Automated extraction of beach bathymetries from video images

1. Automated extraction of beach bathymetries from video images Laura Uunk MSc Thesis prof. dr. S.J.M.H. Hulscher dr. K.M.Wijnberg ir. R. Morelissen

2. Contents • Beach bathymetries by shoreline mapping • Manually mapping shorelines (IBM) • Automatically mapping shorelines (ASM) • Problems encountered • Automated quality control • Automatically vs. manually obtained bathymetries • Beach behaviour • Conclusions

3. Beach bathymetries by shoreline mapping • Argus images • Time exposure images •  10 minute average • Every half hour • Beach bathymetry mapped • Shoreline location • Shoreline elevation • Throughout tidal cycle • Elevation data between • low and high water Timex image of Egmond Coast 3D site, camera 1

4. Manually mapping shorelines (IBM) Interface of the Intertidal Beach Mapper (IBM)

5. Manually mapping shorelines (IBM) • Requires many man-hours • up to 4 hours for one day for one station (5 cameras) • Therefore no daily bathymetries, but monthly • Opportunities of Argus not completely used Automated version was developed (ASM) • Plant • Cerezo and Harley Dutch beach

6. Automatically mapping shorelines (ASM) • Human steps are automated • Definition of the region of interest • based on expected shoreline location on bench-mark bathymetry • Quality control • compare detected points against bench-mark bathymetry Bench-mark bathymetry

7. Automatically mapping shorelines (ASM)

8. Problems encountered • Bad bench-mark bathymetry • bad definition ROI • bad quality control •  Start of a downward spiral • Bad bench mark bathymetry

9. Problems encountered - downward spiral

10. Problems encountered - solutions • Better definition of the Region of Interest • large smoothing scales loess interpolation • better expected shoreline location • extension to edge of image • inclusion of entire shoreline • avoid zigzagging • inclusion of entire shoreline

11. Problems encountered - solutions •  Better expected shoreline location small smoothing scales short time window larger smoothing scales longer time window

12. Problems encountered - solutions • Better definition of the Region of Interest • large smoothing scales loess interpolation • better expected shoreline location • extension to edge of image • inclusion of entire shoreline • avoid zigzagging • inclusion of entire shoreline

13. Problems encountered - solutions

14. Problems encountered - solutions • Double quality control • Two bench-mark bathymetries • 1: small smoothing scales, small time window • 2: large smoothing scales, large time window • Shoreline points first compared to first bathymetry • Points that could not be checked are then compared to second bathymetry

15. Problems encountered - solutions small smoothing scale  more detail  more gaps large smoothing scale  less detail  less gaps

16. Automated quality control • Fixed vertical criterion: Zdif • Sometimes accept points that are wrong • Sometimes reject points that are good

17. Automated quality control •  What value should be used? • ASM was run with three values for Zdif • 0.10 m; • 0.25 m; • 0.50 m • ASM bathymetries compared to IBM bathymetries • Coastal State Indicators (CSIs) • Contours (-0.50 m NAP; 0 m NAP; 0.50 m NAP) • MICL

18. Automated vs. manual 0 m contour for May 7th to 12th 2006 IBM 0.10 m 0.25 m 0.50 m continued 0.25 m

19. Automated vs. manual 0.10 m 0.25 m 0.50 m continued 0.25 m •  No real differences for the different values of Zdif

20. Automated vs. manual – in time

21. Beach behaviour

22. Conclusions • Man-hours are saved by automatically mapping shorelines • Results automated version (ASM) correspond well with results manual version (IBM) • 0 m contour by ASM shows immediate response of the beach to changes in wave height • this was not visible with monthly IBM bathymetries • Opportunities provided by half-hourly Argus images can now be fully exploited • ASM data could be used to e.g. • study storm impact • study influence of nourishments • support management decisions

23. Questions