As I’ve blogged before, this week, the position paper I was contracted to write with two Greek academics was discussed at the UN HQ in New York during a side event. The position paper does not have me as a co-author, yet I’m acknowledged as the drafter of section 2.

Yet the last draft I presented (27 pages) was substantially more than what had been published, and that was fair enough… The position paper focuses more on the vicissitudes of the flag state than on a compendium of existing MPAs in ABNJ and the technologies that can be used to surveil them.

Melvin in Majuro keeping an eye on vessels.

While we discuss MPAs in ABNJ as a totally novel concept, they are not… What is novel is the framework in which they will exist under the BBNJ Agreement. Following my last post on the existing MPAs in the ABNJ, this entry focuses on the present opportunities of Maritime Domain Awareness (MDA) tools for potential surveillance in MPAs in ABNJ.

1 The opportunities of MDA in MPAs in ABNJ

Maritime domain awareness (MDA) refers to the comprehensive understanding of all maritime operations, events, and conditions that may affect security, safety, the economy, or the environment. It involves gathering, integrating, and analysing data from various sources to track vessels, monitor cargo movements, detect shipping practices, and assess specific potential risks and threats.

MDA is essential for governments and commercial partners, including trading and shipping organisations as well as supply chain ecosystem corporations, to manage maritime resources, ensure safe navigation, and combat maritime crime. Technologies such as satellite surveillance, automated identification systems (AIS), vessel monitoring systems (VMS), and machine learning/artificial intelligence (ML/AI) enhance MDA by providing real-time insights and aiding decision-making.

Serious environmental crimes at sea are rarely observed directly, and implicating a vessel suspected of nefarious practices requires gathering intelligence and evidence to establish a case.

Over the past 30 years, scientists have begun using satellite-based remote sensing technologies to maintain real-time records of global and area-specific oceanographic changes, including the temperature and primary productivity of the sea surface, as well as the interactions between humans and the marine environment. Since the 2010s, scientists have also developed technologies and software-based platforms to track and quantify fishing and other human activities across the global ocean. This has emerged as a significant method for evaluating fishing activity and detecting IUU fishing (PEW 2021)[1].

Recent developments in data science, ML/AI, and data visualisation have enabled the harnessing of large-scale spatiotemporal data from numerous sensors. This offers diverse stakeholders access to integrated data products derived from oceanographic, environmental, climate, and anthropogenic activities.

As technology evolves, further integrating the satellite and data-driven capabilities within these platforms with human expertise could strengthen MPA surveillance in ABNJ. However, no technology is a panacea, and a range of factors must be carefully weighed, including costs, access, reliability, coverage, ease of use, and privacy considerations. Furthermore, different data sources must be integrated to yield reliable coverage of ABNJ.[2]

1.1 Vessels Positioning Systems

Automatic identification systems (AIS) and vessel monitoring systems (VMS) have been the key tools for remotely identifying vessel positions[3].

AIS’s primary purpose is safety at sea, including collision avoidance. They are required on vessels of 300 gross tonnage and above engaged in international voyages, cargo ships of 500 gross tonnage and above not engaged in international voyages, and all passenger ships, regardless of size, by the International Maritime Organization (IMO).

Vessel owners outside those ranges may voluntarily install AIS units, and some flag states have additional requirements as part of their safety at sea regulations; therefore, coverage of smaller fishing vessels can vary significantly between flag states.

AIS transponders automatically transmit information such as a ship’s identity, type, position, course, speed, and navigational status while also receiving signals from vessels in the vicinity. These transmissions must be broadcast over an open radio channel and can be freely obtained by anyone.

This availability has led to the widespread use of satellite and terrestrial receivers to collect all accessible AIS signals, enabling global monitoring of vessel positions. AIS is, therefore, the largest and most important source of geospatial ship movement data. However, as vessel tracking was not its original design purpose, it is notoriously complex to analyse and work with.

