A comparison of VMS and AIS data: the effect of data coverage and vessel position recording frequency on estimates of fishing footprints

Assessing the fishing footprint using data integrated from different tracking devices: Issues and opportunities

Gerritsen, H., and Lordan, C. 2011. Integrating vessel monitoring systems (VMS) data with daily catch data from logbooks to explore the spatial distribution of catch and effort at high resolution. – ICES Journal of Marine Science, 68: 245–252. Vessel monitoring systems (VMS) automatically collect positional data from fishing vessels, and the data can be linked to catch data from logbooks to provide a census of spatially resolved catch-and-effort data. The most appropriate and practical method for integrating Irish VMS and logbook data is explored and validated. A simple speed rule is applied to identify VMS records that correspond to fishing activity. The VMS data are then integrated with the catch data from logbooks using date and vessel identifier. Several assumptions were investigated, and the resulting distribution maps of catch and effort appear to be unbiased. The method is illustrated with an example of a time-series of spatially explicit estimates of catch per unit effort. The proposed method is relatively simple and does not require specialist software or computationally intensive methods. It will be possible to generalize this approach to similar datasets that are available within the EU and many other regions. Analysis of integrated VMS and logbook data will allow fisheries data to be analysed on a considerably finer spatial scale than was possible previously, opening up a range of potential applications.

Lee, J., South, A. B., and Jennings, S. 2010. Developing reliable, repeatable, and accessible methods to provide high-resolution estimates of fishing-effort distributions from vessel monitoring system (VMS) data. – ICES Journal of Marine Science, 67: 1260–1271. Vessel monitoring systems (VMS) are used primarily for fisheries enforcement purposes, but also provide information on the spatial and temporal distribution of fishing activity for use in fisheries and environmental assessment and management. A reliable, repeatable, and accessible method using readily available software for estimating fishing effort from unprocessed VMS data is developed, tested, and applied. Caveats associated with the method are identified, and the biases introduced by our assumptions are quantified. Application of the method provides a high-resolution description of gear-specific fishing activity by UK vessels. An index is developed to describe variation in the spatial pattern of fishing effort generated by different gears. The proposed method for VMS analysis involves removing duplicate VMS records and records close to ports, calculating the time interval between successive records to identify periods of activity, linking each record to a vessel and gear type, differentiating fishing and non-fishing activity, and summing fishing records in time and space to estimate fishing effort. The approach is a step towards the development of standardized methods to facilitate wider exchange and use of European VMS data. A clear audit trail for the methods of VMS analysis already used to inform management needs to be documented.

Mismatch between VMS data temporal resolution and fishing activity time scales

Transparency in the global fishing fleet has dramatically increased over the past decade due to the expansion of the Automatic Identification System (AIS) and public Vessel Monitoring System (VMS). Since 2012, the number of vessels tracked has grown more than fivefold, from approximately 20,000 to over 110,000 in 2024 (>100,000 on AIS and ~10,000 on VMS), now covering more than a quarter of the world’s fishing fleet over 12 meters in length. This increase has driven advancements in marine science, supported policies by regional fisheries management organizations, and strengthened enforcement within marine protected areas. Using satellite radar and optical imagery, we provide region-specific estimates of the fraction of industrial fishing vessels publicly tracked, finding that while developed countries lead in use of AIS, developing regions show the fastest growth. This growth is primarily due to an increased fraction of vessels installing tracking devices, not a rise in total fishing activity. Our analysis also shows no evidence of widespread increased AIS disabling despite concerns that using AIS for monitoring may incentivize fishers to disable their devices. Some of the increase in public vessel tracking is driven by proactive voluntary adoption of AIS by fishers, demonstrating the intrinsic value they see in it. However, the majority of increase in AIS use has been a result of national policies mandating or encouraging its use. These policies will be critical to the impact of public tracking in the next decade. Public tracking, if supported by the right policies, enables science and can complement sovereign monitoring systems, especially in international and remote waters. To strengthen transparency in ocean governance, we recommend that countries establish or reinforce AIS regulations, expand public tracking availability while safeguarding privacy, and integrate AIS and VMS data into marine resource management strategies.

