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@InProceedings{EberhardtLuFoSaScTr:2015:DeÁrAg,
               author = "Eberhardt, Isaque Daniel Rocha and Luiz, Alfredo Jos{\'e} Barreto 
                         and Formaggio, Ant{\^o}nio Roberto and Sanches, Ieda Del Arco and 
                         Schultz, Bruno and Trabaquini, Kleber",
          affiliation = "{} and {} and {Instituto Nacional de Pesquisas Espaciais (INPE)} 
                         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 (DATQuaR)",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "5650--5657",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Nowadays the challenge in agricultural estimates using remote 
                         sensing is to produce the estimates and data across the crop 
                         season, in near real-time. The aim of this paper is to build an 
                         approach capable to produce the crop maps of soybean+maize in near 
                         real-time, for Rio Grande do Sul state, using MODIS images. To 
                         generate the near real-time crop maps we used the MODIS 16 days 
                         composites vegetation index (VI) images of NDVI and EVI. This new 
                         approach was called Near Real-Time Crop Fields Detection 
                         (DATQuaR). The MODIS VIs images were aggregated in bimonthly 
                         periods using different ways: average, maximum, minimum and median 
                         of registered values. After that, the image of the previous period 
                         was subtracted from the image of the monitored period, generating 
                         the DATQuaR images. These images were classified by slice using as 
                         limit the occupied area estimate with soybean+maize produced by 
                         random sampling over Landsat image and visual interpretation. The 
                         DATQuaR maps were submitted to 3x3 pixel window mode filter. The 
                         results showed that the best approach was to aggregate the maximum 
                         registered MODIS IVs value in the monitored period and the minimum 
                         value registered in the previous period. In this case the EVI 
                         images and the 3x3 pixel window mode filter were used. Using this 
                         approach the DATQuaR method achieved over 81% (in the worst 
                         period, January/February of 2014) of agreement with random 
                         sampling Landsat pixels classified by visual interpretation.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "1146",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4EF5",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3JM4EF5",
           targetfile = "p1146.pdf",
                 type = "Produ{\c{c}}{\~a}o e previs{\~a}o agr{\'{\i}}cola",
        urlaccessdate = "04 dez. 2020"
}


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