@Article{WiederkehrGCBBSLSM:2020:DiFoSu,
               author = "Wiederkehr, Nat{\'a}lia Cristina and Gama, F{\'a}bio Furlan and 
                         Castro, Paulo B. N. and Bispo, Polyanna da Concei{\c{c}}{\~a}o 
                         and Balzter, Heiko and Sano, Edson E. and Liesenberg, Veraldo and 
                         Santos, Jo{\~a}o Roberto dos and Mura, Jos{\'e} Cl{\'a}udio",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         de Ouro Preto (UFOP)} and {University of Manchester} and 
                         {University of Leicester} and {Empresa Brasileira de Pesquisa 
                         Agropecu{\'a}ria (EMBRAPA)} and {Universidade do Estado de Santa 
                         Catarina (UDESC)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Discriminating forest successional stages, forest degradation, and 
                         land use in central Amazon using ALOS/PALSAR\‐2 
                         full\‐polarimetric data",
              journal = "Remote Sensing",
                 year = "2020",
               volume = "12",
               number = "21",
                pages = "1--30",
                month = "Nov.",
             keywords = "Brazil, Amazon, forest, land use, land cover, forest degradation, 
                         polarimetry, SAR.",
             abstract = "We discriminated different successional forest stages, forest 
                         degradation, and land use classes in the Tapaj{\'o}s National 
                         Forest (TNF), located in the Central Brazilian Amazon. We used 
                         full polarimetric images from ALOS/PALSAR-2 that have not yet been 
                         tested for land use and land cover (LULC) classification, neither 
                         for forest degradation classification in the TNF. Our specific 
                         objectives were: (1) to test the potential of ALOS/PALSAR-2 full 
                         polarimetric images to discriminate LULC classes and forest 
                         degradation; (2) to determine the optimum subset of attributes to 
                         be used in LULC classification and forest degradation studies; and 
                         (3) to evaluate the performance of Random Forest (RF) and Support 
                         Vector Machine (SVM) supervised classifications to discriminate 
                         LULC classes and forest degradation. PALSAR-2 images from 2015 and 
                         2016 were processed to generate Radar Vegetation Index, Canopy 
                         Structure Index, Volume Scattering Index, Biomass Index, and 
                         CloudePottier, van Zyl, FreemanDurden, and Yamaguchi polarimetric 
                         decompositions. To determine the optimum subset, we used principal 
                         component analysis in order to select the best attributes to 
                         discriminate the LULC classes and forest degradation, which were 
                         classified by RF. Based on the variable importance score, we 
                         selected the four first attributes for 2015, alpha, anisotropy, 
                         volumetric scattering, and double-bounce, and for 2016, entropy, 
                         anisotropy, surface scattering, and biomass index, subsequently 
                         classified by SVM. Individual backscattering indexes and 
                         polarimetric decompositions were also considered in both RF and 
                         SVM classifiers. Yamaguchi decomposition performed by RF presented 
                         the best results, with an overall accuracy (OA) of 76.9% and 
                         83.3%, and Kappa index of 0.70 and 0.80 for 2015 and 2016, 
                         respectively. The optimum subset classified by RF showed an OA of 
                         75.4% and 79.9%, and Kappa index of 0.68 and 0.76 for 2015 and 
                         2016, respectively. RF exhibited superior performance in relation 
                         to SVM in both years. Polarimetric attributes exhibited an 
                         adequate capability to discriminate forest degradation and classes 
                         of different ecological succession from the ones with less 
                         vegetation cover.",
                  doi = "10.3390/rs12213512",
                  url = "http://dx.doi.org/10.3390/rs12213512",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-12-03512-v2.pdf",
        urlaccessdate = "27 abr. 2024"
}