Session at ASLO 2021: SS41 Remote sensing of marine debris: principles, scales and applications
Victor Martinez-Vicente, Plymouth Marine Laboratory, email@example.com
Ellen Ramirez, NOAA, firstname.lastname@example.org
Laia Romero, IsardSAT, email@example.com
Francois Galgani, Ifremer, firstname.lastname@example.org
Global, frequent and standardized observations are urgently needed to enhance mapping and long-term monitoring of marine debris. High spatial resolution optical remote sensing from satellites has the potential for detecting floating debris. However, this is a rapidly growing field of research, with many areas of new and exciting research. Rapid progress in the description of the optical properties of marine debris is being used to refine algorithms based on current sensors, as well as helping with the definition of new sensors. An increase in the availability of datasets that can be used for calibration and validation algorithms help assessing uncertainties in marine debris detection, quantification and tracking. Sensors deployed in different platforms (fixed on bridges over rivers, smartphones, on drones, aircrafts, high altitude platforms and satellites) allow for observations at multiple spatio-temporal scales. Combined use of satellite and in situ data with models at different resolutions, warrant developments in the identification of the relevant transport mechanisms of floating marine debris. Demonstrations of the validity of novel remote sensing methods are particularly critical, as they are being considered as tools to support policies in developing regions. This session aims to bring together researchers from different disciplines, including those collecting observations in situ on the whole size spectra of plastic debris, marine optics and remote sensing specialists of visible/near-infrared/shortwave infrared and microwave techniques and modellers, to address the following areas of research: 1) Understanding and describing the processes governing the interaction of marine debris with visible light and electromagnetic waves at other wavelengths through laboratory and field experiments. 2) Reports of in situ observations of the size continuum of marine debris with potential to be used as validation for satellite remote sensing based detection of marine debris. 3) Studies from different deployment platforms (fixed platforms, smartphones, drones, aircrafts, satellite) that address processes affecting the properties and distribution of marine litter at different scales and in different scenarios, including shorelines, rivers, frontal areas and accidental spills. 4) Algorithms (including machine learning techniques) and non-invasive techniques (visible, near-infrared, microwave) targeting detection, quantification and identification of marine litter, demonstrating potential are all welcomed. 5) Demonstrations of operational or pilot studies using remote sensing in combination with modelling and in situ data, with a particular focus on developing countries to support marine litter policy. 6) Discuss the potential of remote sensing of marine debris to support the long term monitoring of trends and the efficiency of reduction measures.
Session at IGARSS : Remote Sensing approaches to detect and characterize marine plastic litter
Dates: 12-14 July 2021
It is estimated that more than 150 million tonnes of plastics have accumulated in the world’s oceans, while 4.6-12.7 million tonnes are added every year (Jambeck et.al, 2015). The well-known properties of plastics such as the durability and strength have increased its consumption drastically but have also made it a serious environmental problem. Plastics degrade very slowly and stay in the ocean for years. They have dramatic impacts on the marine ecosystem and pose risks to human health.
The fate of only 1% of plastic litter is known. Ground data are sparse, with very poor spatial and temporal resolution. The possibility to provide these data on a large scale, possibly at global level, would have a drastic impact not only from a scientific viewpoint, but also in view to have standardised methods to assess the effectiveness of measures implemented to address the marine plastic issue. Remote observations, either from satellite, planes, drones or fixed cameras have the potential to contribute to larger scale standardised detection of plastic litter. Satellites can cover large areas and potentially detect and track accumulation zones and their changes, whereas drones and fixed cameras can monitor hotspot areas, identify single plastics and discriminate the plastics from other litter materials. Although much knowledge is available on the spectral properties of plastics, mainly from industrial sorting, this information cannot simply be used to develop a monitoring system based on remote sensing. The remote sensing of marine plastic litter in an outdoor marine environment has to deal with many issues that do not play a role in an indoor industrial setting. For instance, simply using the Short Wave InfraRed (SWIR) absorption features to discriminate between polymer types will not work when plastics are wet and the water absorbs all reflectance in the SWIR. Hence, new approaches for marine plastic litter detection should be developed, utilizing the shape of the objects and exploiting LIDAR or RADAR based signals.
Research on remote sensing of marine litter is still in its infancy. However, in the last years there has been a steep increase in the number of studies focusing on marine plastic litter. Different technologies are being evaluated and new data processing techniques, including approaches based on artificial intelligence, are developed. This session will provide an overview of the latest scientific insights and an outlook for the future.
