@InProceedings{SilvaFonsKört:2017:SpBaAp,
               author = "Silva, Alexsandro C{\^a}ndido de Oliveira and Fonseca, Leila 
                         Maria Garcia and K{\"o}rting, Thales Sehn",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "A spatiotemporal Bayesian approach to model deforestation",
            booktitle = "Anais...",
                 year = "2017",
         organization = "Workshop dos Cursos de Computa{\c{c}}{\~a}o Aplicada do INPE, 
                         17. (WORCAP)",
             keywords = "Spatiotemporal, Bayesian Network, Deforestation.",
             abstract = "According to the PRODES, a program that monitors deforestation in 
                         the Brazilian Legal Amazon, deforestation rate has increased in 
                         recent years. It jumped almost 30% over last year representing a 
                         sharp increase over the low rates since 2008. Hence, it is 
                         important to monitor the Brazilian Amazon in order to anticipate 
                         preventive actions and establish public policies. Methods of 
                         Bayesian Network have been increasingly used to model 
                         environmental systems. In this work, we present a computer-aided 
                         spatiotemporal Bayesian network method capable to incorporate 
                         experts knowledge and makes predictions about areas more 
                         susceptible to deforestation over time based on spatial and 
                         temporal data observation. The study area is located in the 
                         southwestern of Par{\'a} state around the Novo Progresso 
                         municipally and along the BR-163 highway. The proposed method 
                         takes into account random variables related to the deforestation, 
                         for instance, land use (proximity to pasture and agricultural 
                         areas can influence deforestation), available infrastructure 
                         (roads can give accessibility to the forests) and others spatial 
                         data. This method is intended as a snapshot, modeling what happens 
                         at a certain time in a spatial area. The time line is divided into 
                         a finite number of time instants. An initial instance of the 
                         Bayesian network is designed, which contains the formulation of 
                         the problem at time t=0, that is, the set of random variables and 
                         their dependency relationships. The structure of this initial 
                         Bayesian network is replicated for each instant. In this way, each 
                         network models the annual deforestation of the study area. As 
                         results, we obtained a spatialization of the areas most 
                         susceptible to deforestation over the time. For each studied year 
                         (2010-2015), one Probability Image was computed, in which each 
                         pixel value represents the probability of such area be deforested. 
                         These resulted images can help stakeholders to control or even 
                         reducing deforestation and to support a sustainable development.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP",
      conference-year = "20-22 nov. 2017",
             language = "en",
           targetfile = "Silva_spatiotemporal.pdf",
        urlaccessdate = "21 maio 2024"
}