author = "Serrato, Jeniffer Trejos and Sousa J{\'u}nior, Manoel de 
                         Ara{\'u}jo and Pinz{\'o}n, Federico Pinz{\'o}n and Pardi 
                         Lacruz, Mar{\'{\i}}a Silvia",
          affiliation = "Grupo de Investigaci{\'o}n en Geom{\'a}tica Aplicada - GIGA 
                         Universidad del Valle - Cali, Valle del Cauca, Colombia and 
                         {Universidade Federal de Santa Maria - UFSM} and Universidad del 
                         Valle - Cali, Valle del Cauca, Colombia and {CRECTEALC  Campus 
                title = "Comparaci{\'o}n del modelo markoviano y de regresi{\'o}n para 
                         predicci{\'o}n de cambios en el uso y cobertura del suelo en la 
                         zona central del Departamento del Meta-Colombia",
            booktitle = "Anais...",
                 year = "2011",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                pages = "6532--6539",
         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 = "prediction of change, Markov chain, regression model, land 
                         use/land cover, linear spectral mixture model, predicci{\'o}n de 
                         cambios, cadenas de Markov, modelo de regresi{\'o}n, uso y 
                         cobertura de suelo, modelo lineal de mezcla espectral.",
             abstract = "To propose new methodologies for the sustainable management of the 
                         natural resources, some techniques for the prediction of changes 
                         in the land use have been implemented. Those techniques allow to 
                         estimate tendencies in the landscape dynamics in the time. To 
                         contribute with this purpose, this work compare two empirical 
                         models regression model and Markov chain model - focused in 
                         predicting land use change, verifying advantages and disadvantages 
                         of each one. Land use classes such as oil palm, urban area, 
                         agriculture, water, sand areas, bare soil and forest, were mapped 
                         from classification of three images: a 1988 TM/Landsat, an 
                         ETM+/Landsat 7 from 2000 and a 2005 CCD/CBERS 2 of the year 2005. 
                         Linear spectral mixture model was applied to the different bands. 
                         The area of land use classes were computed in each one of the 
                         images and dates and the regression model with better adjustment 
                         were estimated. With these models, projections for the years 2010, 
                         2012 and 2015 were made. The second prediction were calculated 
                         through the Markov chain transition matrix for the periods 
                         1988-2000 and 2000-2005, and projections were calculated for 2010, 
                         2012 and 2015. Finally, the results of the Markov chain transition 
                         matrix were compared with those obtained by regression models. The 
                         better predictive performance was obtained with the Markov chains 
                         for each of the land use classes. However, the regression models 
                         had good performance for the classes of oil palm and urban areas. 
                         It could also be verified the applicability of these techniques 
                         for agricultural areas under great exploitation.",
  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 = "es",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "3ERPFQRTRW/3A3TJ2H",
                  url = "",
           targetfile = "p0614.pdf",
        urlaccessdate = "2021, Jan. 20"