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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.

, "Spectral region identification versus individual channel selection in supervised dimensionality reduction of hyperspectral image data," J. Appl. Remote Sens. 11(4), 046010 (2017), doi: 10.1117/1.JRS.11.046010. Abstract. Hyperspectral images may be applied to classify objects in a scene. The redundancy in hyperspectral data implies that fewer spectral features might be sufficient for discriminating the objects captured in a scene. The availability of labeled classes of several areas in a scene paves the way for a supervised dimensionality reduction, i.e., using a discrimination measure between the classes in a scene to select spectral features. We show that averaging adjacent spectral channels and using wider spectral regions yield a better class separability than the selection of individual channels from the original hyperspectral dataset. We used a method named spectral region splitting (SRS), which creates a new feature space by averaging neighboring channels. In addition to the common benefits of channel selection methods, the algorithm constructs wider spectral regions when it is useful. Using different class separability measures over various datasets resulted in a better discrimination between the classes than the best-selected channels using the same measure. The reason is that the wider spectral regions led to a reduction in intraclass distances and an improvement in class discrimination. The overall classification accuracy of two hyperspectral scenes gave an increase of about twopercent when using the spectral regions determined by applying SRS. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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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.