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@Article{SaitoArMoSaEuFi:2016:UsGeAn,
               author = "Saito, Nath{\'a}lia Suemi and Arguello, Fernanda Viana Paiva and 
                         Moreira, Maur{\'{\i}}cio Alves and Santos, Alexandre Rosa dos 
                         and Eugenio, Fernando Coelho and Figueiredo, Alvaro Costa",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Univesidade Federal do 
                         Esp{\'{\i}}rito Santo (UFES)} and {Univesidade Federal do 
                         Esp{\'{\i}}rito Santo (UFES)} and {Univesidade Federal do 
                         Esp{\'{\i}}rito Santo (UFES)}",
                title = "Uso da geotecnologia para an{\'a}lise temporal da cobertura 
                         florestal",
              journal = "Cerne",
                 year = "2016",
               volume = "22",
               number = "1",
                pages = "11--18",
                month = "jan./mar.",
             keywords = "Data Mining, GeoDMA, Landscape ecology.",
             abstract = "The landscape ecology metrics associated with data mining can be 
                         used to increase the potential of remote sensing data analysis and 
                         applications, being an important tool for decision making. The 
                         present study aimed to use data mining techniques and landscape 
                         ecology metrics to classify and quantify different types of 
                         vegetation using a multitemporal analysis (2001 and 2011), in 
                         S{\~a}o Lu{\'{\i}}s do Paraitinga city, S{\~a}o Paulo, Brazil. 
                         Object-based image analyses and the C4.5 data-mining algorithm 
                         were used for automated classification. Classification accuracies 
                         were assessed using the kappa index of agreement and the recently 
                         proposed allocation and quantity disagreement measures. Four land 
                         use and land cover classes were mapped, including Eucalyptus 
                         plantations, whose area increased from 4.4% to 8.6%. The automatic 
                         classification showed a kappa index of 0.79 and 0.80, quantity 
                         disagreements of 2% e 3.5% and allocation measures of 5.5% and 5% 
                         for 2001 and 2011, respectively. We therefore concluded that the 
                         data mining method and landscape ecology metrics were efficient in 
                         separating vegetation classes.",
                  doi = "10.1590/01047760201622011935",
                  url = "http://dx.doi.org/10.1590/01047760201622011935",
                 issn = "0104-7760",
             language = "en",
           targetfile = "saito_uso.pdf",
        urlaccessdate = "28 nov. 2020"
}


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