Monitoring river turbidity after a mine tailing dam failure using an empirical model derived from Sentinel-2 imagery
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1590/0001-3765202320220177 http://hdl.handle.net/11449/248783 |
Resumo: | Sudden failure of a mine tailing dam occurred in the municipality of Brumadinho, Minas Gerais, Brazil, on January 25, 2019. Approximately 12 million cubic meters of mine tailings discharged into the Paraopeba River, producing strong environmental and societal impacts, mainly due to a massive increase in turbidity (occasionally exceeding 50,000 Nephelometric Turbidity Units [NTU] (CPRM 2019). Remote sensing is a well-established tool for quantifying spatial patterns of turbidity. However, a few empirical models have been developed to map turbidity in rivers impacted by mine tailings. Thus, this study aimed to develop an empirical model capable of producing turbidity estimates based on images from the Sentinel-2 satellite, using the Paraopeba River as the study area. We found that river turbidity was most strongly correlated with the sensor’s near-infrared band (NIR) (band 8). Thus, we built an empirical single-band model using an exponential function with an (R2 of 0.91) to characterize the spatial-temporal variation of turbidity based on satellite observations of NIR reflectance. Although the role of discharged tailings in the seasonal variation of turbidity is not well understood, the proposed model enabled the monitoring of turbidity variations in the Paraopeba River associated with seasonal resuspension or deposition of mine tailings. Our study shows the capability of single-band models to quantify seasonal variations in turbidity in rivers impacted by mine tailing pollution. |
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Monitoring river turbidity after a mine tailing dam failure using an empirical model derived from Sentinel-2 imageryMine tailingsParaopeba riverRemote SensingSentinel-2turbiditywater qualitySudden failure of a mine tailing dam occurred in the municipality of Brumadinho, Minas Gerais, Brazil, on January 25, 2019. Approximately 12 million cubic meters of mine tailings discharged into the Paraopeba River, producing strong environmental and societal impacts, mainly due to a massive increase in turbidity (occasionally exceeding 50,000 Nephelometric Turbidity Units [NTU] (CPRM 2019). Remote sensing is a well-established tool for quantifying spatial patterns of turbidity. However, a few empirical models have been developed to map turbidity in rivers impacted by mine tailings. Thus, this study aimed to develop an empirical model capable of producing turbidity estimates based on images from the Sentinel-2 satellite, using the Paraopeba River as the study area. We found that river turbidity was most strongly correlated with the sensor’s near-infrared band (NIR) (band 8). Thus, we built an empirical single-band model using an exponential function with an (R2 of 0.91) to characterize the spatial-temporal variation of turbidity based on satellite observations of NIR reflectance. Although the role of discharged tailings in the seasonal variation of turbidity is not well understood, the proposed model enabled the monitoring of turbidity variations in the Paraopeba River associated with seasonal resuspension or deposition of mine tailings. Our study shows the capability of single-band models to quantify seasonal variations in turbidity in rivers impacted by mine tailing pollution.Universidade Estadual de São Paulo (UNESP) Laboratório de Estudos de Bacias (LEBAC), Avenida 24A, 1515, Bela Vista, SPUniversidade Estadual de São Paulo (UNESP) Centro de Estudos Ambientais, Avenida 24A, 1515, Bela Vista, SPUniversidade Estadual de São Paulo (UNESP) Departamento de Geologia Aplicada, Avenida 24A, 1515, Bela Vista, SPUniversidade Estadual de São Paulo (UNESP) Laboratório de Estudos de Bacias (LEBAC), Avenida 24A, 1515, Bela Vista, SPUniversidade Estadual de São Paulo (UNESP) Centro de Estudos Ambientais, Avenida 24A, 1515, Bela Vista, SPUniversidade Estadual de São Paulo (UNESP) Departamento de Geologia Aplicada, Avenida 24A, 1515, Bela Vista, SPUniversidade Estadual Paulista (UNESP)Crioni, Pedro L. B. [UNESP]Teramoto, Elias H. [UNESP]Chang, Hung K. [UNESP]2023-07-29T13:53:34Z2023-07-29T13:53:34Z2023-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1590/0001-3765202320220177Anais da Academia Brasileira de Ciencias, v. 95, n. 1, 2023.1678-26900001-3765http://hdl.handle.net/11449/24878310.1590/0001-37652023202201772-s2.0-85158012152Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAnais da Academia Brasileira de Cienciasinfo:eu-repo/semantics/openAccess2024-04-10T19:22:25Zoai:repositorio.unesp.br:11449/248783Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:33:43.975301Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Monitoring river turbidity after a mine tailing dam failure using an empirical model derived from Sentinel-2 imagery |
