River bed classification using multi-beam echo-sounder backscatter data

The Netherlands form the delta for some of the major river systems of Europe, comprising the Rhine, the Meuse, the Scheldt and the Eems. These rivers are valuable parts of national and international ecological networks and are of high economic importance. A minimum depth should be guaranteed to keep the rivers navigable. This depth depends not only on water discharge but also on river bed topography that changes dynamically in response to discharge fluctuations. Rijkswaterstaat is the Dutch governmental organization that is responsible to maintain the main river systems for both shipping, flood conveyance and ecological purposes. To keep the rivers navigable daily dredging activities are carried out. Furthermore the discharge capacity of the rivers is enlarged and the ecological quality is improved by widening the river and making secondary channels. The river topography and its dynamics are affected by spatial variations in bed sediment composition, thus making knowledge of the spatial sediment distribution highly important. It proved to be sufficient to detect a number of classes to produce classification maps of the bottom. An attractive system to be used for obtaining information on both the river bed bathymetry and sediment composition is the multi-beam echo-sounder (MBES). This sonar emits short pulses of sound towards the river bed to determine the depth and the backscatter strength for a large number of closely-spaced beams. The MBES provides high spatial coverage of an area at moderate costs and within short time. The backscatter strengths are known to be indicative for the sediment types, and consequently have potential with regard to sediment classification. Consequently, the MBES system appears as a good alternative to the conventional, expensive and time-consuming, approach of mapping the river bed composition by taking a large number of physical sediment samples. In the present paper, the results of a novel and fairly simple sediment classification method are presented. The method that developed in the Acoustic Remote Sensing Group of Delft University of technology is briefly described in the following section.