@Article{ReisDutrSantEsca:2020:MuChDe,
               author = "Reis, Mariane Souza and Dutra, Luciano Vieira and Sant'Anna, 
                         Sidnei Jo{\~a}o Siqueira and Escada, Maria Isabel Sobral",
          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)}",
                title = "Multi-source change detection with PALSAR data in the Southern of 
                         Par{\'a} state in the Brazilian Amazon",
              journal = "International Journal of Applied Earth Observation and 
                         Geoinformation",
                 year = "2020",
               volume = "84",
                pages = "101945",
             keywords = "Post-classification change detection, Multi-sensor change 
                         detection, Brazilian Amazon.",
             abstract = "Optical data is broadly used for change detection studies, despite 
                         being hindered by atmospheric conditions. Synthetic Aperture Radar 
                         (SAR) data can be useful for change detection in areas with 
                         frequent cloud coverage as SAR systems are capable of obtaining 
                         images almost independently from atmospheric conditions. This 
                         study aims to verify the difference in results of using SAR data 
                         instead of optical data for change detection purposes. Different 
                         levels of one hierarchical legend and both pixel and region-based 
                         classifiers were used. Change results were evaluated considering 
                         the use of rectangular matrices to incorporate the occurrence of 
                         impossible changes and relative comparison between change maps. 
                         Although the change maps obtained using only optical data were 
                         more accurate than those using either one or two land cover 
                         classifications based on L-band SAR data, the difference in the 
                         accuracy of change maps decreases with the use of less detailed 
                         legends. Additionally, results indicate that L-band SAR and 
                         multi-sensor approaches are adequate for deforestation 
                         identification even if postclassification results did not achieve 
                         global accuracy values superior to 0.86. The most accurate change 
                         detection results obtained in this work were not associated with 
                         the overall accuracy of land cover classifications, but with the 
                         distribution and accuracy of specific land cover classes.",
                  doi = "10.1016/j.jag.2019.101945",
                  url = "http://dx.doi.org/10.1016/j.jag.2019.101945",
                 issn = "0303-2434",
                label = "lattes: 1175464822052393 1 ReisDutrSantEsca:2020:MuChDe",
             language = "en",
           targetfile = "reis_multi.pdf",
        urlaccessdate = "27 abr. 2024"
}