@InProceedings{RizziRudoAdam:2005:EsÁrPl,
               author = "Rizzi, Rodrigo and Rudorff, Bernardo Friedrich Theodor and Adami, 
                         Marcos",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Secretaria de Estado 
                         da Agricultura e do Abastecimento do Paran{\'a} - SEAB}",
                title = "Estimativa de {\'a}rea plantada com soja no Rio Grande do Sul 
                         atrav{\'e}s de amostragem por segmentos quadrados",
            booktitle = "Anais...",
                 year = "2005",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Fonseca, Leila Maria 
                         Garcia",
                pages = "245--252",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 12. (SBSR)",
            publisher = "INPE",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "sampling square method, crop area estimate, soybean, amostragem, 
                         estimativa de area plantada, soja.",
             abstract = "This paper evaluates a sampling square method to estimate soybean 
                         crop area in Rio Grande do Sul State, Brazil. A soybean thematic 
                         map obtained from multitemporal Landsat images classification was 
                         used as reference data. The State area was divided into cells of 1 
                         x 1 km of size and stratified into three soybean area densities 
                         (0-20, 20-40 and >40%) at municipality level. A probabilistic 
                         technique was used to determine four sample rates representing 
                         0.06, 0.12, 0.24 and 0.48% of the study area which were randomly 
                         sampled one hundred times. The soybean area for each sample was 
                         evaluated based on the reference data map. The one hundred 
                         estimates for each sample rate were then compared with the 
                         reference data for the entire study area. Best results were 
                         obtained for the highest sample rate with low Coefficient of 
                         Variation (5.2), indicating that this method is not only suitable 
                         to accurate estimate soybean crop area at State level but it is 
                         also an appropriate alternative for early forecast or when cloud 
                         free images are not available.",
  conference-location = "Goi{\^a}nia",
      conference-year = "16-21 abr. 2005",
                 isbn = "85-17-00018-8",
             language = "Portugu{\^e}s",
         organisation = "Instituto Nacional de Pesquisas Espaciais",
                  ibi = "ltid.inpe.br/sbsr/2004/11.19.13.59",
                  url = "http://urlib.net/ibi/ltid.inpe.br/sbsr/2004/11.19.13.59",
           targetfile = "245.pdf",
                 type = "Agricultura",
        urlaccessdate = "29 abr. 2024"
}