One of the main issues with AIS is that it is not tamper-proof. This allows operators to intentionally manipulate geolocations to appear in the wrong location (spooﬁng) or to turn off transmissions altogether (i.e., a vessel going dark). Static vessel data, such as type and size, is also susceptible to both intentional and unintentional misdeclaration.

Furthermore, many AIS “messages” can overwhelm receivers in busy shipping areas, leading to the loss of some messages. Nevertheless, over time, several messages from any one vessel typically get through due to the high frequency of transmissions occurring every few seconds.

VMS has been a crucial monitoring tool for managing national and regional fisheries for over two decades. They provide a reliable source of vessel position and catch data and are generally mandated by coastal states or regional fisheries management organisations (RFMOs).

Commercial fishers must have a VMS system as part of the licensing process by the flag state, the coastal states (as part of access conditions) and/or the RFMO(s) under which the vessel is to fish. However, the managing nation or RFMO owns the VMS data, which may not necessarily be shared with others across jurisdictions.

A key feature is that the “turning off” of the onboard unit, along with the associated “loss of signal," can trigger an alarm at the VMS monitoring centre. This necessitates that the vessels provide manual position reporting within an agreed-upon timeframe and return to port to repair the VMS unit.

The frequency of transmission of VMS messages is typically far lower than that of AIS messages. Intervals typically vary from one to six hours, depending on the gear and time of year, however, receiving base stations often possess the ability to adjust the message frequency of VMS units remotely.

While VMS systems are almost exclusively in the fisheries domain, as proven and available technology, there are no impediments to their use by other commercial vessels.

1.2 Satellite Surveillance Technologies

A significant challenge for MDA is detecting and monitoring vessels that don’t report their positions, known as dark vessels. These vessels may go dark due to technical failures of position transponders, gaps in coverage by signal receivers, or purposeful shutdowns by vessel operators.

Satellites possess a distinctive advantage: they can continuously observe extensive ocean areas as the viewing radius expands with the observer's altitude. However, each satellite sensor has specific limitations regarding the size of the area that can be scanned (Figure 1)[4].

Most satellites used for vessel detection orbit our planet at altitudes between 300 and 1,000 km (low-Earth orbit) and scan Earth’s surface daily using various sensors. This coverage enables satellites to support comprehensive monitoring of significant ocean areas during operations, allowing patrol assets to investigate dark targets and providing enhanced MDA over extended periods.

The spatial footprints of observation technologies to detect vessel activity at sea concerning the size of the exclusive economic zone of Tuvalu (irregular grey area, 750,000 km2). Thanks@Moritz Lehmann

Three fundamental classes of sensors are routinely used for vessel detection: electro-optical (EO) imagers, synthetic aperture radar (SAR), and radio frequency (RF) geolocation.

1.2.1 Electro-Optical Imagers

Satellite-borne optical imagers produce images of Earth’s surface, commonly referred to as satellite images. Within the size limits given by pixel resolution, vessels can be detected in satellite images. For example, vessels 30 m in length are visible in an image with a 10 m pixel resolution, and a higher pixel resolution is needed for smaller boats.

Optical data is freely available from satellites operated by major space agencies, such as NASA and the European Space Agency (ESA). These satellites routinely cover extensive areas of the planet, providing images with a resolution of 10 metres or more per pixel. Commercial providers supply images with a resolution of around 0.3 metres per pixel, which is currently the finest resolution.

Coverage (imaging footprint) and pixel resolution are generally trade-offs. For example, ESA’s Sentinel-2[5] collects data in continuous strips over land and coastal areas, featuring a swath width of 290 km and a pixel resolution of 10 m. In contrast, images with approximately 50 cm pixel resolution, such as those from the WorldView (Maxar[6]) and SkySat[7] (Planetlabs) constellations, have swath widths ranging from 5 to 15 km and strip lengths from 5 (SkySat) to several tens of kilometres (WorldView). Furthermore, this high-resolution imagery is not collected continuously but rather on demand. Typically, optical sensors pass overhead during the local morning, requiring a cloud-free view for successful imaging.

