@InProceedings{WagnerWebFonDucKle:2007:EsÁrSo,
               author = "Wagner, Ana Paula Luz and Weber, Eliseu Jos{\'e} and Fontana, 
                         Denise Cybis and Ducati, Jorge Ricardo and Klering, Eliana 
                         Veleda",
          affiliation = "{Universidade Federal do Rio Grande do Sul (UFRGS). Centro 
                         Estadual de Pesquisas em Sensoriamento Remoto e Meteorologia 
                         (CEPSRM).} and {Universidade Federal do Rio Grande do Sul (UFRGS). 
                         Centro Estadual de Pesquisas em Sensoriamento Remoto e 
                         Meteorologia (CEPSRM).} and {Universidade Federal do Rio Grande do 
                         Sul (UFRGS). Centro Estadual de Pesquisas em Sensoriamento Remoto 
                         e Meteorologia (CEPSRM). Faculdade de Agronomia. Departamento de 
                         Agrometeorologia e Plantas Forrageiras.} and {Universidade Federal 
                         do Rio Grande do Sul (UFRGS). Centro Estadual de Pesquisas em 
                         Sensoriamento Remoto e Meteorologia (CEPSRM).} and {Universidade 
                         Federal do Rio Grande do Sul (UFRGS). Centro Estadual de Pesquisas 
                         em Sensoriamento Remoto e Meteorologia (CEPSRM).}",
                title = "Estimativa de {\'a}rea de soja no Rio Grande do Sul utilizando 
                         imagens NDVI/MODIS",
            booktitle = "Anais...",
                 year = "2007",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares and Fonseca, Leila Maria Garcia",
                pages = "457--464",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 13. (SBSR).",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "agricultural estimates, NDVI/MODIS, digital classification, 
                         statisticians of spectral distribution, estimativa de {\'a}rea, 
                         NDVI/MODIS, classifica{\c{c}}{\~a}o digital, 
                         estat{\'{\i}}sticas de distribui{\c{c}}{\~a}o espectral.",
             abstract = "The Brazilian agricultural estimates still are strongly based on 
                         subjective surveys. In the last years several researches have been 
                         developed shown that the remote sensing products can be an 
                         important source of objective products. The aim of this study was 
                         to evaluate the application of NDVI MODIS image as a ease and fast 
                         estimator of the soybean crop area in the Rio Grande do Sul state. 
                         The applied methodology uses the temporal dynamic of the soybean 
                         crop through the evaluating of the NDVI behavior in some 
                         characteristic periods of the crop cycle in the main producer 
                         region of the State. The soybean map area produced by a Landsat 
                         image was used as a crop mask for extracting the average and 
                         standard deviation statistics. This information was used to define 
                         NDVI bands corresponding to the implantation and full development 
                         periods of the crop. The cross-matches of these binary images were 
                         used to map soybean areas. The results had shown the possibility 
                         of using this method to map soybean areas in Rio Grande do Sul. 
                         The agreement between the numbers of soybean pixels classified in 
                         the MODIS image related to the soybean areas identified in the 
                         Landsat images was 83.98%, with 0.48 of Kappa. The omission and 
                         commission error was near to 42% each. The difference between the 
                         crop area in the MODIS and Landsat images was 2.38%. When the 
                         comparison was made using MODIS and IBGE data, the difference gone 
                         up to 18.78%. The results were very good in soybean regions with 
                         high heterogeneity caused by the size, technology and crop 
                         calendar, excusing any other type of edition on the classified 
                         image. This methodology allows one to produce good indication of 
                         the soybean area using a simple, economic and fast form.",
  conference-location = "Florian{\'o}polis",
      conference-year = "21-26 abr. 2007",
                 isbn = "978-85-17-00031-7",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "dpi.inpe.br/sbsr@80/2006/11.14.20.31",
                  url = "http://urlib.net/ibi/dpi.inpe.br/sbsr@80/2006/11.14.20.31",
           targetfile = "457-464.pdf",
                 type = "Agricultura",
        urlaccessdate = "28 abr. 2024"
}