Advancing Remote Sensing of Microplastics on the Surface Ocean (03/01/2021-03/01/2024)
Contact Person: Heidi Dierssen (email@example.com)
Funding Agency: NASA Ocean Biology and Biogeochemistry
Summary: Since the 1950’s, positively buoyant plastic objects have been accumulating at the surface of the oceans, transported by currents, wind and waves. Small millimeter-sized pieces (<4.75 mm), known as microplastics, count in trillions at global scale and pose an increasing risk to marine biota. Floating microplastics concentrate along convergence zones in the five major ocean basins, but a comprehensive analysis of the spatial and temporal distributions is lacking and the monitoring tools are not well developed to assess global distributions. Thus far, remote sensing methods have focused on larger macroplastics. Our specific objectives are to: 1) Evaluate geospatial and temporal trends in existing ocean color products across hot spots that may be related to enhanced reflectance from plastics; 2) Propagate estimates of ocean surface hyperspectral reflectance using simple mixed pixel models to the Top of the Atmosphere (TOA) under different microplastic concentrations and atmospheric conditions; 3) Simulate spaceborne ocean color remote sensing observations for different microplastic and atmospheric conditions using robust vector radiative transfer models for coupled ocean-atmosphere systems; 4) Assess microplastic remote sensing detectability using statistical information content assessment in terms of current and future instrument characteristics, microplastic quantity and nature, and external conditions, such as observation geometry and atmospheric state; and 5) Evaluate how results from the above analyses relate to our hypotheses and implications for the remote sensing of microplastic and provide recommendations for new algorithms and instrument design.
AIDMAP (September 2020 – March 2022)
Title: Artificial Intelligence and drones supporting the detection and mapping of floating aquatic plastic litter
Contact Person: Els Knaeps (firstname.lastname@example.org)
Funding Agency: ESA, the European Space Agency (Discovery Element of the Basic Activities – Campaign on Remote Sensing of Plastic Marine Litter)
Summary: The AIDMAP project proposes an Artificial Intelligence (AI)-based approach for the detection of floating marine litter in accumulation zones. Vertical integration of small drone and satellite data will be evaluated for the detection of the marine litter at different spatial scales. These can be complemented by High Altitude Pseudo-Satellite (HAPS) in the longer term to come to a long term sustainable solution. The AIDMAP project therefore responds to the quickly evolving EO landscape with an increasing emphasis on modern, affordable and sustainable technologies, such as Artificial Intelligence, and the launch of (constellations of) small satellites and non-orbiting platforms (such as HAPS) while also exploring the added value of the current Copernicus Sentinel program. Here, study areas in Vietnam are selected to demonstrate the proposed approach.
AIR-SOS: Airborne & satellite observation strategies for marine litter monitoring (June 2020-Sep 2021)
Title: Collecting Multispectral data from floating debris using a seaplane over the Elbe river discharge area to validate current algorithms and methodologies.
Contact Person: Irina Rammos (email@example.com)
Funding Agency: ESA, the European Space Agency (Discovery Element of the Basic Activities – Campaign on Remote Sensing of Plastic Marine Litter)
Summary: Marine Litter is a global issue and can be found in all the seas from the equator to the poles, and in freshwater systems, such as rivers and lakes. Most of the marine litter is plastic and, as plastic production continues to increase, greater impacts are expected. Plastic marine litter dramatically affects marine life and ecosystems and has a great economic impact on coastal communities, tourism, and fisheries. It furthermore poses a concern for human health due to contamination of seafood with plastic particles and associated pollutants.
Urgent questions around marine plastic pathways into the ocean, sinks, trends, and fate remain open, but cannot be answered satisfactorily using ground-based and model-based systems alone. The emerging field of remote sensing for plastic detection is promising for tackling unknowns around marine monitoring, but reliable in situ validation data are required to improve and optimise algorithms and approaches.
The AIR-SOS (AIRborne & Satellite Observation Strategies for marine litter monitoring) study aims to do just that, by collecting high-quality and high-resolution data of floating objects in coastal waters near the mouth of the River Elbe. A seaplane will be used on clear and still (low wind) days to collect data coincident with Copernicus Sentinel-2 satellites overpass. In this way, the project will assess and demonstrate the value of the aircraft as a platform for validation of Sentinel-2 validation.
The ability to fly sensors on General aviation Aircraft at lower cost, at lower altitudes (visual cross-checks) and the possibility to perform in situ measurements (sea-landings) makes this a multi-functional ‘platform’ suitable to for systematic validation of satellite remote sensing detection of marine litter.
Assessment of the Effects of Marine Debris on Ocean Color Signals (2021-2024)
Contact Person: Robert Foster (firstname.lastname@example.org)
Funding Agency: NASA
Summary: The objective of this research is to conduct an investigation into the effects of marine debris upon top of atmosphere (TOA) ocean color signals. Since so little is currently known about the optics of both floating and suspended marine debris, primarily marine plastic, we will first conduct theoretical studies to examine how different types of debris affect changes in the TOA radiance. We will determine the limits of detectability of debris from orbit for current and planned satellite ocean color sensors, followed by an analysis of current and historical remote sensing data from orbiting sensors such as the Hyperspectral Imager for the Coastal Ocean (HICO) and the DESIS Earth Sensing Imaging Spectrometer. This study will seek to address the following science questions: 1) In what ways does the presence of macro- and micro- marine debris affect the top-of atmosphere ocean color signal?; 2) What are the detectability limits for debris with current and planned satellite ocean color missions?; 3) How do percent coverage, debris reflectance, and degree of submersion affect the detectability in the open ocean?; 4) Does marine debris, particularly plastics, alter the polarization of the upwelling radiance?; 5) Do existing remote sensing datasets support the conclusions?
