Cyberinfrastructure Application in Coastal Erosion
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Transcript of Cyberinfrastructure Application in Coastal Erosion
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Cyberinfrastructure Applicationin Coastal Erosion
Xutong Niu
Mapping and GIS Lab
http://shoreline.eng.ohio-state.edu
October 12, 2006
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Outline
• Review of coastal research in Mapping and GIS Lab
• Proposed work in the proposal
• Preliminary research plan in the first year
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Review of Coastal Research
• Coastal Data Integration
• Coastal Mapping and Monitoring
• Coastal Erosion Modeling
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Coastal Data Integration
• Coastal Data– Water level
• Water gauge• Water surfaces (GLFS)• Satellite altimetry
– Bathymetry– Historical shorelines (2D and 3D)– DEMs and orthophotos
• USGS• Aearial photographs• Satellite imagery
– ……
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Water Level
• Water Gauge
http://co-ops.nos.noaa.gov
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Water Level
• Water surfaces (GLFS)
GLFS Hindcast Mean Water Surface 1999-2001
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Altimetry, Gauge, and Bathymetry
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Shorelines
• 2D shoreline (x, y)– USGS DLG (Digital Line Graph)– Historical orthophoto
• 3D shoreline (x, y, z)– Derive from stereo images
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Shorelines
Picture is from http://local.live.com
(bluff lines)
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DEMs and Orthophotos
DEM Generated from IKONOS Stereo Images
3D Shoreline Extracted from IKONOS Stereo Images
LiDAR DEM at Painesville
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Parcel Maps
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Integration of Coastal Data
• Problems– Data from Multiple sensors and models:
• satellite imagery and altimetry, aerial photos, LiDAR, water gauge, and water surfaces
– Formats: • Vector, raster, tabular.
– Resolution and accuracy: • centimeter – sub-meter – meters – kilometers
– Reference system (horizontal and vertical)• Different Coordinate Systems (State plane, UTM, Latitude
and Longitude)• Ellipsoids and Geoids
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Integration of Coastal Data
• Research work– Datum conversion system– Multi-sensor image integration– Integration of water gauge, water surface, and
satellite altimetry – Integration of 3D shoreline, bathymetry,
gauge station, and water surface
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Datum Conversion System
NADCONGEOID99
HTv2.0
IGLD85
VERTCON
3D Helmert Transformation
Diagram of Datum Conversion in Lake Erie
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Z)Y, (X,P
Z)Y, (X,P x
2
1 Z)Y, (X,P
Z)Y, (X,P y
4
3Use Rational functions (RF):
Geometric Processing of IKONOS ImageryGeometric Processing of IKONOS Imagery
Control Points
Checkpoint
1-Refine the RF coefficients using ground control points
2-Transfer the derived coordinates from the vendor-provided RF coefficients using ground
control points
Checkpoint
Control Points
The following two methods are used to improve the RF accuracy:
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Multi-sensor Image Integration
Forward and Backward QuickBird Forward and Backward IKONOS
Forward IKONOS and Backward QuickBird
Forward QuickBird and Backward IKONOS
Backward IKONOS and Backward QuickBird
Forward QuickBird and Forward IKONOS
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Integration of Multi-Sensor Images (Methodology)
VCP: Virtual control points GCP: Ground control points
RPC: Rational polynomial coefficients CKP: Check points
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Integration of Multi-Sensor Images (Results from use of IKONOS, QuickBird, and Aerial Images)
Results obtained from integration of all the images
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Bluffline Extraction by Integration ofLiDAR and Orthophotos
Iterative Closest Points (ICP) Algorithm for Bluffline Refinement
Bluffline extraction (Liu et al. 2005)
LiDAR DSM LiDAR Profile Initial Bluffline from LiDAR (bluff top and toe)
Orthophotos Bluffline Extraction
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Bluffline Extraction by Integration ofLiDAR and Orthophotos
Results in Test Region 1 Results in Test Region 2
Average 3-D difference
Manual and LiDAR line (m)
Manual andOrthoimage line (m)
Manual andrefined line (m)
x y z x y x y z
Region 1 1.41 3.90 0.51 0.89 2.55 0.63 1.23 0.17
Region 2 1.08 2.20 0.88 0.95 1.72 0.52 0.89 0.40
Both regions combined
1.18 2.69 0.77 0.93 1.97 0.55 0.99 0.34
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Integration of water gauge, water surface, and satellite altimetry
Mean Water Level (1999 - 2001)
173.8500
173.9000
173.9500
174.0000
174.0500
174.1000
174.1500
Wate
r L
evel
(m)
Gauge Station Water Surface
Comparison between water gauge data and GLFS water surface
Yearly Comparisons (1999 – 2001) between Altimetry and WSM
