@Article{CarneiroOliSanDobVaz:2020:ExSeSA,
               author = "Carneiro, Arian Ferreira and Oliveira, Willian Vieira de and 
                         Sant'Anna, Sidnei Jo{\~a}o Siqueira and Doblas Prieto, Juan and 
                         Vaz, Daiane Vieira",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Exploiting sentinel-1 SAR time series to detect grasslands in the 
                         northern Brazilian amazon",
              journal = "International Archives of the Photogrammetry, Remote Sensing and 
                         Spatial Information Sciences",
                 year = "2020",
               volume = "43",
               number = "B3",
                pages = "259--265",
                month = "Aug.",
                 note = "2020 24th ISPRS Congress - Technical Commission III; Nice, 
                         Virtual; France; 31 August 2020 through 2 September 2020",
             keywords = "Remote sensing, Time series data, SAR, Image classification, 
                         Grassland detection, Sentinel-1.",
             abstract = "Recent advances in cloud-computing technologies and remote sensing 
                         data availability foster the development of studies based on the 
                         analysis of optical and SAR imagery time series. In this paper, we 
                         assess the potential of Sentinel-1 imagery time series for 
                         grassland detection in the northern Brazilian Amazon. We used the 
                         Google Earth Engine cloud-computing platform as an alternative to 
                         obtain and analyse Sentinel-1 imagery, acquired from 2017 to 2018 
                         over the region of Moju{\'{\i}} dos Campos/PA, Brazil. We 
                         extracted several temporal metrics from the imagery time series 
                         and used the Random Forest algorithm to perform the 
                         classification. In addition, we analysed the time series 
                         considering different channels, including the VV and VH 
                         polarizations, both separately and in combination, and the CR, RGI 
                         and NL indices. We could efficiently discriminate areas of 
                         grasslands from forest and agricultural crops using either VH time 
                         features or features extracted from the combination of both VV and 
                         VH polarizations. The classification map that resulted from the 
                         combination of VV and VH data presented the highest accuracy, with 
                         an overall accuracy of 95.33% and a 0.93 kappa index. Despite 
                         simple, the approach adopted in this paper showed potential to 
                         differ grasslands from areas of agriculture and forest in the 
                         northern Brazilian Amazon.",
                  doi = "10.5194/isprs-archives-XLIII-B3-2020-259-2020",
                  url = "http://dx.doi.org/10.5194/isprs-archives-XLIII-B3-2020-259-2020",
                 issn = "0256-1840",
             language = "en",
           targetfile = "carneiro_exploiting.pdf",
        urlaccessdate = "25 abr. 2024"
}