@InProceedings{TeotiaRibeRamo:2011:InSeRe,
               author = "Teotia, Harendra Singh and Ribeiro, George do Nascimento and 
                         Ramos, Francisco De Assis Pereira",
          affiliation = "{Universidade Federal da Para{\'{\i}}ba – UFPB} and 
                         {Universidade Federal da Para{\'{\i}}ba – UFPB} and 
                         {Universidade Federal da Para{\'{\i}}ba – UFPB}",
                title = "Integra{\c{c}}{\~a}o de Sensoriamento Remoto e SIG 
                         (geoprocessamento) na identifica{\c{c}}{\~a}o dos solos 
                         principais e estratos de vegeta{\c{c}}{\~a}o para planejamento 
                         regional no Estado da Para{\'{\i}}ba",
            booktitle = "Anais...",
                 year = "2011",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "9128--9135",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 15. (SBSR).",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Landsat-TM, Agreste Region, Image Processing, Land Use/Land Cover, 
                         Landsat-TM, Regi{\~a}o Agreste, Processamento de Imagens, Uso da 
                         Terra/Cobertura Vegetal.",
             abstract = "The main objective of this study is the better use of the natural 
                         resources for a part of Agreste region of the state of Paraiba in 
                         northeastern Brazil. Under this study, the classifications 
                         (unsupervised and supervised) were made for the interpretation of 
                         Landsat TM Data, using ERDAS Imagine Software. The soils were 
                         classified into three major groups, such as, Luvissolos, Neossolos 
                         Litolicos and Argissolos. The Land Use and Land Cover 
                         classification was divided into four major classes, such as, 
                         Native Vegetation and Rock-outcrops, Native Vegetation, Degraded 
                         areas and Agricultural areas. According to an average 
                         classification system, the overall classification accuracy was 
                         found approximately 86,00%. It reveals that accuracy of the 
                         classification was considered high and the results were very 
                         satisfactory. The area of each classes was calculated and the 
                         total area of digitally prepared map was approximately 629Km2. The 
                         classes of the system were spectrally homogeneous. The three 
                         principal land limitations encountered in the study area are: lack 
                         of water, surface rockiness and stoniness and susceptibility of 
                         erosion. It was concluded from the study that the Landsat-TM 
                         images are more effective for the detection of major soil groups, 
                         land evaluation and land use/land cover classes for the detailed 
                         regional and local planning, land development and land management 
                         for the Agreste region of the state of Para{\'{\i}}ba. Also, 
                         such type of technology used under this study, may be used for 
                         planning, management and development of any type of climatic 
                         regions, such as Humid, Sub-humid, Agreste, Semi-arid, Arid and 
                         Pantanal.",
  conference-location = "Curitiba",
      conference-year = "30 abr. - 5 maio 2011",
                 isbn = "{978-85-17-00056-0 (Internet)} and {978-85-17-00057-7 (DVD)}",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "3ERPFQRTRW/3A35CJ5",
                  url = "http://urlib.net/ibi/3ERPFQRTRW/3A35CJ5",
           targetfile = "p0125.pdf",
                 type = "Geomorfologia e Solos",
        urlaccessdate = "16 jun. 2024"
}