BLUE (September 2020 – August 2022)
Title: Brillouin – backscatter – fluorescence LIDAR research for Underwater Exploration of marine litter
Contact Person: Valentina Raimondi, CNR-IFAC – Italy (email@example.com)
Funding Agency: ESA, the European Space Agency (Discovery Element of the Basic Activities – Campaign on Remote Sensing of Plastic Marine Litter)
Summary: Our idea is to investigate the potential of LIDAR – space, airborne and ground based – to address plastic marine litter. Recent studies have stressed how plastic litter at the sea surface represents only a small fraction of plastics entering the sea. Hence, the major contribution of our idea would be to investigate remote sensing methods with the potential to provide information on plastics distributed in the water column and its identification. Until now, the contribution of LIDAR to ocean plastic remote sensing has been almost unexplored, except for sporadic bathymetric data from airplane to detect large items. Meanwhile, spaceborne elastic LIDAR has already been used to detect algal blooms in oceanic waters, while fluorescence LIDAR has already been suggested for plastics characterisation in different contexts. The proposed approach is four-fold and aims at investigating the potential of: (1) elastic backscatter and Brillouin LIDAR from space to detect changes in the optical properties of the water column due to microplastics; (2) LIDAR bathymetric data from airplane to detect plastic items by processing airborne LIDAR data acquired over the Great Pacific Garbage Patch; (3) fluorescence LIDAR to identify plastic items from airborne, ship- or ground-based platform (e.g. at river outlets); (4) Raman spectroscopy to identify plastic items and microplastic as for the material type. For the first time, this study will provide an insight on the feasibility of using LIDAR for remote sensing of microplastics and for the characterisation of plastic items in terms of material type.
DL4PlasticLitter (June 2020 – April 2021)
Title: Using Deep Learning Methods For Plastic Litter Detection From Satellite Remote Sensor
Contact Person: Delphine Nobileau (firstname.lastname@example.org) – CAPGEMINI
Summary: Artificial Intelligence (AI) could represent a powerful tool in support of the EO data processing for the detection of marine litter, provided the availability of a sufficiently large dataset of satellite images of marine litter accumulations. However, there are currently no such datasets publicly available yet. To address this issue, in the frame of the DL4PlasticLitter project, we first create realistic synthetic spectra of floating litter accumulations. We simulate combinations of reflectance spectra of seawater and macro-plastic for different observation geometries, different concentrations of chlorophyll and other substances present in the water and we model the radiative transfer to the top of atmosphere and at sensor resolution. Then, we train AI models to learn to differentiate the spectra of modelled accumulations containing plastic. The last step consists in validating the developed AI models with real satellite images of marine litter accumulations.
DOLPHINN (July 2020 – July 2021)
Title: Detection of Ocean Litter Plastics with Hyper-to-multispectral Infrared Neural Networks
Contact Person: Yoland Brown (Yolanda.Brown@mda.space)
Summary: This study seeks to determine the feasibility of using AI to better and more accurately detect and quantify ocean and beach plastics litter from space-borne multispectral data, particularly in the SWIR range. MDA is developing a novel spectral fusion approach that learns to associate multispectral data with full hyperspectral features so that single multispectral images can be used to detect plastics more reliably. Multispectral sensors already on orbit could then better contribute to marine litter detection. Once hyperspectral space assets such as CHIME become available, they could be used for continuous training of multispectral sensors to allow more coverage and revisit in ocean plastic detection and monitoring.
ESAPlastics (January 2020 – May 2021)
Title: Spectrometer for Marine Litter
Contact Person: Hugo Silva ( hugo.m.silva(at)inesctec.pt )
Funding Agency: ESA, the European Space Agency (GSTP)
Summary: The objective of the project is to study, characterize, acquire, and process data from oceanic marine litter samples using heterogeneous sensors information. The project work is divided into three main parts: (i) the in-situ acquisition of marine litter samples from an oceanic marine litter hotspot in Faial Island Azores; (ii) the characterization and identification in laboratory environment of the individual components that are present in the collected marine litter samples i.e., type of material and other chemical elements contained, by using different sensors, e.g. Spectroscopy FTIR, Raman, and LIBS; (iii) performing extensive dataset campaigns using manned and unmanned aerial platforms, for acquiring remote hyperspectral imaging data of artificial marine litter concentrations, fostering the development of automatic methods based on supervised learning approaches for the detection using spatial/spectral information of marine litter concentrations from space.
From Source to Sink (August 2020 – July 2022)
Title: Tackling the plastic debris challenge at its source – Linking EO data with multi-source in-situ data for modelling debris pathways from source to sink
Contact Person: Jonas Franke, Remote Sensing Solutions (email@example.com)
Summary: Monitoring areas closer to plastic marine litter sources such as rivers and estuarine systems has the potential to improve mitigation strategies. Upscaling in-situ litter point data with earth observation (EO) and hydrodynamic models is our central concept. Multi-type in situ data will be collected at various points along the pollution pathway (in our demonstration site in the Po River delta in Italy):
- Imagery from installed cameras on bridges is analyzed to detect floating plastic in rivers using deep-learning (in-situ type 1).
