Burned area mapping in the brazilian savanna using a one-class support vector machine trained by active fires
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.5/14620 |
Resumo: | We used the Visible Infrared Imaging Radiometer Suite (VIIRS) active fire data (375 m spatial resolution) to automatically extract multispectral samples and train a One-Class Support Vector Machine for burned area mapping, and applied the resulting classification algorithm to 300-m spatial resolution imagery from the Project for On-Board Autonomy-Vegetation (PROBA-V). The active fire data were screened to prevent extraction of unrepresentative burned area samples and combined with surface reflectance bi-weekly composites to produce burned area maps. The procedure was applied over the Brazilian Cerrado savanna, validated with reference maps obtained from Landsat images and compared with the Collection 6 Moderate Resolution Imaging Spectrometer (MODIS) Burned Area product (MCD64A1) Results show that the algorithm developed improved the detection of small-sized scars and displayed results more similar to the reference data than MCD64A1. Unlike active fire-based region growing algorithms, the proposed approach allows for the detection and mapping of burn scars without active fires, thus eliminating a potential source of omission error. The burned area mapping approach presented here should facilitate the development of operational-automated burned area algorithms, and is very straightforward for implementation with other sensors |
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Burned area mapping in the brazilian savanna using a one-class support vector machine trained by active firessupport vector machine one classburned areaactive fireCerradoPROBA-VVIIRSWe used the Visible Infrared Imaging Radiometer Suite (VIIRS) active fire data (375 m spatial resolution) to automatically extract multispectral samples and train a One-Class Support Vector Machine for burned area mapping, and applied the resulting classification algorithm to 300-m spatial resolution imagery from the Project for On-Board Autonomy-Vegetation (PROBA-V). The active fire data were screened to prevent extraction of unrepresentative burned area samples and combined with surface reflectance bi-weekly composites to produce burned area maps. The procedure was applied over the Brazilian Cerrado savanna, validated with reference maps obtained from Landsat images and compared with the Collection 6 Moderate Resolution Imaging Spectrometer (MODIS) Burned Area product (MCD64A1) Results show that the algorithm developed improved the detection of small-sized scars and displayed results more similar to the reference data than MCD64A1. Unlike active fire-based region growing algorithms, the proposed approach allows for the detection and mapping of burn scars without active fires, thus eliminating a potential source of omission error. The burned area mapping approach presented here should facilitate the development of operational-automated burned area algorithms, and is very straightforward for implementation with other sensorsMDPIRepositório da Universidade de LisboaPereira, Allan A.Cardoso Pereira, José MiguelLibonati, RenataOom, DuarteSetzer, Alberto W.Morelli, FabianoMachado-Silva, FaustoCarvalho, Luís Marcelo Tavares de2018-01-03T11:30:04Z20172017-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.5/14620engRemote Sens. 2017, 9, 116110.3390/rs9111161info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-06T14:44:39Zoai:www.repository.utl.pt:10400.5/14620Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:00:26.871727Repositório 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 |
Burned area mapping in the brazilian savanna using a one-class support vector machine trained by active fires |
title |
Burned area mapping in the brazilian savanna using a one-class support vector machine trained by active fires |
spellingShingle |
Burned area mapping in the brazilian savanna using a one-class support vector machine trained by active fires Pereira, Allan A. support vector machine one class burned area active fire Cerrado PROBA-V VIIRS |
title_short |
Burned area mapping in the brazilian savanna using a one-class support vector machine trained by active fires |
title_full |
Burned area mapping in the brazilian savanna using a one-class support vector machine trained by active fires |
title_fullStr |
Burned area mapping in the brazilian savanna using a one-class support vector machine trained by active fires |
title_full_unstemmed |
Burned area mapping in the brazilian savanna using a one-class support vector machine trained by active fires |
title_sort |
Burned area mapping in the brazilian savanna using a one-class support vector machine trained by active fires |
author |
Pereira, Allan A. |
author_facet |
Pereira, Allan A. Cardoso Pereira, José Miguel Libonati, Renata Oom, Duarte Setzer, Alberto W. Morelli, Fabiano Machado-Silva, Fausto Carvalho, Luís Marcelo Tavares de |
author_role |
author |
author2 |
Cardoso Pereira, José Miguel Libonati, Renata Oom, Duarte Setzer, Alberto W. Morelli, Fabiano Machado-Silva, Fausto Carvalho, Luís Marcelo Tavares de |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Pereira, Allan A. Cardoso Pereira, José Miguel Libonati, Renata Oom, Duarte Setzer, Alberto W. Morelli, Fabiano Machado-Silva, Fausto Carvalho, Luís Marcelo Tavares de |
dc.subject.por.fl_str_mv |
support vector machine one class burned area active fire Cerrado PROBA-V VIIRS |
topic |
support vector machine one class burned area active fire Cerrado PROBA-V VIIRS |
description |
We used the Visible Infrared Imaging Radiometer Suite (VIIRS) active fire data (375 m spatial resolution) to automatically extract multispectral samples and train a One-Class Support Vector Machine for burned area mapping, and applied the resulting classification algorithm to 300-m spatial resolution imagery from the Project for On-Board Autonomy-Vegetation (PROBA-V). The active fire data were screened to prevent extraction of unrepresentative burned area samples and combined with surface reflectance bi-weekly composites to produce burned area maps. The procedure was applied over the Brazilian Cerrado savanna, validated with reference maps obtained from Landsat images and compared with the Collection 6 Moderate Resolution Imaging Spectrometer (MODIS) Burned Area product (MCD64A1) Results show that the algorithm developed improved the detection of small-sized scars and displayed results more similar to the reference data than MCD64A1. Unlike active fire-based region growing algorithms, the proposed approach allows for the detection and mapping of burn scars without active fires, thus eliminating a potential source of omission error. The burned area mapping approach presented here should facilitate the development of operational-automated burned area algorithms, and is very straightforward for implementation with other sensors |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017 2017-01-01T00:00:00Z 2018-01-03T11:30:04Z |
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://hdl.handle.net/10400.5/14620 |
url |
http://hdl.handle.net/10400.5/14620 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Remote Sens. 2017, 9, 1161 10.3390/rs9111161 |
dc.rights.driver.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 |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
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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1799131091822444544 |