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Tipo de ReferênciaJournal Article
Sitemtc-m21c.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34R/43F8FGB
Repositóriosid.inpe.br/mtc-m21c/2020/10.23.14.03
Última Atualização2020:10.23.14.03.31 self-uploading-INPE-MCTI-GOV-BR
Metadadossid.inpe.br/mtc-m21c/2020/10.23.14.03.31
Última Atualização dos Metadados2021:04.08.18.16.06 self-uploading-INPE-MCTI-GOV-BR
DOI10.3390/ijgi9100564
ISSN2220-9964
Rótuloself-archiving-INPE-MCTIC-GOV-BR
Chave de CitaçãoBarbozaCastilloTASRSBOE:2020:MoWiNo
TítuloMonitoring wildfires in the northeastern peruvian amazon using landsat-8 and sentinel-2 imagery in the GEE platform
Ano2020
MêsOct.
Data de Acesso13 abr. 2021
Tipo de Trabalhojournal article
Número de Arquivos1
Tamanho3806 KiB
Área de contextualização
Autor1 Barboza Castillo, Elgar
2 Turpo Cayo, Efrain Yury
3 Almeida, Cláudia Maria de
4 Salas López, Rolando
5 Rojas Briceño, Nilton Beltrán
6 Silva López, Jhonsy Omar
7 Barrena Gurbillón, Miguel Ángel
8 Oliva, Manuel
9 Espinoza-Villar, Raul
Identificador de Curriculo1
2
3 8JMKD3MGP5W/3C9JGS3
ORCID1 0000-0002-9628-8138
2 0000-0002-6691-9867
3 0000-0002-6523-3169
4 0000-0003-2184-6761
5 0000-0002-5352-6140
6 0000-0002-2618-7526
7 0000-0002-4200-4101
8
9 0000-0002-1355-9060
Grupo1
2
3 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
Afiliação1 Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM)
2 Universidad Nacional Agraria La Molina
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM)
5 Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM)
6 Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM)
7 Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM)
8 Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM)
9 Universidad Nacional Agraria La Molina
Endereço de e-Mail do Autor1 ebarboza@indes-ces.edu.pe
2 eturpo@lamolina.edu.pe
3 claudia.almeida@inpe.br
4 rsalas@indes-ces.edu.pe
5 nrojas@indes-ces.edu.pe
6 jsilva@indes-ces.edu.pe
7 miguel.barrena@untrm.edu.pe
8 soliva@indes-ces.edu.pe
9 respinoza@lamolina.edu.pe
RevistaISPRS International Journal of Geo-Information
Volume9
Número10
Páginase564
Tipo SecundárioPRE PI
Nota SecundáriaB3_GEOCIÊNCIAS B5_CIÊNCIAS_AMBIENTAIS
Histórico2020-10-23 14:03:32 :: simone -> administrator ::
2020-10-23 14:03:32 :: administrator -> simone :: 2020
2020-10-23 14:05:22 :: simone -> administrator :: 2020
2021-01-04 13:26:48 :: administrator -> self-uploading-INPE-MCTI-GOV-BR :: 2020
2021-04-08 18:16:06 :: self-uploading-INPE-MCTI-GOV-BR -> simone :: 2020
Área de conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteudoExternal Contribution
Tipo de Versãopublisher
Palavras-Chaveremote sensing, GIS, spectral analysis, burn severity, forests, vegetation cover, biodiversity.
ResumoDuring the latest decades, the Amazon has experienced a great loss of vegetation cover, in many cases as a direct consequence of wildfires, which became a problem at local, national, and global scales, leading to economic, social, and environmental impacts. Hence, this study is committed to developing a routine for monitoring fires in the vegetation cover relying on recent multitemporal data (20172019) of Landsat-8 and Sentinel-2 imagery using the cloud-based Google Earth Engine (GEE) platform. In order to assess the burnt areas (BA), spectral indices were employed, such as the Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2), and Mid-Infrared Burn Index (MIRBI). All these indices were applied for BA assessment according to appropriate thresholds. Additionally, to reduce confusion between burnt areas and other land cover classes, further indices were used, like those considering the temporal differences between pre and post-fire conditions: differential Mid-Infrared Burn Index (dMIRBI), differential Normalized Burn Ratio (dNBR), differential Normalized Burn Ratio 2 (dNBR2), and differential Near-Infrared (dNIR). The calculated BA by Sentinel-2 was larger during the three-year investigation span (16.55, 78.50, and 67.19 km2 ) and of greater detail (detected small areas) than the BA extracted by Landsat-8 (16.39, 6.24, and 32.93 km2 ). The routine for monitoring wildfires presented in this work is based on a sequence of decision rules. This enables the detection and monitoring of burnt vegetation cover and has been originally applied to an experiment in the northeastern Peruvian Amazon. The results obtained by the two satellites imagery are compared in terms of accuracy metrics and level of detail (size of BA patches). The accuracy for Landsat-8 and Sentinel-2 in 2017, 2018, and 2019 varied from 82.791.4% to 94.598.5%, respectively.
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