Dimitri LAGUE, Géosciences RennesResponsable Scientifique de la plateforme Lidar Topo-Bathymétrique Nantes-Rennes(OSUR/CNRS/Université Rennes 1)dimitri.lague@univ-rennes1.frwww.lidar-nantes-rennes.eu

Modèle Numérique de Terrain à 30 m(couverture mondiale)

Modèle Numérique de Terrain à 1 m(lidar aéroporté)

Haute Résolution TopographiqueDonnées Topographiques Standard

C. Crosby, Opentopography presentation

Otira gorge, Southern Alps, New-Zealand

zones ennoyées

99 % des calculs et figures présentées dans ce talk ont été réalisées avec CloudCompare

Funded by:EEC Marie-CurieCNRS/INSU/EC2CO

CloudCompare (D. Girardeau-Montaut, EDF R&D)

CANUPO (Brodu and Lague, 2012)

3D CLASSIFICATION

M3C2 (Lague et al., 2013)

2D or 3D Point Cloud Differencing

qCANUPO and qM3C2 plugins (2014)

~ 2D surfaceCalculation pointscalled « Core points »

Neighborhood ballof a given diameter

Scene elements are characterized by different

combination of dimensionalityover scales ranging from e.g. 2

cm to 1.5 m

•Vegetation is clearly distinct from bedrock only at scale > 20-50 cm•Vegetation cannot be clearly separated from gravels at any single scale•Gravels and water surface have similar dimensionality signature at any single scale

Raw point cloud Automatically classified point cloud

N individual clouds of boulders

Segmentation

35 m

Classification accuracy on

vegetation is up to 99 % in denselyscanned zones

3 mVegetation segmentation

(J. Leroux’s PhD)

Rangitikei river, New-Zealand

Changes in visibility

3D surface normal orientation

Cobble bed

Variable roughness in

space and time

Flat cliff Rockfall

10 m 10 m 10 m

Roughness creates uncertainty in the comparison of surfaces

3D difference of surface mesh(e.g. 3D inspection software)

+ 3D normal calculation

- meshing of rough surfaces

○ Uncontrolled interpolation

- no confidence interval

3D closest point distance (e.g Girardeau-Montaut et al., 2005, Cloudcompare, ICP)

+ Very fast, 3D but not oriented (no normal calculation)

- dependent on point spacing and changes in visibility

- no confidence intervals

Ref

Compared

shadow

n

n

DoD

True surface changealong local normal

1: Normal direction calculation on cloud 1→ Oriented difference

2: Average distance between the two PC → Noise and roughness averaging

Averagingscale

4: Distance smaller than confidence interval →statistically not significant

5: No intercept with other cloud→ no calculation→ no need to trim the data

CLOUD 1

CLOUD 2

3: Local confidence interval calculation→ Local roughness→ Local point density→ Global registration

2009 2011

Best case : ±4 mm

Debris: ~ 4 cm

Set by registration error

Set by surface roughness

3D map of confidence interval

Target based registration

qCANUPOvegetation

removal on raw data

qM3C23D difference

Lague et al., ISPRS, 2013

Single flood erosion map

10 m

1/01/2009 1/01/2010 1/01/2011 1/01/2012

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Stage @ Rangitikei at Mangaweka

~ Sediment

transport level

Feb 09-Feb1010 eventsHmax= 6 m

Feb10-Dec1021 eventsHmax= 5.9 m

Dec10-Feb112 eventsHmax=6.5 m

Feb11-Dec117 eventsHmax=5.5 m

(with S. Bonnet, GET)

12/2010-02/2011

02/2011-11/20111/01/2009 1/01/2010 1/01/2011 1/01/2012

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Stage @ Rangitikei at Mangaweka

~ Sediment

transport level

Color range +- 2 cm

Averagingscale

CLOUD 1

CLOUD 2

Option 1: Vertical mode→ no normal calculation→ FasterHorizontal mode→ Bank retreat

Option 2: CORE POINTS→ Calculation on a subset of points

of arbitrary geometry usingthe RAW DATA

CLOUD 1

CLOUD 2

Grid of regularly sampled core points (CloudCompareRasterization)

(Wagner, Lague, Morhig, Passalacqua, Shaw, Moffett, GRL, 2017)

Uncertainty map combining roughnessand registration effects

Mesure et modélisation de la dynamique des prés salésen contexte mégatidal

Projet EC2CO/DRIL 2012-2014

Dimitri Lague, Jérôme Leroux (PhD)

