Multispectral Remote Sensing for mapping the areas affected by the techno-industrial disaster of the oil spill on Brazilian beaches
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Anais da Academia Brasileira de Ciências (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000401102 |
Resumo: | Abstract Since the emergence in August 2019 of portions of varying sizes of crude oil on several beaches in the Northeast region of Brazil, various studies have been conducted to identify the source of the disaster and estimate the damage caused. This article aims to contribute to this scientific effort in order to describe an extensive mapping that used Remote Sensing data of the impacted areas and its correlation with socioeconomic typology of the municipalities directly affected. The research was based on the list of 201 oiled beaches published on October 28th, 2019 in Technical Note from the Brazilian Institute of the Environment and Renewable Natural Resources (IBAMA, 2019). Applying the Supervised Classification to images from the MSI/Sentinel-2 sensor, a methodology for beach cartography was defined, then geographical sections were subsequently classified and quantified by thematic classes. This thematic mapping fostered to obtain a proxy of the possible impacted areas up to that date, generating an “Atlas of Beaches Affected by Oil” with 402 maps of the affected beaches, which is available on the Joaquim Nabuco Foundation website. This mapping is unprecedented and it becomes important for the environmental monitoring of these areas. This article was prepared from a request for the special edition of the AABC journal. |
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Multispectral Remote Sensing for mapping the areas affected by the techno-industrial disaster of the oil spill on Brazilian beachesdisasteroil spillRemote Sensingsocial vulnerabilityBrazilAbstract Since the emergence in August 2019 of portions of varying sizes of crude oil on several beaches in the Northeast region of Brazil, various studies have been conducted to identify the source of the disaster and estimate the damage caused. This article aims to contribute to this scientific effort in order to describe an extensive mapping that used Remote Sensing data of the impacted areas and its correlation with socioeconomic typology of the municipalities directly affected. The research was based on the list of 201 oiled beaches published on October 28th, 2019 in Technical Note from the Brazilian Institute of the Environment and Renewable Natural Resources (IBAMA, 2019). Applying the Supervised Classification to images from the MSI/Sentinel-2 sensor, a methodology for beach cartography was defined, then geographical sections were subsequently classified and quantified by thematic classes. This thematic mapping fostered to obtain a proxy of the possible impacted areas up to that date, generating an “Atlas of Beaches Affected by Oil” with 402 maps of the affected beaches, which is available on the Joaquim Nabuco Foundation website. This mapping is unprecedented and it becomes important for the environmental monitoring of these areas. This article was prepared from a request for the special edition of the AABC journal.Academia Brasileira de Ciências2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000401102Anais da Academia Brasileira de Ciências v.94 suppl.2 2022reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765202220210308info:eu-repo/semantics/openAccessFREIRE,NEISON C.F.CAMPOS,LUIS HENRIQUE R.QUEIROZ,VINICIUSSOUZA,LUCAS B.V.SILVA,MAYARA C.eng2022-05-20T00:00:00Zoai:scielo:S0001-37652022000401102Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2022-05-20T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)false |
dc.title.none.fl_str_mv |
Multispectral Remote Sensing for mapping the areas affected by the techno-industrial disaster of the oil spill on Brazilian beaches |
title |
Multispectral Remote Sensing for mapping the areas affected by the techno-industrial disaster of the oil spill on Brazilian beaches |
spellingShingle |
Multispectral Remote Sensing for mapping the areas affected by the techno-industrial disaster of the oil spill on Brazilian beaches FREIRE,NEISON C.F. disaster oil spill Remote Sensing social vulnerability Brazil |
title_short |
Multispectral Remote Sensing for mapping the areas affected by the techno-industrial disaster of the oil spill on Brazilian beaches |
title_full |
Multispectral Remote Sensing for mapping the areas affected by the techno-industrial disaster of the oil spill on Brazilian beaches |
title_fullStr |
Multispectral Remote Sensing for mapping the areas affected by the techno-industrial disaster of the oil spill on Brazilian beaches |
title_full_unstemmed |
Multispectral Remote Sensing for mapping the areas affected by the techno-industrial disaster of the oil spill on Brazilian beaches |
title_sort |
Multispectral Remote Sensing for mapping the areas affected by the techno-industrial disaster of the oil spill on Brazilian beaches |
author |
FREIRE,NEISON C.F. |
author_facet |
FREIRE,NEISON C.F. CAMPOS,LUIS HENRIQUE R. QUEIROZ,VINICIUS SOUZA,LUCAS B.V. SILVA,MAYARA C. |
author_role |
author |
author2 |
CAMPOS,LUIS HENRIQUE R. QUEIROZ,VINICIUS SOUZA,LUCAS B.V. SILVA,MAYARA C. |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
FREIRE,NEISON C.F. CAMPOS,LUIS HENRIQUE R. QUEIROZ,VINICIUS SOUZA,LUCAS B.V. SILVA,MAYARA C. |
dc.subject.por.fl_str_mv |
disaster oil spill Remote Sensing social vulnerability Brazil |
topic |
disaster oil spill Remote Sensing social vulnerability Brazil |
description |
Abstract Since the emergence in August 2019 of portions of varying sizes of crude oil on several beaches in the Northeast region of Brazil, various studies have been conducted to identify the source of the disaster and estimate the damage caused. This article aims to contribute to this scientific effort in order to describe an extensive mapping that used Remote Sensing data of the impacted areas and its correlation with socioeconomic typology of the municipalities directly affected. The research was based on the list of 201 oiled beaches published on October 28th, 2019 in Technical Note from the Brazilian Institute of the Environment and Renewable Natural Resources (IBAMA, 2019). Applying the Supervised Classification to images from the MSI/Sentinel-2 sensor, a methodology for beach cartography was defined, then geographical sections were subsequently classified and quantified by thematic classes. This thematic mapping fostered to obtain a proxy of the possible impacted areas up to that date, generating an “Atlas of Beaches Affected by Oil” with 402 maps of the affected beaches, which is available on the Joaquim Nabuco Foundation website. This mapping is unprecedented and it becomes important for the environmental monitoring of these areas. This article was prepared from a request for the special edition of the AABC journal. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
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=S0001-37652022000401102 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000401102 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0001-3765202220210308 |
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 |
Academia Brasileira de Ciências |
publisher.none.fl_str_mv |
Academia Brasileira de Ciências |
dc.source.none.fl_str_mv |
Anais da Academia Brasileira de Ciências v.94 suppl.2 2022 reponame:Anais da Academia Brasileira de Ciências (Online) instname:Academia Brasileira de Ciências (ABC) instacron:ABC |
instname_str |
Academia Brasileira de Ciências (ABC) |
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ABC |
institution |
ABC |
reponame_str |
Anais da Academia Brasileira de Ciências (Online) |
collection |
Anais da Academia Brasileira de Ciências (Online) |
repository.name.fl_str_mv |
Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC) |
repository.mail.fl_str_mv |
||aabc@abc.org.br |
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1754302872085856256 |