@Article{MaedaForShiBalHan:2009:PrFoFi,
               author = "Maeda, Eduardo Eiji and Formaggio, Antonio Roberto and 
                         Shimabukuro, Yosio Edemir and Balue Arcoverde, Gustavo Felipe and 
                         Hansen, Matthew C.",
          affiliation = "Instituto Nacional de Pesquisas Espaciais (INPE), Univ Helsinki, 
                         Dept Geog, FIN-00014 Helsinki, Finland and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and S Dakota State Univ, Geog Informat Sci Ctr Excellence, 
                         Pierre, SD USA",
                title = "Predicting forest fire in the Brazilian Amazon using MODIS imagery 
                         and artificial neural networks",
              journal = "International Journal of Applied Earth Observation and 
                         Geoinformation",
                 year = "2009",
               volume = "11",
               number = "4",
                pages = "265--272",
                month = "Aug.",
             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.",
                  doi = "10.1016/j.jag.2009.03.003",
                  url = "http://dx.doi.org/10.1016/j.jag.2009.03.003",
                 issn = "1569-8432",
             language = "en",
           targetfile = "maeda.pdf",
        urlaccessdate = "01 maio 2024"
}