@InProceedings{MoserMcNiLiOlVi:2015:EsCaSa,
               author = "Moser, Paolo and McRoberts, Ronald and Nicoletti, Adilson Luiz and 
                         Lingner, D{\'e}bora Vanessa and Oliveira, Laio Zimermann and 
                         Vibrans, Alexander Christian",
                title = "M{\'e}todos para quantifica{\c{c}}{\~a}o da acur{\'a}cia de 
                         estimativas de cobertura florestal a partir mapas baseados em 
                         sensoriamento remoto e dados de invent{\'a}rio terrestre: um 
                         estudo de caso em Santa Catarina, Brasil",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "5951--5958",
         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 = "Estimation of area of forest cover from remote sensing-based maps 
                         is a very useful technique when data and qualified are available. 
                         However, there is no a definitive conclusion about techniques that 
                         compensate misclassification errors in the process. In addition, 
                         estimating the bias of the estimates is an incipient technique. A 
                         case study was conducted in the state of Santa Catarina in 
                         southern Brazil, where estimates of four remote sensing-based land 
                         cover maps were compared, using the estimate of Santa Catarina 
                         Forest and Floristic Inventory as the ground truth. The inventory 
                         data to assess the overall accuracy and the bias for the four 
                         maps. The routine of analysis is based in X steps: (1) 
                         classification errors; (2) construction of error matrices, (3) 
                         calculating accuracy measures (4) estimating bias and (5) 
                         constructing 95% confidence intervals for the estimates. To 
                         correct the existing bias, a model-assisted estimator was used. 
                         After the adjustments, the estimates for three of the four maps 
                         were closer to the plot-based, simple random sampling estimates 
                         than before adjustment. However, an independent t-test showed that 
                         three of the four maps were statistically significantly different 
                         from the simple random sampling estimate. The model-assisted 
                         estimator is an easily implemented technique for adjusting for 
                         estimated classification bias and for constructing real confidence 
                         intervals.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "1228",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4ERD",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM4ERD",
           targetfile = "p1228.pdf",
                 type = "Floresta e vegeta{\c{c}}{\~a}o",
        urlaccessdate = "20 maio 2024"
}