Space-Temporal analysis of suspended sediment in low concentration reservoir by remote sensing

Detalhes bibliográficos
Autor(a) principal: Chelotti,Giancarlo Brugnara
Data de Publicação: 2019
Outros Autores: Martinez,Jean Michel, Roig,Henrique Llacer, Olivietti,Diogo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: RBRH (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312019000100221
Resumo: ABSTRACT The study of small reservoirs with low suspended sediment concentration (CSS) is still a challenge for remote sensing. In this work we estimate CSS from the optical properties of water and orbital imagery. Campaigns were carried out at selected dates according to the calendar of sensor passages, rainfall seasonality and hydrograph of the reservoir for the collection of surface water samples and field spectroradiometry. The calibration between CSS and spectral behavior generated CSS estimation models from MODIS and Landsat 8 data, allowing investigation of their temporal and spatial behavior. The MODIS model generated a time series of CSS from 2000 to 2017, presenting R2 = 0.8105 and RMSE% = 39.91%. The Landsat 8 model allowed the spatial analysis of CSS, with R2 = 0.8352 and RMSE% = 15.12%. The combination of the proposed models allowed the temporal and spatial analysis of the CSS and its relationships with the rainfall regime and the quota variation of the Descoberto reservoir (DF). The results showed that the use of orbital data complements the CSS information obtained by the traditional methods of collecting and analyzing water quality in low CSS reservoirs.
id ABRH-1_7cdf62fc85bae9890b00a080580f9c7e
oai_identifier_str oai:scielo:S2318-03312019000100221
network_acronym_str ABRH-1
network_name_str RBRH (Online)
repository_id_str
spelling Space-Temporal analysis of suspended sediment in low concentration reservoir by remote sensingWater qualityHydrological and environmental modelingRemote SensingMODISLandsat 8ABSTRACT The study of small reservoirs with low suspended sediment concentration (CSS) is still a challenge for remote sensing. In this work we estimate CSS from the optical properties of water and orbital imagery. Campaigns were carried out at selected dates according to the calendar of sensor passages, rainfall seasonality and hydrograph of the reservoir for the collection of surface water samples and field spectroradiometry. The calibration between CSS and spectral behavior generated CSS estimation models from MODIS and Landsat 8 data, allowing investigation of their temporal and spatial behavior. The MODIS model generated a time series of CSS from 2000 to 2017, presenting R2 = 0.8105 and RMSE% = 39.91%. The Landsat 8 model allowed the spatial analysis of CSS, with R2 = 0.8352 and RMSE% = 15.12%. The combination of the proposed models allowed the temporal and spatial analysis of the CSS and its relationships with the rainfall regime and the quota variation of the Descoberto reservoir (DF). The results showed that the use of orbital data complements the CSS information obtained by the traditional methods of collecting and analyzing water quality in low CSS reservoirs.Associação Brasileira de Recursos Hídricos2019-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312019000100221RBRH v.24 2019reponame:RBRH (Online)instname:Associação Brasileira de Recursos Hídricos (ABRH)instacron:ABRH10.1590/2318-0331.241920180061info:eu-repo/semantics/openAccessChelotti,Giancarlo BrugnaraMartinez,Jean MichelRoig,Henrique LlacerOlivietti,Diogoeng2019-04-29T00:00:00Zoai:scielo:S2318-03312019000100221Revistahttps://www.scielo.br/j/rbrh/https://old.scielo.br/oai/scielo-oai.php||rbrh@abrh.org.br2318-03311414-381Xopendoar:2019-04-29T00:00RBRH (Online) - Associação Brasileira de Recursos Hídricos (ABRH)false
dc.title.none.fl_str_mv Space-Temporal analysis of suspended sediment in low concentration reservoir by remote sensing
title Space-Temporal analysis of suspended sediment in low concentration reservoir by remote sensing
spellingShingle Space-Temporal analysis of suspended sediment in low concentration reservoir by remote sensing
Chelotti,Giancarlo Brugnara
Water quality
Hydrological and environmental modeling
Remote Sensing
MODIS
Landsat 8
title_short Space-Temporal analysis of suspended sediment in low concentration reservoir by remote sensing
title_full Space-Temporal analysis of suspended sediment in low concentration reservoir by remote sensing
title_fullStr Space-Temporal analysis of suspended sediment in low concentration reservoir by remote sensing
title_full_unstemmed Space-Temporal analysis of suspended sediment in low concentration reservoir by remote sensing
title_sort Space-Temporal analysis of suspended sediment in low concentration reservoir by remote sensing
author Chelotti,Giancarlo Brugnara
author_facet Chelotti,Giancarlo Brugnara
Martinez,Jean Michel
Roig,Henrique Llacer
Olivietti,Diogo
author_role author
author2 Martinez,Jean Michel
Roig,Henrique Llacer
Olivietti,Diogo
author2_role author
author
author
dc.contributor.author.fl_str_mv Chelotti,Giancarlo Brugnara
Martinez,Jean Michel
Roig,Henrique Llacer
Olivietti,Diogo
dc.subject.por.fl_str_mv Water quality
Hydrological and environmental modeling
Remote Sensing
MODIS
Landsat 8
topic Water quality
Hydrological and environmental modeling
Remote Sensing
MODIS
Landsat 8
description ABSTRACT The study of small reservoirs with low suspended sediment concentration (CSS) is still a challenge for remote sensing. In this work we estimate CSS from the optical properties of water and orbital imagery. Campaigns were carried out at selected dates according to the calendar of sensor passages, rainfall seasonality and hydrograph of the reservoir for the collection of surface water samples and field spectroradiometry. The calibration between CSS and spectral behavior generated CSS estimation models from MODIS and Landsat 8 data, allowing investigation of their temporal and spatial behavior. The MODIS model generated a time series of CSS from 2000 to 2017, presenting R2 = 0.8105 and RMSE% = 39.91%. The Landsat 8 model allowed the spatial analysis of CSS, with R2 = 0.8352 and RMSE% = 15.12%. The combination of the proposed models allowed the temporal and spatial analysis of the CSS and its relationships with the rainfall regime and the quota variation of the Descoberto reservoir (DF). The results showed that the use of orbital data complements the CSS information obtained by the traditional methods of collecting and analyzing water quality in low CSS reservoirs.
publishDate 2019
dc.date.none.fl_str_mv 2019-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312019000100221
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312019000100221
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/2318-0331.241920180061
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Associação Brasileira de Recursos Hídricos
publisher.none.fl_str_mv Associação Brasileira de Recursos Hídricos
dc.source.none.fl_str_mv RBRH v.24 2019
reponame:RBRH (Online)
instname:Associação Brasileira de Recursos Hídricos (ABRH)
instacron:ABRH
instname_str Associação Brasileira de Recursos Hídricos (ABRH)
instacron_str ABRH
institution ABRH
reponame_str RBRH (Online)
collection RBRH (Online)
repository.name.fl_str_mv RBRH (Online) - Associação Brasileira de Recursos Hídricos (ABRH)
repository.mail.fl_str_mv ||rbrh@abrh.org.br
_version_ 1754734701892861952