@InProceedings{RosanAlcâ:2015:DeÁrQu,
               author = "Rosan, Thais Michele and Alc{\^a}ntara, Enner Herenio",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "{"}Detec{\c{c}}{\~a}o de {\'a}reas queimadas e severidade a 
                         partir do {\'{\i}}ndice espectral \ΔNBR{"}",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "526--533",
         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 = "The use of fire is a recurrent practice in areas where 
                         deforestation is expanding, mainly for cleaning grazing areas and 
                         agriculture maintenance. The frequent episodes of drought in the 
                         Amazon region during the last three decades caused an increase of 
                         susceptibility of vegetation and it has contributed for the 
                         increase of fires. These fires cause several changes in the 
                         ecosystem, changing biosphere-atmosphere components. Thus, remote 
                         sensing become an essential tool for monitoring and analyzing the 
                         impacts of fires, as it provides temporal and spatial coverage of 
                         fire events in areas of difficult access. Therefore, this work 
                         aims to assess the potential of \ΔNBR index in detecting 
                         burned areas and the fire severity in two scenes of the OLI sensor 
                         aboard Landsat-8, for the June and August of 2013. To minimize 
                         atmospheric effects in sensor data acquisition, we used the QUAC 
                         (Quick Atmospheric Correction) algorithm. Moreover, to detect the 
                         variations of burned areas and fire severity, we applied the 
                         radiometric normalization in Landsat-8 data. The results 
                         demonstrated that the \ΔNBR index presented an optimal 
                         distinction between areas affected by fire, with high severity, 
                         areas not burned and areas with vegetation regrowth. Accordingly, 
                         the \ΔNBR index presented an excellent agreement on 
                         delimiting burned areas.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "104",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM45D2",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM45D2",
           targetfile = "p0104.pdf",
                 type = "Floresta e vegeta{\c{c}}{\~a}o",
        urlaccessdate = "27 abr. 2024"
}