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

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 unce...

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Detalles Bibliográficos
Autores principales: de Souza, Erico N., Boerder, Kristina, Matwin, Stan, Worm, Boris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4930218/
https://www.ncbi.nlm.nih.gov/pubmed/27367425
http://dx.doi.org/10.1371/journal.pone.0158248
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author de Souza, Erico N.
Boerder, Kristina
Matwin, Stan
Worm, Boris
author_facet de Souza, Erico N.
Boerder, Kristina
Matwin, Stan
Worm, Boris
author_sort de Souza, Erico N.
collection PubMed
description 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.
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spelling pubmed-49302182016-07-18 Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning de Souza, Erico N. Boerder, Kristina Matwin, Stan Worm, Boris PLoS One Research Article 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. Public Library of Science 2016-07-01 /pmc/articles/PMC4930218/ /pubmed/27367425 http://dx.doi.org/10.1371/journal.pone.0158248 Text en © 2016 de Souza et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
de Souza, Erico N.
Boerder, Kristina
Matwin, Stan
Worm, Boris
Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning
title Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning
title_full Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning
title_fullStr Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning
title_full_unstemmed Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning
title_short Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning
title_sort improving fishing pattern detection from satellite ais using data mining and machine learning
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4930218/
https://www.ncbi.nlm.nih.gov/pubmed/27367425
http://dx.doi.org/10.1371/journal.pone.0158248
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