@InProceedings{FreitasAMBLWRASCFLBMCPCBFABBR:2023:ReFiPr,
               author = "Freitas, Ana Larissa R. de and Anderson, Liana O. and Morton, 
                         Douglas and Bett, Philip E. and Le{\~a}o, Paulo H. A. and 
                         Williams, Karina and Reis, Jo{\~a}o B. C. and Alvarado, Swanni T. 
                         and Silva J{\'u}nior, Celso Henrique Leite and Cardoso, Murilo 
                         and Fernandes, Valesca R. and Lima, Ylza and Brito, 
                         Fabr{\'{\i}}cio and Medeiros, Thais Pereira and Campanharo, 
                         Wesley Augusto and Pess{\^o}a, Ana C. M. and Carvalho, 
                         Nath{\'a}lia Silva de and Burton, Chantelle and Ferraz, Tiago and 
                         Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de and Braga, Eduarda V. 
                         and Bezerra, Denilson and Rodrigues, Ta{\'{\i}}ssa",
          affiliation = "{Centro Nacional de Monitoramento e Alertas de Desastres Naturais 
                         (CEMADEN) } and {Centro Nacional de Monitoramento e Alertas de 
                         Desastres Naturais (CEMADEN) } and {National Aeronautics and Space 
                         Administration (NASA)} and {Met Office} and {Centro Nacional de 
                         Monitoramento e Alertas de Desastres Naturais (CEMADEN) } and {Met 
                         Office} and {Centro Nacional de Monitoramento e Alertas de 
                         Desastres Naturais (CEMADEN) } and {Universidad del Rosario} and 
                         {University of California} and {Secretaria de Meio ambiente de 
                         Goi{\'a}s} and {Weather and Climate Monitoring Center of the 
                         state of Mato Grosso do Sul} and {Secretary of State for the 
                         Environment and Indigenous Policies} and {Universidade CEUMA} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and 
                         {Centro Nacional de Monitoramento e Alertas de Desastres Naturais 
                         (CEMADEN) } and {Instituto Nacional de Pesquisas Espaciais (INPE)} 
                         and {Met Office} and UEMA and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {University of the Tocantina Region of 
                         Maranh{\~a}o} and {Universidade Federal do Maranh{\~a}o (UFMA)} 
                         and {University of the Tocantina Region of Maranh{\~a}o}",
                title = "Fire patterns in brazilian municipalities: relationship with the 
                         fire probability forecast",
            booktitle = "Anais...",
                 year = "2023",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de and Sanches, Ieda DelArco",
                pages = "e156159",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 20. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Fire pattern, Fire probability forecast, Climate, Anthropic use.",
             abstract = "Brazil contains some of the most fire-prone regions in the world, 
                         and this motivates the development of a fire probability forecast 
                         system. CEMADEN has collaborations with the states of Acre (AC), 
                         Goi{\'a}s (GO), Maranh{\~a}o (MA) and Mato Grosso do Sul (MS), 
                         which use the CEMADEN fire probability forecast data to guide 
                         prevention and mitigation actions. In this study, we aim to 
                         identify the fire pattern by municipality and the relation between 
                         the fire occurrence and fire probability forecast product for each 
                         state, using the 2022 January-September period as a case study. 
                         Our results demonstrate that AC state has the highest average 
                         burned area. GO and MA exhibit similar fire patterns and fire 
                         probability forecasts. MS municipalities reach the highest values 
                         in three of the fire patterns. We identified that some adjustments 
                         could be applied to the fire probability forecast data, aiming to 
                         improve its adherence to the spatial gradient.",
  conference-location = "Florian{\'o}polis",
      conference-year = "02-05 abril 2023",
                 isbn = "978-65-89159-04-9",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/493Q4MH",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/493Q4MH",
           targetfile = "156159.pdf",
                 type = "Queimadas e Inc{\^e}ndios Florestais",
        urlaccessdate = "03 maio 2024"
}