Alain Crave, Nicolas Brodu, Philippe Davy

CNRS, Geosciences Rennes, OSUR

Bruno Goffé

CNRS, CEREGE

Cendrine Mony, Lise Provost (M2)

Ecobio, Rennes

Romaric Verney

DYNECO, IFREMER, Brest

Tim Marjoribanks

Geography Dpt, Durham University, UK

Insu

Cassini map, 1756

• More than 8 km of marsh expansion correspongin to ~30 km² of coastal wetlandsconverted to agriculture by dykes

• Marsh expansion is still ongoing and threatened to surround the Mt St Michel islandMajor civil works have been undertaken to restore its maritime character

• Temps caractéristiques de réponse ?• Tendance vers l’équilibre ?• Reversabilité ?

Besoin de modélisation numérique aux échelles de temps séculaires

Review of Geophysics, 2012

Baie du Mt St Michel

Polderisation

Chenalisation

Niveau Marin

Impact/mécanismes d’accrétion liés à la végétation en contexte méga-tidal ? Facteur de contrôle de la migration des chenaux tidaux ? Changement d’échelle temporel/spatial

Low tide High Spring tideLast week-end

Max tidal range ~ 14 mmega-tidal regime

Tide travel distance ~ 15 km

Tide dominated environment-> very little wave action

• Impact de la végétation pionnière sur l’accrétion ?• Facteurs de contrôle de la migration latérale ?

Mt St Michel

1. Leroux, J., Lague, D. and Crave, A., Meander dynamics in megatidal salt marshes: 1 - vegetation and hydrosedimentary controls on point bar accretion, in prep for Earth Surf Processes and Landforms2. Lague, D. and Leroux, J., Meander dynamics in megatidal salt marshes: 2 - bank erosion, coupling with point bar accretion and extreme events, in prep for Earth Surf Processes and Landforms

Hiver

Ete

100 m

View2

Raw point cloud : 30 million points

2007

Fixed targets3 mm registration error

Point density ~ 1 pt/cm²

TLS

ADVs Altus

Up looking ADCP

Some issues when using high resolution lidar data

Non-uniform point density

Vegetation and shadow effects

CloudCompare (D. Girardeau-Montaut, EDF R&D)

CANUPO (Brodu and Lague, 2012)

3D CLASSIFICATION

M3C2 (Lague et al., 2013)

2D or 3D Point Cloud Differencing

Raw data : 7 Oct

Raw data : 8 Oct

Vegetation classification

Accretion/erosion map

Mean= 0.0106 mStd = 0.085 m

Accretion in pioneer vegetation during 2 spring tides

• High accuracy : detect 5 mm change at 95 % confidence interval• High resolution : ~ 1 pt/cm²

• Captures vegetation structure• Captures heterogeneity of topographic change• Direct spatial upscaling from cm to 100’s m

M3C2 grid of difference

Vegetation layer

CC: closest point calculation

Distance to closest vegetation calculation

Topographic change within 1 m of vegetation Topographic change away from vegetation

Segmentation by distance to vegetation

Increasederosion

Ma

rsh

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Up-looking ADCP in the channel

TLS

ADVsAltus

Bedload traps and sedimentation plates

2 ADV and 1 high resolution sonar(ALTUS)

• 1 turbidimeter profiler• 1 camera taking hourly pictures

ADCP, 1 ADV and altus courtesy of IFREMER, R. Verney

Flow velocities during 1 tide

Pe

ak

Ve

loci

ty(m

/s)

Flood

EbbKey results :• Uniform flow velocities during flood/large

velocity gradients during ebb• Ebb dominance during spring tides• Non-linear relationship between Vebb and HWL• Peak Ebb velocity proportional to tidal prism

Overmarsh tidesunderrmarsh tides

• Textbook translation of the meander BUT• No balance on an annual level between inner bar accretion and bank erosion

Spring cycle : channel erosion & migration

+-1 m channel bed elevation variations

Neap cycle : channel accretion

Systematic pattern during extreme events:• Outer bank erosion• Landward channel erosion• Seaward channel accretion

On the point bar:• Assymetric trail bar

sedimentation• Large areas without significant

topographic change awayfrom vegetation

• Peak retreat rate can reach 2-3 m/tide (= up to 6 m/day)• Rapid increase of erosion with HWL for overmarsh tide

related to non-linear increase of ebb-velocity with HWL• Consistent with non-linear bank erosion law

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