@InProceedings{VericaJúRiSiBePaJo:2017:TéMiDa,
               author = "Verica, Weverton Rodrigo and J{\'u}nior, Cl{\'o}vis Cechim and 
                         Richetti, Jonathan and Silva, La{\'{\i}}za Cavalcante de 
                         Albuquerque and Becker, Willyan Ronaldo and Paludo, Alex and 
                         Johann, Jerry Adriani",
                title = "T{\'e}cnicas de minera{\c{c}}{\~a}o de dados aplicadas a 
                         imagens MODIS para mapeamento de culturas de ver{\~a}o no estado 
                         do Paran{\'a}",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "4337--4342",
         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 = "The objective of this work was to develop a methodology for 
                         mapping cultivated areas with summer crops (soybean and corn) in 
                         the state of Paran{\'a} using MODIS sensor images for the crop 
                         year of 2013/2014. For classification the Random Forest algorithm 
                         was used. It is hard to differ soybean from corn with MODIS. Due 
                         to the heterogeneous and high spectral-temporal dynamics of corn 
                         and soybean, including proximity or distinction in the sowing and 
                         initial development in mesoregions of the state the Random Forest 
                         algorithm was applied in order to present a clear differentiation 
                         between crops. For the evaluation of the spatial accuracy of the 
                         mapping, the Landsat8 / OLI satellite images were used. These 
                         images served as reference to generate the error matrix. The 
                         proposed method obtained a kappa index of 0.9678, which is 
                         considered excellent, and a global of 98.61%, which also 
                         represents an excellent index. However, the results obtained from 
                         the mapping underestimated the area destined to the culture of 
                         Corn in about 50% and overestimated the soybean area by about 24%. 
                         Besides that, the methodology was successful in mapping soybean 
                         and corn crops in the state of Paran{\'a} for the crop year 
                         2013/2014, since the method is fast and inexpensive. Thus, the 
                         results indicate that the method is efficient for mapping the 
                         summer crops. Nevertheless, it is necessary to make some 
                         improvements to minimize the difference between the mapping result 
                         and the official data.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59412",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSM2SR",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSM2SR",
           targetfile = "59412.pdf",
                 type = "Agricultura e pecu{\'a}ria",
        urlaccessdate = "27 abr. 2024"
}