@InProceedings{MorasFoFiJúBaHoBo:2017:ClMáVe,
               author = "Moras Filho, Luiz Ot{\'a}vio and Figueiredo, Evandro Orfan{\'o} 
                         and J{\'u}nior, Marcos Ant{\^o}nio Isaac and Barros, Vanessa 
                         Cabral Costa de and Hott, Marcos Cicarini and Borges, 
                         Lu{\'{\i}}s Ant{\^o}nio Coimbra",
                title = "Classificador de m{\'a}xima verossimilhan{\c{c}}a aplicado 
                         {\`a} identifica{\c{c}}{\~a}o de esp{\'e}cies nativas na 
                         Floresta Amaz{\^o}nica",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "1605--1610",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Among a variety of digital classification methods based on remote 
                         sensing images, the Maximum Likelihood (ML) is widely used in 
                         environmental studies, mainly for land cover and vegetation 
                         analysis. This study aimed to evaluate the effectiveness of 
                         supervised classification by ML technique in a forest management 
                         area of dense ombrophilous forest, using one RapidEye image. With 
                         this purpose, it was conducted the census of species over 30 cm in 
                         diameter at breast height and calculated the Cover Value Index 
                         (CVI), and selected the 20 species with the highest CVI as a 
                         parameter for classification in a Geographic Information System. 
                         13 of the 20 species selected in the study area were not 
                         identified by the classification method, and among the seven 
                         identified species, two were underestimated and the others were 
                         overestimated. Both the maximum likelihood technique and the 
                         spatial resolution of the image used were not suitable for 
                         supervised classification of native vegetation, with Kappa index 
                         of 0.05 and global accuracy of 5.53%. Studies using spectral 
                         characterization in leaf level supported by higher or hyper 
                         spectral and spatial resolution images are recommended to increase 
                         the accuracy of classification.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59504",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSLNUL",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSLNUL",
           targetfile = "59504.pdf",
                 type = "Floresta e outros tipos de vegeta{\c{c}}{\~a}o",
        urlaccessdate = "27 abr. 2024"
}