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@Article{FurtadoSilvNovo:2016:DuFuC,
               author = "Furtado, Luiz Felipe de Almeida and Silva, Thiago Sanna Freire and 
                         Novo, Evlyn M{\'a}rcia Le{\~a}o de Moraes",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Estadual Paulista (UNESP)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)}",
                title = "Dual-season and full-polarimetric C band SAR assessment for 
                         vegetation mapping in the Amazon varzea wetlands",
              journal = "Remote Sensing of Environment",
                 year = "2016",
               volume = "174",
                pages = "212--222",
                month = "Mar.",
             keywords = "PoISAR, Wetlands, Polarimetric decomposition, Multitemporal, 
                         Mapping accuracy.",
             abstract = "This study answered the following questions: 1) Is polarimetric 
                         C-band SAR (PoISAR) more efficient than dual polarization 
                         (dual-pol) C-band SAR for mapping varzea floodplain vegetation 
                         types, when using images of a single hydrological period? 2) Are 
                         single-season C-band PoISAR images more accurate for mapping 
                         varzea vegetation types than dual-season dual-pol C-band SAR 
                         images? 3) What are the most efficient polarimetric descriptors 
                         for mapping varzea vegetation types? We applied the Random Forests 
                         algorithm to classify dual-pol SAR images and polarimetric 
                         descriptois derived from two full-polarimetric Radarsat-2 C-band 
                         images acquired during the low and high water seasons of Lago 
                         Grande de Curuai floodplain, lower Amazon, Brazil. We used the 
                         Kappa index of agreement (kappa), Allocation Disagreement (AD) and 
                         Quantity Disagreement (QD), and Producer's and User's accuracy 
                         measurements to assess the classification results. Our results 
                         showed that single-season full-polarimetric C-band data can yield 
                         more accurate classifications than single-season dual-pol C-band 
                         SAR imagery and similar accuracies to dual-season dual-pol C-band 
                         SAR classifications. Still, dual season PoISAR achieved the 
                         highest accuracies, showing that seasonality is paramount for 
                         obtaining high accuracies in wetland land cover classification, 
                         regardless of SAR image type. On average, single-season 
                         classifications of low-water periods were less accurate than 
                         high-water classifications, likely due to plant phenology and 
                         flooding conditions. Classifications using model-based 
                         polarimetric decompositions (such as Freeman-Durden, Yamaguchi and 
                         van Zyl) produced the highest accuracies (kappa greater than 0.8; 
                         AD ranging from 7.5% to 2.5%; QD ranging from 15% to 12%), while 
                         eigenvector-based decompositions such as Touzi and Cloude-Pottier 
                         had the worst accuracies (kappa ranging from 0.5 to 0.7; AD 
                         greater than 10%; QD smaller than 10%). Vegetation types with 
                         dense canopies (Shrubs, Floodable Forests and Emergent 
                         Macrophytes), whose classification is challenging using C-band, 
                         were accurately classified using dual-season full-polarimetric SAR 
                         data, with Producer's and User's accuracies between 80% and 90%. 
                         We conclude that full polarimetric C-band imagery can yield very 
                         accurate classifications of varzea vegetation (kappa similar to 
                         0.8, AD similar to 3% and QD similar to 10%) and can be used as an 
                         operational tool for forested wetland mapping.",
                  doi = "10.1016/j.rse.2015.12.013",
                  url = "http://dx.doi.org/10.1016/j.rse.2015.12.013",
                 issn = "0034-4257",
             language = "en",
           targetfile = "1_furtado_dual.pdf",
        urlaccessdate = "30 nov. 2020"
}


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