| 1. Identity statement | |
| Reference Type | Journal Article |
| Site | mtc-m16c.sid.inpe.br |
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
| Identifier | 8JMKD3MGP8W/35N7S3E |
| Repository | sid.inpe.br/mtc-m18@80/2009/07.24.14.53 (restricted access) |
| Last Update | 2010:09.20.12.02.03 (UTC) marciana |
| Metadata Repository | sid.inpe.br/mtc-m18@80/2009/07.24.14.53.26 |
| Metadata Last Update | 2020:04.28.17.48.52 (UTC) administrator |
| Secondary Key | INPE--PRE/ |
| DOI | 10.1016/j.jag.2009.03.003 |
| ISSN | 1569-8432 |
| Citation Key | MaedaForShiBalHan:2009:PrFoFi |
| Title | Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks  |
| Year | 2009 |
| Month | Aug. |
| Access Date | 2024, May 18 |
| Type of Work | journal article |
| Secondary Type | PRE PI |
| Number of Files | 1 |
| Size | 790 KiB |
| 2. Context | |
| Author | 1 Maeda, Eduardo Eiji 2 Formaggio, Antonio Roberto 3 Shimabukuro, Yosio Edemir 4 Balue Arcoverde, Gustavo Felipe 5 Hansen, Matthew C. |
| Resume Identifier | 1 2 8JMKD3MGP5W/3C9JGJQ 3 8JMKD3MGP5W/3C9JJCQ |
| Group | 1 DSR-OBT-INPE-MCT-BR 2 DSR-OBT-INPE-MCT-BR 3 DSR-OBT-INPE-MCT-BR 4 DSR-OBT-INPE-MCT-BR |
| Affiliation | 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 |
| Journal | International Journal of Applied Earth Observation and Geoinformation |
| Volume | 11 |
| Number | 4 |
| Pages | 265-272 |
| Secondary Mark | B1_GEOCIÊNCIAS |
| History (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. Content and structure | |
| Is the master or a copy? | is the master |
| Content Stage | completed |
| Transferable | 1 |
| Content Type | External Contribution |
| Version Type | publisher |
| Keywords | artificial neural network back propagation forest fire land cover land use change MODIS NDVI prediction satellite imagery satellite sensor Brazil Mato Grosso South America |
| Abstract | 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. |
| Area | SRE |
| Arrangement | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Predicting forest fire... |
| doc Directory Content | access |
| source Directory Content | there are no files |
| agreement Directory Content | there are no files |
| 4. Conditions of access and use | |
| Language | en |
| Target File | maeda.pdf |
| User Group | administrator marciana |
| Reader Group | administrator marciana |
| Visibility | shown |
| Archiving Policy | denypublisher denyfinaldraft |
| Read Permission | deny from all and allow from 150.163 |
| Update Permission | not transferred |
| 5. Allied materials | |
| Mirror Repository | sid.inpe.br/mtc-m18@80/2008/03.17.15.17.24 |
| Next Higher Units | 8JMKD3MGPCW/3ER446E |
| Dissemination | WEBSCI |
| Host Collection | sid.inpe.br/mtc-m18@80/2008/03.17.15.17 |
| 6. Notes | |
| Empty Fields | 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. Description control | |
| e-Mail (login) | simone |
| update | |