@InProceedings{MorettiFerrMattTren:2011:AvMéCl,
               author = "Moretti, Ana Isabel Pasztor and Ferreira, Marcos C{\'e}sar and 
                         Mattos, Eliana Corr{\^e}a Aguirre de and Trentin, Gracieli",
          affiliation = "{Universidade Estadual de Campinas - UNICAMP} and {Universidade 
                         Estadual de Campinas - UNICAMP} and {Universidade Estadual de 
                         Campinas - UNICAMP} and {Universidade Estadual de Campinas - 
                         UNICAMP}",
                title = "Avalia{\c{c}}{\~a}o de m{\'e}todos de classifica{\c{c}}{\~a}o 
                         supervisionada para o mapeamento da cobertura vegetal nativa da 
                         {\'A}rea de Prote{\c{c}}{\~a}o Ambiental Fern{\~a}o Dias a 
                         partir de imagem Landsat",
            booktitle = "Anais...",
                 year = "2011",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "7279--7285",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 15. (SBSR).",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "remote sensing, image processing, forest surveys, sensoriamento 
                         remoto, processamento de imagens, levantamentos florestais.",
             abstract = "The loss of biodiversity is highly influenced by forest 
                         fragmentation, caused by the replacement of native vegetal 
                         coverage for urban, industrial, pasture and agriculture areas. The 
                         identification and classification of forest fragments are 
                         important for its conservation, monitoring and for support actions 
                         of environmental planning. In this context, the aim of this work 
                         is to map the forest fragments of APA Fern{\~a}o Dias from two 
                         supervised classification methods of remote sensing images: the 
                         Maximum Likelihood and the one based on fuzzy logic. The Maximum 
                         Likelihood, a boolean method, considers the balance of averages 
                         distances from statistical parameters and the classification 
                         result is presented in the form of polygons with sharp boundaries 
                         between different land uses. The other one, based on fuzzy logic, 
                         does not have sharply defined boundaries and an element may have 
                         partial and multiple membership, providing a better representation 
                         for geographical phenomena that cannot be described by a single 
                         attribute. Resulting images of both methods showed that APA 
                         Fern{\~a}o Dias has significant forest coverage area. The 
                         classified image resulted of Maximum Likelihood showed that the 
                         semideciduous forest covered the largest area (18,14%) and is 
                         distributed throughout APA and around of the tropical rain forest 
                         fragments, in its lowest altitudes. The classified image resulted 
                         of the method based on fuzzy logic attributed a larger number of 
                         pixels with possibility to belong to the tropical rain forest 
                         (28,57%) and revealed the transition zone of tropical rain forest 
                         to the semideciduous forest, representing the reality of APA 
                         Fern{\~a}o Dias more efficiently.",
  conference-location = "Curitiba",
      conference-year = "30 abr. - 5 maio 2011",
                 isbn = "{978-85-17-00056-0 (Internet)} and {978-85-17-00057-7 (DVD)}",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "3ERPFQRTRW/3A3NMB2",
                  url = "http://urlib.net/ibi/3ERPFQRTRW/3A3NMB2",
           targetfile = "p1434.pdf",
                 type = "Processamento de Imagens",
        urlaccessdate = "11 maio 2024"
}