author = "Cordeiro, Carlos Leandro de Oliveira and Rossetti, Dilce de 
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Mapping vegetation in a late Quaternary landform of the Amazonian 
                         wetlands using object-based image analysis and decision tree 
              journal = "International Journal of Remote Sensing",
                 year = "2015",
               volume = "36",
               number = "13",
                pages = "3397--3422",
             abstract = "Fan-shaped morphologies related to late Quaternary residual 
                         megafan depositional systems are common features over wide areas 
                         in northern Amazonia. These features were formed by ancient 
                         distributary drainage systems that are in great contrast to 
                         tributary drainage networks that typify the modern Amazon basin. 
                         The surfaces of the Amazonian megafans constitute vegetacional 
                         mosaic wetlands with different campinarana types. A 
                         fine-scale-resolution investigation is required to provide 
                         detailed classification maps for the various campinarana and 
                         surrounding forest types associated with the Amazonian megafans. 
                         This approach remains to be presented, despite its relevance for 
                         analysing the relationship between stages of plant succession and 
                         sedimentary dynamics associated with the evolution of megafans. In 
                         this work, we develop a methodology for classifying vegetation 
                         over a fan-shaped megafan palaeoform from a northern Amazonian 
                         wetland. The approach included object-based image analysis (OBIA) 
                         and data-mining (DM) techniques combining Advanced Spaceborne 
                         Thermal Emission and Reflection Radiometer (ASTER) images, 
                         land-cover fractions derived by the linear spectral mixing model, 
                         synthetic aperture radar (SAR) images, and the digital elevation 
                         model (DEM) acquired during the Shuttle Radar Topography Mission 
                         (SRTM). The DEM, vegetation fraction, and ASTER band 3 were the 
                         most useful parameters for defining the forest classes. The 
                         normalized difference vegetation index (NDVI), ASTER band 1, 
                         vegetation fraction, and the Advanced Land Observing Satellite 
                         (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR) 
                         transmitting and receiving horizontal polarization (HH) and 
                         transmitting horizontal and receiving vertical polarization (HV) 
                         were all effective in distinguishing the wetland classes 
                         campinarana and Mauritia. Tests of statistical significance 
                         indicated the overall accuracies and kappa coefficients (kappa) of 
                         88% and 0.86 for the final map, respectively. The allocation 
                         disagreement coefficient of 5% and a quantity disagreement value 
                         of 7% further attested the statistical significance of the 
                         classification results. Hence, in addition to water, exposed soil, 
                         and deforestation areas, OBIA and DM were successful for 
                         differentiating a large number of open (forest, wood, shrub, and 
                         grass campinaranas), forest (terra firme, varzea, igapo, and 
                         alluvial), as well as Mauritia wetland classes in the inner and 
                         outer areas of the studied megafan.",
                  doi = "10.1080/01431161.2015.1060644",
                  url = "http://dx.doi.org/10.1080/01431161.2015.1060644",
                 issn = "0143-1161",
             language = "en",
        urlaccessdate = "26 nov. 2020"