River bed classification using multi-beam echo-sounder backscatter data
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1 River bed classification using multi-beam echo-sounder backscatter data Niels Kinneging Mirjam Snellen Dimitrios Eleftherakis Dick Simons Erik Mosselman Arjan Sieben 13 November 2012
2 transport water management 2 13 November 2012
3 main inland waterway between Rotterdam and Germany 3 13 November 2012
4 4 13 November 2012
5 5 13 November 2012
6 Discharge Rhine November 20126
7 Riverbed morphology 13 November 20127
8 Variatie bodemligging Variations bottom topography Variatie bodemligging 13 November 20128
9 Summarizing Safe navigation Variation in river discharge Dynamic riverbed topography 9 13 November 2012
10 Measures Flooding areas River dams Dredging Regular monitoring Knowledge on sediment composition 13 November
11 Acoustic remote sensing for sediment classification Multibeam echosounders Bathymetry Backscatter strength Signals affected by sediment properties Classification based on backscatter strength November 2012
12 A Bayesian approach to seafloor classification Averaged backscatter measurements per beam Corrections for propagation losses, slopes and footprint No corrections for angle-dependence of the backscatter strength Variations of seafloor type along the swath are accounted for Errors in transducer calibration do not pose a problem Method accounts for the ping-to-ping variability of the backscatter strength November 2012
13 MBES backscatter data Averaged backscatter measurements per beam considered Normally distributed averaged beam backscatter values guaranteed by averaging over pings and beams Classification carried out for each set of combined beams (maximally 7 beams) November 2012
14 For all sets of beam 1. Non-linear curve fitting of BS histogram and determine number of acoustic classes Bayesian method 2. Identification of acoustic classes 3. Assigning sediment types to acoustic classes 4. Mapping November 2012
15 MBES sediment classification (step 1) Non-linear curve fitting Fit a model to the histogram of selected measured backscatter strengths Employ goodness-of-fit criterion to decide upon the number of sediment types November 2012
16 MBES sediment classification (step 2 and 3) Acoustic class identification The number of classes is equal to the number of PDFs and the borders of the classes are the intersections of the PDFs. A sediment type is assigned to each of the measurements based on backscatter value Assign sediment type to acoustic class November 2012
17 MBES sediment classification (step 4) Mapping the classification results Amiri-Simkooei et al., JASA 126, November 2012
18 River Waal Very shallow water Backscatter measurements taken with a 300 khz MBES system Samples taken at various positions along the river: coarse sediments(-5 < Mz < 0) November 2012
19 Waal bathymetry Mapping the classification results November 2012
20 Sediment mean grain sizes in The Netherlands November 2012
21 Results Dordtse Kil November 2012
22 Conclusions Sediment classification from multibeam echosounder data An MBES classification method is available that discriminates between sediment types in the most optimal way Simple Highly effective Low cost Extensively tested (also for very fine grains, not shown in this presentation) Very detailed sediment maps (almost 100%) No need for calibrated sonars November 2012
23 Future work Implementation for other MBES-systems Implementation in operational environment MBES amplitude calibration November 2012
24 Thanks for your attention
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