@InProceedings{CrivelariCostaRoDeOlLaSi:2023:EmInLa,
               author = "Crivelari Costa, Patr{\'{\i}}cia Monique and Rossi, Fernando 
                         Saragosa and Della Silva, Jo{\~a}o Lucas and Oliveira 
                         J{\'u}nior, Jos{\'e} Wagner de and La Scala J{\'u}nior, Newton 
                         and Silva J{\'u}nior, Carlos Antonio",
          affiliation = "{Universidade do Estado de Mato Grosso (UNEMAT)} and {} and 
                         {Universidade do Estado de Mato Grosso (UNEMAT)} and {Universidade 
                         Federal de Mato Grosso (UFMT)} and {Universidade Estadual Paulista 
                         (UNESP)} and {Universidade do Estado de Mato Grosso (UNEMAT)}",
                title = "Emissions induced by land use and land cover change in the legal 
                         Amazon from remotely sensed data",
            booktitle = "Anais...",
                 year = "2023",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de and Sanches, Ieda DelArco",
                pages = "e155623",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 20. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "LULCC, carbon dioxide, Google Earth Engine, remote sensing.",
             abstract = "The Amazon rainforest is a tropical forest and has the greatest 
                         biodiversity in the world. Changes in land use and land cover are 
                         one of the main factors in the degradation of tropical forests, 
                         which contribute to the emission of greenhouse gases. Thus, the 
                         objective of this research was to detect the emissions induced by 
                         the change of land use and land cover in the Legal Amazon through 
                         multispectral images of the MODIS sensor in the period 2009-2019. 
                         The land use and land cover data, the gross primary production 
                         (GPP) and the flux of carbon dioxide (CO2flux) were used. The 
                         parameters were obtained using JavaScript language as input in the 
                         Google Earth Engine platform. CHIRPS dataset was used to ascertain 
                         the regularities of precipitation over the time series.",
  conference-location = "Florian{\'o}polis",
      conference-year = "02-05 abril 2023",
                 isbn = "978-65-89159-04-9",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/492Q3DP",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/492Q3DP",
           targetfile = "155623.pdf",
                 type = "Monitoramento e modelagem ambiental",
        urlaccessdate = "03 maio 2024"
}