In summary, optical satellite sensors provide intuitive images that sometimes even allow the identification of vessels from visual comparisons. Large-scale, repeat coverage of coastal waters is achieved by persistently monitoring platforms at resolutions allowing the detection of vessels 20 m in length or larger. High-resolution imagery must be requested for specific areas and times, and some providers allow users to order imagery through web-based systems.

*Information on a selection of satellites with optical imagers suitable for monitoring maritime domains (to decrease footprint size). Thanks@Moritz Lehmann*

1.2.2 Synthetic Aperture Radar

Satellite-based synthetic aperture radar (SAR) systems transmit microwave radiation and measure echoes from the backscattered signals. By utilising longer wavelengths than optical systems, radar can penetrate clouds with minimal distortion. This capability enables measurements in all weather conditions, a significant advantage for operations under a range of environmental conditions.

Several SAR satellites provide free and commercial imagery over regions from 25 to 225,000 km2 at spatial resolutions between 0.35 and 50 metres. Some of the SAR satellites that have been used for ship detection include (in order of decreasing swath width):

● Radarsat-2[8]: Canadian commercial C-band radar satellite with a dedicated large-scale ship detection acquisition mode at 450 km swath width; this satellite must be tasked to acquire data in areas of interest.

● Sentinel-1[9]: ESA C-band radar satellite providing freely available data through the Copernicus Programme; Sentinel-1 scans land and coastal oceans at 250 km swath width and cannot be tasked.

● TerraSAR-X/TanDEM-X[10]: Twin satellite X-band constellation by the German Aerospace Centre; scanning modes for ship detection include ScanSAR at 100 km swath width and Wide ScanSAR at 270 km swath width.

● ICEYE[11]: Finnish space startup offering tasking of its SAR constellation with a range of acquisition modes at a swath width of 5 to 100 km.

● Capella[12]: Space startup company providing high-resolution SAR data at a swath width of 5 km. Capella achieves short revisit periods through a growing constellation of small satellites.

Very few satellites can deliver large-area SAR imagery with suitable resolution for vessel detection. The Radarsat-2 satellite has demonstrated promising results for large ocean regions. Using the Ship Detection (DVWF) mode, Radarsat-2 can cover an area of about 225,000 km² with a pixel size of 20 metres. The minimum size of a typical metal-hulled vessel that can be reliably detected from Radarsat-2 images in DVWF mode is approximately 30 metres.

A SAR image is a reflectivity map where the intensity of the backscattered signal depends on the physical properties of the reflecting surface. Surfaces that are relatively flat and smooth, such as the ocean, reflect the transmitted energy away from the satellite, resulting in dark areas within the SAR image. Rough and complex surfaces, such as those of vessels, reflect energy back to the satellite, providing a greater return at the radar receiver; this results in bright spots within the SAR image. Detecting vessels requires separating spots of high signal intensity that may indicate ships from a noisy background. This is a processing-intensive task, making it possible for vessels to be missed or for noise to be mistaken for a vessel.

1.2.3 Radiofrequency Geolocation

Recent advancements in satellite technology for maritime domain awareness have concentrated on techniques to detect and geolocate vessels by analysing their radio frequency (RF) emissions. The main source of RF signals from ships is navigational radars.

These powerful beacons emit beams of RF energy around the horizon at a rate typically ranging from 20 to 60 times per minute. Most vessels, while at sea, will have one or more navigational radars continuously operating, emitting intense pulses in the S- and X-bands. Other types of RF emissions may also occur, such as the use of VHF for radio communications.

RF emissions from navigational radar propagate into space, where satellites can detect them. By measuring the angle of arrival at the satellite, one can derive the emitter's location with limited accuracy. Such data collection occurs during a brief interval (a few seconds) when a satellite in low-Earth orbit is passing overhead. The main advantage of RF geolocation over SAR imaging is the ability to cover a larger area (up to 10 million km² in a single pass) at a lower cost.

This is a promising new, albeit still maturing, technology. Some of the commercial companies providing RF detection products include (in alphabetical order):

● HawkEye 360[13]: US geospatial analytics company uses three-satellite satellite constellations to collect and geolocate RF signals. The RF data collected includes UHF and VHF radio communications, X- and S-band marine radars, and L-band mobile satellite devices.

