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Integration of Multi-Source Spatial Information for Coastal Management and Decision Making

Ron Li

Mapping and GIS LaboratoryThe Ohio State University

Email: li.282@osu.edu URL: http://shoreline.eng.ohio-state.edu/research/diggov/DigiGov.html

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Project Goal and Objectives

• Goal:

– Investigate and develop technologies to enhance the operational capabilities of federal, state, and local agencies responsible for coastal management and decision making

• Objectives:– Enhance capabilities for handling spatio-temporal coastal databases,– Build a fundamental basis of coastal geospatial information for inter-

governmental agency operations, and – Provide innovative tools for all levels of governmental agencies to increase

efficiency and reduce operating costs.

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Research TasksResearch Tasks

Geospatial database, digital models

GPS Buoy, Satellite Altimetry

IKONOS, SAR/INSAR,LIDAR,

etc.

Pilot erosion awareness system

ODNR Permitting System

Sea model assimilation

Modeling of water surface

Shoreline and bathymetric mapping, change detection, and coastal DEM generation

Modeling of coastal terrain and changes, modeling of erosion and environmental changes

Digital shoreline generation from digital CTM and WSM, tide-coordinated

shoreline calculation, shoreline prediction

Coastal spatial-temporal modeling: uncertainty and shoreline generalization

Visualization of spatial and temporal changes, Internet-based visualization of operational results

from distributed databases

Integration with government geospatial databases

Support of government decision making

Great Lakes Forecasting System

Hydrodynamic modeling, hind- and forecasting

On-Site Mobile Wireless System

Digital shoreline production and future shoreline prediction

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Pilot SitesPilot Sites

Tampa Bay (Florida)

Lake Erie (Ohio)

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Multi-source Spatial Data

Multi-Source Data:

• Water gauge data • Buoy data• Bathymetric data • Satellite altimetry data (TOPEX/POSEIDON )• Hydrological water surfaces (GLFS)• DEMs derived from high-resolution IKONOS satellite images

and aerial photographs• GPS survey data

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Gauge, Altimetry and Bathymetry

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Gauge Data

High and low water levels at the tide-gauge stationsat Cleveland and Marblehead, Ohio

Cleveland (1955-2001) Marblehead (1975-1995)

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Water Surfaces

Grid size: 2km

GLFS Hindcast Mean Water Surface 1999-2001

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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

Check Points

Refine the RF coefficients using ground control points

Transfer the derived coordinates from the vendor-provided RF coefficients using ground control points

Check Points

Control Points

The following two methods are used to improve the RF accuracy:

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DEM Generated from IKONOS Stereo Images

3D Shoreline Extracted from IKONOS Stereo Images

Products from 1m IKONOS Stereo ImagesProducts from 1m IKONOS Stereo Images

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Coastal Terrain Model

• Generation Process– Bathymetry Data (NOAA)– DEMs: USGS DEM, IKONOS DEM, and Aerial Photo DEM– Datum: NAVD 88

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Water Level Comparison between GLFS Water Surface and Altimetry (Long Track)

174.3

174.35

174.4

174.45

174.5

174.55

174.6

174.65

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Point ID

Wat

er L

evel

(met

er)

GLFS MWL (92-01)

altimetry (NAVD88)

10 year water level variation comparison(GLFS water surface and T/P Altimetry)

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Digital Shoreline

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Shoreline Erosion Awareness System

Painesville Shoreline Erosion Awareness System

Collaboration with Lake County Planning Commission, OH

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ODNR Coastal Structure Permit System

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Integrating Wireless Technology, Internet-based GIS, and Spatial DataIntegrating Wireless Technology, Internet-based GIS, and Spatial Data

Lake Erie

PDA

Cell phone

GPS Satellite

On-site MobileSpatial Subsystem

Server

Computer

Computer

Shoreline ErosionAwareness Subsystem

Permit Subsystem

Internet

Coastal Structure

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Seagrass in Tampa Bay, FL

Seagrasses (Greening, 2000; Johansson, 2000)• Maintain water clarity by trapping fine sediments and particles with their leaves; • Provide shelter for many fishes, crustaceans, and shellfish; • Provide food for many marine animals and water birds; • Reduce the impact from waves and currents; and• Is an integral component of shallow water nutrient cycling process.

In Tampa Bay, turtle grass and shoal grass are dominant, and widgeon grass, manatee grass, and star grass are also found.

Turtle grass Shoal grass Manatee grass

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Historical Background

Facts about seagrass coverage in Tampa Bay, FL (Johansson, 2000):• In late 1800s, approximately 31,000 ha of seagrass were present in Tampa Bay • In 1950, 16,500 ha seagrass coverage, about 50% losses.• In 1982, 8,800 ha seagrass coverage, more than 70% losses of the historical

seagrass coverage.

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Causes of Seagrass Loss

Reasons of seagrass losses:• Excessive loading of nutrients from the watershed, or eutrophication (Greening, 2004)• Population growth of the bay area and increase in commercial activities (Johansson,

2000);• Various dredging operations and shoreline developments (1950s – 1970s). Increase

turbidity of water column and sediment deposition on the meadows (Johansson, 2000).

Factors influencing seagrass growth:• Water quality (Janicki and Wade,1996)

– Nitrogen– Chlorophyll – Turbidity

• Light attenuation (Janicki Environmental, Inc., 1996)• Water depth (Fonseca et al., 2002)• Wave effect (Fonseca et al., 2002)• Offshore sandbar (Fonseca et al., 2002)

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Related Physical Factors

• Water depth :– Bathymetry (SHOALS)– Water level (Gauge stations)– Coastal changes (shoreline positions)

• Offshore sandbar– Bathymetry– Hydrodynamic variables (wave,

current) • Wave and current effects

– Water level– Hydrodynamic variables (wave,

current, temperature,...)• Spatial distribution of seagrass

– Patchy or Continuous– Seagrass mapping (High-resolution

satellite imagery)

Bathymetric / Topographic Merged DEM

Video Clip of Seagrass

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• High-resolution seagrass mapping and 3D shoreline change detection– QuickBird Imagery

• Sub-meter stereo panchromatic imagery

• 2.5 m stereo multispectral imagery– IKONOS Imagery

• One-meter stereo panchromatic imagery

• Observe seagrass changes and temporal patterns

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Integration of physical modeling and bioinformatics

• Advantages of physical modeling– Detailed coverage information in seagrass mapping and monitoring

based on high-resolution satellite imagery– Quantitative numerical hydrodynamic modeling of wave and current

effects on seagrass degradation and restoration– Highly-accurate physical environment change monitoring based on

three-dimensional satellite imagery and GPS– Highly-accurate bathymetry with SHOALS technology– Integration of physical monitoring and modeling capabilities and

ecological factors• Collaborators

– Keith Bedford (hydrodynamic modeling, OSU)– Holy Greening (Tampa Bay Estuary Program)– Mark Luther (Ocean Modeling and Prediction, USF)– NOAA and USGS

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Research Plan

Analysis of spatial distribution patterns of seagrass and environment– Spatial locations of seagrass

coverage: Patchy and Continuous– Water quality– Bathymetry– Water surface and current modeling– Shoreline changes

Prediction– Very fine analysis and modeling of a

small area– Extension of the result to the entire

bay