@InProceedings{NegriLuzFrerCasa:2023:DeÁrQu,
               author = "Negri, Rog{\'e}rio Galente and Luz, Andr{\'e}a E. O. and Frery, 
                         Alejandro C. O. and Casaca, Wallace C. O.",
          affiliation = "{Universidade Estadual Paulista (UNESP)} and {Universidade 
                         Estadual Paulista (UNESP)} and {Victoria University of Wellington 
                         (VUW)} and {Universidade Estadual Paulista (UNESP)}",
                title = "Detec{\c{c}}{\~a}o de {\'a}reas queimadas utilizando dados 
                         temporais e modelagem estat{\'{\i}}stica n{\~a}o 
                         supervisionada",
            booktitle = "Anais...",
                 year = "2023",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de and Sanches, Ieda DelArco",
                pages = "e155775",
         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 = "Queimadas, {\'{\i}}ndices espectrais, s{\'e}ries temporais, 
                         n{\~a}o supervisionado, Forest fires, spectral index, time 
                         series, unsupervise.",
             abstract = "As queimadas florestais t{\^e}m aumentado significativamente nos 
                         {\'u}ltimos anos. Eventos desta natureza motivam o 
                         desenvolvimento de metodologias automatizadas para o devido 
                         mapeamentos e monitoramento. Este trabalho introduz um m{\'e}todo 
                         capaz de mapear de forma acurada {\'a}reas afetadas por fogo 
                         utilizando modelagem estat{\'{\i}}stica e series temporais de 
                         imagens de sensoriamento remoto. A avalia{\c{c}}{\~a}o desta 
                         proposta {\'e} realizada por dois estudos de caso envolvendo 
                         {\'a}reas de floresta no Brasil com frequente hist{\'o}rico de 
                         queimadas. S{\~a}o utilizadas imagens obtidas pelos 
                         sat{\'e}lites Landsat-8 e Sentinel-2 e apresentadas 
                         compara{\c{c}}{\~o}es com um m{\'e}todo alternativo. ABSTRACT: 
                         The frequency of forest fires has increased significantly in 
                         recent years across the planet. Events of this nature motivate the 
                         development of automated methodologies aimed at mapping areas 
                         affected by fire. In this context, we propose a method capable of 
                         accurately mapping areas affected by fire using time series of 
                         remotely sensed multispectral images by statistical modeling. In 
                         order to evaluate the introduced proposal, we carry out a case 
                         study on a region in Brazil with recurrent history of forest 
                         fires. Furthermore, images obtained by the Landsat-8 satellite are 
                         used in this case study. Comparisons with an alternative method 
                         are included in this analysis.",
  conference-location = "Florian{\'o}polis",
      conference-year = "02-05 abril 2023",
                 isbn = "978-65-89159-04-9",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/4937678",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/4937678",
           targetfile = "155775.pdf",
                 type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
        urlaccessdate = "12 maio 2024"
}