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

PloS one, 2016-07, Vol.11 (7), p.e0158248-e0158248 [Peer Reviewed Journal]

COPYRIGHT 2016 Public Library of Science ;COPYRIGHT 2016 Public Library of Science ;2016 de Souza et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2016 de Souza et al 2016 de Souza et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0158248 ;PMID: 27367425

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Title:
Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning
Author: de Souza, Erico N ; Boerder, Kristina ; Matwin, Stan ; Worm, Boris
Tsikliras, Athanassios C.
Subjects: Algorithms ; Analysis ; Animals ; Aquatic sciences ; Artificial intelligence ; Biology and Life Sciences ; Chionoecetes opilio ; Coastal fisheries ; Coastal waters ; Computer and Information Sciences ; Computer science ; Conservation ; Data analysis ; Data mining ; Data Mining - methods ; Data processing ; Earth Sciences ; Ecology ; Ecology and Environmental Sciences ; Environmental protection ; Filtration ; Fisheries ; Fisheries - statistics & numerical data ; Fisheries management ; Fishery management ; Fishing ; Fishing bobbers ; Fishing equipment ; Fishing gear ; Fishing tackle ; Fishing vessels ; Ground stations ; Human influences ; Identification systems ; Information systems ; International conferences ; Learning algorithms ; Machine Learning ; Markov chains ; Methods ; Monte Carlo simulation ; Multilayers ; Oceans ; Pattern Recognition, Automated - methods ; Physical sciences ; Protection and preservation ; Research and Analysis Methods ; Satellite communications ; Seiners ; Signal processing ; Spacecraft ; Spatial distribution ; Temporal distribution ; Trawlers
Is Part Of: PloS one, 2016-07, Vol.11 (7), p.e0158248-e0158248
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.
Publisher: United States: Public Library of Science
Language: English
Identifier: ISSN: 1932-6203
EISSN: 1932-6203
DOI: 10.1371/journal.pone.0158248
PMID: 27367425
Source: PLoS OA刊
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Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning

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