Delimitation of flooded areas based on sentinel-1 sar data processed through machine learning: a study case from central amazon, Brazil
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://scielo.pt/scielo.php?script=sci_arttext&pid=S0430-50272023000200087 |
Resumo: | Abstract Delimitation of areas subject to flooding is crucial to understand water dynamics and fluvial changes. This study analyzed the potential of C-band Synthetic Aperture Radar (SAR) images acquired by the Sentinel-1 satellite in 2017, 2018, and 2019 to delineate flooded areas in the Central Amazon. The images were processed by the Artificial Neural Network Multi-Layer Perceptron (ANN-MLP) and two K-Nearest Neighbor (KNN-7 and KNN-11) machine learning (ML) classifiers. Pre-processing of Single Look Complex (SLC) SAR images involved the following methodological steps: orbit-file application; radiometric calibration (σ0); Range-Doppler terrain correction; speckle noise filtering; and conversion of linear data to backscattering coefficients (units in dB). We applied the Lee filter, with a window size of 3x3, for speckle filtering. A set of 6000 randomly distributed samples for training (70%), validation (20%), and test (10%) was obtained based on visual interpretation of Sentinel-2 optical satellite image acquired in the same years of SAR images. We found the largest flooded areas in 2019 in the study area (municipality of Parintins and Urucará, Amazonas River, Brazil): 6244km2 by the ANN-MLP classifier; 6268km2 by KNN-7; and 6290km2 by KNN-11, while the smallest flooded areas were found in 2018: 5364km2 by ANN-MLP; 5412km2 by KNN-7; and 5535km2 by KNN-11. The three classifiers presented Kappa coefficients between 0.77 and 0.91. ANN-MLP showed the best accuracy. The presence of shadow effects in the SAR images increased the commission errors. |
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Delimitation of flooded areas based on sentinel-1 sar data processed through machine learning: a study case from central amazon, BrazilRemote sensingwater resourcesimage classifiersinundationAbstract Delimitation of areas subject to flooding is crucial to understand water dynamics and fluvial changes. This study analyzed the potential of C-band Synthetic Aperture Radar (SAR) images acquired by the Sentinel-1 satellite in 2017, 2018, and 2019 to delineate flooded areas in the Central Amazon. The images were processed by the Artificial Neural Network Multi-Layer Perceptron (ANN-MLP) and two K-Nearest Neighbor (KNN-7 and KNN-11) machine learning (ML) classifiers. Pre-processing of Single Look Complex (SLC) SAR images involved the following methodological steps: orbit-file application; radiometric calibration (σ0); Range-Doppler terrain correction; speckle noise filtering; and conversion of linear data to backscattering coefficients (units in dB). We applied the Lee filter, with a window size of 3x3, for speckle filtering. A set of 6000 randomly distributed samples for training (70%), validation (20%), and test (10%) was obtained based on visual interpretation of Sentinel-2 optical satellite image acquired in the same years of SAR images. We found the largest flooded areas in 2019 in the study area (municipality of Parintins and Urucará, Amazonas River, Brazil): 6244km2 by the ANN-MLP classifier; 6268km2 by KNN-7; and 6290km2 by KNN-11, while the smallest flooded areas were found in 2018: 5364km2 by ANN-MLP; 5412km2 by KNN-7; and 5535km2 by KNN-11. The three classifiers presented Kappa coefficients between 0.77 and 0.91. ANN-MLP showed the best accuracy. The presence of shadow effects in the SAR images increased the commission errors.Centro de Estudos Geográficos2023-08-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articletext/htmlhttp://scielo.pt/scielo.php?script=sci_arttext&pid=S0430-50272023000200087Finisterra - Revista Portuguesa de Geografia n.123 2023reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAPenghttp://scielo.pt/scielo.php?script=sci_arttext&pid=S0430-50272023000200087Magalhães,Ivo Augusto LopesCarvalho Junior,Osmar Abilio deSano,Edson Eyjiinfo:eu-repo/semantics/openAccess2024-02-06T16:58:24Zoai:scielo:S0430-50272023000200087Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:15:09.846500Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Delimitation of flooded areas based on sentinel-1 sar data processed through machine learning: a study case from central amazon, Brazil |
