Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina)

Autores
Bonansea, Matias; Rodriguez, Claudia; Pinotti, Lucio Pedro; Ferrero, Susana Beatriz
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The application of remote sensing technology to water quality monitoring has special significance for lake management at regional scales. Many studies have proposed algorithms between Landsat data and in-situ water quality parameters using classical regression models. The novelty of this paper is that we developed algorithms to determine log-transformed chlorophyll-a concentration (Chl-a) and Secchi disk transparency (SDT) in Río Tercero reservoir using Landsat TM and ETM+ imagery, ancillary environmental factors and linear mixed models (LMM), obtaining an increase in the accuracy of the estimates. The validation results showed that LMM with spatial correlation structure that take into account water surface temperature (WST) and rainfall were the most suitable method for estimating these parameters. WST derived from the Landsat thermal band was also validated. The algorithms were used to generate quantitative maps providing spatially and temporally rich information on patterns of water quality throughout the reservoir. Water quality features related to the hydrogeomorphology of the reservoir, typical seasonality and influx from the cooling system of a local nuclear reactor were identified in the time series maps.
Fil: Bonansea, Matias. Universidad Nacional de Rio Cuarto. Facultad de Agronomia y Veterinaria. Cátedra de Ecología; Argentina. Universidad Nacional de Rio Cuarto. Facultad de Cs.exactas Fisicoquimicas y Naturales. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Cordoba. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente.; Argentina
Fil: Rodriguez, Claudia. Universidad Nacional de Rio Cuarto. Facultad de Agronomia y Veterinaria. Cátedra de Ecología; Argentina
Fil: Pinotti, Lucio Pedro. Universidad Nacional de Rio Cuarto. Facultad de Cs.exactas Fisicoquimicas y Naturales. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Cordoba. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente.; Argentina. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas, Fisicoquímicas y Naturales. Departamento de Geología; Argentina
Fil: Ferrero, Susana Beatriz. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas, Físico-Químicas y Naturales. Departamento de Matemática; Argentina
Materia
REMOTE SENSING
RESERVOIR
LANDSAT
WATER QUALITY
LINEAR MIXED MODELS
ALGORITHMS
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:11336/102510
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina)Bonansea, MatiasRodriguez, ClaudiaPinotti, Lucio PedroFerrero, Susana BeatrizREMOTE SENSINGRESERVOIRLANDSATWATER QUALITYLINEAR MIXED MODELSALGORITHMShttps://purl.org/becyt/ford/2.7https://purl.org/becyt/ford/2The application of remote sensing technology to water quality monitoring has special significance for lake management at regional scales. Many studies have proposed algorithms between Landsat data and in-situ water quality parameters using classical regression models. The novelty of this paper is that we developed algorithms to determine log-transformed chlorophyll-a concentration (Chl-a) and Secchi disk transparency (SDT) in Río Tercero reservoir using Landsat TM and ETM+ imagery, ancillary environmental factors and linear mixed models (LMM), obtaining an increase in the accuracy of the estimates. The validation results showed that LMM with spatial correlation structure that take into account water surface temperature (WST) and rainfall were the most suitable method for estimating these parameters. WST derived from the Landsat thermal band was also validated. The algorithms were used to generate quantitative maps providing spatially and temporally rich information on patterns of water quality throughout the reservoir. Water quality features related to the hydrogeomorphology of the reservoir, typical seasonality and influx from the cooling system of a local nuclear reactor were identified in the time series maps.Fil: Bonansea, Matias. Universidad Nacional de Rio Cuarto. Facultad de Agronomia y Veterinaria. Cátedra de Ecología; Argentina. Universidad Nacional de Rio Cuarto. Facultad de Cs.exactas Fisicoquimicas y Naturales. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Cordoba. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente.; ArgentinaFil: Rodriguez, Claudia. Universidad Nacional de Rio Cuarto. Facultad de Agronomia y Veterinaria. Cátedra de Ecología; ArgentinaFil: Pinotti, Lucio Pedro. Universidad Nacional de Rio Cuarto. Facultad de Cs.exactas Fisicoquimicas y Naturales. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Cordoba. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente.; Argentina. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas, Fisicoquímicas y Naturales. Departamento de Geología; ArgentinaFil: Ferrero, Susana Beatriz. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas, Físico-Químicas y Naturales. Departamento de Matemática; ArgentinaElsevier Science Inc2015-03-01info: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/11336/102510Bonansea, Matias; Rodriguez, Claudia; Pinotti, Lucio Pedro; Ferrero, Susana Beatriz; Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina); Elsevier Science Inc; Remote Sensing of Environment; 158; 1; 1-3-2015; 28-410034-4257CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0034425714004544info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rse.2014.10.032info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:37:06Zoai:ri.conicet.gov.ar:11336/102510instacron: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 09:37:07.144CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina)
title Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina)
spellingShingle Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina)
Bonansea, Matias
REMOTE SENSING
RESERVOIR
LANDSAT
WATER QUALITY
LINEAR MIXED MODELS
ALGORITHMS
title_short Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina)
title_full Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina)
title_fullStr Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina)
title_full_unstemmed Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina)
title_sort Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina)
dc.creator.none.fl_str_mv Bonansea, Matias
Rodriguez, Claudia
Pinotti, Lucio Pedro
Ferrero, Susana Beatriz
author Bonansea, Matias
author_facet Bonansea, Matias
Rodriguez, Claudia
Pinotti, Lucio Pedro
Ferrero, Susana Beatriz
author_role author
author2 Rodriguez, Claudia
Pinotti, Lucio Pedro
Ferrero, Susana Beatriz
author2_role author
author
author
dc.subject.none.fl_str_mv REMOTE SENSING
RESERVOIR
LANDSAT
WATER QUALITY
LINEAR MIXED MODELS
ALGORITHMS
topic REMOTE SENSING
RESERVOIR
LANDSAT
WATER QUALITY
LINEAR MIXED MODELS
ALGORITHMS
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 application of remote sensing technology to water quality monitoring has special significance for lake management at regional scales. Many studies have proposed algorithms between Landsat data and in-situ water quality parameters using classical regression models. The novelty of this paper is that we developed algorithms to determine log-transformed chlorophyll-a concentration (Chl-a) and Secchi disk transparency (SDT) in Río Tercero reservoir using Landsat TM and ETM+ imagery, ancillary environmental factors and linear mixed models (LMM), obtaining an increase in the accuracy of the estimates. The validation results showed that LMM with spatial correlation structure that take into account water surface temperature (WST) and rainfall were the most suitable method for estimating these parameters. WST derived from the Landsat thermal band was also validated. The algorithms were used to generate quantitative maps providing spatially and temporally rich information on patterns of water quality throughout the reservoir. Water quality features related to the hydrogeomorphology of the reservoir, typical seasonality and influx from the cooling system of a local nuclear reactor were identified in the time series maps.
Fil: Bonansea, Matias. Universidad Nacional de Rio Cuarto. Facultad de Agronomia y Veterinaria. Cátedra de Ecología; Argentina. Universidad Nacional de Rio Cuarto. Facultad de Cs.exactas Fisicoquimicas y Naturales. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Cordoba. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente.; Argentina
Fil: Rodriguez, Claudia. Universidad Nacional de Rio Cuarto. Facultad de Agronomia y Veterinaria. Cátedra de Ecología; Argentina
Fil: Pinotti, Lucio Pedro. Universidad Nacional de Rio Cuarto. Facultad de Cs.exactas Fisicoquimicas y Naturales. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Cordoba. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente.; Argentina. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas, Fisicoquímicas y Naturales. Departamento de Geología; Argentina
Fil: Ferrero, Susana Beatriz. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas, Físico-Químicas y Naturales. Departamento de Matemática; Argentina
description The application of remote sensing technology to water quality monitoring has special significance for lake management at regional scales. Many studies have proposed algorithms between Landsat data and in-situ water quality parameters using classical regression models. The novelty of this paper is that we developed algorithms to determine log-transformed chlorophyll-a concentration (Chl-a) and Secchi disk transparency (SDT) in Río Tercero reservoir using Landsat TM and ETM+ imagery, ancillary environmental factors and linear mixed models (LMM), obtaining an increase in the accuracy of the estimates. The validation results showed that LMM with spatial correlation structure that take into account water surface temperature (WST) and rainfall were the most suitable method for estimating these parameters. WST derived from the Landsat thermal band was also validated. The algorithms were used to generate quantitative maps providing spatially and temporally rich information on patterns of water quality throughout the reservoir. Water quality features related to the hydrogeomorphology of the reservoir, typical seasonality and influx from the cooling system of a local nuclear reactor were identified in the time series maps.
publishDate 2015
dc.date.none.fl_str_mv 2015-03-01
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/11336/102510
Bonansea, Matias; Rodriguez, Claudia; Pinotti, Lucio Pedro; Ferrero, Susana Beatriz; Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina); Elsevier Science Inc; Remote Sensing of Environment; 158; 1; 1-3-2015; 28-41
0034-4257
CONICET Digital
CONICET
url http://hdl.handle.net/11336/102510
identifier_str_mv Bonansea, Matias; Rodriguez, Claudia; Pinotti, Lucio Pedro; Ferrero, Susana Beatriz; Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina); Elsevier Science Inc; Remote Sensing of Environment; 158; 1; 1-3-2015; 28-41
0034-4257
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0034425714004544
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rse.2014.10.032
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 Elsevier Science Inc
publisher.none.fl_str_mv Elsevier Science Inc
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
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score 13.069144

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