- Water samples from estuaries and coastal areas using manta trawls are used to quantify plastic litter abundances (in-situ type 2)
- Drone imagery along the shoreline is acquired for accumulation analyses (in-situ type 3)
- Beach samples through field surveys (in situ type 4)
Sentinel-2 and -3, together with VHR data such as WorldView-3, are used to monitor discharging rivers and their estuaries (water constituents and river plume detection). Integration of these situ-data, multi-scale EO and hydrodynamic modelling serves as the development basis, allowing for the first time a monitoring of real-world debris transport pathways. Such source-to-sink monitoring systems can be used to identify environmental, economic, human health and safety-related impacts of plastic litter and would support targeted efforts of both off- and onshore-based clean-up projects.
GLIMPS (November 2020 – December 2021)
Title: Global Monitoring of Microplastics using GNSS-Reflectometry
Contact Person: Dr. Clarizia (firstname.lastname@example.org)
Funding Agency: ESA, the European Space Agency (Discovery Element of the Basic Activities)
Summary: The goal of GLIMPS is to produce global maps of the microplastics concentration in the oceans using GNSS-R data and algorithms based on machine learning. This will deliver information about the location and distribution of microplastics, which will be complementary to that provided by in situ measurements and ocean circulation models.
The idea is built upon the assumption that microplastics and associated surfactants dampen the waves, reducing ocean surface roughness; and that this reduction in roughness can be sensed by satellite radar. GNSS-Reflectometry is a radar-based remote sensing technique which only requires cheap, lightweight and low-power receivers to be implemented, since it exploits existing GNSS transmitters of opportunity. The wealth of existing GNSS transmitters, and the nature of GNSS-R receivers, makes it easy to build a constellation, addressing the need for high space-time sampling that is crucial for monitoring microplastics from space efficiently.
HyperDrone (June 2020 – December 2021)
Title: Hyperdrone: Development of spectroradiometric proxies of shoreline marine plastic debris for satellite validation using remotely piloted aircrafts
Contact Person: Aser Mata (email@example.com)
Summary: Developing instruments and algorithms for satellite remote sensing of ocean plastic needs standardised global in situ observations. This project plans to collect hyperspectral data from plastic targets to develop a standardised indicator for in situ radiometric detection of plastic debris on the shoreline, with a view to being deployed globally on different platforms.
Field campaigns will be carried out that will include hand-held hyperspectral spectrometers (SVC) and state of the art hyperspectral imagers (BaySpec OCI-F and the Headwall Co-aligned VNIR+SWIR sensors) mounted on drone platforms flying at different altitudes. Spectra from different plastic targets will be collected on the shoreline in real conditions meeting traceability standards and with uncertainty estimates for each dataset (to be made freely available upon completion of the project). Taking advantage of the SWIR spectral features of plastic materials, HyperDrone aims to develop proxies for plastic detection on the shoreline and assess subpixel detection. Using an atmospheric radiative transfer model, we will simulate at-satellite sensor radiances to provide guidance on sensor requirements as well as model signal unmixing for retrieval of plastic pixel coverage.
LOCATE (June 2020 – May 2021)
Title: Prediction of plastic hot-spots in coastal regions using satellite derived plastic detection, cleaning data and numerical simulations in a coupled system
Summary: The LOCATE project concerns the identification of plastic hotspots in coastal waters and on the shore by using a coupled system integrating satellite derived information, regional coastal models, the computation of lagrangian plastic trajectories and information from cleaning campaigns. Satellite derived hydrodynamic information (Sentinel-1 SAR and Sentinel-3 altimeter) is being used to validate eulerian hydrodynamic simulations in coastal waters and will be also used to produce model inputs and data assimilation (bathymetry, hydrodynamic variables). Moreover, Sentinel-2 optical information is used to derive water quality (turbidity) which potentially can be correlated to plastic inputs, cleaning data and numerical simulations. Eulerian hydrodynamic simulations are produced using the numerical model COAWST and validated with Satellite hydrodynamic information (wave height, water surface elevation). Daily hydrodynamic forecasting outputs produced at the Catalan coast in Spain are stored in a dedicate web page at three different coastal grid domains (with grid sizes of 2500m, 350m and 70m). Simulations of plastic dispersion in the nested domains are obtained using the Parcels software. Plastic accumulation regions will be then identified and tracked in space and time and contrasted with cleaning data. The developed system will answer a high demand of a more efficient local to regional management of coastal plastic pollution by helping to identify hotspots of plastic accumulations in time and space. The developed system will answer a high demand of a more efficient local to regional management of coastal plastic pollution by helping to identify hotspots of plastic accumulations in time and space. The forecasting system will be made publicly available.
Marine Litter Aggregation Forecast (Sept 2020 – Dec 2021)
Contact Person: Mario Castro de Lera (firstname.lastname@example.org)
Summary: The main goal of this project is to provide a new estimation of long-term marine litter accumulation areas at global scale taking advantage of numerical reanalysis databases of historical met-ocean conditions. Different machine learning techniques are applied to generate long-term climate-based series of those environmental variables that may affect the drift of marine litter over the sea surface such as currents, wind and wave-induced Stokes drift. The generated series will feed a state-of-the-art Lagrangian model in order to simulate the global long-term evolution of marine litter transport through the ocean surface. As marine debris sources coastal cities, river outputs and shipping routes are considered. Besides the main goal of the project, the proposed methodology has also the potential for interesting secondary achievements, such as providing the estimation of global scale marine litter distribution for specific past dates or predicting the expected future location and distribution of marine litter patches up to approximately six months ahead.
