@Article{GuimarãesGaloNarvSilv:2020:CoTeDa,
               author = "Guimar{\~a}es, Ulisses Silva and Galo, Maria de Lourdes Bueno 
                         Trindade and Narvaes, Igor da Silva and Silva, Arnaldo de 
                         Queiroz",
          affiliation = "{Sistema de Prote{\c{c}}{\~a}o da Amaz{\^o}nia (SIPAM)} and 
                         {Universidade Estadual Paulista (UNESP)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and {Universidade Federal do 
                         Par{\'a} (UFPA)}",
                title = "Cosmo-SkyMed and TerraSAR-X datasets for geomorphological mapping 
                         in the eastern of Marajo Island, Amazon coast",
              journal = "Geomorphology",
                 year = "2020",
               volume = "350",
                pages = "UNSP 106934",
                month = "Feb.",
             keywords = "Synthetic aperture radar, Amazon coastal environments, Random 
                         Forest.",
             abstract = "The Amazon coast is marked by the high discharge of sediments and 
                         freshwater, macrotidal influence, a wide continental shelf, 
                         extensive flood plains and lowered plateaus which make it unique 
                         as a delta and estuary landscape. Further, the tropical climate 
                         imposes heavy rains and incessant cloudiness that render microwave 
                         systems more suitable for cartography. This study proposed to 
                         recognize and map the Amazon coastal environments through the 
                         X-band Synthetic Aperture Radar, provided by Cosmo-SkyMed (CSK) 
                         and TerraSAR-X (TSX) systems. The SAR datasets consisted of 
                         interferometric and stereo pairs, restricted to single-revisit and 
                         obtained with small interval (1-11 days), under steeper (theta < 
                         35 degrees) and shallow (theta >= 35 degrees) incidence angles, 
                         and during the rainy and dry seasons. From the 4 acquisitions of 
                         X-band SAR data, attributes such as the backscattering 
                         coefficient, coefficient of variation, texture, coherence, and 
                         Digital Surface Model (DSM) were derived, adding each variable in 
                         5 scenarios. These combinations resulted in 20 models, which were 
                         submitted individually to the machine learning (ML) classification 
                         approach by Random Forest (RF). The backscattering and altimetry 
                         described the coastal environments which shared ambiguity and high 
                         dispersion, with the lowest separability for vegetated 
                         environments such as Mangrove, Vegetated Coastal Plateau and 
                         Vegetated Fluvial Marine Terrace. The coherence provided by 
                         interferometry was weak (<0.44), even during the dry season, in 
                         the other hand, the cross-correlation was strong (>0.60), during 
                         the rainy and dry season showing more suitability for 
                         radargrammetry on the Amazon coast. The RF models resulted in 
                         Kappa coefficient between 0.39 to 0.70, indicating that the use of 
                         X-band SAR images at an incidence angle greater than 44 degrees 
                         and obtained in the dry season is more appropriated for the 
                         morphological mapping. The RF models given by TSX achieved the 
                         higher mapping accuracies per scenario of SAR products, in order 
                         of 0.48 to 0.63. Despite this, the best classification was carried 
                         out by 19 RF model with 0.70, provided by CSK in shallow incidence 
                         composed by intensity, texture, coherence and stereo DSM. The CSK 
                         and TSX data allowed to map the Amazon coast precisely, based on 
                         X-band at single polarization, high spatial resolution and 
                         revisit, which has demonstrated the support for detailed 
                         cartography scale (1:50,000) and frequent updating (monthly up to 
                         yearly).",
                  doi = "10.1016/j.geomorph.2019.106934",
                  url = "http://dx.doi.org/10.1016/j.geomorph.2019.106934",
                 issn = "0169-555X",
             language = "en",
           targetfile = "guimaraes_cosmo.pdf",
        urlaccessdate = "27 abr. 2024"
}