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@Article{FonsecaArLiShArAn:2016:MoFiPr,
               author = "Fonseca, Marisa Gesteira and Arag{\~a}o, Luiz Eduardo Oliveira e 
                         Cruz de and Lima, Andr{\'e} and Shimabukuro, Yosio Edemir and 
                         Arai, Eg{\'{\i}}dio and Anderson, Liana O.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Centro Nacional de Monitoramento e Alertas de 
                         Desastres Naturais (CEMADEN)}",
                title = "Modelling fire probability in the Brazilian Amazon using the 
                         maximum entropy method",
              journal = "International Journal of Wildland Fire",
                 year = "2016",
               volume = "25",
               number = "9",
                pages = "955--969",
             keywords = "anthropogenic ignition, climate, machine learning, MESS analysis, 
                         MODIS, tropical forest.",
             abstract = "Fires are both a cause and consequence of important changes in the 
                         Amazon region. The development and implementation of better fire 
                         management practices and firefighting strategies are important 
                         steps to reduce the Amazon ecosystems' degradation and carbon 
                         emissions from land-use change in the region. We extended the 
                         application of the maximum entropy method (MaxEnt) to model fire 
                         occurrence probability in the Brazilian Amazon on a monthly basis 
                         during the 2008 and 2010 fire seasons using fire detection data 
                         derived from satellite images. Predictor variables included 
                         climatic variables, inhabited and uninhabited protected areas and 
                         land-use change maps. Model fit was assessed using the area under 
                         the curve (AUC) value (threshold-independent analysis), binomial 
                         tests and model sensitivity and specificity (threshold-dependent 
                         analysis). Both threshold-independent (AUC\≤0.9190.004) 
                         and threshold-dependent evaluation indicate satisfactory model 
                         performance. Pasture, annual deforestation and secondary 
                         vegetation are the most effective variables for predicting the 
                         distribution of the occurrence data. Our results show that MaxEnt 
                         may become an important tool to guide on-the-ground decisions on 
                         fire prevention actions and firefighting planning more effectively 
                         and thus to minimise forest degradation and carbon loss from 
                         forest fires in Amazonian ecosystems.",
                  doi = "10.1071/WF15216",
                  url = "http://dx.doi.org/10.1071/WF15216",
                 issn = "1049-8001",
             language = "en",
        urlaccessdate = "04 dez. 2020"
}


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