@InProceedings{FigueiredoCavaValeFigu:2009:AvAcCl,
               author = "Figueiredo, Symone Maria de Melo and Cavalcante, Luciana Mendes 
                         and Valentim, Judson Ferreira and Figueiredo, Evandro 
                         Orfan{\'o}",
          affiliation = "{Universidade Federal do Acre/AC} and {Empresa Brasileira de 
                         Pesquisa Agropecu{\'a}ria Amaz{\^o}nia Oriental /PA} and 
                         {Empresa Brasileira de Pesquisa Agropecu{\'a}ria/AC} and {Empresa 
                         Brasileira de Pesquisa Agropecu{\'a}ria/AC}",
                title = "Avalia{\c{c}}{\~a}o da acur{\'a}cia de 
                         classifica{\c{c}}{\~a}o digital de imagens no mapeamento de 
                         {\'a}reas de pastagens degradadas em Rio Branco, Acre",
            booktitle = "Anais...",
                 year = "2009",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "5789--5796",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 14. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "an{\'a}lise de mistura espectral, NDVI, minera{\c{c}}{\~a}o de 
                         dados, exatid{\~a}o do mapeamento, Amaz{\^o}nia, linear spectral 
                         unmixing, NDVI, data mining, mapping accuracy, Amazon.",
             abstract = "The purpose of this work was to evaluate the effect of using 
                         several image classification schemes for mapping of degraded 
                         pasture in the municipality of Rio Branco-AC, Brazil. 
                         Multi-spectral Landsat data, fraction images and images 
                         representing the Normalized Difference Vegetation Index (NDVI) 
                         were used as attributes in the classification procedures in order 
                         to map forest, degraded pasture, high and low pasture, water and 
                         bare soil. Four classification schemes were used: a) maximum 
                         likelihood using Landsat images; b) maximum likelihood using 
                         fraction images; c) isodata using Landsat images; and d) decision 
                         tree using Landsat images, fraction images and NDVI. Results 
                         showed that all classification methods used were efficient ranging 
                         from very good to excellent classification according to the Kappa 
                         coefficient. The scheme using the maximum likelihood 
                         classification algorithm performed better than the others the 
                         mapping of degraded pasture.",
  conference-location = "Natal",
      conference-year = "25-30 abr. 2009",
                 isbn = "978-85-17-00044-7",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "dpi.inpe.br/sbsr@80/2008/11.14.12.12",
                  url = "http://urlib.net/ibi/dpi.inpe.br/sbsr@80/2008/11.14.12.12",
           targetfile = "5789-5796.pdf",
                 type = "Mudan{\c{c}}a de Uso e Cobertura da Terra",
        urlaccessdate = "01 maio 2024"
}