Improving maritime traffic surveillance in inland waterways using the robust fusion of AIS and visual data

The maritime Automatic Identification System (AIS) data is obtained from many different terrestrial and satellite sources. AIS data enables safety, security, environmental protection and the economic efficiency of the maritime sector. The quality of AIS receivers is not controlled in the same manner as AIS transmitters. This has led to a situation where AIS data is not as clean as it should/could be. Added to this is the lack of accuracy and standards in entering the voyage data by the mariners such as next port of call into the AIS equipment installed on vessels. By using analytics IMIS Global Limited has been able to process the AIS data stream to eliminate a large portion of the faulty data. This has allowed the resultant AIS data to be used for more accurate detailed analysis such as the long-term vessel track, port arrival events and port departure events. New data that is derived from processing AIS data has enhanced the information available to maritime authorities enabling a significant increase in safety, security, environmental protection and economic growth. The next generation of maritime data communications technology being based AIS. This is known as the VHF Data Exchange System (VDES) and this technology now enables further opportunities. The value from the large volumes of AIS data is extracted by visual, streaming, historical and prescriptive data analytics. The datAcron project is showing the way with regards to the processing and use of AIS and resultant trajectory data.

The synthetic aperture radar (SAR) instrument of Sentinel-1 is a remote sensing technology being developed to enable the detection of vessel distribution. The purpose of this research is to study fishing-vessel detection using SAR Sentinel-1 data. In this study, the constant false alarm rate method (CFAR) for Sentinel-1 data is used for the detection of fishing vessels in Indramayu sea waters. The data used to detect ships includes SAR Sentinel-1A images and vessel monitoring system (VMS) data acquired on 8 March and 20 March 2018. SAR Sentinel-1 imagery data is obtained through pre-processing and object identification using Sentinel Application Platform (SNAP) software. Overlay analysis is then used to enable discrimination of immovable and movable objects and validation of ships detected from SAR Sentinel-1 imagery is performed using VMS data. From overlay analysis, 46 ships were detected on 8 March 2018 and 39 ships on 20 March 2018. Of all the ship points detected using SAR Sentinel-1, 7.06% could be detected by VMS data while 92.94% could not. The number of ships detected by SAR Sentinel-1 is greater than those detected by VMS because not all ships use VMS devices.

Monitoring fishery activity is essential for resource planning and guaranteeing fisheries sustainability. Large fishing vessels constantly and continuously communicate their positions via Automatic Identification System (AIS) or Vessel Monitoring Systems (VMSs). These systems can use radio or Global Positioning System (GPS) devices to transmit data. Processing and integrating these big data with other fisheries data allows for exploring the relations between socio-economic and ecosystem assets in marine areas, which is fundamental in fishery monitoring. In this context, estimating actual fishing activity from time series of AIS and VMS data would enhance the correct identification of fishing activity patterns and help assess regulations' effectiveness. However, these data might contain gaps because of technical issues such as limited coverage of the terrestrial receivers or saturated transmission bands. Other sources of data gaps are adverse meteorological conditions and voluntary switch-offs. Gaps may also include hidden (unreported) fishing activity whose quantification would improve actual fishing activity estimation. This paper presents a workflow for AIS/VMS big-data analysis that estimates potential unreported fishing activity hotspots in a marine area. The workflow uses a statistical spatial analysis over vessel speeds and coordinates and a multi-source data integration approach that can work on multiple areas and multiple analysis scales. Specifically, it (i) estimates fishing activity locations and rebuilds data gaps, (ii) estimates the potential unreported fishing hour distribution and the unreported-over-total ratio of fishing hours at a 0.01° spatial resolution, (iii) identifies potential unreported fishing activity hotspots, (iv) extracts the stocks involved in these hotspots (using global-scale repositories of stock and species observation data) and raises an alert about their possible endangered, threatened, and protected (ETP) status. The workflow is also a free-to-use Web Service running on an open science-compliant cloud computing platform with a Web Processing Service (WPS) standard interface, allowing efficient big data processing. As a study case, we focussed on the Adriatic Sea. We reconstructed the monthly reported and potential unreported trawling activity in 2019, using terrestrial AIS data with a 5-min sampling period, containing ~50 million records transmitted by ~1,600 vessels. The results highlight that the unreported fishing activity hotspots especially impacted Italian coasts and some forbidden and protected areas. The potential unreported activity involved 33 stocks, four of which were ETP species in the basin. The extracted information agreed with expert studies, and the estimated trawling patterns agreed with those produced by the Global Fishing Watch.

