Estimation of chlorophyll-a concentration using Landsat 8 in the Cassaffousth reservoir

Autores
Ledesma, María Micaela; Bonansea, Matias; Ledesma, Claudia; Rodríguez, Claudia; Carreño, Joel; Pinotti, Lucio Pedro
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The physico-chemical and biological composition of a reservoir's effluents directly influences water quality. The values of variables such as high values of concentrations of chlorophyll-a (Chl-a) are indicators of pollution. The objective of this work was to monitor the trophic status and water quality of the Cassaffousth reservoir (Córdoba, Argentina) through the development of statistical models based on field data and satellite information. During 2016 and 2017, samples were taken bimonthly. Seven sampling sites were selected and physico-chemical and biological parameters were assessed. By using regression techniques, Landsat 8 information was related with field data to construct and validate a statistical model to determine the distribution of Chl-a in the reservoir (R2 = 0.87). The generated algorithm was used to generate maps which contained information about the dynamics of Chl-a in the entire reservoir. Remote sensing techniques can be used to expand the knowledge of the dynamics of the Cassaffousth reservoir. Moreover, these techniques can be used as baselines for the development of an early warning system for this and other reservoirs in the region.
Fil: Ledesma, María Micaela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Río Cuarto. Facultad de Agronomía y Veterinaria. Departamento de Estudios Básicos y Agropecuarios; Argentina
Fil: Bonansea, Matias. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente; Argentina
Fil: Ledesma, Claudia. Universidad Nacional de Río Cuarto; Argentina
Fil: Rodríguez, Claudia. Universidad Nacional de Río Cuarto; Argentina
Fil: Carreño, Joel. Universidad Nacional de Río Cuarto; Argentina
Fil: Pinotti, Lucio Pedro. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente; Argentina
Materia
CHLOROPHYLL-A
LANDSAT 8
PREDICTIVE MODELS
RESERVOIR
WATER QUALITY
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/123226
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/123226
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Estimation of chlorophyll-a concentration using Landsat 8 in the Cassaffousth reservoirLedesma, María MicaelaBonansea, MatiasLedesma, ClaudiaRodríguez, ClaudiaCarreño, JoelPinotti, Lucio PedroCHLOROPHYLL-ALANDSAT 8PREDICTIVE MODELSRESERVOIRWATER QUALITYhttps://purl.org/becyt/ford/2.7https://purl.org/becyt/ford/2The physico-chemical and biological composition of a reservoir's effluents directly influences water quality. The values of variables such as high values of concentrations of chlorophyll-a (Chl-a) are indicators of pollution. The objective of this work was to monitor the trophic status and water quality of the Cassaffousth reservoir (Córdoba, Argentina) through the development of statistical models based on field data and satellite information. During 2016 and 2017, samples were taken bimonthly. Seven sampling sites were selected and physico-chemical and biological parameters were assessed. By using regression techniques, Landsat 8 information was related with field data to construct and validate a statistical model to determine the distribution of Chl-a in the reservoir (R2 = 0.87). The generated algorithm was used to generate maps which contained information about the dynamics of Chl-a in the entire reservoir. Remote sensing techniques can be used to expand the knowledge of the dynamics of the Cassaffousth reservoir. Moreover, these techniques can be used as baselines for the development of an early warning system for this and other reservoirs in the region.Fil: Ledesma, María Micaela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Río Cuarto. Facultad de Agronomía y Veterinaria. Departamento de Estudios Básicos y Agropecuarios; ArgentinaFil: Bonansea, Matias. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente; ArgentinaFil: Ledesma, Claudia. Universidad Nacional de Río Cuarto; ArgentinaFil: Rodríguez, Claudia. Universidad Nacional de Río Cuarto; ArgentinaFil: Carreño, Joel. Universidad Nacional de Río Cuarto; ArgentinaFil: Pinotti, Lucio Pedro. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente; ArgentinaIWA Publishing2019-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/123226Ledesma, María Micaela; Bonansea, Matias; Ledesma, Claudia; Rodríguez, Claudia; Carreño, Joel; et al.; Estimation of chlorophyll-a concentration using Landsat 8 in the Cassaffousth reservoir; IWA Publishing; Water Science and Technology: Water Supply; 19; 7; 11-2019; 2021-20271606-97491607-0798CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://iwaponline.com/ws/article/doi/10.2166/ws.2019.080/67786/Estimation-of-chlorophylla-concentration-usinginfo:eu-repo/semantics/altIdentifier/doi/10.2166/ws.2019.080info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:47:03Zoai:ri.conicet.gov.ar:11336/123226instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:47:03.853CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Estimation of chlorophyll-a concentration using Landsat 8 in the Cassaffousth reservoir
title Estimation of chlorophyll-a concentration using Landsat 8 in the Cassaffousth reservoir
spellingShingle Estimation of chlorophyll-a concentration using Landsat 8 in the Cassaffousth reservoir
Ledesma, María Micaela
CHLOROPHYLL-A
LANDSAT 8
