A method to automatically detect fish aggregations using horizontally scanning sonar
Abstract Pelagic fishes are a major source of protein and unsaturated fatty acids, and robust management is critical to avoid overfishing. Fisheries management is often supported by indices from scientific acoustic-trawl surveys, where vertically aligned echo sounders and trawl samples are used to p...
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Online Access: | http://dx.doi.org/10.1093/icesjms/fsy029 http://academic.oup.com/icesjms/article-pdf/75/5/1803/31236692/fsy029.pdf |
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croxfordunivpr:10.1093/icesjms/fsy029 2023-12-31T10:21:24+01:00 A method to automatically detect fish aggregations using horizontally scanning sonar Vatnehol, Sindre Peña, Hector Handegard, Nils Olav Demer, David 2018 http://dx.doi.org/10.1093/icesjms/fsy029 http://academic.oup.com/icesjms/article-pdf/75/5/1803/31236692/fsy029.pdf en eng Oxford University Press (OUP) http://creativecommons.org/licenses/by/4.0/ ICES Journal of Marine Science volume 75, issue 5, page 1803-1812 ISSN 1054-3139 1095-9289 Ecology Aquatic Science Ecology, Evolution, Behavior and Systematics Oceanography journal-article 2018 croxfordunivpr https://doi.org/10.1093/icesjms/fsy029 2023-12-06T08:44:59Z Abstract Pelagic fishes are a major source of protein and unsaturated fatty acids, and robust management is critical to avoid overfishing. Fisheries management is often supported by indices from scientific acoustic-trawl surveys, where vertically aligned echo sounders and trawl samples are used to provide an estimate of abundance. Survey biases may be introduced when fish are located near the sea surface or if they avoid the survey vessel. Horizontally scanning acoustic equipment, such as fish-detection sonars, have been proposed as a method to quantify such biases; however, manual interpretation of the data hamper further development. An automated method for identifying fish aggregations within large volumes of sonar data has been developed. It exploits the fact that near-stationary targets, i.e. a fish school, have distinct patterns through the data. The algorithm is not instrument specific, and was tested on data collected from several acoustic-trawl surveys in the Norwegian Sea. The automatic algorithm had a similar performance to manual interpretation, and the main cause of discrepancies was aggregations overlooked in the manual work. These discrepancies were substantially reduced in a second round of manual interpretation. We envision that this method will facilitate a labour efficient and more objective analysis of sonar data and provide information to support fisheries management for pelagic fish. Article in Journal/Newspaper Norwegian Sea Oxford University Press (via Crossref) ICES Journal of Marine Science 75 5 1803 1812 |
institution |
Open Polar |
collection |
Oxford University Press (via Crossref) |
op_collection_id |
croxfordunivpr |
language |
English |
topic |
Ecology Aquatic Science Ecology, Evolution, Behavior and Systematics Oceanography |
spellingShingle |
Ecology Aquatic Science Ecology, Evolution, Behavior and Systematics Oceanography Vatnehol, Sindre Peña, Hector Handegard, Nils Olav A method to automatically detect fish aggregations using horizontally scanning sonar |
topic_facet |
Ecology Aquatic Science Ecology, Evolution, Behavior and Systematics Oceanography |
description |
Abstract Pelagic fishes are a major source of protein and unsaturated fatty acids, and robust management is critical to avoid overfishing. Fisheries management is often supported by indices from scientific acoustic-trawl surveys, where vertically aligned echo sounders and trawl samples are used to provide an estimate of abundance. Survey biases may be introduced when fish are located near the sea surface or if they avoid the survey vessel. Horizontally scanning acoustic equipment, such as fish-detection sonars, have been proposed as a method to quantify such biases; however, manual interpretation of the data hamper further development. An automated method for identifying fish aggregations within large volumes of sonar data has been developed. It exploits the fact that near-stationary targets, i.e. a fish school, have distinct patterns through the data. The algorithm is not instrument specific, and was tested on data collected from several acoustic-trawl surveys in the Norwegian Sea. The automatic algorithm had a similar performance to manual interpretation, and the main cause of discrepancies was aggregations overlooked in the manual work. These discrepancies were substantially reduced in a second round of manual interpretation. We envision that this method will facilitate a labour efficient and more objective analysis of sonar data and provide information to support fisheries management for pelagic fish. |
author2 |
Demer, David |
format |
Article in Journal/Newspaper |
author |
Vatnehol, Sindre Peña, Hector Handegard, Nils Olav |
author_facet |
Vatnehol, Sindre Peña, Hector Handegard, Nils Olav |
author_sort |
Vatnehol, Sindre |
title |
A method to automatically detect fish aggregations using horizontally scanning sonar |
title_short |
A method to automatically detect fish aggregations using horizontally scanning sonar |
title_full |
A method to automatically detect fish aggregations using horizontally scanning sonar |
title_fullStr |
A method to automatically detect fish aggregations using horizontally scanning sonar |
title_full_unstemmed |
A method to automatically detect fish aggregations using horizontally scanning sonar |
title_sort |
method to automatically detect fish aggregations using horizontally scanning sonar |
publisher |
Oxford University Press (OUP) |
publishDate |
2018 |
url |
http://dx.doi.org/10.1093/icesjms/fsy029 http://academic.oup.com/icesjms/article-pdf/75/5/1803/31236692/fsy029.pdf |
genre |
Norwegian Sea |
genre_facet |
Norwegian Sea |
op_source |
ICES Journal of Marine Science volume 75, issue 5, page 1803-1812 ISSN 1054-3139 1095-9289 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1093/icesjms/fsy029 |
container_title |
ICES Journal of Marine Science |
container_volume |
75 |
container_issue |
5 |
container_start_page |
1803 |
op_container_end_page |
1812 |
_version_ |
1786832162559885312 |