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...

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Published in:ICES Journal of Marine Science
Main Authors: Woodd-Walker, RS, Watkins, JL, Brierley, Andrew Stuart
Format: Article in Journal/Newspaper
Language:English
Published: 2003
Subjects:
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
id ftunstandrewcris:oai:research-portal.st-andrews.ac.uk:publications/05ffb027-cac6-4016-9d47-1f8c2a568190
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spelling 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
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