| 1. Identificação | |
| Tipo de Referência | Artigo em Revista Científica (Journal Article) |
| Site | mtc-m16c.sid.inpe.br |
| Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
| Identificador | 8JMKD3MGP8W/35N7S3E |
| Repositório | sid.inpe.br/mtc-m18@80/2009/07.24.14.53 (acesso restrito) |
| Última Atualização | 2010:09.20.12.02.03 (UTC) marciana |
| Repositório de Metadados | sid.inpe.br/mtc-m18@80/2009/07.24.14.53.26 |
| Última Atualização dos Metadados | 2020:04.28.17.48.52 (UTC) administrator |
| Chave Secundária | INPE--PRE/ |
| DOI | 10.1016/j.jag.2009.03.003 |
| ISSN | 1569-8432 |
| Chave de Citação | MaedaForShiBalHan:2009:PrFoFi |
| Título | Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks  |
| Ano | 2009 |
| Mês | Aug. |
| Data de Acesso | 13 maio 2024 |
| Tipo de Trabalho | journal article |
| Tipo Secundário | PRE PI |
| Número de Arquivos | 1 |
| Tamanho | 790 KiB |
| 2. Contextualização | |
| Autor | 1 Maeda, Eduardo Eiji 2 Formaggio, Antonio Roberto 3 Shimabukuro, Yosio Edemir 4 Balue Arcoverde, Gustavo Felipe 5 Hansen, Matthew C. |
| Identificador de Curriculo | 1 2 8JMKD3MGP5W/3C9JGJQ 3 8JMKD3MGP5W/3C9JJCQ |
| Grupo | 1 DSR-OBT-INPE-MCT-BR 2 DSR-OBT-INPE-MCT-BR 3 DSR-OBT-INPE-MCT-BR 4 DSR-OBT-INPE-MCT-BR |
| Afiliação | 1 Instituto Nacional de Pesquisas Espaciais (INPE), Univ Helsinki, Dept Geog, FIN-00014 Helsinki, Finland 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Instituto Nacional de Pesquisas Espaciais (INPE) 4 Instituto Nacional de Pesquisas Espaciais (INPE) 5 S Dakota State Univ, Geog Informat Sci Ctr Excellence, Pierre, SD USA |
| Revista | International Journal of Applied Earth Observation and Geoinformation |
| Volume | 11 |
| Número | 4 |
| Páginas | 265-272 |
| Nota Secundária | B1_GEOCIÊNCIAS |
| Histórico (UTC) | 2010-03-12 14:13:01 :: marciana -> administrator :: 2010-05-11 01:09:36 :: administrator -> marciana :: 2011-08-31 14:44:02 :: marciana -> administrator :: 2009 2013-02-22 16:26:58 :: administrator -> marciana :: 2009 2013-03-08 17:20:42 :: marciana -> administrator :: 2009 2016-06-04 22:32:00 :: administrator -> marciana :: 2009 2016-08-19 11:33:50 :: marciana -> administrator :: 2009 2016-08-19 11:44:38 :: administrator -> marciana :: 2009 2016-10-04 17:05:42 :: marciana -> administrator :: 2009 2020-04-28 17:48:52 :: administrator -> simone :: 2009 |
| 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 | artificial neural network back propagation forest fire land cover land use change MODIS NDVI prediction satellite imagery satellite sensor Brazil Mato Grosso South America |
| Resumo | The presented work describes a methodology that employs artificial neural networks (ANN) and multitemporal imagery from the MODIS/Terra-Aqua sensors to detect areas of high risk of forest fire in the Brazilian Amazon. The hypothesis of this work is that due to characteristic land use and land cover change dynamics in the Amazon forest, forest areas likely to be burned can be separated from other land targets. A study case was carried out in three municipalities located in northern Mato Grosso State, Brazilian Amazon. Feedforward ANNs, with different architectures, were trained with a backpropagation algorithm, taking as inputs the NDVI values calculated from MODIS imagery acquired during five different periods preceding the 2005 fire season. Selected samples were extracted from areas where forest fires were detected in 2005 and from other non-burned forest and agricultural areas. These samples were used to train, validate and test the ANN. The results achieved a mean squared error of 0.07. In addition, the model was simulated for an entire municipality and its results were compared with hotspots detected by the MODIS sensor during the year. A histogram analysis showed that the spatial distribution of the areas with fire risk were consistent with the fire events observed from June to December 2005. The ANN model allowed a fast and relatively precise method to predict forest fire events in the studied area. Hence, it offers an excellent alternative for supporting forest fire prevention policies, and in assisting the assessment of burned areas, reducing the uncertainty involved in currently used method. |
| Área | SRE |
| Arranjo | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Predicting forest fire... |
| Conteúdo da Pasta doc | acessar |
| Conteúdo da Pasta source | não têm arquivos |
| Conteúdo da Pasta agreement | não têm arquivos |
| 4. Condições de acesso e uso | |
| Idioma | en |
| Arquivo Alvo | maeda.pdf |
| Grupo de Usuários | administrator marciana |
| Grupo de Leitores | administrator marciana |
| Visibilidade | shown |
| Política de Arquivamento | denypublisher denyfinaldraft |
| Permissão de Leitura | deny from all and allow from 150.163 |
| Permissão de Atualização | não transferida |
| 5. Fontes relacionadas | |
| Repositório Espelho | sid.inpe.br/mtc-m18@80/2008/03.17.15.17.24 |
| Unidades Imediatamente Superiores | 8JMKD3MGPCW/3ER446E |
| Divulgação | WEBSCI |
| Acervo Hospedeiro | sid.inpe.br/mtc-m18@80/2008/03.17.15.17 |
| 6. Notas | |
| Campos Vazios | alternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress electronicmailaddress format isbn label lineage mark nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate session shorttitle sponsor subject tertiarymark tertiarytype url |
| 7. Controle da descrição | |
| e-Mail (login) | simone |
| atualizar | |