Marine Macro Litter Drift Forecasting Service (May 2018 – December 2020)
Contact Person: Anne Vallette (email@example.com)
Funding Agency: Mercator Ocean (CMEMS)
The LITTER-TEP (Thematic Exploitation Platform) will provide a macro-litter beaching forecast service for local authorities, government agents, NGOs and environmental protection agencies. The fate of marine macro-litter can be forecast by modelling drift using Lagrangian models and bulk fluxes using Eulerian models. These will be parameterised using CMEMS products forecasting wave, wind and current data and supplemented with models of settlement, sinking and resuspension. Results of these simulations will be posted on a dedicated LITTER-TEP web portal along with a geo-browser to locate litter events associated with an area-of-interest (AOI), historical records and statistics. The expected results are maps of likely stretches of coastline that will be affected by marine-litter grounding subsequent to storm events. These will enable local agencies to forecast when and where clean-up operations will be needed.
MARLISAT (June 2020 – December 2021)
Title: A full-range plastic marine litter monitoring service to support cleaning and littering reduction actions by mapping hotspots, pathways and littering sources
Contact Person: Marc Lucas (firstname.lastname@example.org)
Summary: A combined satellite-based solution is proposed to map plastic marine litter pathways and accumulation areas relying onto three innovative developments: 1) Utilising machine learning alongside an augmented land cover classification, accumulations of plastic litter across coastal and riparian zones will be mapped.. 2) Based on the MAR-GE/T beacons with GPS positions relayed through the Argos satellite system, the proposed novel satellite tracker will be specifically designed to track plastic litter pathways, with better precision and realistic behavior. This miniaturized device will have a reduced environmental footprint. 3) Ocean surface currents and winds play a primary role in the transport and dispersal of plastic marine litters. A multi-year observations of ocean currents from space, in a synergetic use of satellite sensors (altimetry, scatterometers,…) and in-situ buoys, will be computed and used within a Lagrangian drift modelling tool to simulate the plastic litters pathways and map the hotspots. The combination of these three components will constitute a full-range monitoring system of plastic marine litter, from littering sources determination to hotspots and pathways identification, thus improving marine litter collection efforts.
ML-OPSI (June 2020 – September 2021)
Title: A Simulator for Marine Litter Observation from Space
Contact Person: Theodora Papadopoulou (email@example.com)
Summary: A breadboard for end-to-end (E2E) Marine Litter Optical Performance Simulations (ML-OPSI) is being designed in the frame of the ESA Discovery Campaign to support Earth Observation scientists with the design of computational experiments for Operations Research. The ML-OPSI breadboard will estimate Marine Litter signal at Top-Of-Atmosphere (TOA) from a set of Bottom-Of-Atmosphere (BOA) scenarios representing the various case studies by the community (e.g., windrows, frontal areas, river mouths, sub-tropical gyres), coming from synthetic data or from real observations. It is a modular, pluggable and extensible framework, promoting re-use and be adapted for different missions, sensors and scenarios.
The breadboard consists of the OPSI components for the simulation and the Marine Litter model components for the detection of marine litter. It shall consider the changes caused in the water reflectance and properties due to marine litter, exploiting gathered information of plastic polymers, different viewing geometries, and atmospheric conditions as naturally occurring.
Marine Litter scenarios of reference shall be built based on in-situ campaigns, to reflect the true littering conditions at each case, both in spatial distribution and composition. The breadboard shall be validated over artificial targets at sea in field campaigns as relevant.
MUSS2 (December 2020 – June 2022)
Title: Multi-Model synthetic S2-HS (hyperspectral) data for marine/plastic debris characterization
Contact Person: Jonathan Cheung-Wai Chan (Jonathan.Chan@vub.be)
Summary: For effective identification and tracking of marine plastic, the sensor data we want is hyperspectral (HS) images including the wavebands at SWIR (1000-2500 nm) with a high spatial resolution around 0.5 m. These characteristics are non-existent in current EO missions. We propose to use novel spectral and spatial enhancement method to generate simulated EO HS SWIR from Sentinel 2 MSI using spectral response function modelling. For spatial enhancement, we apply spatial superresolution through a CNN (Convolutional Neural Network) based 2 branch feature extraction model for capturing detail feature in spatial and spectral domain for plastic debris identification.
Ocean Plastics Polarization Properties OP³ (August 2020 – July 2022)
Title: Characterization of light polarization properties of virgin and marine-harvested plastic litter toward remote-sensing mapping of ocean plastics
Summary: Measurements of polarization state of water-leaving light have been shown to be a significant tool to disentangle complex aquatic light signal to retrieve water constituents. On another hand, subsurface plastic marine litter (PML) might induce surfactants from “bio-fouling” production. In turn, those surfactants will smooth away capillary waves which can be detectable through polarimetric remote sensing. In the context of the future launch the satellite mission PACE (NASA) and 3MI-Sentinel-5 (ESA, EUMETSAT), embarking hyperspectral radiometers and polarimeters, we propose to fully characterize the polarization signature of PML in relation to other natural seawater constituents through: (i) laboratory experimentation, (ii) in-situ measurements, (iii) existing polarization data from older satellite missions (e.g., PARASOL). For this characterization up-to-date polarimetric sensors will be exploited as well as theoretical modeling of light propagation in PML contaminated waters (accumulation zones in the open ocean and estuarine systems) focusing on the water-leaving polarization and surface surfactants/roughness. This is foreseen as an important complementary effort along those of the Copernicus framework of operational satellite missions Sentinel 1 and 2 which have been shown to have potential monitoring application for PML.