● Unseenlabs[14]: French company operating a constellation of satellites detecting x- and S-band marine radar signals.

1.2.4 Night Light Satellite Detection

Night-light satellite detection for vessels employs Earth Observation (EO) satellite sensors to identify and monitor ships based on their nighttime light emissions. This technology is invaluable for tracking maritime activities such as fishing, cargo transport, or illegal operations like smuggling. However, its effectiveness is particularly pronounced with squid jiggers that operate at night and utilise 1000 watts of lighting per side per metre of total length.

Persistent night-time imagery is collected by the Visible Infrared Imaging Radiometer Suite (VIIRS)[15], operated by the US National Oceanic and Atmospheric Administration (NOAA). This sensor provides daily global coverage at 500 m per pixel. Cloud cover presents a significant challenge for this detection technology, as it can obscure night-light signals. Light pollution in coastal areas complicates the distinction between vessel lights and other light sources (e.g., coastal cities), necessitating advanced filtering and analysis. Furthermore, it has proven ineffective with smaller vessels.

1.3 The role of MDA data software-driven platforms

The proliferation of ocean-monitoring technologies and diverse data sources has significantly enhanced the capacity to monitor marine ecosystems. This underscores the necessity for practical and pragmatic data integration and visualisation technologies, sometimes referred to as "fusion" platforms.

Over the last decade, these MDA data-receiving, processing, and presentation platforms have become pivotal in ensuring compliance. By utilising satellite imagery, AIS/VMS, artificial intelligence (AI), and its subset, machine learning (ML), and enhanced by computer vision technologies, these fusion platforms provide the capability to analyse large volumes of data to identify patterns and detect anomalies. Algorithms can examine present and historical data to predict potential compliance risks, enabling proactive risk management.

Analytics from the fusion platform can provide risk insights, improve report accuracy, and reduce time and resource requirements. This enables diverse stakeholders across multiple jurisdictions to decentralise work.

Some examples of presently used platforms in fisheries are:

OceanMind[16] is a UK-based non-profit organisation. It began in 2013 as a technology demonstrator that incorporated machine learning algorithms to automatically identify fishing activity and generate alerts, allowing expert users to investigate illegal fishing. In 2018, it became an independent non-profit organisation, with the Satellite Applications Catapult, Pew Charitable Trusts, and the Draper Richards Kaplan Foundation as founding funders.

Since its inception, it has supported marine enforcement and compliance by leveraging satellite information and data analytics, combined with extensive marine enforcement expertise, to assist authorities in monitoring and enforcing their marine protected areas. They have pioneered innovative remote monitoring and surveillance technologies, compiling best practices for enforcement that support both flag state responsibility and port state authority.

Currently, as a contractor to the UK government, it concentrates on MPA surveillance. Since 2016, it has been monitoring the Pitcairn Islands MPA (850,000 km²) to assess fishing activity and analyse compliance with MPA restrictions.

Global Fishing Watch[17] was founded in 2015 through a collaboration between three partners: Oceana, SkyTruth and Google. While initially focusing only on commercial fishing, it has since worked on integrating data and technology to support the effective design, management, and monitoring of MPAs since 2020.

Its Marine Manager Portal makes diverse ocean datasets accessible. It presents actionable information to inform managers to rapidly collate, assess, and analyse scientific data integral to MPA governance. The portal provides near-real-time, dynamic, and interactive data on ocean conditions, biology, and human-use activities to support marine spatial planning, marine protected area design and management, and scientific research.

Skylight[18] is based at the Allen Institute for AI (Ai2), a non-profit organisation based in the USA that has been conducting AI research and engineering to serve the common good since 2019. Recognising the critical role of enforcement, it provides operationally relevant information and insights to under-resourced states and managers of marine protected areas at no cost.

While initially focusing on IUU fishing, partnerships with organisations like the International Union for Conservation of Nature (IUCN) enable Skylight to provide support to developing countries at no cost.

