Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETMþ images

We have used Landsat-5 TM and Landsat-7 ETMþ images together with simultaneous ground-truth data at sample points in the Do~nana marshes to predict water turbidity and depth from band reflectance using Generalized Additive Models. We have point samples for 12 different dates simultaneous with 7 Land...

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Detalhes bibliográficos
Autores: Bustamante, Javier, Pacios, Fernando, Díaz-Delgado, Ricardo, Aragonés, David
Formato: artículo
Fecha de publicación:2009
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/46891
Acesso em linha:http://hdl.handle.net/10261/46891
Access Level:acceso abierto
Palavra-chave:Water turbidity
Water depth
Marshland
Remote sensing
Wetlands
GAM
GLM
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spelling Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETMþ imagesBustamante, JavierPacios, FernandoDíaz-Delgado, RicardoAragonés, DavidWater turbidityWater depthMarshlandRemote sensingWetlandsGAMGLMWe have used Landsat-5 TM and Landsat-7 ETMþ images together with simultaneous ground-truth data at sample points in the Do~nana marshes to predict water turbidity and depth from band reflectance using Generalized Additive Models. We have point samples for 12 different dates simultaneous with 7 Landsat-5 and 5 Landsat-7 overpasses. The best model for water turbidity in the marsh explained 38% of variance in ground-truth data and included as predictors band 3 (630e690 nm), band 5 (1550e1750 nm) and the ratio between bands 1 (450e520 nm) and 4 (760e900 nm). Water turbidity is easier to predict for water bodies like the Guadalquivir River and artificial ponds that are deep and not affected by bottom soil reflectance and aquatic vegetation. For the latter, a simple model using band 3 reflectance explains 78.6% of the variance. Water depth is easier to predict than turbidity. The best model for water depth in the marsh explains 78% of the variance and includes as predictors band 1, band 5, the ratio between band 2 (520e600 nm) and band 4, and bottom soil reflectance in band 4 in September, when the marsh is dry. The water turbidity and water depth models have been developed in order to reconstruct historical changes in Do~nana wetlands during the last 30 years using the Landsat satellite images time series.Peer reviewedElsevierConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]201220122009info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501application/pdfhttp://hdl.handle.net/10261/46891reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésDíaz-Delgado, Ricardo; Afán, Isabel; Aragonés, David; García, Diego; Bustamante, Javier; 2019; Water Turbidity Masks Doñana 1984/2019 (v1.0) [Dataset]; Zenodo; https://doi.org/10.5281/zenodo.3519043; http://hdl.handle.net/10261/285756http://dx.doi.org/10.1016/j.jenvman.2007.08.021Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/468912026-05-22T06:33:51Z
dc.title.none.fl_str_mv Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETMþ images
title Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETMþ images
spellingShingle Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETMþ images
Bustamante, Javier
Water turbidity
Water depth
Marshland
Remote sensing
Wetlands
GAM
GLM
title_short Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETMþ images
title_full Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETMþ images
title_fullStr Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETMþ images
title_full_unstemmed Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETMþ images
title_sort Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETMþ images
dc.creator.none.fl_str_mv Bustamante, Javier
Pacios, Fernando
Díaz-Delgado, Ricardo
Aragonés, David
author Bustamante, Javier
author_facet Bustamante, Javier
Pacios, Fernando
Díaz-Delgado, Ricardo
Aragonés, David
author_role author
author2 Pacios, Fernando
Díaz-Delgado, Ricardo
Aragonés, David
author2_role author
author
author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Water turbidity
Water depth
Marshland
Remote sensing
Wetlands
GAM
GLM
topic Water turbidity
Water depth
Marshland
Remote sensing
Wetlands
GAM
GLM
description We have used Landsat-5 TM and Landsat-7 ETMþ images together with simultaneous ground-truth data at sample points in the Do~nana marshes to predict water turbidity and depth from band reflectance using Generalized Additive Models. We have point samples for 12 different dates simultaneous with 7 Landsat-5 and 5 Landsat-7 overpasses. The best model for water turbidity in the marsh explained 38% of variance in ground-truth data and included as predictors band 3 (630e690 nm), band 5 (1550e1750 nm) and the ratio between bands 1 (450e520 nm) and 4 (760e900 nm). Water turbidity is easier to predict for water bodies like the Guadalquivir River and artificial ponds that are deep and not affected by bottom soil reflectance and aquatic vegetation. For the latter, a simple model using band 3 reflectance explains 78.6% of the variance. Water depth is easier to predict than turbidity. The best model for water depth in the marsh explains 78% of the variance and includes as predictors band 1, band 5, the ratio between band 2 (520e600 nm) and band 4, and bottom soil reflectance in band 4 in September, when the marsh is dry. The water turbidity and water depth models have been developed in order to reconstruct historical changes in Do~nana wetlands during the last 30 years using the Landsat satellite images time series.
publishDate 2009
dc.date.none.fl_str_mv 2009
2012
2012
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/46891
url http://hdl.handle.net/10261/46891
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Díaz-Delgado, Ricardo; Afán, Isabel; Aragonés, David; García, Diego; Bustamante, Javier; 2019; Water Turbidity Masks Doñana 1984/2019 (v1.0) [Dataset]; Zenodo; https://doi.org/10.5281/zenodo.3519043; http://hdl.handle.net/10261/285756
http://dx.doi.org/10.1016/j.jenvman.2007.08.021
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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