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Automated extraction of beach

bathymetries from video images

Laura Uunk MSc Thesis

prof. dr. S.J.M.H. Hulscher dr. K.M.Wijnberg

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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

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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

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Manually mapping shorelines (IBM)

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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

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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

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Automatically mapping shorelines (ASM)

Database with shoreline points Shoreline points

within time window

Bench-mark bathymetry Shoreline elevation

Region of interest

Detected shoreline points

Accepted shoreline points Acceptance criterion

Detection method Elevation model start / next time step

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Problems encountered

Bad bench-mark bathymetry

> bad definition ROI

> bad quality control  Start of a downward spiral

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Problems encountered - downward spiral

Database with shoreline points Shoreline points

within time window

Bench-mark bathymetry Shoreline elevation

Region of interest

Detected shoreline points

Accepted shoreline points Acceptance criterion

Detection method Elevation model start / next time step

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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

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Problems encountered - solutions

 Better expected shoreline location larger smoothing scales

longer time window small smoothing scales

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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

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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

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Problems encountered - solutions

small smoothing scale  more detail  more gaps

large smoothing scale  less detail  less gaps

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Automated quality control

Fixed vertical criterion: Zdif

Sometimes accept points that are wrong

Sometimes reject points that are good

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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)

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Automated vs. manual

0 m contour for May 7th to 12th 2006

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Automated vs. manual

0.10 m 0.25 m 0.50 m continued 0.25 m

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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

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