Ocean Scan (July 2020 – October 2021)
Title: Ocean Scan: Marine litter database from Earth and space
Contact Person: Laia Romero (firstname.lastname@example.org)
Summary: Satellite remote sensing has demonstrated great potential to become a breakthrough in the mapping of marine litter. One limiting factor for its full development is the access to reliable, extensive, and consistent ground truth of plastic and litter occurrence in aquatic environments. During the past two decades, the amount of in-situ data and information about marine litter has greatly increased, especially during the last five years, however this information is sparsely located in different databases and often lacks the needed requirements or remote sensing technology research.
Ocean Scan was designed to address this problem, by becoming the first inclusive global labelled database to integrate in-situ observations of marine plastic and litter with satellite data. In Ocean Scan, data will be presented on a global interactive map, where users can access information about marine litter in-situ observations and associated EO products. A web portal and a mobile application will provide user friendly access to the platform for data upload, consultation and download.
Designed to maximise interoperability and scalability and to ensure a consistent data format and schema to fit the requirements of remote sensing technologies, the database will be free of charge and open to everybody, upon user registration.
By ensuring data provenance and different levels of privacy for uploaded observations, Ocean Scan will provide a unified reference point for marine plastic and litter observations to support and promote international collaboration and research.
Plastic Detection: Black Sea Test/PDBS (May 2020– May 2021)
Title: Plastic waste and the Black Sea, monitoring litter at sea and on the land from Sentinel-2 data
ContactPerson: Noelia ABASCAL-ZORRILLA (BSPlastics@argans.co.uk)
Summary: An EO processor for the detection of marine debris was originally developed and validated in different areas which were known for the presence of big patches of plastic. The current Argans Ltd. detector, based on the analysis of marine debris spectral reflectance for already well-known indices, was developed for fainter signals and proves to be a semi-robust litter and plastics detector. However, an assessment of the densities and volumes of plastics that could be detected in comparison to big patches, needed to be performed. The Black Sea, a semi-enclosed basin with numerous litter inflows by huge watershed rivers and with only a spillway at the Bosporus, is an ideal test area for the further development of the marine detector and the development of a land-litter detector. Therefore, the objective of the project is to be able to effectively use Sentinel-2 to identify both floating rafts of marine litter and sites of unconsolidated waste on land, providing information on both source and output areas. The detector is tuned to the probabilities of detection and false alarms, fixed by the operator. A Bayesian approach combined with an assessment of the diagnosis ability of the detector (represented by a ROC curve) allows an adjustment of the detector’s thresholds according to the environmental, viewing conditions and the a-priori knowledge of plastics presence delivered by a litter drift model deployed in the Black Sea.
Plastic Monitor (April 2021 – March 2022)
Title: Detecting riverine plastic conglomerations, fluxes and pathways in Indonesia
Contact Person: Marieke Eleveld (email@example.com) – Deltares
Summary: The general objective of Plastic Monitor is to assess detection of heavy plastic pollution loads in rivers by satellite imaging and demonstrate how it can enhance the quantification and monitoring of plastic input into the marine environment.
Although none of the satellite mission concepts were specifically designed for the detection of plastic debris, there is potential for some sensors to be used in the detection of plastics. Therefore, our idea is centred around using a multi-sensor method, where satellite images from different sensors are analysed. This monitoring method is applied to rivers, to detect plastic litter before it reaches the oceans. This will be achieved together with a new plastic capture system, which first concentrates and then removes plastic floating in rivers and can, in this way, bring added value for monitoring of plastic fluxes. In the analysis we will develop advanced data science techniques to get information about the aquatic environment. The plastic detection capacity of existing sensors will lead to recommendations to inspire ESA’s future mission design.
Plastic Plants (September 2020 – August 2023)
Title: Detecting water hyacinth patches as a proxy for riverine plastic transport
Contact Person: Louise Schreyers (firstname.lastname@example.org)
Summary: This project aims to develop and implement an algorithm to automatically detect floating macroplastic accumulation in rivers using remote sensing. It combines the detection of floating water hyacinths using mainly Sentinel-2 with in-situ estimates of macroplastic amounts carried by this invasive aquatic weed. Water hyacinths typically form large patches of several meters of width and length, and can thus be detected from space. Preliminary results show that they can aggregate as much as 80% of floating plastic debris in tropical rivers. The detection tool and field measurements will focus on the Saigon river, Vietnam, a river highly invaded by hyacinths. Our main scope is to quantify floating macroplastic transport and accumulation for the Saigon river over several years, using water hyacinths as a proxy. We will rely on robust in-situ data collected over one year, characterizing the share of macroplastic entangled in hyacinths and its spatiotemporal variability. The algorithms and spectral libraries may serve for future applications, notably for plastic monitoring in other tropical river systems. Given that tropical rivers invaded by hyacinths typically overlap with the highest plastic polluted waterways, this detection system could serve for global monitoring purposes.
