@Article{SimioniGuaNasRuiBel:2020:InMuAn,
               author = "Simioni, Jo{\~a}o Paulo Delapasse and Guasselli, Laurindo Antonio 
                         and Nascimento, Victor Fernandez and Ruiz, Lu{\'{\i}}s Fernando 
                         Chimelo and Belloli, Tassia Fraga",
          affiliation = "{Universidade Federal do Rio Grande do Sul (UFRGS)} and 
                         {Universidade Federal do Rio Grande do Sul (UFRGS)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         do Rio Grande do Sul (UFRGS)} and {Universidade Federal do Rio 
                         Grande do Sul (UFRGS)}",
                title = "Integration of multi\‑sensor analysis and decision tree for 
                         evaluation of dual and quad\‑Pol SAR in L\‑ and 
                         C\‑bands applied for marsh delineation",
              journal = "Environment Development and Sustainability",
                 year = "2020",
               volume = "22",
               number = "6",
                pages = "5603--5620",
                month = "Aug.",
             keywords = "Data mining, Hydromorphic soils, Polarization, Wetlands.",
             abstract = "Marsh is a wetland type characterized by hydromorphic soils, 
                         herbaceous vegetation, aquatic and emergent vegetation; usually, 
                         the apparent water surface does not exceed 25% of the area. 
                         Multi-polarized active remote sensors with different frequencies 
                         have characteristics that make them ideal for mapping and 
                         delineating marsh areas since they provide information on canopy 
                         roughness, vegetation moisture and amount of biomass. Therefore, 
                         the main objective of this study is to develop a method based on 
                         multi-frequency radar satellites images to delineate marsh areas 
                         using decision tree classification. In order to reach this 
                         objective, we sought to answer the following questions: (1) Are 
                         L-band SAR images more efficient for marshes delineation than 
                         C-band SAR images? (2) Is multi-sensor (L and C-band) integration 
                         more accurate for marsh areas delineation than a single sensor? 
                         and (3) What are the most efficient channels for marshes 
                         delineation? Our findings showed that L-band images present 
                         greater proportion correct (PC) for marshes delineation compared 
                         to C-band images. However, the greatest PC was found using 
                         integration of Alos Palsar 1 and Sentinel 1 satellites images, 
                         reaching more than 72% of correctness. Regarding the polarization 
                         importance to Alos Palsar 1 image, HVVH presented the highest 
                         importance, with 29%, followed by VH and HV polarizations, both 
                         with 28%. For Sentinel 1 image, the most important polarization 
                         was VH, with 22%, followed by VV + VH that presented 20%. HVVH 
                         polarization was the most important in Alos and Sentinel images 
                         integration, with 35%, followed by Alos Palsar HV and VH, with 34 
                         and 33%, respectively. Thus, we concluded that the method based on 
                         SAR multi-frequency data integration used in this study can be 
                         easily applied by other researchers interested in marsh 
                         delineation since the radar images used are freely available and 
                         can be processed and manipulated in free GIS software.",
                  doi = "10.1007/s10668-019-00442-0",
                  url = "http://dx.doi.org/10.1007/s10668-019-00442-0",
                 issn = "1387-585X",
             language = "en",
           targetfile = "Simioni2020_Article_IntegrationOfMulti-sensorAnaly.pdf",
        urlaccessdate = "28 abr. 2024"
}