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Delineation of aquatic plants and estimation of its surface extent are crucial to the efficient control of its proliferation, and this information can be derived accurately with fine resolution remote sensing products. However, small swath and low observation frequency associated with them may be prohibitive for application to large water bodies with rapid proliferation and dynamic floating aquatic plants. The information can be derived from products with large swath and high observation frequency, but with coarse resolution; and the quality of so derived information must be eventually assessed using finer resolution data. In this study, we evaluate two methods: Normalized Difference Vegetation Index (NDVI) slicing and maximum likelihood in terms of delineation; and two methods: Gutman and Ignatov's NDVI-based fractional cover retrieval and linear spectral unmixing in terms of area estimation of aquatic plants from 300 m Medium Resolution Imaging Spectrometer (MERIS) data, using as reference results obtained with 30 m Landsat-7 ETM+. Our results show for delineation, that maximum likelihood with an average classification accuracy of 80% is better than NDVI slicing at 75%, both methods showing larger errors over sparse vegetation. In area estimation, we found that Gutman and Ignatov's method and spectral unmixing produce almost the same root mean square (RMS) error of about 0.10, but the former shows larger errors of about 0.15 over sparse vegetation while the latter remains invariant. Where an endmember spectral library is available, we OPEN ACCESS Remote Sens. 2014, 6 7763 recommend the spectral unmixing approach to estimate extent of vegetation with coarse resolution data, as its performance is relatively invariant to the fragmentation of aquatic vegetation cover.

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Knowledge of the present-day activity of river channels in distal fluvial systems strongly contributes to the reconstruction of past branching and avulsion processes. Established remote sensing techniques can be applied to monitor the formation of flooding planes (crevasse splays) and channel activity. In this research variations in the amplitude in Synthetic Aperture Radar images are interpreted as soil moisture changes. Interferometric SAR showed minor phase changes during dry season and loss of coherence after peak run-off. After peak discharge during the dry season in 2009 reactivation of multiple avulsed river paths and crevasse channels was detected. These results show that analysis of SAR images can contribute to the monitoring of fluvial systems. It is expected that these initial results will be confirmed by field data and analysis of alternative remote sensing data sources.

Monitoring and mapping of alluvial surfaces and distal fluvial system has an important role for studying the depositional basin and river behaviour at its terminus. Experiences show rivers in semi-arid areas get smaller through their terminus and create a complex pattern at downstream parts. Remote sensing images can be used to monitor distal fluvial system and reveal changes of channels activity. In this study, we examine the feasibility of mapping and identifying the changes over time of a semi-arid area by Landsat ETM+ images. The study area is at the terminus of a fluvial system in Bolivia. Change detection techniques were applied to emerge the temporal and spatial changes. We used precipitation data of the area for better interpreting the images in different dates. The ETM+ image analysis results show changes in river morphology. It was also observed by the visible bands that the reflectance of abandoned channels increased after several consecutive weeks of high precipitation. The changes in dry seasons are more observable by the infrared bands. The study shows that Landsat ETM+ images in combination with field work data have a good potential to identify temporal and spatial changes at river morphology in a qualitative manner.