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@Article{SartoriImaMurNovSil:2011:MaMaSp,
               author = "Sartori, L. R. and Imai, N. N and Mura, J. C. and Novo, E. M. L. 
                         D. M and Silva, T. S. F.",
          affiliation = "Sao Paulo State University (UNESP), Presidente Prudente 19060-190, 
                         Brazil and Sao Paulo State University (UNESP), Presidente Prudente 
                         19060-190, Brazil and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         Geography Department, University of Victoria, Victoria, BC V8W 
                         3R4, Canada",
                title = "Mapping macrophyte species in the amazon floodplain wetlands using 
                         fully polarimetric ALOS/PALSAR data",
              journal = "IEEE Transactions on Geoscience and Remote Sensing",
                 year = "2011",
               volume = "49",
               number = "12 PART 1",
                pages = "4717--4728",
                month = "Dec.",
             keywords = "Amazon floodplain, classification, macrophyte species, Phased 
                         Array Type L-Band Synthetic Aperture Radar (PALSAR) data, 
                         polarimetric decomposition, radar polarimetry.",
             abstract = "The purpose of this paper was to evaluate attributes derived from 
                         fully polarimetric PALSAR data to discriminate and map macrophyte 
                         species in the Amazon floodplain wetlands. Fieldwork was carried 
                         out almost simultaneously to the radar acquisition, and macrophyte 
                         biomass and morphological variables were measured in the field. 
                         Attributes were calculated from the covariance matrix [C] derived 
                         from the single-look complex data. Image attributes and macrophyte 
                         variables were compared and analyzed to investigate the 
                         sensitivity of the attributes for discriminating among species. 
                         Based on these analyses, a rule-based classification was applied 
                         to map macrophyte species. Other classification approaches were 
                         tested and compared to the rule-based method: a classification 
                         based on the Freeman-Durden and Cloude-Pottier decomposition 
                         models, a hybrid classification (Wishart classifier with the input 
                         classes based on the H/a plane), and a statistical-based 
                         classification (supervised classification using Wishart distance 
                         measures). The findings show that attributes derived from fully 
                         polarimetric L-band data have good potential for discriminating 
                         herbaceous plant species based on morphology and that estimation 
                         of plant biomass and productivity could be improved by using these 
                         polarimetric attributes.",
                  doi = "10.1109/TGRS.2011.2157972",
                  url = "http://dx.doi.org/10.1109/TGRS.2011.2157972",
                 issn = "0196-2892",
             language = "en",
           targetfile = "05995161.pdf",
        urlaccessdate = "17 jan. 2021"
}


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