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@InProceedings{SilveiraSouzZeil:2015:AvDeIm,
               author = "Silveira, S{\'e}rgio Wagner Gripp da and Souza, Gracyeli Santos 
                         and Zeilhofer, Peter",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Avalia{\c{c}}{\~a}o de Desempenho de Imagens MODIS no Estudo da 
                         Din{\^a}mica de Inunda{\c{c}}{\~a}o do Pantanal 
                         Mato-Grossense",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "4286--4291",
         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 preliminary study examines the applicability of multi-year 
                         MOD13Q1 16-day composites for inundation monitoring in the 
                         Northern Pantanal floodplain. Therefore, five-year field 
                         measurements of water levels with an average weekly temporal 
                         resolution during the flooding period from 51 gauges were compiled 
                         and binarized for Logistic Regression model development and 
                         accuracy assessment. Average inundation period in the northern 
                         Pantanal lasts between three and five months. Thus, to reduce 
                         model bias, a total of 80 MODIS composites were selected, half 
                         from the flood, half from the dry season. For the calibration 
                         period, a Nagelkerke R2 coefficient of 0.595 and an overall 
                         classification accuracy of 83.6% were obtained, this if only 
                         pixels with a PR = 0 quality were used. For the validation period 
                         which only included composites not used for calibration, overall 
                         accuracy decayed to 74.4%. If pixels down to a QA of 1100 were 
                         included, overall accuracy further decayed significantly to 71.3%. 
                         Poorest classification results were obtained for semi-deciduous 
                         forests developed on Cordilheira elevations (false positives) and 
                         for temporary flooded, mono-dominant Vochysia divergens forests 
                         (Cambarazal). Best results were obtained for open Savannah 
                         formations. Great performance differences between the calibration 
                         and validation periods indicate that RL models should be developed 
                         on a yearly basis, presuming the availability of a permanent flood 
                         monitoring network. Future studies should further consider the 
                         antagonist effects between high accuracies versus gap-free 
                         monitoring including low quality pixels and test for the 
                         applicability of time series filling algorithms.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "840",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4CKB",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3JM4CKB",
           targetfile = "p0840.pdf",
                 type = "Monitoramento e modelagem ambiental",
        urlaccessdate = "28 nov. 2020"
}


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