PLASTICSURF (October 2020 – October 2023)
Title: Can the microbial communities in the oceans help satellites to monitor micro-plastic pollution?
Contact Person: Armando Marino (email@example.com)
Summary: We propose to tackle the problem of monitoring plastic from another perspective. We aim at observing the effects of plastic on the microbial environment and, as a consequence, on ocean surface characteristics. This project brings together three pieces of research: a) Plastic in the ocean is heavily colonised by microbes; b) Microbes in water produce substances (surfactants) that dampen small waves; and c) Synthetic Aperture Radar (SAR) can identify surfactants as dark areas or stripes in images.
We discovered that several ESA Sentinel-1 satellite images acquired over the garbage patches (Atlantic, Indian and Pacific oceans) present the same dark features we associate with surfactants. We observed that such features are not correlated with high chlorophyll-a and therefore microbes naturally occurring in the ocean (i.e. phytoplankton). Our hypothesis is that these dark patches are the signature of micro-plastics.
Our experiments will show whether or not plastic-dwelling microbes can produce enough surfactants to be visible from space. We will use plastic submerged in fish cages in Scotland and also lab experiments. A ground radar and Sentinel-1 images will be used to check for surfactants around cages while the ground radar will be used with the lab tanks.
PLUXIN (September 2020 – September 2023)
Title: Plastic Flux for Innovation and Business Opportunities in Flanders
Contact Person: https://pluxin.be/nl/contact
Funding Agency: https://www.blauwecluster.be/
Summary: PLUXIN focuses on plastic at the source prior to reaching the marine environment in rivers and canals by studying a critical knowledge gap about the whereabouts of plastics and about their flux towards the marine environment. This information is crucial to fast track cost-efficient plastic remediation measures. A central objective in this project is to develop a two-dimensional-horizontal (2DH) plastic dispersal model. The model will be calibrated and validated with experiments and field sampling data. In this context, plastics will be identified from remote sensing data through image recognition algorithms (‘Deep Learning’) captured from fixed cameras on the bridges and drone acquisitions, hence resulting in an automated plastic detection method. This information in combination with in situ sampling will validate the 2DH-model. Main object of the project is through remote sensing and in-situ observations in combination with numerical models contribute to our understanding of the sources, circulation patterns and fate of plastic in the aquatic environment.
REACT (June 2020 – June 2021)
Title: Crowdsourcing, Copernicus and Hyperspectral Satellite Data for Marine Plastic Litter Detection, Quantification and Tracking
Contact Person: Antonello Aiello (firstname.lastname@example.org)
Summary: Earth Observation by satellite can contribute to marine plastic litter monitoring thanks to its global synoptic point of view. However, remote sensing of marine plastic litter is in its infancy, and it is a significant scientific and technological challenge. REACT is focused on presenting a Proof-of-Concept on remote sensing of marine plastic litter. The project aims to develop a methodology to detect plastic litter onshore or close to the shoreline and offshore. The methodology exploits data fusion of multispectral (i.e., Sentinel-2, WorldView) and hyperspectral satellite data (i.e., PRISMA), together with in situ data collection, and takes advantage of two different approaches. The first one based on spectral signature unmixing, and the second one on artificial intelligence methodologies.
REACT aims at filling the gap related to: 1) The fundamental relationships between marine plastic and the reflectance captured in satellite remotely-sensed imagery, with current and future remote sensing instruments; 2) The sensitivity of existing sensors on marine plastic litters; 3) The identification of the satellite combination delivering the most useful fused-data; 4) The definition of spectral features and spatial scales recommended for future missions (CubeSat/small satellite missions) to be matched with major satellites as Sentinel-2.
Remote Sensing of Marine Debris: Potentials and Limitations (June 2021 – May 2024)
Contact Person: Chuanmin Hu (email@example.com)
Funding Agency: NASA
Summary: Despite several pioneering studies showing potential in remote detection of marine debris using optical means, there are still technical challenges to be addressed. The project is to address these challenges with the following objectives: (1) To compile a spectral library of various types of marine debris as well as other floating matters. This will be through literature search, data mining, and laboratory and field experiments; (2) To determine the resolution requirements (spatial, spectral, radiometric) and optimal bands as well as potentials/limitations of current and future sensors in mapping and quantifying marine debris. This will be through radiative transfer simulations and sensitivity analysis using the endmember spectra and realistic measurement conditions; (3) To develop and evaluate practical approaches for several sensors to maximize their potentials in mapping marine debris; (4) To make recommendations on future satellite missions as well as on algorithms and approaches toward remote sensing of marine debris.
Satellite FRONTs for detection of Anthropogenic plastic Litter /FRONTAL (September 2020 – September 2022)
Title: Development of a risk index for floating marine litter in coastal areas by combining optical and SAR techniques with numerical models
Contact Person: Victor Martinez Vicente (firstname.lastname@example.org)
Summary: Fronts in coastal and oceanic regions are hot-spots for rich and diverse marine life, where floating marine debris also tends to accumulate.