PREDICTIVE MODELS
RESERVOIR
WATER QUALITY
title_short Estimation of chlorophyll-a concentration using Landsat 8 in the Cassaffousth reservoir
title_full Estimation of chlorophyll-a concentration using Landsat 8 in the Cassaffousth reservoir
title_fullStr Estimation of chlorophyll-a concentration using Landsat 8 in the Cassaffousth reservoir
title_full_unstemmed Estimation of chlorophyll-a concentration using Landsat 8 in the Cassaffousth reservoir
title_sort Estimation of chlorophyll-a concentration using Landsat 8 in the Cassaffousth reservoir
dc.creator.none.fl_str_mv Ledesma, María Micaela
Bonansea, Matias
Ledesma, Claudia
Rodríguez, Claudia
Carreño, Joel
Pinotti, Lucio Pedro
author Ledesma, María Micaela
author_facet Ledesma, María Micaela
Bonansea, Matias
Ledesma, Claudia
Rodríguez, Claudia
Carreño, Joel
Pinotti, Lucio Pedro
author_role author
author2 Bonansea, Matias
Ledesma, Claudia
Rodríguez, Claudia
Carreño, Joel
Pinotti, Lucio Pedro
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv CHLOROPHYLL-A
LANDSAT 8
PREDICTIVE MODELS
RESERVOIR
WATER QUALITY
topic CHLOROPHYLL-A
LANDSAT 8
PREDICTIVE MODELS
RESERVOIR
WATER QUALITY
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.7
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv The physico-chemical and biological composition of a reservoir's effluents directly influences water quality. The values of variables such as high values of concentrations of chlorophyll-a (Chl-a) are indicators of pollution. The objective of this work was to monitor the trophic status and water quality of the Cassaffousth reservoir (Córdoba, Argentina) through the development of statistical models based on field data and satellite information. During 2016 and 2017, samples were taken bimonthly. Seven sampling sites were selected and physico-chemical and biological parameters were assessed. By using regression techniques, Landsat 8 information was related with field data to construct and validate a statistical model to determine the distribution of Chl-a in the reservoir (R2 = 0.87). The generated algorithm was used to generate maps which contained information about the dynamics of Chl-a in the entire reservoir. Remote sensing techniques can be used to expand the knowledge of the dynamics of the Cassaffousth reservoir. Moreover, these techniques can be used as baselines for the development of an early warning system for this and other reservoirs in the region.
Fil: Ledesma, María Micaela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina. Universidad Nacional de Río Cuarto. Facultad de Agronomía y Veterinaria. Departamento de Estudios Básicos y Agropecuarios; Argentina
Fil: Bonansea, Matias. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente; Argentina
Fil: Ledesma, Claudia. Universidad Nacional de Río Cuarto; Argentina
Fil: Rodríguez, Claudia. Universidad Nacional de Río Cuarto; Argentina
Fil: Carreño, Joel. Universidad Nacional de Río Cuarto; Argentina
Fil: Pinotti, Lucio Pedro. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente; Argentina
description The physico-chemical and biological composition of a reservoir's effluents directly influences water quality. The values of variables such as high values of concentrations of chlorophyll-a (Chl-a) are indicators of pollution. The objective of this work was to monitor the trophic status and water quality of the Cassaffousth reservoir (Córdoba, Argentina) through the development of statistical models based on field data and satellite information. During 2016 and 2017, samples were taken bimonthly. Seven sampling sites were selected and physico-chemical and biological parameters were assessed. By using regression techniques, Landsat 8 information was related with field data to construct and validate a statistical model to determine the distribution of Chl-a in the reservoir (R2 = 0.87). The generated algorithm was used to generate maps which contained information about the dynamics of Chl-a in the entire reservoir. Remote sensing techniques can be used to expand the knowledge of the dynamics of the Cassaffousth reservoir. Moreover, these techniques can be used as baselines for the development of an early warning system for this and other reservoirs in the region.
publishDate 2019
dc.date.none.fl_str_mv 2019-11
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/123226
Ledesma, María Micaela; Bonansea, Matias; Ledesma, Claudia; Rodríguez, Claudia; Carreño, Joel; et al.; Estimation of chlorophyll-a concentration using Landsat 8 in the Cassaffousth reservoir; IWA Publishing; Water Science and Technology: Water Supply; 19; 7; 11-2019; 2021-2027
1606-9749
1607-0798
CONICET Digital
CONICET
url http://hdl.handle.net/11336/123226
identifier_str_mv Ledesma, María Micaela; Bonansea, Matias; Ledesma, Claudia; Rodríguez, Claudia; Carreño, Joel; et al.; Estimation of chlorophyll-a concentration using Landsat 8 in the Cassaffousth reservoir; IWA Publishing; Water Science and Technology: Water Supply; 19; 7; 11-2019; 2021-2027
1606-9749
1607-0798
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://iwaponline.com/ws/article/doi/10.2166/ws.2019.080/67786/Estimation-of-chlorophylla-concentration-using
info:eu-repo/semantics/altIdentifier/doi/10.2166/ws.2019.080
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv IWA Publishing
publisher.none.fl_str_mv IWA Publishing
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613467257962496
score 13.070432

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