@Article{BelloliGuaKupRuiSim:2022:ClBaOb,
               author = "Belloli, Tassia Fraga and Guasselli, Laurindo Antonio and Kuplich, 
                         Tatiana Mora and Ruiz, Luis Fernando Chimelo and Simioni, 
                         Jo{\~a}o Paulo Delapasse",
          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 {Santos Lab} and 
                         {Universidade Federal do Rio Grande do Sul (UFRGS)}",
                title = "Classificacao Baseada em Objeto de Tipologias de Cobertura Vegetal 
                         em Area {\'U}mida Integrando Imagens Opticas e SAR",
              journal = "Revista Brasileira de Cartografia",
                 year = "2022",
               volume = "74",
               number = "1",
                pages = "67--83",
                month = "Jan.",
             keywords = "OBIA, Random Forest, Sentinel-1 e 2A, Vegetation mapping in 
                         marshes.",
             abstract = "Accurately mapping the boundaries of wetlands and patterns of 
                         vegetation cover is an essential step for rapid assessment and 
                         management of wetlands. The Object-Based Image Analysis (OBIA) as 
                         from machine learning and fusion of optical and radar data has 
                         advantages over other techniques for mapping vegetation cover in 
                         wetlands ecosystems. This study aims to classify vegetation cover 
                         typologies in wetlands, integrating optical and SAR images from 
                         the Sentinel-1 and 2A satellites and the Random Forest algorithm 
                         in OBIA classification, using Banhado Grande, located in the Rio 
                         Grande do Sul as a case study. As a result, the VH and VV 
                         polarizations of Sentinel-1 obtained the highest relevance in the 
                         classification (18.6%). Among the optical bands, the greatest 
                         relevance occurred for the Red Edge and Medium Infrared bands. 
                         From the optical attributes, the classification obtained an 
                         accuracy of 86.2%. When the most important SAR attributes were 
                         inserted, the accuracy increased to 91.3%. The Emergent Macrophyte 
                         (ME) class, corresponding to the species Scirpus giganteus, 
                         achieved the best accuracy of the classifier (91%), with an 
                         estimated area of 1,507 ha. We conclude that the integration of 
                         images combined with the classification method made it possible to 
                         delimit the extent of vegetation typologies and the total area of 
                         the ecosystem. Accurate results show that this methodological 
                         approach can be expanded to other subtropical palustrine 
                         wetlands.",
                  doi = "10.14393/rbcv74n1-61277",
                  url = "http://dx.doi.org/10.14393/rbcv74n1-61277",
                 issn = "0560-4613 and 1808-0936",
             language = "pt",
           targetfile = "belloli_2022_classifica{\c{c}}{\~a}o.pdf",
        urlaccessdate = "06 jun. 2024"
}