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River bed classification using multibeam echosounder backscatter data

Niels Kinneging, Mirjam Snellen, Dick Simons, Erik Mosselman, Arjan Sieben


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. This topography and its dynamics are affected by spatial variations in bed sediment composition, thus making knowledge of the spatial sediment distribution highly important.

Up to now this knowledge is obtained by laborious and extensive sampling campaigns. However, these are expensive and yield a poor spatial representation for the heterogeneous river bed sediments. An efficient method that offers a complete map of grain sizes at the river bed surface would be very profitable. We therefore adapted the method of Simons and Snellen (2009) for sea floor classification to classification of the river bed. The method employs the backscatter information obtained from multibeam echosounder measurements and discriminates between sediments in the most optimal way. The method uses the averaged backscatter data per beam. It, therefore, is independent on the quality of the MBES calibration. Also, its performance is insensitive to seafloor type variation along the MBES swathe and corrections for the angular dependence of the backscatter are not needed. The method accounts for the ping-to-ping variability of the backscatter intensity. It estimates both the number of sediment types present in the survey area and their backscatter strength probability density function at a certain angle. By means of dedicated ground truthing a map of the grain size distribution of the river bed can be produced with a reduced number of sampling points.

The method has been tested on different parts of the Dutch rivers with shallow water and river bed types that range from silt, clay and peat to sand, gravel and pebbles.  The resulting bed type classification proved to be efficient and provided very useful information for morphological studies on river bed changes. Figure 1 shows an example of applying the method to a part of the riverWaal.

We argue that further improvement of the system can be obtained, when it is adapted for various multibeam systems of different vendors. The calibration of the multibeam systems can be improved to cover amplitude calibration as well as travel time calibration. Also integration of the method into the standard acquisition systems is discussed.


D.G. Simons and M. Snellen, A Bayesian approach to seafloor classification using multi-beam echo-sounder backscatter data, October 2009, Applied Acoustics, Volume 70, issue 10, pages 1258-1268.