The goal of FRONTAL is to develop a prototype of a risk index for the accumulation of marine plastic debris at fronts. The approach is to combine state-of-the-art optical processing techniques of direct detection (from Copernicus Sentinel-2 MSI), validate the retrieval (using existing in situ datasets) and combine the results with front detection algorithms applied to thermal, optical and SAR satellite imagery. Opportunistically, we will take advantage of hyperspectral satellite data to explore the improvement of algorithms with collocated datasets.
In addition to mapping the risk areas for accumulation, their connectivity to the pathways into the ocean, through numerical dispersion models of coarse and high spatial resolution will be investigated. In doing so, we aim to provide a tool to local and regional policy makers to identify areas where intervention would be more effective.
As a case study, we are working in collaboration with local stakeholders in Da Nang (Vietnam).
SMART (May 2021 – May 2023)
Title: diStributed AI systeM for mArine plastic debRis moniToring (SMART)
Website: SMART project (under construction); AI Moonshot challenge (https://www.moonshotchallenge.ai);
Funding Agency: Portuguese Space Agency – Portugal Space (https://ptspace.pt) in partnership with FCT, ANI, ESA, Unbabel and the support of the Web Summit
Summary: SMART is an intelligent framework based on deep physics-informed learning, which combines automatic identification and classification of floating plastic debris from satellite images, spatiotemporal modelling of plastic accumulations with high-resolution numerical ocean modelling, physics-guided machine learning and a distributed system of sensors mounted on low-cost marine autonomous vehicles for long-term deployment and validation of the model results. This unique combination will allow to bypass the need of running full ocean numerical models at small-scale simulation grids, which brings numerical instabilities and are unable to assess uncertainty about the spatiotemporal predictions. Instead, the final outcome for the end-user will be a probability of plastic occurrence map at any time step required in the past and in the future. The probability of plastic occurrence map will allow authorities to devise strategies for ocean clean-up while making decisions under uncertainty. The project will be developed using two pilot sites in the North Atlantic and the results obtained will be validated in situ using low-cost marine autonomous vehicles, which will collect samples at key sensitive regions predicted by the model.
SPOTS (September 2020 – December 2021)
Title: Spectral Properties of Submerged and Biofouled Marine Plastic Litter
Contact Person: Robin de Vries, The Ocean Cleanup (email@example.com)
Summary: The SPOTS project will take a closer look at the influence of biofouling and water depth on the spectral reflectance of plastics. By varying the water depth and degree of biofouling in a systematic way and a controlled lab and outdoor environment, we will gather a more detailed dataset and predictive model about the influence of both these factors on the hyperspectral footprint of plastic litter. Besides debris from the marine environment, we will also investigate coastal and riverine plastic litter.
TISPLALI (September 2021 – March 2022)
Title: Thermal Infrared Sensing of marine Plastic Litter
Contact Person: Lonneke Goddijn-Murphy (firstname.lastname@example.org)
Summary: This project explores the potential of thermal infrared remote sensing for the detection of floating marine plastic litter and how it could complement other remote sensing methods such as those in the optical spectrum. For example, thermal infrared sensing does not depend on the presence of daylight and can look through light snow and rain. Some plastic materials that are transparent in the optical spectrum may appear opaque in the thermal spectrum. We focus on the consequences of the presence of sunlight and of different air and sea temperatures on the thermal infrared signal of plastic floating in water. The aim is to verify a thermal radiance model using imaging long-wave infrared (7.5 – 13.5 μm), near-infrared (850 nm), and visible colour (RGB) cameras, a drone, and plastic targets deployed at sea. This involves drone surveys during day- and night-time hours, and in summer as well as winter to cover a range of conditions. We support our findings with experiments in the laboratory where we can create a more controlled environment. One of these experiments is looking at plastic litter that has spent time in marine water, to study the effect of biofouling of the plastic surface on thermal infrared radiance leaving this surface.
TRACE (August 2020 – January 2022)
Title: TRACE: Detection and tracking of large marine litter based on high-resolution remote sensing time series, machine learning and ocean current modelling
Contact Person: Mathias Bochow (email@example.com)
Summary: Using daily high-resolution optical (PlanetScope), SAR (Sentinel-1), and hyperspectral (PRISMA) satellite data, this project aims to obtain precise and reliable data on large pieces of floating litter, regarding their quantity, trajectories and accumulation zones, material properties, floating depth, and sources. To achieve this goal we will develop a scalable (current test area: Adriatic Sea) fully-automatic remote sensing based detection and tracking system of large marine litter and accumulation zones and couple it with oceanographic forecasting. After being operationally online the derived information will be published with a delay of 3-4 days on a web-map-server and may serve as a basis for the recovery of floating litter, for the elimination of its sources, and for preventing its dispersal.
WASP (May 2020 – October 2021)
Title: Mapping Windrows as Proxy for marine litter monitoring from space
Contact Person: Manuel Arias (firstname.lastname@example.org)
Summary: WASP is a data processor, developed in the frame of the ESA Discovery 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. The entire Sentinel-2 archive over the Mediterranean Sea will be processed and following an in-depth analysis, a database of the identified proxies will be created over the area. The final product will be a map of 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.