title |
Delimitation of flooded areas based on sentinel-1 sar data processed through machine learning: a study case from central amazon, Brazil |
spellingShingle |
Delimitation of flooded areas based on sentinel-1 sar data processed through machine learning: a study case from central amazon, Brazil Magalhães,Ivo Augusto Lopes Remote sensing water resources image classifiers inundation |
title_short |
Delimitation of flooded areas based on sentinel-1 sar data processed through machine learning: a study case from central amazon, Brazil |
title_full |
Delimitation of flooded areas based on sentinel-1 sar data processed through machine learning: a study case from central amazon, Brazil |
title_fullStr |
Delimitation of flooded areas based on sentinel-1 sar data processed through machine learning: a study case from central amazon, Brazil |
title_full_unstemmed |
Delimitation of flooded areas based on sentinel-1 sar data processed through machine learning: a study case from central amazon, Brazil |
title_sort |
Delimitation of flooded areas based on sentinel-1 sar data processed through machine learning: a study case from central amazon, Brazil |
author |
Magalhães,Ivo Augusto Lopes |
author_facet |
Magalhães,Ivo Augusto Lopes Carvalho Junior,Osmar Abilio de Sano,Edson Eyji |
author_role |
author |
author2 |
Carvalho Junior,Osmar Abilio de Sano,Edson Eyji |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Magalhães,Ivo Augusto Lopes Carvalho Junior,Osmar Abilio de Sano,Edson Eyji |
dc.subject.por.fl_str_mv |
Remote sensing water resources image classifiers inundation |
topic |
Remote sensing water resources image classifiers inundation |
description |
Abstract Delimitation of areas subject to flooding is crucial to understand water dynamics and fluvial changes. This study analyzed the potential of C-band Synthetic Aperture Radar (SAR) images acquired by the Sentinel-1 satellite in 2017, 2018, and 2019 to delineate flooded areas in the Central Amazon. The images were processed by the Artificial Neural Network Multi-Layer Perceptron (ANN-MLP) and two K-Nearest Neighbor (KNN-7 and KNN-11) machine learning (ML) classifiers. Pre-processing of Single Look Complex (SLC) SAR images involved the following methodological steps: orbit-file application; radiometric calibration (σ0); Range-Doppler terrain correction; speckle noise filtering; and conversion of linear data to backscattering coefficients (units in dB). We applied the Lee filter, with a window size of 3x3, for speckle filtering. A set of 6000 randomly distributed samples for training (70%), validation (20%), and test (10%) was obtained based on visual interpretation of Sentinel-2 optical satellite image acquired in the same years of SAR images. We found the largest flooded areas in 2019 in the study area (municipality of Parintins and Urucará, Amazonas River, Brazil): 6244km2 by the ANN-MLP classifier; 6268km2 by KNN-7; and 6290km2 by KNN-11, while the smallest flooded areas were found in 2018: 5364km2 by ANN-MLP; 5412km2 by KNN-7; and 5535km2 by KNN-11. The three classifiers presented Kappa coefficients between 0.77 and 0.91. ANN-MLP showed the best accuracy. The presence of shadow effects in the SAR images increased the commission errors. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-08-01 |
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://scielo.pt/scielo.php?script=sci_arttext&pid=S0430-50272023000200087 |
url |
http://scielo.pt/scielo.php?script=sci_arttext&pid=S0430-50272023000200087 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://scielo.pt/scielo.php?script=sci_arttext&pid=S0430-50272023000200087 |
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 |
Centro de Estudos Geográficos |
publisher.none.fl_str_mv |
Centro de Estudos Geográficos |
dc.source.none.fl_str_mv |
Finisterra - Revista Portuguesa de Geografia n.123 2023 reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799137252281942016 |