Comparing unmanned aerial multispectral and hyperspectral imagery for harmful algal bloom monitoring in artificial ponds used for fish farming

Detalhes bibliográficos
Autor(a) principal: Olivetti, Diogo
Data de Publicação: 2023
Outros Autores: Cicerelli, Rejane Ennes, Martinez, Jean-Michel, Almeida, Tati de, Casari, Raphael Augusto das Chagas Noqueli, Borges, Henrique Dantas, Roig, Henrique Llacer
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UnB
Texto Completo: http://repositorio2.unb.br/jspui/handle/10482/46917
https://doi.org/10.3390/drones7070410
https://orcid.org/0000-0002-8199-5163
https://orcid.org/0000-0003-3281-8512
Resumo: This work aimed to assess the potential of unmanned aerial vehicle (UAV) multi- and hyper-spectral platforms to estimate chlorophyll-a (Chl-a) and cyanobacteria in experimental fishponds in Brazil. In addition to spectral resolutions, the tested platforms differ in the price, payload, imaging system, and processing. Hyperspectral airborne surveys were conducted using a push-broom system 276-band Headwall Nano-Hyperspec camera onboard a DJI Matrice 600 UAV. Multispectral airborne surveys were conducted using a global shutter-frame 4-band Parrot Sequoia camera onboard a DJI Phantom 4 UAV. Water quality field measurements were acquired using a portable fluorometer and laboratory analysis. The concentration ranged from 14.3 to 290.7 µg/L and from 0 to 112.5 µg/L for Chl-a and cyanobacteria, respectively. Forty-one Chl-a and cyanobacteria bio-optical retrieval models were tested. The UAV hyperspectral image achieved robust Chl-a and cyanobacteria assessments, with RMSE values of 32.8 and 12.1 µg/L, respectively. Multispectral images achieved Chl-a and cyanobacteria retrieval with RMSE values of 47.6 and 35.1 µg/L, respectively, efficiently mapping the broad Chl-a concentration classes. Hyperspectral platforms are ideal for the robust monitoring of Chl-a and CyanoHABs; however, the integrated platform has a high cost. More accessible multispectral platforms may represent a trade-off between the mapping efficiency and the deployment costs, provided that the multispectral cameras offer narrow spectral bands in the 660–690 nm and 700–730 nm ranges for Chl-a and in the 600–625 nm and 700–730 nm spectral ranges for cyanobacteria.
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spelling Olivetti, DiogoCicerelli, Rejane EnnesMartinez, Jean-MichelAlmeida, Tati deCasari, Raphael Augusto das Chagas NoqueliBorges, Henrique DantasRoig, Henrique LlacerUniversity of Brasília, Institute of GeosciencesUniversity of Brasília, Institute of GeosciencesUniversity of Brasília, Institute of GeosciencesCentre National de la Recherche Scientifique (CNRS), Géosciences Environnement Toulouse (GET), UMR5563, Institut de Recherche pour le Développement (IRD), Université Toulouse 3,University of Brasília, Institute of GeosciencesUniversity of Brasília, Institute of GeosciencesUniversity of Brasília, Institute of Geoscienceshttps://orcid.org/0000-0002-0729-57672023-11-28T14:00:29Z2023-11-28T14:00:29Z2023-06-21OLIVETTI, Diogo et al. Comparing unmanned aerial multispectral and hyperspectral imagery for harmful algal bloom monitoring in artificial ponds used for fish farming. Drones, [S. l.], v. 7, n. 7, 410. DOU: https://doi.org/10.3390/drones7070410. Disponível em: https://www.mdpi.com/2504-446X/7/7/410.http://repositorio2.unb.br/jspui/handle/10482/46917https://doi.org/10.3390/drones7070410https://orcid.org/0000-0002-8199-5163https://orcid.org/0000-0003-3281-8512engMDPICopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).info:eu-repo/semantics/openAccessComparing unmanned aerial multispectral and hyperspectral imagery for harmful algal bloom monitoring in artificial ponds used for fish farminginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleÁguaSensoriamento remotoAeronaves remotamente pilotadasDronesClorofila-aCianobactériaThis work aimed to assess the potential of unmanned aerial vehicle (UAV) multi- and hyper-spectral platforms to estimate chlorophyll-a (Chl-a) and cyanobacteria in experimental fishponds in