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Dimitri LAGUE, Géosciences RennesResponsable Scientifique de la plateforme Lidar Topo-Bathymétrique Nantes-Rennes(OSUR/CNRS/Université Rennes 1)[email protected]
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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
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Otira gorge, Southern Alps, New-Zealand
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zones ennoyées
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99 % des calculs et figures présentées dans ce talk ont été réalisées avec CloudCompare
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Funded by:EEC Marie-CurieCNRS/INSU/EC2CO
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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)
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~ 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
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•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
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Raw point cloud Automatically classified point cloud
N individual clouds of boulders
Segmentation
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35 m
Classification accuracy on
vegetation is up to 99 % in denselyscanned zones
3 mVegetation segmentation
(J. Leroux’s PhD)
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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
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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
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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
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Best case : ±4 mm
Debris: ~ 4 cm
Set by registration error
Set by surface roughness
3D map of confidence interval
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Target based registration
qCANUPOvegetation
removal on raw data
qM3C23D difference
Lague et al., ISPRS, 2013
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Single flood erosion map
10 m
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1/01/2009 1/01/2010 1/01/2011 1/01/2012
2500
3000
3500
4000
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5000
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6000
6500
7000
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@ R
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ei a
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Time
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)
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12/2010-02/2011
02/2011-11/20111/01/2009 1/01/2010 1/01/2011 1/01/2012
2500
3000
3500
4000
4500
5000
5500
6000
6500
7000
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@ R
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ga
we
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Time
Stage @ Rangitikei at Mangaweka
~ Sediment
transport level
Color range +- 2 cm
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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)
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(Wagner, Lague, Morhig, Passalacqua, Shaw, Moffett, GRL, 2017)
Uncertainty map combining roughnessand registration effects
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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
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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
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• 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
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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
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• 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
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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
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Some issues when using high resolution lidar data
Non-uniform point density
Vegetation and shadow effects
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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
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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
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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
ele
vati
on
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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
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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
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• 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
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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
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• 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
ത𝐸 =
𝑇𝑖𝑑𝑒 𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛
𝑘(𝑉(𝐻𝑊𝐿) − 𝑉𝐶)𝑛