@InProceedings{PinheiroBorgSano:2017:ApReNe,
               author = "Pinheiro, Priscila Santos and Borges, Elane Fiuza and Sano, Edson 
                         Eyji",
                title = "Mapeamento Multitemporal de queimadas na bacia do rio Grande-BA: 
                         aplica{\c{c}}{\~a}o de uma Rede Neural Artificial em produtos 
                         MODIS",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "5591--5598",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Fire in the Cerrado biome is used as a management tool. In 
                         agriculture, the fire is used for the cleaning of the pastures, as 
                         well as regrowth of the vegetation to serve as food for the herd. 
                         However, recurring fire practices in this environment end up 
                         causing severe damage to the environment. Thus, remote sensing, 
                         combined with other practices, is important in terms of monitoring 
                         and conservation of the landscape. In this way, this paper aimed 
                         to map the areas of burn scars to the Rio Grande-BA basin, from 
                         2005 to 2014, through the EVI of the sensor MODIS and an 
                         artificial neural network. For the collection of input samples 
                         from the neural network a Graphical User Interface (GUI) was 
                         created, where the user is able to arbitrate which input data and 
                         their possible percentages, the number of samples, as well as the 
                         window size of scanning of incoming data. The network was trained 
                         in the MATLAB, with the backpropagtion algorithm. For the 
                         validation of the neural network a manual vetorization was used 
                         from data from the Landsat and Resourcesat series for the same 
                         period analyzed. Next, from the confusion matrix, errors of 
                         omission and commission, global accuracy and Kappa index were 
                         generated. Where the data were found in the latter classification 
                         with qualities ranging from good to very good and global accuracy 
                         ranging from 65% to 82%.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59330",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMBCE",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMBCE",
           targetfile = "59330.pdf",
                 type = "An{\'a}lise de s{\'e}ries temporais de imagens de 
                         sat{\'e}lite",
        urlaccessdate = "27 abr. 2024"
}