AIS (Automatic Identification System) data received from moving vessels over an area of interest can be of very much interest for deriving maritime trajectory patterns. In this paper, a novel approach to extract course patterns from AIS data of vessels is presented. From machine learning and natural language processing principles, a topic model might be used for extracting implicit patterns underlying massive and unstructured collection of incoming data. To apply topic model to AIS data, PQk-means vector quantization to convert AIS data record to code documents is introduced. Then, a topic model is applied to extract course patterns from AIS data. In fact, courses, not only encompasses trajectory locations, but also headings and speeds, are recognized by the proposed algorithm. The performance of PQk-means is evaluated using the relative root mean square error and elapsed time. The potential of the approach is illustrated by a series of experimental results derived from practical AIS data set in a region of North West France.

Optimization of an artificial neural network for identifying fishing set positions from VMS data: An example from the Peruvian anchovy purse seine fishery

Estimating geo-referenced fishing effort is vital to develop advice for effective fisheries management. Many studies in recent decades have attempted to obtain complete, high-resolution effort data from vessel monitoring systems (VMSs). The main challenge in this regard is to develop a classification method for differentiating fishing activities (e.g., fishing days) from nonfishing activities in VMS data. This study developed a simple, novel classification criterion for a large-scale tuna longline (LTLL) fishery that has not been studied before. LTLL operations were first explored using observer data. Three approaches were designed for developing fishing-day classification criteria, using maximizing sum of sensitivity and specificity (SS) as the major performance measure and minimizing difference of SS as a reference. At least one VMS report with speed in the range of 2–5 kn (1 kn = 1.852 km·h–1) detected during the time-of-day period of 14:00–23:00 h was recommended as the criterion for defining a fishing day. Possible explanations for the differences between the estimated fishing days from VMS data and those reported on logbooks are discussed; most causes were related to specific features of the fishery.

Illegal, unregulated, and unreported (IUU) fishing poses a major threat to effective management of marine resources, affecting biodiversity and communities dependent on these coastal resources. Spatiotemporal patterns of industrial fisheries in developing countries are often poorly understood, and global efforts to describe spatial patterns of fishing vessel activity are currently based on automatic identification system (AIS) data. However, AIS is often not a legal requirement on fishing vessels, likely resulting in underestimates of the scale and distribution of legal and illegal fishing activity, which could have significant ramifications for targeted enforcement efforts and the management of fisheries resources. To help address this knowledge gap, we analyzed 3 years of vessel monitoring system (VMS) data in partnership with the national fisheries department in the Republic of the Congo to describe the behavior of national and distant-water industrial fleets operating in these waters. We found that the spatial footprint of the industrial fisheries fleet encompassed over one-quarter of the Exclusive Economic Zone. On average, 73% of fishing activity took place on the continental shelf (waters shallower than 200 m). Our findings highlight that VMS is not acting as a deterrent or being effectively used as a proactive management tool. As much as 33% (13% on average) of fishing effort occurred in prohibited areas set aside to protect biodiversity, including artisanal fisheries resources, and the distant-water fleet responsible for as much as 84% of this illegal activity. Given the growth in industrial and distant-water fleets across the region, as well as low levels of management and enforcement, these findings highlight that there is an urgent need for the global community to help strengthen regional and national capacity to analyze national scale data sets if efforts to combat IUU fishing are to be effective.

Defining high-resolution dredge fishing grounds with Automatic Identification System (AIS) data

Spatial patterns of fishing activity inside the Gorringe bank MPA based on VMS, AIS and e-logbooks data