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@Article{EberhardtLuizFormSanc:2015:DeÁrAg,
               author = "Eberhardt, Isaque Daniel Rocha and Luiz, Alfredo Jos{\'e} Barreto 
                         and Formaggio, Ant{\^o}nio Roberto and Sanches, Ieda Del Arco",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Embrapa 
                         Meio Ambiente} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Detec{\c{c}}{\~a}o de {\'a}reas agr{\'{\i}}colas em tempo 
                         quase real com imagens Modis",
              journal = "Pesquisa Agropecu{\'a}ria Brasileira",
                 year = "2015",
               volume = "50",
               number = "7",
                pages = "605--614",
                month = "jul",
             keywords = "Crop monitoring, DATQuaR, Remote sensing, Summer crop maps.",
             abstract = "The objective of this work was to develop a method to identify and 
                         monitor, in near real-time, crop field areas cultivated with 
                         temporary summer crops, using Modis orbital images, in the state 
                         of Rio Grande do Sul, Brazil. The methodology was called near 
                         real-time detection of crop fields (DATQuaR) and uses Modis sensor 
                         images of the NDVI and EVI vegetation indices (VIs) from 16-day 
                         composites. Four different metrics were used to aggregate the 
                         values of VIs per pixel, in the bimonthly periods evaluated: 
                         average, maximum, minimum, and median. To generate the images 
                         (ImDATQuaR), the aggregated image for the previous period was 
                         subtracted from the aggregated image for the monitored period. 
                         These images were classified by slicing and compared with the 
                         reference classes obtained by the visual interpretation of 
                         randomly selected pixels in Landsat images. Each ImDATQuaR image 
                         generated two DATQuaR maps: one with a 3x3 pixel window mode 
                         filter and another without filtering. The best DATQuaR map is 
                         produced using EVI images and filtering-by subtracting the image 
                         of minimum value for the previous period from the image of maximum 
                         value for the monitored period-and achieves agreement with the 
                         reference over 81%.",
                  doi = "10.1590/S0100-204X2015000700010",
                  url = "http://dx.doi.org/10.1590/S0100-204X2015000700010",
                 issn = "0100-204X",
             language = "en",
           targetfile = "eberhardt_deteccao.pdf",
        urlaccessdate = "28 nov. 2020"
}


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