Assessing topographic features and population abundance in an Antarctic penguin colony through UAV-based deep-learning models

Penguins play an essential biochemical role in the Antarctic ecosystem, being the study of their dynamics of utmost importance to understand their environment, behaviour and populational trends in the current climate change scenario. In this study, we used multi-rotor Unmanned Aerial Vehicles (UAVs)...

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Detalhes bibliográficos
Autores: Belyaev, Oleg, Román, Alejandro, Belliure, Josabel, Navarro, Gabriel, Barbero, Luis, Tovar-Sánchez, Antonio
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/378279
Acesso em linha:http://hdl.handle.net/10261/378279
https://api.elsevier.com/content/abstract/scopus_id/85203536787
Access Level:acceso abierto
Palavra-chave:UAV
Deep-learning
Chinstrap penguin
Pygoscelis antarcticus
Breeding site features
Vapour col
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spelling Assessing topographic features and population abundance in an Antarctic penguin colony through UAV-based deep-learning modelsBelyaev, OlegRomán, AlejandroBelliure, JosabelNavarro, GabrielBarbero, LuisTovar-Sánchez, AntonioUAVDeep-learningChinstrap penguinPygoscelis antarcticusBreeding site featuresVapour colPenguins play an essential biochemical role in the Antarctic ecosystem, being the study of their dynamics of utmost importance to understand their environment, behaviour and populational trends in the current climate change scenario. In this study, we used multi-rotor Unmanned Aerial Vehicles (UAVs) along the coast of the chinstrap penguin (Pygoscelis antarcticus) colony of Vapour Col (Deception Island, Antarctica) to map potential sites of biochemical interactions with the surrounding sea water. Several runoff discharge points were identified, where a precise placing of environmental sampling station is suggested. Additionally, UAVs were used in combination with Object Detection Architectures to obtain the chinstrap colony population size. Applying a simulation for clutch initiation dates due to our off-laying peak count, we obtained an estimated range of 13,250 to 22,000 breeding pairs in the 2021/2022 breeding season, also suggesting an alternative approach using chinstrap chicks as proxy to estimate adult numbers. This research shows the utility of UAV-deep learning for environment characterization and wildlife monitoring, providing a solid framework for upcoming studies in the area.This research has been funded by the Spanish Government projects PiMetAn (ref. RTI2018-098048-BI00), DICHOSO (PID2021-1257830B-100), University of Cádiz EQC2018-004446-P, CSIC EQC2018-004275-P and EQC2019-005721. O. Belyaev is supported by the Spanish Predoctoral Grant (Ref: PRE2022-103391). A. Román is supported by the Spanish FPU Grant (Ref: FPU19/04557). J. Belliure is supported by the grant PID2019-108597RB-100 (PERPANTAR project) from the Spanish Agency of Research (Spain). This research is part of the POLARCSIC research initiatives. We warmly thank the military staff of the Spanish Antarctic Base Gabriel de Castilla, the crew of the BIO Hespérides oceanographic vessel and the Marine Technology Unit (UTM-CSIC) for their logistic support, without which the XXXV Spanish Antarctic campaign and this research would not have been possible.Peer reviewedElsevierAgencia Estatal de Investigación (España)Universidad de CádizConsejo Superior de Investigaciones Científicas (España)CSIC - Plataforma Temática Interdisciplinar del CSIC Polar CSIC (PTI POLARCSIC)CSIC - Unidad de Tecnología Marina (UTM)Barbero, Luis [0000-0002-3513-2025]Tovar-Sánchez, Antonio [0000-0003-4375-1982]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/378279https://api.elsevier.com/content/abstract/scopus_id/85203536787reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-098048-B-I00info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-125783OB-I00info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108597RB-I00The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1016/j.jag.2024.104124https://doi.org/10.1016/j.jag.2024.104124Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3782792026-05-22T06:33:51Z
dc.title.none.fl_str_mv Assessing topographic features and population abundance in an Antarctic penguin colony through UAV-based deep-learning models
title Assessing topographic features and population abundance in an Antarctic penguin colony through UAV-based deep-learning models
spellingShingle Assessing topographic features and population abundance in an Antarctic penguin colony through UAV-based deep-learning models
Belyaev, Oleg
UAV
Deep-learning
Chinstrap penguin
Pygoscelis antarcticus
Breeding site features
Vapour col
title_short Assessing topographic features and population abundance in an Antarctic penguin colony through UAV-based deep-learning models
title_full Assessing topographic features and population abundance in an Antarctic penguin colony through UAV-based deep-learning models
title_fullStr Assessing topographic features and population abundance in an Antarctic penguin colony through UAV-based deep-learning models
title_full_unstemmed Assessing topographic features and population abundance in an Antarctic penguin colony through UAV-based deep-learning models
title_sort Assessing topographic features and population abundance in an Antarctic penguin colony through UAV-based deep-learning models
dc.creator.none.fl_str_mv Belyaev, Oleg
Román, Alejandro
Belliure, Josabel
Navarro, Gabriel
Barbero, Luis
Tovar-Sánchez, Antonio
author Belyaev, Oleg
author_facet Belyaev, Oleg
Román, Alejandro
Belliure, Josabel
Navarro, Gabriel
Barbero, Luis
Tovar-Sánchez, Antonio
author_role author
author2 Román, Alejandro
Belliure, Josabel
Navarro, Gabriel
Barbero, Luis
Tovar-Sánchez, Antonio
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación (España)
Universidad de Cádiz
Consejo Superior de Investigaciones Científicas (España)
CSIC - Plataforma Temática Interdisciplinar del CSIC Polar CSIC (PTI POLARCSIC)
CSIC - Unidad de Tecnología Marina (UTM)
Barbero, Luis [0000-0002-3513-2025]
Tovar-Sánchez, Antonio [0000-0003-4375-1982]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv UAV
Deep-learning
Chinstrap penguin
Pygoscelis antarcticus
Breeding site features
Vapour col
topic UAV
Deep-learning
Chinstrap penguin
Pygoscelis antarcticus
Breeding site features
Vapour col
description Penguins play an essential biochemical role in the Antarctic ecosystem, being the study of their dynamics of utmost importance to understand their environment, behaviour and populational trends in the current climate change scenario. In this study, we used multi-rotor Unmanned Aerial Vehicles (UAVs) along the coast of the chinstrap penguin (Pygoscelis antarcticus) colony of Vapour Col (Deception Island, Antarctica) to map potential sites of biochemical interactions with the surrounding sea water. Several runoff discharge points were identified, where a precise placing of environmental sampling station is suggested. Additionally, UAVs were used in combination with Object Detection Architectures to obtain the chinstrap colony population size. Applying a simulation for clutch initiation dates due to our off-laying peak count, we obtained an estimated range of 13,250 to 22,000 breeding pairs in the 2021/2022 breeding season, also suggesting an alternative approach using chinstrap chicks as proxy to estimate adult numbers. This research shows the utility of UAV-deep learning for environment characterization and wildlife monitoring, providing a solid framework for upcoming studies in the area.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/378279
https://api.elsevier.com/content/abstract/scopus_id/85203536787
url http://hdl.handle.net/10261/378279
https://api.elsevier.com/content/abstract/scopus_id/85203536787
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-098048-B-I00
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-125783OB-I00
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108597RB-I00
The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1016/j.jag.2024.104124
https://doi.org/10.1016/j.jag.2024.104124
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
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