@InProceedings{SilvaJúniorTeoDelRosNan:2023:NeApSo,
               author = "Silva J{\'u}nior, Carlos Antonio da and Teodoro, Paulo Eduardo 
                         and Della Silva, Jo{\~a}o Lucas and Rossi, Fernando Saragosa and 
                         Nanni, Marcos Rafael",
          affiliation = "{Universidade do Estado de Mato Grosso (UNEMAT)} and {Universidade 
                         Federal de Mato Grosso do Sul (UFMS)} and {Universidade do Estado 
                         de Mato Grosso (UNEMAT)} and {Universidade Estadual Paulista 
                         (UNESP)} and {Universidade Estadual de Maring{\'a} (UEM)}",
                title = "Perpendicular crop enhancement index: a new approach to soybean 
                         monitoring using time-series",
            booktitle = "Anais...",
                 year = "2023",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de and Sanches, Ieda DelArco",
                pages = "e156226",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 20. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Spatial distribution, vegetation index, PCEI, digital image 
                         processing, data mining.",
             abstract = "In Brazil, despite the improvements with respect the technological 
                         knowledge, agricultural areas are often estimated in loco. Here, 
                         soybean areas in Paran{\'a}, Brazil, using MODIS imagery were 
                         mapped. We applied the vegetation index PCEI (Perpendicular Crop 
                         Enhancement Index) and threshold determination for the automation 
                         of soybean area discrimination by geo-object (GEOBIA). For this, 
                         vegetation indices (NDVI, EVI and CEI) and the development of the 
                         PCEI were used with the aid of timeseries images from the 
                         TERRA/MODIS. By geo-objects and decision tree based on data mining 
                         support analysis, the new vegetation index was determined. Kappa 
                         and Overall Accuracy statistics were applied to evaluate 
                         classification precision. Regarding the ground line, R and R² were 
                         above 0.92 and 0.84, respectively (p<0.01). The test results 
                         indicate that the proposed methodology is efficient for mapping 
                         soybean distribution. Thus, this study allows automated mapping of 
                         with soybean crops areas at large scales.",
  conference-location = "Florian{\'o}polis",
      conference-year = "02-05 abril 2023",
                 isbn = "978-65-89159-04-9",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/495GU42",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/495GU42",
           targetfile = "156226.pdf",
                 type = "An{\'a}lise de s{\'e}ries temporais de imagens de 
                         sat{\'e}lite",
        urlaccessdate = "11 maio 2024"
}