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 (κ) 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, várzea, igapó, and alluvial), as well as Mauritia wetland classes in the inner and outer areas of the studied megafan.

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Macrophyte rafts can enhance fish dispersal in the Amazon River basin, and determining whether raft properties (e.g., size and plant species richness) can predict fish species richness and composition is important in order to understand the underlying factors of fish dispersal. We tested for a relationship between the plant species richness and fish species richness in the rafts and determined whether there exists a significant pattern of concordance between rafts composition and fish assemblages in a River-Lake system close to Manaus, Amazonas, Brazil. We estimated the cover of each species of macrophyte and collected fish in 20 macrophyte rafts of different sizes. Macrophyte species richness was not a good predictor of fish species richness. We found a significant correlation between the compositional similarities of macrophytes and fishes when the data for presence/absence were analyzed, but not when abundance data were used. However, the congruence patterns were clearly related to raft size, and we found a correlation between plants and fishes, using both presence/absence and abundance data, when only large rafts were used in the analysis. For small rafts, there were no significant correlations using any type of data. These findings show that the composition of fish assemblage dispersal in the rafts depends on the composition of macrophytes of which the rafts are composed and on stochastic processes of raft splitting.

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