Cyberinfrastructure Applicationin Coastal Erosion

Xutong Niu

Mapping and GIS Lab

http://shoreline.eng.ohio-state.edu

October 12, 2006

Outline

• Review of coastal research in Mapping and GIS Lab

• Proposed work in the proposal

• Preliminary research plan in the first year

Review of Coastal Research

• Coastal Data Integration

• Coastal Mapping and Monitoring

• Coastal Erosion Modeling

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

– ……

Water Level

• Water Gauge

http://co-ops.nos.noaa.gov

Water Level

• Water surfaces (GLFS)

GLFS Hindcast Mean Water Surface 1999-2001

Altimetry, Gauge, and Bathymetry

Shorelines

• 2D shoreline (x, y)– USGS DLG (Digital Line Graph)– Historical orthophoto

• 3D shoreline (x, y, z)– Derive from stereo images

Shorelines

Picture is from http://local.live.com

(bluff lines)

DEMs and Orthophotos

DEM Generated from IKONOS Stereo Images

3D Shoreline Extracted from IKONOS Stereo Images

LiDAR DEM at Painesville

Parcel Maps

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

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

Datum Conversion System

NADCONGEOID99

HTv2.0

IGLD85

VERTCON

3D Helmert Transformation

Diagram of Datum Conversion in Lake Erie

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:

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

Integration of Multi-Sensor Images (Methodology)

VCP: Virtual control points GCP: Ground control points

RPC: Rational polynomial coefficients CKP: Check points

Integration of Multi-Sensor Images (Results from use of IKONOS, QuickBird, and Aerial Images)

Results obtained from integration of all the images

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

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

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

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

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

ODNR Coastal Structure Permit System

On-site Mobile Spatial System

Coastal Erosion Area Maps for Lake County by Ohio Division of Natural Resources

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

Mathematical Model

Flow chart for model

Using polynomial

Error analysis diagram

Analysis diagram for shorelines of year 2000

Based on transects digitized from the Coastal Erosion Area map

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

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

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

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jtti

ti

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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

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

Experimental Results of the Shoreline Model

Experiment 3 (WSM constraint)• Real Shorelines

• historical Lake Erie shoreline• Real WSM• Different Initial Positions

Water

Land

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

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