@InProceedings{FerrazSimõAlveXaud:2017:UsReSe,
               author = "Ferraz, Rodrigo and Sim{\~o}es, Margareth and Alves, Andrei Olak 
                         and Xaud, Haron Abrahim Magalh{\~a}es",
                title = "Use of Remote Sensing to assess Ecosystem Integrity of the 
                         Brazilian Amazon rainforest A Bayesian approach",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "7923--7929",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Biodiversity supports many ecosystem services that are very 
                         important for climate change mitigation and adaptation. There is a 
                         functional link between the tropical forest ecosystem biodiversity 
                         and their capacity for carbon uptake and storage as well as 
                         regulation of evapotranspiration flux. Nevertheless, land use 
                         changes and agriculture expansion reduce the ecosystems integrity 
                         modifying the functions related directly to the ecosystem 
                         services. The relationship between biodiversity loss and the 
                         impacts on ecosystem services of tropical forests, in face of the 
                         ongoing global climate change needs to be better quantified. In 
                         this work, we considered the concept of Ecosystem Integrity (EI), 
                         which represents the connection of biodiversity with the ability 
                         of ecosystems to sustain the processes of self-organization. 
                         Bayesian Networks (BBN-Bayesian Belief Network) can provide 
                         metrics for the generation of Ecosystem Integrity Index, from the 
                         training of probabilistic relationships of evidence obtained 
                         through Remote Sensing data. The objective of this work is to 
                         present the methodological approach and the results of EI mapping, 
                         elaborated at the regional scale for different patterns of 
                         phyto-ecologic landscape of the Brazilian Amazon. The modelling 
                         was based on learning from the parameters (data-driven model) 
                         through the use of the Expectation Maximization algorithm. For the 
                         validation of this probabilistic model, an evaluation was carried 
                         out in controlled areas with field observation by experts. Results 
                         showed that it is possible to generate an Ecosystem Integrity 
                         Index at regional scale using a probabilistic model based on 
                         Bayesian Belief Networks (BBN), and totally free web-available 
                         satellite products.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59379",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMGLD",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMGLD",
           targetfile = "59379.pdf",
                 type = "Degrada{\c{c}}{\~a}o de florestas",
        urlaccessdate = "27 abr. 2024"
}