| 1. Identificação | ||||
| Tipo de Referência | Artigo em Revista Científica (Journal Article) | |||
| Site | mtc-m21d.sid.inpe.br | |||
| Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identificador | 8JMKD3MGP3W34T/4A63TQ2 | |||
| Repositório | sid.inpe.br/mtc-m21d/2023/11.03.17.15 (acesso restrito) | |||
| Última Atualização | 2023:11.03.17.15.53 (UTC) simone | |||
| Repositório de Metadados | sid.inpe.br/mtc-m21d/2023/11.03.17.15.53 | |||
| Última Atualização dos Metadados | 2024:01.02.17.16.52 (UTC) administrator | |||
| DOI | 10.1016/j.rse.2023.113798 | |||
| ISSN | 0034-4257 | |||
| Chave de Citação | DalagnolWGBOSBPSFSA:2023:MaTrFo | |||
| Título | Mapping tropical forest degradation with deep learning and Planet NICFI data  | |||
| Ano | 2023 | |||
| Mês | Dec. | |||
| Data de Acesso | 12 maio 2024 | |||
| Tipo de Trabalho | journal article | |||
| Tipo Secundário | PRE PI | |||
| Número de Arquivos | 1 | |||
| Tamanho | 10262 KiB | |||
| 2. Contextualização | ||||
| Autor | 1 Dalagnol, Ricardo 2 Wagner, Fabien Hubert 3 Galvão, Lênio Soares 4 Braga, Daniel 5 Osborn, Fiona 6 Sagang, Le Bienfaiteur 7 Bispo, Polyanna da Conceição 8 Payne, Matthew 9 Silva Junior, Celso 10 Favrichon, Samuel 11 Silgueiro, Vinicius 12 Anderson, Liana O. | |||
| Identificador de Curriculo | 1 2 3 8JMKD3MGP5W/3C9JHLF | |||
| Grupo | 1 2 3 DIOTG-CGCT-INPE-MCTI-GOV-BR 4 SER-SRE-DIPGR-INPE-MCTI-GOV-BR | |||
| Afiliação | 1 University of California 2 University of California 3 Instituto Nacional de Pesquisas Espaciais (INPE) 4 Instituto Nacional de Pesquisas Espaciais (INPE) 5 CTrees 6 University of California 7 University of Manchester 8 University of Manchester 9 University of California 10 NASA-Jet Propulsion Laboratory 11 Instituto Centro de Vida (ICV) 12 Centro Nacional de Monitoramento e Alertas de Desastres Naturais (CEMADEN) | |||
| Endereço de e-Mail do Autor | 1 dalagnol@ucla.edu 2 3 lenio.galvao@inpe.br | |||
| Revista | Remote Sensing of Environment | |||
| Volume | 298 | |||
| Páginas | e113798 | |||
| Nota Secundária | A1_INTERDISCIPLINAR A1_GEOCIÊNCIAS A1_ENGENHARIAS_I A1_CIÊNCIAS_BIOLÓGICAS_I A1_CIÊNCIAS_AMBIENTAIS A1_CIÊNCIAS_AGRÁRIAS_I A1_BIODIVERSIDADE | |||
| Histórico (UTC) | 2023-11-03 17:15:53 :: simone -> administrator :: 2023-11-03 17:15:55 :: administrator -> simone :: 2023 2023-11-03 17:17:23 :: simone -> administrator :: 2023 2024-01-02 17:16:52 :: administrator -> simone :: 2023 | |||
| 3. Conteúdo e estrutura | ||||
| É a matriz ou uma cópia? | é a matriz | |||
| Estágio do Conteúdo | concluido | |||
| Transferível | 1 | |||
| Tipo do Conteúdo | External Contribution | |||
| Tipo de Versão | publisher | |||
| Palavras-Chave | Amazon Fire Forest degradation Logging U-net | |||
| Resumo | Tropical rainforests from the Brazilian Amazon are frequently degraded by logging, fire, edge effects and minor unpaved roads. However, mapping the extent of degradation remains challenging because of the lack of frequent high-spatial resolution satellite observations, occlusion of understory disturbances, quick recovery of leafy vegetation, and limitations of conventional reflectance-based remote sensing techniques. Here, we introduce a new approach to map forest degradation caused by logging, fire, and road construction based on deep learning (DL), henceforth called DL-DEGRAD, using very high spatial (4.77 m) and bi-annual to monthly temporal resolution of the Planet NICFI imagery. We applied DL-DEGRAD model over forests of the state of Mato Grosso in Brazil to map forest degradation with attributions from 2016 to 2021 at six-month intervals. A total of 73,744 images (256 × 256 pixels in size) were visually interpreted and manually labeled with three semantic classes (logging, fire, and roads) to train/validate a U-Net model. We predicted the three classes over the study area for all dates, producing accumulated degradation maps biannually. Estimates of accuracy and areas of degradation were performed using a probability design-based stratified random sampling approach (n = 2678 samples) and compared it with existing operational data products at the state level. DL-DEGRAD performed significantly better than all other data products in mapping logging activities (F1-score = 68.9) and forest fire (F1-score = 75.6) when compared with the Brazil's national maps (SIMEX, DETER, MapBiomas Fire) and global products (UMD-GFC, TMF, FireCCI, FireGFL, GABAM, MCD64). Pixel-based spatial comparison of degradation areas showed the highest agreement with DETER and SIMEX as Brazil official data products derived from visual interpretation of Landsat imagery. The U-Net model applied to NICFI data performed as closely to a trained human delineation of logged and burned forests, suggesting the methodology can readily scale up the mapping and monitoring of degraded forests at national to regional scales. Over the state of Mato Grosso, the combined effects of logging and fire are degrading the remaining intact forests at an average rate of 8443 km2 year−1 from 2017 to 2021. In 2020, a record degradation area of 13,294 km2 was estimated from DL-DEGRAD, which was two times the areas of deforestation. | |||
| Área | SRE | |||
| Arranjo 1 | urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Mapping tropical forest... | |||
| Arranjo 2 | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Mapping tropical forest... | |||
| Conteúdo da Pasta doc | acessar | |||
| Conteúdo da Pasta source | não têm arquivos | |||
| Conteúdo da Pasta agreement |
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| 4. Condições de acesso e uso | ||||
| Idioma | en | |||
| Arquivo Alvo | 1-s2.0-S0034425723003498-main.pdf | |||
| Grupo de Usuários | simone | |||
| Grupo de Leitores | administrator simone | |||
| Visibilidade | shown | |||
| Política de Arquivamento | denypublisher allowfinaldraft24 | |||
| Permissão de Leitura | deny from all and allow from 150.163 | |||
| Permissão de Atualização | não transferida | |||
| 5. Fontes relacionadas | ||||
| Unidades Imediatamente Superiores | 8JMKD3MGPCW/3F3NU5S 8JMKD3MGPCW/46KUATE | |||
| Lista de Itens Citando | sid.inpe.br/bibdigital/2013/10.18.22.34 1 sid.inpe.br/mtc-m21/2012/07.13.14.53.28 1 | |||
| Divulgação | WEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS. | |||
| Acervo Hospedeiro | urlib.net/www/2021/06.04.03.40 | |||
| 6. Notas | ||||
| Campos Vazios | alternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes number orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url | |||
| 7. Controle da descrição | ||||
| e-Mail (login) | simone | |||
| atualizar | ||||