Identification of Southern Ocean acoustic targets using aggregation backscatter and shape characteristics
Acoustic surveys for biomass estimation require accurate identification of echoes from the target species. In one objective technique for identifying Antarctic krill, the difference between mean volume-backscattering strength at two frequencies is used, but can misclassify small krill and other plan...
Published in: | ICES Journal of Marine Science |
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Language: | English |
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2003
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Online Access: | https://research-portal.st-andrews.ac.uk/en/publications/05ffb027-cac6-4016-9d47-1f8c2a568190 https://doi.org/10.1016/S1054-3139(03)00062-6 http://www.scopus.com/inward/record.url?scp=0038538500&partnerID=8YFLogxK |
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ftunstandrewcris:oai:research-portal.st-andrews.ac.uk:publications/05ffb027-cac6-4016-9d47-1f8c2a568190 2024-11-10T14:36:22+00:00 Identification of Southern Ocean acoustic targets using aggregation backscatter and shape characteristics Woodd-Walker, RS Watkins, JL Brierley, Andrew Stuart 2003-06 https://research-portal.st-andrews.ac.uk/en/publications/05ffb027-cac6-4016-9d47-1f8c2a568190 https://doi.org/10.1016/S1054-3139(03)00062-6 http://www.scopus.com/inward/record.url?scp=0038538500&partnerID=8YFLogxK eng eng info:eu-repo/semantics/restrictedAccess Woodd-Walker , RS , Watkins , JL & Brierley , A S 2003 , ' Identification of Southern Ocean acoustic targets using aggregation backscatter and shape characteristics ' , ICES Journal of Marine Science , vol. 60 , pp. 641-649 . https://doi.org/10.1016/S1054-3139(03)00062-6 acoustics artificial neural network Euphausia superba krill linear discriminant analysis South Georgia Southern Ocean zooplankton ANTARCTIC KRILL FISH SCHOOLS SPECIES IDENTIFICATION BIOLOGICAL PATCHINESS CLASSIFICATION ABUNDANCE GEORGIA EDGE ATLANTIC NETWORKS article 2003 ftunstandrewcris https://doi.org/10.1016/S1054-3139(03)00062-6 2024-10-24T00:01:10Z Acoustic surveys for biomass estimation require accurate identification of echoes from the target species. In one objective technique for identifying Antarctic krill, the difference between mean volume-backscattering strength at two frequencies is used, but can misclassify small krill and other plankton. Here, we investigate ways to improve target identification by including characteristics of backscattering energy and morphology of aggregations. To do this, multi-frequency acoustic data were collected concurrently with target fishing of Antarctic krill and other euphausiid and salp aggregations. Parameter sets for these known aggregations were collated and used to develop empirical classifications. Both linear discriminant-function analysis (DFA) and the artificial neural network technique were employed. In both cases, acoustic-backscattering energy parameters were most important for discriminating between Antarctic krill and other zooplankton. However, swarm morphology and other parameters improved the discrimination, particularly between krill and salps. Our study suggests that for krill-biomass estimates, a simple DFA based on acoustic-energy parameters is a substantial improvement over current dB-difference acoustic methods; but studies requiring the discrimination of zooplankton other than krill must still be supported by target fishing. (C) 2003 International Council for the Exploration of the Sea. Published by Elsevier Science Ltd. All rights reserved. Article in Journal/Newspaper Antarc* Antarctic Antarctic Krill Euphausia superba Southern Ocean University of St Andrews: Research Portal Antarctic Southern Ocean ICES Journal of Marine Science 60 3 641 649 |
institution |
Open Polar |
collection |
University of St Andrews: Research Portal |
op_collection_id |
ftunstandrewcris |
language |
English |
topic |
acoustics artificial neural network Euphausia superba krill linear discriminant analysis South Georgia Southern Ocean zooplankton ANTARCTIC KRILL FISH SCHOOLS SPECIES IDENTIFICATION BIOLOGICAL PATCHINESS CLASSIFICATION ABUNDANCE GEORGIA EDGE ATLANTIC NETWORKS |