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DataAcquisition
Time
Average Elevation of Shoreline
Standard Deviation of Shoreline
Water Level from Nearest Gauge Stations
IKONOS2004-07-08 16:17 GMT
0.285 m 0.615 m
Port Manatee: -0.1870m (predicted)
St. Petersburg: -0.1546m
Port of Tampa: -0.1734m
QuickBird2003-09-12 15:58 GMT
- 0.217 m 0.439 m Port Manatee: -0.017 m
Tampa Bay, FL
Legend
IK_Cal
IK_Cal
cock
<VALUE>
-5.125 - -3.375
-3.375- -0.7
-0.7 - -0.6
-0.6 - -0.5
-0.5 - -0.4
-0.4 - -0.3
-0.3 - -0.2
-0.2 - -0.1
-0.1 - 0
0 - 0.065
orthopo_001000.img
Value
High : 2040
Low : 0
orthopo_159082_pan_0000000.img
Value
High : 2040
Low : 0
utmgrid
Value
High : 31.9283
Low : -29.7271
IKONOSShoreline
QuickBirdShoreline
Integration of LiDAR Bathymetry, Water Gauge Data and 3-D Shorelines
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Coastal Mobile GIS
Lake Erie
Server
Computer
PDA
Cell phone
GPSSatellite
Computer
Shoreline ErosionAwareness Subsystem
Permit Subsystem
On-site MobileSpatial Subsystem
Computer
Internet
Coastal Structure
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ODNR Coastal Structure Permit System
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On-site Mobile Spatial System
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Coastal Erosion Area Maps for Lake County by Ohio Division of Natural Resources
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Establishing correspondence
This mathematical model can be envisioned as a similarity transformation that transforms the shoreline segment Sm,
(k-1)at time (k-1) to the segment Sm,k at time k by the action of the erosion
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Mathematical Model
Flow chart for model
Using polynomial
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Error analysis diagram
Analysis diagram for shorelines of year 2000
Based on transects digitized from the Coastal Erosion Area map
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Coastal Erosion Modeling
Shoreline Avg. error Max. error Min.error
Predicted 16.38 ft 93.05 ft 0.00 ft
Erosion rate- Based 16.22 ft 133.01 ft 0.00 ft
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Snake-based Tide-coordinated Shoreline Model
),,...,,(Z
),,...,,(Y
),,...,,(X
210
210
210
sccccF
sbbbbF
saaaaF
nZ
nY
nX
t1 < t2 < …… < tm
t1
t3
t2
MLLW
f(X, Y, Z)t1
F(X, Y, Z)
f(X, Y, Z)t3
f(X, Y, Z)t2
Minimize Energy Function:
Pi
Tide gauges
Instantaneous shoreline at time t1
Tide-coordinated shoreline
Instantaneous shoreline at time t3
Instantaneous shoreline at time t2
External force
Protection structure
1
0 extint
1
0)(C)(C)(C dssEsEdssEE snakesnake
Constraints:Water Surface, Gauge Stations, and Coastal Structures
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Snake-based Shoreline Model(Model Equations)
External Forces
)),y,x(z()(z
)),y,x(y()(y)),y,x(x()(x
111z11
t
111y11
t
111x11
t
tttt
tttt
tttt
zfIK
zfIKzfIK
2)(C
)()(C
)(2
2
22
int
s
ss
s
ssE
J
j
I
i
jtti
J
j
I
i
jtti
ti
jti
ti
jti
ti
jtiext ddzzyyxxE
1 1
)(
1 1
)(222
1
0 extint
1
0)(C)(C)(C dssEsEdssEE snakesnakeEnergy Function:
Internal Energy:
External Energy:
Evolution Function:
Constraints:
....11 )(,, LWCTg
tg
tg
tg
tg
tg TWzandyyxx
...11 )(,, MCTs
ts
ts
ts
ts
ts TMzandyyxx
kkkkkk
...),( MCTti
ti
ti yxMz
Gauge Stations
Coastal Structures
Water Surface Model
xEf x ext
yEf y ext
zEf z ext
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Experimental Results of the Shoreline Model
Experiment 1 (WSM constraint)• Simulated Shorelines
• straight line segments• Simulated WSM
• flat plane• Different Initial Positions
Experiment 2 (WSM constraint)• Simulated Shorelines
• straight line segments• Real WSM• Different Initial Positions
Historical lines Initial line Results line
Case 1
Case 2
Case 1
Case 2
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Experimental Results of the Shoreline Model
Experiment 3 (WSM constraint)• Real Shorelines
• historical Lake Erie shoreline• Real WSM• Different Initial Positions
Water
Land
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Proposed Work in Proposal
• 3.1 Multi-Model Multi-Sensor Data Integration Service– Universal spatial data transfer and conversion service
• Convert spatial data to GML to transfer them between GRID nodes– Multi-model Integration Service (Dr. Bedford)– Multi-sensor 3-D Mapping Service
• Application services with distributed data
• 3.2 Querying Service– Coastal data repository in Mapping and GIS lab.
• Provide data access– Metadata indexing (Hakan)
• 4.2 Coastal Erosion Prediction and Analysis– Suggested area: Painesville, OH– Conduct coastal erosion pattern analysis– Create an application service
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Preliminary Research Plan
• In Proposal: “We will investigate different coastal data formats, integrate them with the virtual and wrapper middleware system and finish the development of multi- data/model integration services, query services, and scalable data mining services. We will also implement time-series analysis algorithms for forecasting and nowcasting application.”
• Working in Mapping and GIS Lab– Coastal data format conversion
– Integration with the virtual and wrapper middleware system
– Multi-sensor integration service