integration of multi-source spatial information for coastal management and decision making
DESCRIPTION
Integration of Multi-Source Spatial Information for Coastal Management and Decision Making. Ron Li Mapping and GIS Laboratory The Ohio State University Email: [email protected] URL: http://shoreline.eng.ohio-state.edu/research/diggov/DigiGov.html. Project Goal and Objectives. Goal: - PowerPoint PPT PresentationTRANSCRIPT
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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: [email protected] 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