@InProceedings{MaedaForShiArcLim:2009:FoFiRi,
               author = "Maeda, Eduardo Eiji and Formaggio, Ant{\^o}nio Roberto and 
                         Shimabukuro, Yosio Edemir and Arcoverde, Gustavo Felipe Balu{\'e} 
                         and Lima, Andr{\'e}",
          affiliation = "{INPE/University of Helsinki} and INPE and INPE and INPE and 
                         INPE",
                title = "Forest fire risk mapping in the Brazilian Amazon using MODIS 
                         images and artificial neural networks",
            booktitle = "Anais...",
                 year = "2009",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "1425--1432",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 14. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "forest fire, artificial neural networks, Amazon forest, MODIS.",
             abstract = "The present work describes a methodology based on Artificial 
                         Neural Networks (ANN) and multi-temporal images from the 
                         MODIS/Terra-Aqua sensors in order to detect areas with high risk 
                         of forest fire in the Brazilian Amazon. The hypothesis of this 
                         work is that, due to the characteristics of land use and land 
                         cover change dynamics in the Amazon forest, the temporal spectral 
                         profile of forest areas preparing to be burned can be separated 
                         from other areas. A study case was carried out in three 
                         municipalities in the north region of 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 images acquired during five 
                         different periods preceding the forest fire season. Samples were 
                         extracted from areas where forest fires were detected in 2005, and 
                         also from forest and agricultural areas. These samples were 
                         divided to train, to validate and to test the ANN. The tests 
                         results achieved a mean squared error of around 0.07. When 
                         simulated in an entire municipality, the ANN model was efficient 
                         in showing the spatial distribution of the forest fire 
                         probability, which was coherent with the fire events observed in 
                         the following months.",
  conference-location = "Natal",
      conference-year = "25-30 abr. 2009",
                 isbn = "978-85-17-00044-7",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "dpi.inpe.br/sbsr@80/2008/11.17.09.57",
                  url = "http://urlib.net/ibi/dpi.inpe.br/sbsr@80/2008/11.17.09.57",
           targetfile = "1425-1432.pdf",
                 type = "An{\'a}lise e Aplica{\c{c}}{\~a}o de Imagens Multitemporais",
        urlaccessdate = "15 jun. 2024"
}