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@InProceedings{JesusSetMorCânMel:2015:EfCoAt,
               author = "Jesus, Silvia Cristina de and Setzer, Alberto and Morelli, Fabiano 
                         and C{\^a}ndido, Pietro de Almeida and Melchiori, Arturo 
                         Emiliano",
          affiliation = "{} and {} and {Instituto Nacional de Pesquisas Espaciais (INPE)} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Efeito da corre{\c{c}}{\~a}o atmosf{\'e}rica na 
                         classifica{\c{c}}{\~a}o de {\'{\i}}ndices espectrais para o 
                         mapeamento de {\'a}reas queimadas",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "368--375",
         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 = "This paper investigates the impact of atmospheric correction (AC) 
                         for medium-resolution imagery in the mapping of fire scars when 
                         using automatic classification based on the spectral composite 
                         indexes NBR, dNDVI and dNBR. 11 Landsat-5/TM scenes of a same 
                         Cerrado area in 2005-2006 provided 9 time-consecutive pairs in 
                         which a visual analysis provided the reference mapping of burned 
                         areas. Automatic digital classification of the three indexes with 
                         10 output classes, including one specific for burn scars, was 
                         compared with and without the use of the so-called 6S AC 
                         algorithm. Results show that atmospherically corrected and 
                         uncorrected data are highly correlated (R2\≈1). The values 
                         in the contingency tables for both procedures are not 
                         significantly different; considering AC and non-AC values for all 
                         the data, the overall accuracy is above 99% for both, the product 
                         accuracy for scars is 79.6% and 82.6%, and the user accuracy is 
                         92.2% and 94.3%, respectively. In conclusion, the mapping of fire 
                         scars in medium-resolution imagery doesnt require atmospheric 
                         correction when the most common indexes for burned area estimates 
                         are used with automatic classification, what may simplify the 
                         processing chain of large image datasets; however, this may not be 
                         the case in non-automatic classification, when surface reflectance 
                         thresholds are defined for individual scenes and applications.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "76",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM458B",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3JM458B",
           targetfile = "p0076.pdf",
                 type = "Degrada{\c{c}}{\~a}o de florestas",
        urlaccessdate = "02 dez. 2020"
}


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