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.
Selecting bands with the lowest redundant information from a hyperspectral dataset is always desirable. This study obtains the most independent bandset by using a state-of-the-art single metric measuring the overall dependence of multivariate systems.
One of the main steps in hyperspectral image classification is the selection of bands that provide the best separability among classes. It is usually understood that the selected bands for classification must contain a large amount of information, and the correlation among selected bands should be small to avoid redundancy. At the same time for optimal classification, class separability should be at maximum value. The question arises whether the most informative spectral regions are really the same as the most discriminant ones for a given set of classes. Answering the question, we developed a new method named Spectral Region Splitting (SRS) to identify interesting spectral regions. This article concludes that the optimal informative and the optimal separable spectral regions are not identical. Furthermore, the cause of the difference is proven theoretically.
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