spellingShingle |
acoustics artificial neural network Euphausia superba krill linear discriminant analysis South Georgia Southern Ocean zooplankton ANTARCTIC KRILL FISH SCHOOLS SPECIES IDENTIFICATION BIOLOGICAL PATCHINESS CLASSIFICATION ABUNDANCE GEORGIA EDGE ATLANTIC NETWORKS Woodd-Walker, RS Watkins, JL Brierley, Andrew Stuart Identification of Southern Ocean acoustic targets using aggregation backscatter and shape characteristics |
topic_facet |
acoustics artificial neural network Euphausia superba krill linear discriminant analysis South Georgia Southern Ocean zooplankton ANTARCTIC KRILL FISH SCHOOLS SPECIES IDENTIFICATION BIOLOGICAL PATCHINESS CLASSIFICATION ABUNDANCE GEORGIA EDGE ATLANTIC NETWORKS |
description |
Acoustic surveys for biomass estimation require accurate identification of echoes from the target species. In one objective technique for identifying Antarctic krill, the difference between mean volume-backscattering strength at two frequencies is used, but can misclassify small krill and other plankton. Here, we investigate ways to improve target identification by including characteristics of backscattering energy and morphology of aggregations. To do this, multi-frequency acoustic data were collected concurrently with target fishing of Antarctic krill and other euphausiid and salp aggregations. Parameter sets for these known aggregations were collated and used to develop empirical classifications. Both linear discriminant-function analysis (DFA) and the artificial neural network technique were employed. In both cases, acoustic-backscattering energy parameters were most important for discriminating between Antarctic krill and other zooplankton. However, swarm morphology and other parameters improved the discrimination, particularly between krill and salps. Our study suggests that for krill-biomass estimates, a simple DFA based on acoustic-energy parameters is a substantial improvement over current dB-difference acoustic methods; but studies requiring the discrimination of zooplankton other than krill must still be supported by target fishing. (C) 2003 International Council for the Exploration of the Sea. Published by Elsevier Science Ltd. All rights reserved. |
format |
Article in Journal/Newspaper |
author |
Woodd-Walker, RS Watkins, JL Brierley, Andrew Stuart |
author_facet |
Woodd-Walker, RS Watkins, JL Brierley, Andrew Stuart |
author_sort |
Woodd-Walker, RS |
title |
Identification of Southern Ocean acoustic targets using aggregation backscatter and shape characteristics |
title_short |
Identification of Southern Ocean acoustic targets using aggregation backscatter and shape characteristics |
title_full |
Identification of Southern Ocean acoustic targets using aggregation backscatter and shape characteristics |
title_fullStr |
Identification of Southern Ocean acoustic targets using aggregation backscatter and shape characteristics |
title_full_unstemmed |
Identification of Southern Ocean acoustic targets using aggregation backscatter and shape characteristics |
title_sort |
identification of southern ocean acoustic targets using aggregation backscatter and shape characteristics |
publishDate |
2003 |
url |
https://research-portal.st-andrews.ac.uk/en/publications/05ffb027-cac6-4016-9d47-1f8c2a568190 https://doi.org/10.1016/S1054-3139(03)00062-6 http://www.scopus.com/inward/record.url?scp=0038538500&partnerID=8YFLogxK |
geographic |
Antarctic Southern Ocean |
geographic_facet |
Antarctic Southern Ocean |
genre |
Antarc* Antarctic Antarctic Krill Euphausia superba Southern Ocean |
genre_facet |
Antarc* Antarctic Antarctic Krill Euphausia superba Southern Ocean |
op_source |
Woodd-Walker , RS , Watkins , JL & Brierley , A S 2003 , ' Identification of Southern Ocean acoustic targets using aggregation backscatter and shape characteristics ' , ICES Journal of Marine Science , vol. 60 , pp. 641-649 . https://doi.org/10.1016/S1054-3139(03)00062-6 |
op_rights |
info:eu-repo/semantics/restrictedAccess |
op_doi |
https://doi.org/10.1016/S1054-3139(03)00062-6 |
container_title |
ICES Journal of Marine Science |
container_volume |
60 |
container_issue |
3 |
container_start_page |
641 |
op_container_end_page |
649 |
_version_ |
1815349032572157952 |