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 larger smoothing scales longer time window small smoothing scales short 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 7 th to 12 th 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