Coastline monitoring Virginie Lafon

Cyril Mallet

S. Capo, A. Dehouck

E. Maneux, A. Michot

C. Proisy, R. Walker First BreTel Workshop

May 31 – June 1st 2012 Saint Malo, France

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30/05/2012 First BreTel Workshop 2

About the coastlines

Geomorphology Substrate type

Bedrock, sand beach, tidal flats, salt marsh and mangrove …

Forcing

Evolution

Forecast Population

Management strategy

About the monitoring

• Sandy beaches and dunes – Shallow water sandbank – Dune foot

• Rocky coasts – Cliff foot

• Maritime wetlands – Salt marsh contour

• Mangrove wetlands • Articificial coasts

– Dykes, seawalls …

– Waterline (lakes, internal

seas)

• Terrestrial acquisition – DGPS

– Theodolite

– Scanner 3D

– Photo / video surveys

• Airborne RS – Pictures (RGB + NIR)

– Hyperspectral imagery

– Lidar

• Satellite RS – MS / Hyperspec Imagery

– SAR Imagery

Survey indicators Monitoring approaches

From the :

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Dune foot monitoring

X JNGCGC, NEREUS publication, 2010

Radiometric calibration / Image mosaic

Unsupervised /Supervised classification

Sobel filtering

Raster facies / polylines

Validation (DGPS)

Mean coastline positionning error : 10 m (2 m resolution space image)

Partnership

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Dune foot forecast and management strategy

Hazard mapping - Weak - Medium - Strong

NEREUS publication, 2012

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Demonstrators of operational services

• Automatic detection

• Change detection analysis

• Conservation status analysis

• Dune erosion / restoration

Window on GMES publication, 2012

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Shallow water bathymetry

Réflectance image (rXS1)

depth (z) in 3 steps

SP -

16/04/2010

Sem

i an

aly

tical

mo

del Empirical

calibration

Sentinel-2 Preparatory Symposium, 2012

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Sandbank monitoring

Breaching

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Integrated analysis

55 m

55 m

14 m

2000 2003 2005

- 04/2009 - 04/2010 - 09/2010

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Oyster farming / oyster banks

THR Satellite data

Texture analysis +

Segmentation

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Polder monitoring

• Polder evolution survey

• Island breaching and flooding monitoring – Mecanism study

K2 18/03/2009 K2 05/03/2010 K2 15/03/2010

• Evolution scenarii proposition • 3-year project (2012 -> 2015)

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Mangrove monitoring

SAVI-based automatic detection

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Conclusions

• Satellite imagery allows locating the coastline over large muddy sandy, rocky littoral (with / without vegetation cover)

• Satellite imagery allows describing time (historical) / space evolution of the coastline

• Satellite observation constitute a powerful and low cost solution for coastline surveying at a national scale

• Also, medium- to long-term satellite-based observations participate to evolution processes analysis and they provide numerical simulation validation cases

• Automatic operational services are being demonstrated e.g. in Aquitaine

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Thank you for

your attention

Scale

+1 m

0 hydro

- 1 m

- 2 m

- 3 m