Brazil. In addition to spectral resolutions, the tested platforms differ in the price, payload, imaging system, and processing. Hyperspectral airborne surveys were conducted using a push-broom system 276-band Headwall Nano-Hyperspec camera onboard a DJI Matrice 600 UAV. Multispectral airborne surveys were conducted using a global shutter-frame 4-band Parrot Sequoia camera onboard a DJI Phantom 4 UAV. Water quality field measurements were acquired using a portable fluorometer and laboratory analysis. The concentration ranged from 14.3 to 290.7 µg/L and from 0 to 112.5 µg/L for Chl-a and cyanobacteria, respectively. Forty-one Chl-a and cyanobacteria bio-optical retrieval models were tested. The UAV hyperspectral image achieved robust Chl-a and cyanobacteria assessments, with RMSE values of 32.8 and 12.1 µg/L, respectively. Multispectral images achieved Chl-a and cyanobacteria retrieval with RMSE values of 47.6 and 35.1 µg/L, respectively, efficiently mapping the broad Chl-a concentration classes. Hyperspectral platforms are ideal for the robust monitoring of Chl-a and CyanoHABs; however, the integrated platform has a high cost. More accessible multispectral platforms may represent a trade-off between the mapping efficiency and the deployment costs, provided that the multispectral cameras offer narrow spectral bands in the 660–690 nm and 700–730 nm ranges for Chl-a and in the 600–625 nm and 700–730 nm spectral ranges for cyanobacteria.Instituto de Geociências (IG)reponame:Repositório Institucional da UnBinstname:Universidade de Brasília (UnB)instacron:UNBLICENSElicense.txtlicense.txttext/plain102http://repositorio2.unb.br/jspui/bitstream/10482/46917/2/license.txtaed4704d04bb260d4decd80db311aaa5MD52open accessORIGINALARTIGO_ComparingUnmannedAerial.pdfARTIGO_ComparingUnmannedAerial.pdfapplication/pdf19360915http://repositorio2.unb.br/jspui/bitstream/10482/46917/1/ARTIGO_ComparingUnmannedAerial.pdfa508d5e42d309a215eb4414b863f060cMD51open access10482/469172023-11-28 11:13:35.95open accessoai:repositorio2.unb.br:10482/46917U3VibWlzc8OjbyBlZmV0aXZhZGEgZGUgYWNvcmRvIGNvbSBsaWNlbsOnYSBjb25jZWRpZGEgcGVsbyBhdXRvciBlL291IGRldGVudG9yIGRvcyBkaXJlaXRvcyBhdXRvcmFpcy4KBiblioteca Digital de Teses e DissertaçõesPUBhttps://repositorio.unb.br/oai/requestopendoar:2023-11-28T14:13:35Repositório Institucional da UnB - Universidade de Brasília (UnB)false
dc.title.pt_BR.fl_str_mv Comparing unmanned aerial multispectral and hyperspectral imagery for harmful algal bloom monitoring in artificial ponds used for fish farming
title Comparing unmanned aerial multispectral and hyperspectral imagery for harmful algal bloom monitoring in artificial ponds used for fish farming
spellingShingle Comparing unmanned aerial multispectral and hyperspectral imagery for harmful algal bloom monitoring in artificial ponds used for fish farming
Olivetti, Diogo
Água
Sensoriamento remoto
Aeronaves remotamente pilotadas
Drones
Clorofila-a
Cianobactéria
title_short Comparing unmanned aerial multispectral and hyperspectral imagery for harmful algal bloom monitoring in artificial ponds used for fish farming
title_full Comparing unmanned aerial multispectral and hyperspectral imagery for harmful algal bloom monitoring in artificial ponds used for fish farming
title_fullStr Comparing unmanned aerial multispectral and hyperspectral imagery for harmful algal bloom monitoring in artificial ponds used for fish farming
title_full_unstemmed Comparing unmanned aerial multispectral and hyperspectral imagery for harmful algal bloom monitoring in artificial ponds used for fish farming
title_sort Comparing unmanned aerial multispectral and hyperspectral imagery for harmful algal bloom monitoring in artificial ponds used for fish farming
author Olivetti, Diogo
author_facet Olivetti, Diogo
Cicerelli, Rejane Ennes
Martinez, Jean-Michel
Almeida, Tati de
Casari, Raphael Augusto das Chagas Noqueli
Borges, Henrique Dantas
Roig, Henrique Llacer
author_role author
author2 Cicerelli, Rejane Ennes
Martinez, Jean-Michel
Almeida, Tati de
Casari, Raphael Augusto das Chagas Noqueli
Borges, Henrique Dantas
Roig, Henrique Llacer
