@InProceedings{FelixTavCouAntEsq:2023:UsSéTe,
               author = "Felix, Filipe Castro and Tavares, Andr{\'e} Silva and Coutinho, 
                         Alexandre Camargo and Antunes, Jo{\~a}o Francisco 
                         Gon{\c{c}}alves and Esquerdo, J{\'u}lio C{\'e}sar Dalla Mora",
          affiliation = "{Embrapa Agricultura Digital} and {Embrapa Agricultura Digital} 
                         and {Embrapa Agricultura Digital} and {Embrapa Agricultura 
                         Digital} and {Embrapa Agricultura Digital}",
                title = "Uso de s{\'e}ries temporais Sentinel-2 para mapeamento de classes 
                         de cobertura vegetal no norte de Rond{\^o}nia",
            booktitle = "Anais...",
                 year = "2023",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de and Sanches, Ieda DelArco",
                pages = "e156076",
         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 = "desmatamento, sits, Self-organizing Maps, Random Forest, SWIR, 
                         deforestation, sits, Self-organizing Maps, Random Forest, SWIR.",
             abstract = "Atualmente, os sistemas de observa{\c{c}}{\~a}o da Terra 
                         produzem grandes volumes de imagens que permitem o monitoramento 
                         de diversos fen{\^o}menos espa{\c{c}}o-temporais. Neste 
                         contexto, este estudo visou explorar o uso de s{\'e}ries 
                         temporais do sat{\'e}lite Sentinel-2 e do algoritmo Random Forest 
                         na classifica{\c{c}}{\~a}o supervisionada do uso e cobertura da 
                         terra na regi{\~a}o do munic{\'{\i}}pio de Buritis-RO, sudoeste 
                         da Amaz{\^o}nia, que {\'e} caracterizada pela expans{\~a}o 
                         agr{\'{\i}}cola acelerada nas {\'u}ltimas d{\'e}cadas. Para 
                         isso, foram avaliados dois cen{\'a}rios: (I) esta{\c{c}}{\~a}o 
                         seca (tr{\^e}s meses) e (II) um ano agr{\'{\i}}cola, a fim de 
                         determinar qual o per{\'{\i}}odo mais adequado ao mapeamento 
                         dessa regi{\~a}o. O cen{\'a}rio (II) apresentou a maior 
                         acur{\'a}cia (88,66%), por{\'e}m nossos resultados demonstraram 
                         que a esta{\c{c}}{\~a}o seca e o uso das bandas short wavelength 
                         infrared (SWIR) foram determinantes nos mapeamentos, sendo 
                         indicadas para abordagens futuras de mapeamento dessa regi{\~a}o. 
                         ABSTRACT: Nowadays, earth observation systems produce large 
                         volumes of images that allow the monitoring of several 
                         spatiotemporal phenomena. In this context, we aimed to explore the 
                         use of satellite image time series of Sentinel-2 and Random Forest 
                         algorithm to the supervised classification of the land use and 
                         land cover (LULC) at the region of Buritis-RO, southwestern of 
                         Brazilian Amazon, which represents an area of intense expansion of 
                         agricultural frontiers. Then, two scenarios were evaluated: (I) 
                         dry season, and (II) one year, aiming to determine which period is 
                         most suitable for mapping the region. Scenario II presented the 
                         best map, with an accuracy of 88.66%. However, our results showed 
                         that the dry season and the use of short wavelength infrared 
                         (SWIR) bands were determinants for the mapping. Therefore, we 
                         indicate these bands for future approaches that aim to map this 
                         region.",
  conference-location = "Florian{\'o}polis",
      conference-year = "02-05 abril 2023",
                 isbn = "978-65-89159-04-9",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/495DT6S",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/495DT6S",
           targetfile = "156073.pdf",
                 type = "An{\'a}lise de s{\'e}ries temporais de imagens de 
                         sat{\'e}lite",
        urlaccessdate = "04 maio 2024"
}