@InProceedings{TrabaquiniLuEbScFoAt:2015:MeMoAg,
               author = "Trabaquini, Kleber and Luiz, Alfredo Jos{\'e} Barreto and 
                         Eberhardt, Isaque Daniel Rocha and Schultz, Bruno and Formaggio, 
                         Ant{\^o}nio Roberto and Atzberger, Clement",
          affiliation = "{} and {} and {} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Metodologia para monitoramento agr{\'{\i}}cola com emprego de 
                         imagens orbitais e amostragem estat{\'{\i}}stica",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "4482--4489",
         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 = "Brazil still has not a system based in earth observation images to 
                         map and monitoring the aimed crops in large scale. Many programs 
                         have been made with Landsat-like and MODIS data to monitoring 
                         crops in Brazil, but only the CANASAT has worked in operation 
                         level. The clouds and unit products (UPS) size in Brazil, have not 
                         permitted the use these data to correct classify maize, sugarcane 
                         and soybean. The use of sample frame and visual pixels 
                         classification with multitemporal OLI images could be a solution 
                         to monitor these three crops. The goal of this study was evaluate 
                         the sample frame performance to maize (c1), soybean (c2) and 
                         sugarcane (c3) in Paran{\'a} (PR) State using OLI images and 
                         pixel visual classification. Were used four periods to classify 
                         20.000 random pixels over all the Paran{\'a} State: (p1) Nov/Dec, 
                         (p2) Jan/Feb, (p3) Mar/Apr and (p4) May/Jun. Each period was 
                         compost for 4 OLI images, and 5.000 pixels were classified as c1, 
                         c2, c3 and others. IBGE data from 2012 were used to determinate 
                         the number of random pixels in each PR mesoregion/stratum. The 
                         Stratified Random Sample by Maximum Corrected (SRSMC) showed good 
                         performance for tree crops. The coefficient of variation (CV) for 
                         each period ranged of 1.42 for soybean in p2 until 16.87 for 
                         soybean in p4. The sugarcane CVs have not varied ( and maize CV 
                         had the minimum value (2.16) in p4.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "877",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4CR5",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM4CR5",
           targetfile = "p0877.pdf",
                 type = "Produ{\c{c}}{\~a}o e previs{\~a}o agr{\'{\i}}cola",
        urlaccessdate = "27 abr. 2024"
}