@Article{MarquesCarvalhoAlmEscAlvLac:2023:SiPrUr,
               author = "Marques Carvalho, R{\^o}mulo and Almeida, Cl{\'a}udia Maria de 
                         and Escobar Silva, Elton Vicente and Alves, Rayanna Barroso de 
                         Oliveira and Lacerda, Camila Souza dos Anjos",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and Instituto Federal de Educa{\c{c}}{\~a}o, 
                         Ci{\^e}ncia e Tecnologia do Sul de Minas (IF SuldeMinas)",
                title = "Simulation and prediction of urban land use change considering 
                         Multiple classes and transitions by means of random change 
                         Aalocation algorithms",
              journal = "Remote Sensing",
                 year = "2023",
               volume = "15",
               number = "1",
                pages = "e90",
                month = "Jan.",
             keywords = "cellular automata (CA), digital terrain model, Google Earth, 
                         orbital images.",
             abstract = "The great majority of the world population resides nowadays in 
                         urban areas. Understanding their physical and social structure, 
                         and especially their urban land use pattern dynamics throughout 
                         time, becomes crucial for successful, effective management of such 
                         areas. This study is committed to simulate and predict urban land 
                         use change in a pilot city belonging to the S{\~a}o Paulo 
                         Metropolitan Region, southeast of Brazil, by means of a cellular 
                         automata model associated with the Markov chain. This model is 
                         driven by data derived from orbital and airborne remotely sensed 
                         images and is parameterized by the Bayesian weights of evidence 
                         method. Several layers related to infrastructure and biophysical 
                         aspects of the pilot city, S{\~a}o Caetano do Sul, were used as 
                         evidence in the simulation process. Alternative non-stationary 
                         scenarios were generated for the short-run, and the results 
                         obtained from past simulations were statistically validated using 
                         a multiresolution goodness-of-fit metric relying on fuzzy logic. 
                         The best simulations reached fuzzy similarity indices around 
                         0.250.58 for small neighborhood windows when an exponential decay 
                         approach was employed for the analysis, and approximately 0.650.95 
                         when a constant decay and larger windows were considered. The 
                         adopted Bayesian inference method proved to be a good 
                         parameterization approach for simulating processes of urban land 
                         use change involving multiple classes and transitions.",
                  doi = "10.3390/rs15010090",
                  url = "http://dx.doi.org/10.3390/rs15010090",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-15-00090-v3.pdf",
        urlaccessdate = "01 maio 2024"
}