In 2020, UNODC/GMCP entered a partnership with Skylight and has since introduced the platform to law enforcement agencies in over 40 countries worldwide. By facilitating access to Skylight and providing training customised to each agency’s mission and specific threats, UNODC has enhanced these countries’ abilities to identify and combat maritime crimes.

Starboard.nz[19] is a New Zealand-based subscription fusion platform providing advanced global maritime monitoring tools. The platform focuses on detecting and analysing vessel activities. It integrates data from multiple sources to monitor activities in the legal maritime domain.

Working holistically, they are contractors to the NZ and Australian governments, integrating vessel intelligence for Customs, Biosecurity, Immigration, Defence, and Fisheries data feeds. They also work with the 17 Pacific Islands Fisheries Forum Agency[20] (FFA) member states, focusing on fisheries-specific issues such as transhipments, zone intrusions, and port state measures.

They pioneered the integration of weather and oceanographic data into their platform algorithms to enhance situational awareness and decision-making in the maritime domain. Additionally, they focused on improving the user experience (UX) of their software interface for various users, including fishers, defence, customs, and biosecurity professionals. This emphasis on usability significantly reduced frustration and errors, especially in time-sensitive scenarios involving multiple jurisdictions and stakeholders, such as RFMOs, NGOs, and governmental agencies.

Outside the fisheries realm:

SkyTruth[21] is a technology nonprofit focused on conservation that employs satellite imagery, machine learning, and big data to make concealed environmental issues visible, quantifiable, and actionable. Its Oceans programme documents the extent of oil spills and detects vessel pollution at sea due to bilge dumping. Its Cerulean[22] platform is a global system for tracking ocean oil pollution, identifying oil slicks based on SAR imagery, and pinpointing nearby vessels and offshore infrastructure that may be responsible. SkyTruth has also established a collaboration with Global Fishing Watch.

EarthRanger[23] advocates for the protection of terrestrial wildlife and habitats. Founded in 2015, it has become a product of Ai2 and has integrated with Skylight to enhance maritime monitoring and conservation efforts. It integrates and visualises real-time and historical data, including information from field reports, GPS-tracked wildlife collars, ranger movements, and remote sensors. This unified view helps protected area managers make informed decisions about protecting wildlife, addressing human-wildlife conflicts, combating poaching, and efficiently managing ecological monitoring.

The tool has been deployed globally in over 250 protected areas, connecting communities and authorities and pioneering participative reporting.

References

[1] Emerging Marine Monitoring Technologies Enable More Effective Management of Protected Areas. https://www.pewtrusts.org/en/research-and-analysis/articles/2021/09/27/emerging-marine-monitoring-technologies-enable-more-effective-management-of-protected-areas

[2] STRONG High Seas project, 2018. Workshop summary: Technological tools for MCS in ABNJ, Sciences Po, Paris. https://www.prog-ocean.org/wp-content/uploads/2018/08/MCS-Workshop-I-summary-final.pdf

[3] Lehmann, M & Charley, M, 2023. The importance of maritime domain awareness in fighting illegal, unreported and unregulated fishing. SPC Fisheries Newsletter # 169. https://coastfish.spc.int/publications/bulletins/fisheries-newsletter/538

[4] Source: Satellite “dark vessel” detection for MDA. INFOFISH International 6/2022

[5] https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2

[6] https://www.maxar.com/maxar-intelligence/products/satellite-imagery

[7] https://earth.esa.int/eogateway/missions/skysat

[8] https://www.asc-csa.gc.ca/eng/satellites/radarsat2/

[9] https://sentinels.copernicus.eu/web/sentinel/copernicus/sentinel-1

[10] https://earth.esa.int/eogateway/missions/terrasar-x-and-tandem-x

[11] https://www.iceye.com

[12] https://www.capellaspace.com

[13] https://www.he360.com

[14] https://unseenlabs.space

[15] https://www.nesdis.noaa.gov/our-satellites/currently-flying/joint-polar-satellite-system/visible-infrared-imaging-radiometer-suite-viirs