Toward the integrated marine debris observing system

Plastics and other artificial materials pose new risks to the health of the ocean. Anthropogenic debris travels across large distances and is ubiquitous in the water and on shorelines, yet, observations of its sources, composition, pathways, and distributions in the ocean are very sparse and inaccurate. Total amounts of plastics and other man-made debris in the ocean and on the shore, temporal trends in these amounts under exponentially increasing production, as well as degradation processes, vertical fluxes, and time scales are largely unknown. Present ocean circulation models are not able to accurately simulate drift of debris because of its complex hydrodynamics. In this paper we discuss the structure of the future integrated marine debris observing system (IMDOS) that is required to provide long-term monitoring of the state of this anthropogenic pollution and support operational activities to mitigate impacts on the ecosystem and on the safety of maritime activity. The proposed observing system integrates remote sensing and in situ observations. Also, models are used to optimize the design of the system and, in turn, they will be gradually improved using the products of the system. Remote sensing technologies will provide spatially coherent coverage and consistent surveying time series at local to global scale. Optical sensors, including high-resolution imaging, multi- and hyperspectral, fluorescence, and Raman technologies, as well as SAR will be used to measure different types of debris. They will be implemented in a variety of platforms, from hand-held tools to ship-, buoy-, aircraft-, and satellite-based sensors. A network of in situ observations, including reports from volunteers, citizen scientists and ships of opportunity, will be developed to provide data for calibration/validation of remote sensors and to monitor the spread of plastic pollution and other marine debris. IMDOS will interact with other observing systems monitoring physical, chemical, and biological processes in the ocean and on shorelines as well as the state of the ecosystem, maritime activities and safety, drift of sea ice, etc. The synthesized data will support innovative multi-disciplinary research and serve a diverse community of users

Unsupervised Reconstruction of Sea Surface Currents from AIS Maritime Traffic Data Using Trainable Variational Models

International audienceThe estimation of ocean dynamics is a key challenge for applications ranging from climate modeling to ship routing. State-of-the-art methods relying on satellite-derived altimetry data can hardly resolve spatial scales below ∼100 km. In this work we investigate the relevance of AIS data streams as a new mean for the estimation of the surface current velocities. Using a physics-informed observation model, we propose to solve the associated the ill-posed inverse problem using a trainable variational formulation. The latter exploits variational auto-encoders coupled with neural ODE to represent sea surface dynamics. We report numerical experiments on a real AIS dataset off South Africa in a highly dynamical ocean region. They support the relevance of the proposed learning-based AIS-driven approach to significantly improve the reconstruction of sea surface currents compared with state-of-the-art methods, including altimetry-based one

Unsupervised reconstruction of sea surface currents from AIS maritime traffic data using learnable variational models

International audienceSpace oceanography missions, especially altimeter missions,have considerably improved the observation of sea surfacedynamics over the last decades. They can however hardlyresolve spatial scales below∼100km. Meanwhile the AIS(Automatic Identification System) monitoring of the mar-itime traffic implicitly conveys information on the underlyingsea surface currents as the trajectory of ships is affected bythe current. Here, we show that an unsupervised variationallearning scheme provides new means to elucidate how AISdata streams can be converted into sea surface currents. Theproposed scheme relies on a learnable variational frame-work and relate to variational auto-encoder approach coupledwith neural ODE (Ordinary Differential Equation) solvingthe targeted ill-posed inverse problem. Through numericalexperiments on a real AIS dataset, we demonstrate how theproposed scheme could significantly improve the reconstruc-tion of sea surface currents from AIS data compared withstate-of-the-art methods, including altimetry-based one

Monitoring the Greater Agulhas Current with AIS Data Information

Over the core region of the Agulhas Current, new estimations of ocean surface velocities are reported using the increasing dataset from the Automatic Identification System (AIS), initially designed to monitor vessel traffic. A two‐step strategy is suggested. A first guess is evaluated from the collective behavior of vessels for a given space‐time interval. Individual vessel trajectories are then re‐analyzed and interpolated. Applied during year 2016, these ocean surface current estimates are demonstrated to well determine the intensity of surface currents. The improved spatial resolution helps the decomposition of the optimally interpolated surface current vector field between irrotational ≈ 80‐90 % and divergence‐free components ≈ 10‐20 %, e.g. Helmholtz‐Hodge decomposition. Comparisons are performed between in situ drifting‐buoys and represent up to 25 % gain in respect of the altimetry gridded current (for the meridional component). Others comparisons with data collected during the ACT experiment (Agulhas Current Time‐series), as well as with the mean Doppler‐derived surface currents obtained from satellite synthetic aperture radar (SAR) measurements also reveal a significant benefit of using the AIS derived estimates. Comparisons with the Sea Surface Temperature from MODIS sensors confirm the occurrence of meandering events for the current path. For the Agulhas Current region, the high density of vessel traffic can provide new means to study and monitor intense upper ocean currents with more detailed resolution and precision. Plain Language Summary Today, for security reasons, merchant ships transmit location, speed, heading and course‐over‐ground information through the Automatic Identification System (AIS). These messages are a new source of information to complement ocean surface current measurements. In this paper, the intense traffic off the South African coast can result in a selection of more than 150 ships per day. Daily analyses and correlations are reported and illustrate links with observed changes in sea surface temperature. In view of the existing need to establish a more comprehensive monitoring system for the Agulhas Current, these results encourage the systematic usage of the increasingly available amount of AIS data as complementary to traditional in situ and altimeter measurements for routine quantitative monitoring of heat and mass transport in this region

Toward the Integrated Marine Debris Observing System

Plastics and other artificial materials pose new risks to the health of the ocean. Anthropogenic debris travels across large distances and is ubiquitous in the water and on shorelines, yet, observations of its sources, composition, pathways, and distributions in the ocean are very sparse and inaccurate. Total amounts of plastics and other man-made debris in the ocean and on the shore, temporal trends in these amounts under exponentially increasing production, as well as degradation processes, vertical fluxes, and time scales are largely unknown. Present ocean circulation models are not able to accurately simulate drift of debris because of its complex hydrodynamics. In this paper we discuss the structure of the future integrated marine debris observing system (IMDOS) that is required to provide long-term monitoring of the state of this anthropogenic pollution and support operational activities to mitigate impacts on the ecosystem and on the safety of maritime activity. The proposed observing system integrates remote sensing and in situ observations. Also, models are used to optimize the design of the system and, in turn, they will be gradually improved using the products of the system. Remote sensing technologies will provide spatially coherent coverage and consistent surveying time series at local to global scale. Optical sensors, including high-resolution imaging, multi- and hyperspectral, fluorescence, and Raman technologies, as well as SAR will be used to measure different types of debris. They will be implemented in a variety of platforms, from hand-held tools to ship-, buoy-, aircraft-, and satellite-based sensors. A network of in situ observations, including reports from volunteers, citizen scientists and ships of opportunity, will be developed to provide data for calibration/validation of remote sensors and to monitor the spread of plastic pollution and other marine debris. IMDOS will interact with other observing systems monitoring physical, chemical, and biological processes in the ocean and on shorelines as well as the state of the ecosystem, maritime activities and safety, drift of sea ice, etc. The synthesized data will support innovative multi-disciplinary research and serve a diverse community of users