@InProceedings{RodriguesSouzScafLass:2023:MaMaSt,
               author = "Rodrigues, Fl{\'a}vio Henrique and Souza Filho, Carlos Roberto de 
                         and Scafutto, Rebecca Del’Papa Moreira and Lassalle, Guillaume",
          affiliation = "{Universidade Estadual de Campinas (UNICAMP)} and {Universidade 
                         Estadual de Campinas (UNICAMP)} and {Universidade Estadual de 
                         Campinas (UNICAMP)} and {Universidade Estadual de Campinas 
                         (UNICAMP)}",
                title = "Mangrove mapping strategies using Google Earth Engine and 
                         Landsat-8 and Sentinel-2 imagery 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 = "e156061",
         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 = "Mangrove mapping, Google Earth Engine.",
             abstract = "Vegetation indices based on remote sensing data have been widely 
                         used for mangrove monitoring. Nowadays, the availability of 
                         cloud-based platforms allows the processing of large datasets of 
                         orbital imagery with moderate spatial and spectral resolutions 
                         such as the computation of numerous vegetation spectral indices to 
                         map coastal vegetated wetlands. This study presents the 
                         performance of the Mangrove Vegetation Index (MVI) and image 
                         classification algorithms, embedded in the Google Earth Engine, 
                         applied to Landsat-8 and Sentinel-2 data, to map tracts of 
                         mangroves in Aracaju (Sergipe, Brazil). Results reveal that the 
                         Cobweb clustering algorithm applied to MVIderived from Landsat-8 
                         data favors reliable and practical mangrove mapping, considering 
                         the broad diversity of vegetation conditions in this habitat.",
  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/494DLGL",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/494DLGL",
           targetfile = "156061.pdf",
                 type = "Floresta e outros tipos de vegeta{\c{c}}{\~a}o",
        urlaccessdate = "11 maio 2024"
}