title |
Monitoring river turbidity after a mine tailing dam failure using an empirical model derived from Sentinel-2 imagery |
spellingShingle |
Monitoring river turbidity after a mine tailing dam failure using an empirical model derived from Sentinel-2 imagery Crioni, Pedro L. B. [UNESP] Mine tailings Paraopeba river Remote Sensing Sentinel-2 turbidity water quality |
title_short |
Monitoring river turbidity after a mine tailing dam failure using an empirical model derived from Sentinel-2 imagery |
title_full |
Monitoring river turbidity after a mine tailing dam failure using an empirical model derived from Sentinel-2 imagery |
title_fullStr |
Monitoring river turbidity after a mine tailing dam failure using an empirical model derived from Sentinel-2 imagery |
title_full_unstemmed |
Monitoring river turbidity after a mine tailing dam failure using an empirical model derived from Sentinel-2 imagery |
title_sort |
Monitoring river turbidity after a mine tailing dam failure using an empirical model derived from Sentinel-2 imagery |
author |
Crioni, Pedro L. B. [UNESP] |
author_facet |
Crioni, Pedro L. B. [UNESP] Teramoto, Elias H. [UNESP] Chang, Hung K. [UNESP] |
author_role |
author |
author2 |
Teramoto, Elias H. [UNESP] Chang, Hung K. [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Crioni, Pedro L. B. [UNESP] Teramoto, Elias H. [UNESP] Chang, Hung K. [UNESP] |
dc.subject.por.fl_str_mv |
Mine tailings Paraopeba river Remote Sensing Sentinel-2 turbidity water quality |
topic |
Mine tailings Paraopeba river Remote Sensing Sentinel-2 turbidity water quality |
description |
Sudden failure of a mine tailing dam occurred in the municipality of Brumadinho, Minas Gerais, Brazil, on January 25, 2019. Approximately 12 million cubic meters of mine tailings discharged into the Paraopeba River, producing strong environmental and societal impacts, mainly due to a massive increase in turbidity (occasionally exceeding 50,000 Nephelometric Turbidity Units [NTU] (CPRM 2019). Remote sensing is a well-established tool for quantifying spatial patterns of turbidity. However, a few empirical models have been developed to map turbidity in rivers impacted by mine tailings. Thus, this study aimed to develop an empirical model capable of producing turbidity estimates based on images from the Sentinel-2 satellite, using the Paraopeba River as the study area. We found that river turbidity was most strongly correlated with the sensor’s near-infrared band (NIR) (band 8). Thus, we built an empirical single-band model using an exponential function with an (R2 of 0.91) to characterize the spatial-temporal variation of turbidity based on satellite observations of NIR reflectance. Although the role of discharged tailings in the seasonal variation of turbidity is not well understood, the proposed model enabled the monitoring of turbidity variations in the Paraopeba River associated with seasonal resuspension or deposition of mine tailings. Our study shows the capability of single-band models to quantify seasonal variations in turbidity in rivers impacted by mine tailing pollution. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-29T13:53:34Z 2023-07-29T13:53:34Z 2023-01-01 |
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.uri.fl_str_mv |
http://dx.doi.org/10.1590/0001-3765202320220177 Anais da Academia Brasileira de Ciencias, v. 95, n. 1, 2023. 1678-2690 0001-3765 http://hdl.handle.net/11449/248783 10.1590/0001-3765202320220177 2-s2.0-85158012152 |
url |
http://dx.doi.org/10.1590/0001-3765202320220177 http://hdl.handle.net/11449/248783 |
identifier_str_mv |
Anais da Academia Brasileira de Ciencias, v. 95, n. 1, 2023. 1678-2690 0001-3765 10.1590/0001-3765202320220177 2-s2.0-85158012152 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Anais da Academia Brasileira de Ciencias |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
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1808128826924859392 |