author2_role author
author
author
author
author
author
dc.contributor.affiliation.pt_BR.fl_str_mv University of Brasília, Institute of Geosciences
University of Brasília, Institute of Geosciences
University of Brasília, Institute of Geosciences
Centre National de la Recherche Scientifique (CNRS), Géosciences Environnement Toulouse (GET), UMR5563, Institut de Recherche pour le Développement (IRD), Université Toulouse 3,
University of Brasília, Institute of Geosciences
University of Brasília, Institute of Geosciences
University of Brasília, Institute of Geosciences
dc.contributor.orcid.none.fl_str_mv https://orcid.org/0000-0002-0729-5767
dc.contributor.author.fl_str_mv Olivetti, Diogo
Cicerelli, Rejane Ennes
Martinez, Jean-Michel
Almeida, Tati de
Casari, Raphael Augusto das Chagas Noqueli
Borges, Henrique Dantas
Roig, Henrique Llacer
dc.subject.keyword.pt_BR.fl_str_mv Água
Sensoriamento remoto
Aeronaves remotamente pilotadas
Drones
Clorofila-a
Cianobactéria
topic Água
Sensoriamento remoto
Aeronaves remotamente pilotadas
Drones
Clorofila-a
Cianobactéria
description This work aimed to assess the potential of unmanned aerial vehicle (UAV) multi- and hyper-spectral platforms to estimate chlorophyll-a (Chl-a) and cyanobacteria in experimental fishponds in Brazil. In addition to spectral resolutions, the tested platforms differ in the price, payload, imaging system, and processing. Hyperspectral airborne surveys were conducted using a push-broom system 276-band Headwall Nano-Hyperspec camera onboard a DJI Matrice 600 UAV. Multispectral airborne surveys were conducted using a global shutter-frame 4-band Parrot Sequoia camera onboard a DJI Phantom 4 UAV. Water quality field measurements were acquired using a portable fluorometer and laboratory analysis. The concentration ranged from 14.3 to 290.7 µg/L and from 0 to 112.5 µg/L for Chl-a and cyanobacteria, respectively. Forty-one Chl-a and cyanobacteria bio-optical retrieval models were tested. The UAV hyperspectral image achieved robust Chl-a and cyanobacteria assessments, with RMSE values of 32.8 and 12.1 µg/L, respectively. Multispectral images achieved Chl-a and cyanobacteria retrieval with RMSE values of 47.6 and 35.1 µg/L, respectively, efficiently mapping the broad Chl-a concentration classes. Hyperspectral platforms are ideal for the robust monitoring of Chl-a and CyanoHABs; however, the integrated platform has a high cost. More accessible multispectral platforms may represent a trade-off between the mapping efficiency and the deployment costs, provided that the multispectral cameras offer narrow spectral bands in the 660–690 nm and 700–730 nm ranges for Chl-a and in the 600–625 nm and 700–730 nm spectral ranges for cyanobacteria.
publishDate 2023
dc.date.accessioned.fl_str_mv 2023-11-28T14:00:29Z
dc.date.available.fl_str_mv 2023-11-28T14:00:29Z
dc.date.issued.fl_str_mv 2023-06-21
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.citation.fl_str_mv OLIVETTI, Diogo et al. Comparing unmanned aerial multispectral and hyperspectral imagery for harmful algal bloom monitoring in artificial ponds used for fish farming. Drones, [S. l.], v. 7, n. 7, 410. DOU: https://doi.org/10.3390/drones7070410. Disponível em: https://www.mdpi.com/2504-446X/7/7/410.
dc.identifier.uri.fl_str_mv http://repositorio2.unb.br/jspui/handle/10482/46917
dc.identifier.doi.pt_BR.fl_str_mv https://doi.org/10.3390/drones7070410
dc.identifier.orcid.none.fl_str_mv https://orcid.org/0000-0002-8199-5163
https://orcid.org/0000-0003-3281-8512
identifier_str_mv OLIVETTI, Diogo et al. Comparing unmanned aerial multispectral and hyperspectral imagery for harmful algal bloom monitoring in artificial ponds used for fish farming. Drones, [S. l.], v. 7, n. 7, 410. DOU: https://doi.org/10.3390/drones7070410. Disponível em: https://www.mdpi.com/2504-446X/7/7/410.
url http://repositorio2.unb.br/jspui/handle/10482/46917
https://doi.org/10.3390/drones7070410
https://orcid.org/0000-0002-8199-5163
https://orcid.org/0000-0003-3281-8512
dc.language.iso.fl_str_mv eng
language eng
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