@InProceedings{PinheiroChagJúniAnjo:2013:UsDaSe,
               author = "Pinheiro, Helena Saraiva Koenow and Chagas, C{\'e}sar da Silva 
                         and J{\'u}nior, Waldir de Carvalho and Anjos, L{\'u}cia Helena 
                         Cunha dos",
                title = "Uso de dados de sensoriamento remoto em mapeamento digital de 
                         solos  por redes neurais artificiais",
            booktitle = "Anais...",
                 year = "2013",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "9240--9247",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 16. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "The current demand for spatial information and the advance in 
                         processing of the machines has changed the course of the soil 
                         survey. The digital soil mapping arises in the context of improved 
                         the pedological surveys products through the use of GIS tools, 
                         knowledge of the genesis, morphology, and classification of soils, 
                         for selection appropriate attributes to represent reality, and 
                         produce a soil survey with accuracy and efficiency (cost x time). 
                         The approach used in digital soil mapping is based on the 
                         classical concepts of soil genesis and relations with terrain 
                         attributes. The objective was evaluating the effects of using 
                         remote sensing data in digital soil mapping of the river basin 
                         Guapi-Macacu (RJ). Were analyzed five different sets of 
                         attributes, in order to assess the contribution of three indices 
                         derived from remote sensing, in the performance of the 
                         classification by neural networks. They are: (i) all variables, 
                         (ii) all except clay minerals, (iii) all except iron oxide, (iv) 
                         all except NDVI, (v) all except the three indices. Image 
                         processing was performed with ERDAS and other attributes in ArcGIS 
                         v.10. The analyzes showed statistical differences between 
                         classifiers, highlighting the contribution of data derived from 
                         remote sensing. The criteria used in the evaluation (statistical 
                         indexes and concordance with the control points) indicate the set 
                         (iv) as the most suitable to represent the soil map of the area, 
                         although better performance of statistical indices has been 
                         obtained by the set (i).",
  conference-location = "Foz do Igua{\c{c}}u",
      conference-year = "13-18 abr. 2013",
                 isbn = "{978-85-17-00066-9 (Internet)} and {978-85-17-00065-2 (DVD)}",
                label = "1443",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "3ERPFQRTRW34M/3E7GLCH",
                  url = "http://urlib.net/ibi/3ERPFQRTRW34M/3E7GLCH",
           targetfile = "p1443.pdf",
                 type = "Solos e Umidade do Solo",
        urlaccessdate = "04 jun. 2024"
}