@InProceedings{MedeirosRudoShim:1996:ImLaEs,
               author = "Medeiros, Ana Maria P. and Rudorff, Bernardo Friedrich Theodor and 
                         Shimabukuro, Yosio Edemir",
          affiliation = "{Instituto Brasileiro de Geografia e Estat{\'{\i}}stica (IBGE). 
                         Diretoria de Geoci{\^e}ncias.} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Imagens Landsat na estimativa de {\'a}reas de 
                         cana-de-a{\c{c}}{\'u}car, milho e soja",
            booktitle = "Anais...",
                 year = "1996",
               editor = "Krug, Thelma",
                pages = "33--38",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 8. (SBSR).",
            publisher = "INPE",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "AGRONOMIA, CANA-DE-ACUCAR, MILHO, SOJA, ESTIMATIVA, IMAGENS 
                         LANDSAT.",
             abstract = "Precise estimation of planted areas with agricultural crops of 
                         relevant interest to the national economy is of fundamental 
                         importance to several aspects such as: transportation and 
                         commercialization of agricultural production. In the present work 
                         remote sensing techniques were used to estimate areas planted with 
                         sugarcane soybean and maize in the counties of Aramina, Buritizal, 
                         Ituverava and Ipua, located in north of Sao Paulo state, for the 
                         crop year of 1994/95. A Landsat Tm image from 10 January 1995 was 
                         selected to perform the digital classification using a maximum 
                         likelihood classifier. Topographic charts were used to obtained 
                         the county limits that were digitized and registered to the 
                         Landsat image. Sample areas, over the Landsat image, were selected 
                         based mainly on information acquired during field work, in order 
                         to train the classifier. Area estimation data for each crop, per 
                         county, were obtained from the Systematic Survey Agricultural 
                         Production (LSPA)from the Brazilian Institute of Geography and 
                         Statistics (IBGE)and used to compare with the results from the 
                         digital thematic classification. The planted areas with sugarcane, 
                         soybean and maize were underestimated by 23 (9,930 ha), 25 (13.627 
                         ha)and 94(50.817 ha)in relation to the estimated area by the LSPA. 
                         Based on the large differences of area estimation, at the county 
                         level, observed between the satellite image and the LSPA, there is 
                         no doubt that one of the parts are in error. The procedure adopted 
                         during work was performed with state of the art remote sensing 
                         techniques and it is believed that these results are quite close 
                         to reality. However, it is recommended that the work should be 
                         continued, especially, to verify if multitemporal analysis could 
                         improve the area estimation and also to detected error source.",
  conference-location = "Salvador",
      conference-year = "14-19 abr. 1996",
                 isbn = "85-17-00014-5",
                label = "7535",
             language = "Pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais",
                  ibi = "sid.inpe.br/deise/1999/02.01.11.30",
                  url = "http://urlib.net/ibi/sid.inpe.br/deise/1999/02.01.11.30",
           targetfile = "T85.pdf",
                 type = "Agricultura",
        urlaccessdate = "05 maio 2024"
}