@InProceedings{FonsecaLimAndShiAra:2015:AvPrMo,
               author = "Fonseca, Marisa Gesteira and Lima, Andr{\'e} and Anderson, Liana 
                         Oighenstein and Shimabukuro, Yosio Edemir and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
          affiliation = "{} and {} and {} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Avalia{\c{c}}{\~a}o preliminar da modelagem de queimadas na 
                         Amaz{\^o}nia brasileira utilizando o princ{\'{\i}}pio de 
                         M{\'a}xima Entropia",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "1868--1875",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Climate change, forest fragmentation, and the increase in 
                         secondary vegetation cover are expected to amplify fire incidence 
                         in the Amazon. The negative impacts of forest fires on 
                         biodiversity, precipitation and the dynamics of atmospheric 
                         circulation, human health, forest structure, biomass and carbon 
                         stock have been recognized in the literature. The development and 
                         implementation of better fire management practices and 
                         firefighting strategies are therefore important steps to reduce 
                         forest degradation and carbon emissions from land use change in 
                         the region. Here we extend the application of Maximum Entropy 
                         method (Maxent) to model fire risk in the Brazilian Amazon using 
                         an innovative combination of climatic variables (sea surface 
                         temperature anomalies, precipitation and accumulated water 
                         deficit), inhabited and uninhabited protected areas and land use 
                         (deforestation, pasture, and forest regeneration) maps. The model 
                         was calibrated to forecast hot pixels occurrence in September 2008 
                         and September 2010, two years of contrasting fire incidence. Tests 
                         were carried out to determine the regularization multiplier (a 
                         user defined parameter that influences model complexity) that 
                         maximizes model fit. Model fit was assessed using the AUC value 
                         (threshold independent analysis), binomial tests and model 
                         sensitivity and specificity (threshold dependent analysis). Both 
                         threshold dependent and independent model evaluations showed that 
                         Maxent can be successfully used in operational routines for 
                         monthly hot pixels occurrence prediction and hence applied in 
                         prevention programs and firefighting planning.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "370",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM49D9",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM49D9",
           targetfile = "p0370.pdf",
                 type = "Monitoramento e modelagem ambiental",
        urlaccessdate = "27 abr. 2024"
}