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Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning

Author

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  • Erico N de Souza
  • Kristina Boerder
  • Stan Matwin
  • Boris Worm

Abstract

A key challenge in contemporary ecology and conservation is the accurate tracking of the spatial distribution of various human impacts, such as fishing. While coastal fisheries in national waters are closely monitored in some countries, existing maps of fishing effort elsewhere are fraught with uncertainty, especially in remote areas and the High Seas. Better understanding of the behavior of the global fishing fleets is required in order to prioritize and enforce fisheries management and conservation measures worldwide. Satellite-based Automatic Information Systems (S-AIS) are now commonly installed on most ocean-going vessels and have been proposed as a novel tool to explore the movements of fishing fleets in near real time. Here we present approaches to identify fishing activity from S-AIS data for three dominant fishing gear types: trawl, longline and purse seine. Using a large dataset containing worldwide fishing vessel tracks from 2011–2015, we developed three methods to detect and map fishing activities: for trawlers we produced a Hidden Markov Model (HMM) using vessel speed as observation variable. For longliners we have designed a Data Mining (DM) approach using an algorithm inspired from studies on animal movement. For purse seiners a multi-layered filtering strategy based on vessel speed and operation time was implemented. Validation against expert-labeled datasets showed average detection accuracies of 83% for trawler and longliner, and 97% for purse seiner. Our study represents the first comprehensive approach to detect and identify potential fishing behavior for three major gear types operating on a global scale. We hope that this work will enable new efforts to assess the spatial and temporal distribution of global fishing effort and make global fisheries activities transparent to ocean scientists, managers and the public.

Suggested Citation

  • Erico N de Souza & Kristina Boerder & Stan Matwin & Boris Worm, 2016. "Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-20, July.
  • Handle: RePEc:plo:pone00:0158248
    DOI: 10.1371/journal.pone.0158248
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    References listed on IDEAS

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    1. Walker, E. & Bez, N., 2010. "A pioneer validation of a state-space model of vessel trajectories (VMS) with observers’ data," Ecological Modelling, Elsevier, vol. 221(17), pages 2008-2017.
    2. Fabrizio Natale & Maurizio Gibin & Alfredo Alessandrini & Michele Vespe & Anton Paulrud, 2015. "Mapping Fishing Effort through AIS Data," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-16, June.
    3. Vermard, Youen & Rivot, Etienne & Mahévas, Stéphanie & Marchal, Paul & Gascuel, Didier, 2010. "Identifying fishing trip behaviour and estimating fishing effort from VMS data using Bayesian Hidden Markov Models," Ecological Modelling, Elsevier, vol. 221(15), pages 1757-1769.
    4. Lavielle, Marc, 1999. "Detection of multiple changes in a sequence of dependent variables," Stochastic Processes and their Applications, Elsevier, vol. 83(1), pages 79-102, September.
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    Cited by:

    1. Floriane Cardiec & Sophie Bertrand & Matthew J Witt & Kristian Metcalfe & Brendan J Godley & Catherine McClellan & Raul Vilela & Richard J Parnell & François le Loc’h, 2020. "“Too Big To Ignore”: A feasibility analysis of detecting fishing events in Gabonese small-scale fisheries," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-19, June.
    2. Elizabeth R. Selig & Shinnosuke Nakayama & Colette C. C. Wabnitz & Henrik Österblom & Jessica Spijkers & Nathan A. Miller & Jan Bebbington & Jessica L. Decker Sparks, 2022. "Revealing global risks of labor abuse and illegal, unreported, and unregulated fishing," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    3. Fouzi Harrag & Ali Alshehri, 2023. "Applying Data Mining in Surveillance: Detecting Suspicious Activity on Social Networks," International Journal of Distributed Systems and Technologies (IJDST), IGI Global, vol. 14(1), pages 1-24, January.
    4. Sanaz Honarmand Ebrahimi & Marinus Ossewaarde & Ariana Need, 2021. "Smart Fishery: A Systematic Review and Research Agenda for Sustainable Fisheries in the Age of AI," Sustainability, MDPI, vol. 13(11), pages 1-20, May.
    5. Solomon Amoah Owiredu & Kwang-Il Kim, 2021. "Spatio-Temporal Fish Catch Assessments Using Fishing Vessel Trajectories and Coastal Fish Landing Data from around Jeju Island," Sustainability, MDPI, vol. 13(24), pages 1-18, December.
    6. Edison D. Macusi & Andre Chagas da Costa-Neves & Christian Dave Tipudan & Ricardo P. Babaran, 2023. "Closed Season and the Distribution of Small-Scale Fisheries Fishing Effort in Davao Gulf, Philippines," World, MDPI, vol. 4(1), pages 1-16, January.

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