@InProceedings{ValerianoRoss:2019:MoAnRe,
               author = "Valeriano, M{\'a}rcio de Morisson and Rossetti, Dilce de 
                         F{\'a}tima",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Modal analysis for the regionalization of local landform data in 
                         Central Amaz{\^o}nia sedimentary refief",
            booktitle = "Anais...",
                 year = "2019",
               editor = "Gherardi, Douglas Francisco Marcolino and Sanches, Ieda DelArco 
                         and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
                pages = "2397--2400",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 19. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "SRTM, DEM, landform, Amaz{\^o}nia.",
             abstract = "This paper describes a procedure to regionalize local landform 
                         information derived from SRTM for broad scale studies of the 
                         central Amaz{\^o}nia relief. Modal landforms of mapping units 
                         were assigned to terrain segments based on elevation data. Top 
                         landforms prevailed among first mode classes, and a second mode 
                         was then assigned, considering only the less frequent landforms 
                         related to fluvial incisions. The combinations of first and second 
                         modes were evaluated through detailed examinations of local DEM 
                         and landforms together with the available geological data. After 
                         regrouping the most associate classes, the final modal landform 
                         combination map showed the effectiveness of the method for 
                         regionalization of landform data.",
  conference-location = "Santos",
      conference-year = "14-17 abril 2019",
                 isbn = "978-85-17-00097-3",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3U2J3DH",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3U2J3DH",
           targetfile = "97407.pdf",
                 type = "Geomorfologia",
        urlaccessdate = "28 abr. 2024"
}