, the relatively small sea surface area typically investigated during offshore research expeditions often is too small to account for the low areal concentrations of macroplastics. Given the importance of macroplastic in the global ocean plastic mass balance, quantitative information on the spatiotemporal distribution of macroplastics afloat in the surface ocean are urgently needed.

By now, our location-enabled action camera's on-board vessels of opportunity have recorded a vast amount of optical data from the North Pacific and North Atlantic Ocean (approximately 1 million images). By selection and labelling of occurrences of debris in images, we have trained an object detection and localization algorithm. We use the camera’s intrinsic parameters to estimate relevant sampling parameters, such as size and distance of each object detected. An overview of numerical concentrations is generated by combining the object detection solution with bulk processing of the optical data. The first results are promising and well-comparable to sampling methods applicable to smaller debris size classes, such as surface neuston nets.

How to cite: de Vries, R., Egger, M., Mani, T., and Lebreton, L.: Vessel-based optical data and artificial intelligence for sampling mega-plastic concentrations on the high seas, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15204, https://doi.org/10.5194/egusphere-egu21-15204, 2021.

13:53–13:55
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EGU21-15243
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ECS
Aikaterini Kikaki, Ioannis Kakogeorgiou, Paraskevi Mikeli, Dionysios E. Raitsos, and Konstantinos Karantzalos

Plastic debris in the global ocean is considered an essential issue with severe implications for human health and marine ecosystems. Remote sensing is a useful tool for detecting and identifying marine pollution; however, there are still few studies and benchmark datasets for developing monitoring solutions for marine plastic debris detection from high-resolution satellite data.

Here, we present an annotated plastic debris dataset from different geographical regions, seasons, and years, including annotations for sea surface features (e.g., foam), objects (e.g., ship) and floating macroalgae species such as Sargassum. Our dataset is based on high-resolution multispectral satellite observations collected mainly for the period 2014-2020 over the Gulf of Honduras (Caribbean Sea). Over this region, large plastic debris masses and Sargassum macroalgae blooms have been frequently reported, suggesting that it is an ideal region to examine satellite sensors' effectiveness in plastic debris identification, as well as monitoring along with sea surface circulation and meteorological data.

We also present a set of machine learning classification frameworks for marine debris detection on high-resolution satellite imagery, comparing qualitatively and quantitatively their overall performance. The new algorithms were validated against different regions that have been reported as major plastic polluted areas, as well as their performance was compared to well-established FAI and new promising FDI. This benchmark study can trigger more research and developement efforts towards the systematic detection and monitoring of marine plastic pollution.

How to cite: Kikaki, A., Kakogeorgiou, I., Mikeli, P., Raitsos, D. E., and Karantzalos, K.: Detecting and Classifying Marine Plastic Debris from high-resolution multispectral satellite data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15243, https://doi.org/10.5194/egusphere-egu21-15243, 2021.

13:55–13:57
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EGU21-15275
Manuel Arias, Romain Sumerot, James Delaney, Fatimatou Coulibaly, Andres Cozar, Stefano Aliani, Giuseppe Suaria, Theodora Papadopoulou, and Paolo Corradi

WASP (Windrows AS Proxies) is a data processor, developed in the frame of the European Space Agency (ESA) OSIP Campaign, exploiting Copernicus Sentinel-2 L1C images to detect and catalogue the presence of filaments of floating marine debris with high probability of containing man-made litter. WASP takes advantage of the prototype EO data processor developed in the frame of ESA project  “Earth Observation (EO) Track for Marine Litter (ML) in the Mediterranean Sea” that successfully proved for first time that Copernicus Sentinel-2 data can detect the presence of marine litter accumulations as proxies of plastic litter content.

WASP puts significant effort in masking unneeded data that has been source of false-positive detections, including sun glint and clouds. Also, a new spectral analysis technique has been employed to identify the most promising Copernicus Sentinel-2 bands to be used in the detection of such filaments, which has also led to the construction of a novel spectral index WASP Spectral Index (WSI). This index enables the detection of filaments of floating debris.

The images processed using WSI are transformed into binary masks to be analysed by a deterministic object classifier, which looks at the geometry and shapes of the detections to identify ML windrows within them and separate them from background noise and/or false positives. This enables automatic processing and classification of the images, which makes possible to generate regional and/or local databases of remote-sensed floating debris, which can be exploited by means of geostatistics to support research and monitoring of marine litter in the environment.

These implementations are also supported with the introduction of advanced super-resolution techniques that are downscaling the spatial resolution of the bands to 10m, well beyond the simple interpolation, yielding better quality on the results.

In a preliminary assessment, the implemented proposed algorithm has proven to be successful in identifying windrows even when those are too thin to be visible in True Colour images by the naked eye. Nevertheless, some drawbacks/limitations have been found, principally associated to residual limitations when removing bad data, and with the special case of the problematic wave glint, well known in the Sentinel-2 data but of difficult solution.

Once the entire Sentinel-2 archive over the Mediterranean Sea is processed and following an in-depth analysis, a database of the identified proxies, including spatial and temporal patterns will be created over this initial region. The final EO product will be a map of on sub-mesoscale marine debris concentrations in the Mediterranean Sea based on Copernicus Sentinel-2. The product will consist on a census of these structures for each processed tile for the Mediterranean Sea, with potential for global scalability. Scientific research, cleaning activities and policy making on marine litter are only a few of the activities that could benefit from such a product.

This activity collaborates on the “Remote Sensing of Marine Litter and Debris” IOCCG taskforce.

How to cite: Arias, M., Sumerot, R., Delaney, J., Coulibaly, F., Cozar, A., Aliani, S., Suaria, G., Papadopoulou, T., and Corradi, P.: Mapping Windrows as Proxies for Marine Litter Monitoring from Space (WASP), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15275, https://doi.org/10.5194/egusphere-egu21-15275, 2021.

13:57–13:59
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EGU21-16384
Yangrong Ling, Lauren Biermann, Mark Manuel, Ellen Ramirez, Austin Coates, Megan Gallagher, and Davida Streett

Since 2014, the NOAA Satellite Analysis Branch has used high resolution optical satellite imagery in an effort to detect ghost nets (derelict fishing gear) and other large plastic debris in the Pacific Ocean and its atolls in support of clean-up efforts (by the NOAA Pacific Islands Fisheries Science Center, Ocean Voyages Institute, etc.). Until recently, reliable detection has proven challenging. With the application of Worldview imagery matched to in situ information on known net locations, we have been able to extract spectral signatures of floating plastics and use these to detect and identify other instances of plastic debris. Using ENVI’s Spectral Angle Mapper (SAM) target detection method, a number of likely locations of nets/plastics in the Pearl and Hermes atoll in the Northwestern Hawaiian Islands (NWHI) were highlighted. The resulting locations of the 41 debris detections were strikingly similar to the distributions along the coast reported in surveys, and are consistent with those that would be expected due to the seasonal ocean currents. This satellite imagery analysis procedure will be repeated shortly before the next NWHI clean-up effort, which will better enable us to support the removal of ghost nets and other marine plastics, and also assess the accuracy and rapid reproducibility of the technique.

How to cite: Ling, Y., Biermann, L., Manuel, M., Ramirez, E., Coates, A., Gallagher, M., and Streett, D.: Satellite Detection of Ghost Nets and Plastic Debris in Pacific Atolls, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16384, https://doi.org/10.5194/egusphere-egu21-16384, 2021.

13:59–14:15