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

de Souza EN, Boerder K, Matwin S, Worm B - PLoS ONE (2016)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Big Data Analytics Institute, Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada.

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.

No MeSH data available.


Related in: MedlinePlus

Results for longliner number 1 from Table 2 (Accuracy: 46%).Matching results for fishing activity presented in blue, expert labels in green and the algorithm’s fishing activity predictions in red. Empty circles represent non-fishing activity as identified by algorithm and expert. Map data by Natural Earth.
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pone.0158248.g008: Results for longliner number 1 from Table 2 (Accuracy: 46%).Matching results for fishing activity presented in blue, expert labels in green and the algorithm’s fishing activity predictions in red. Empty circles represent non-fishing activity as identified by algorithm and expert. Map data by Natural Earth.

Mentions: Figs 7 and 8 illustrate the results of the fishing prediction for tracks 8 and 1 in Table 2, respectively. These tracks were chosen because these vessels presented the highest and lowest accuracy results in the longliner data.


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

de Souza EN, Boerder K, Matwin S, Worm B - PLoS ONE (2016)

Results for longliner number 1 from Table 2 (Accuracy: 46%).Matching results for fishing activity presented in blue, expert labels in green and the algorithm’s fishing activity predictions in red. Empty circles represent non-fishing activity as identified by algorithm and expert. Map data by Natural Earth.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4930218&req=5

pone.0158248.g008: Results for longliner number 1 from Table 2 (Accuracy: 46%).Matching results for fishing activity presented in blue, expert labels in green and the algorithm’s fishing activity predictions in red. Empty circles represent non-fishing activity as identified by algorithm and expert. Map data by Natural Earth.
Mentions: Figs 7 and 8 illustrate the results of the fishing prediction for tracks 8 and 1 in Table 2, respectively. These tracks were chosen because these vessels presented the highest and lowest accuracy results in the longliner data.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Big Data Analytics Institute, Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada.

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.

No MeSH data available.


Related in: MedlinePlus