1. Multi-scale characterization of near-shore environment using Open source GIS technology
Helena Mitasova and Tom Drake
Department of Marine, Earth and Atmospheric Sciences
North Carolina State University, Raleigh, NC
Advisor: R.S. Harmon, ARO

2. GIS and the Environment
Monitoring
processing imagery and site environmental data,
spatial access to data through Internet (USGS, EPA)
Analysis and risk assessment
integration of multiple-source data + spatial analysis
Prediction, modeling, simulation
numerical modeling for prediction of impacts
Planning and decision support
cost effective prevention and conservation
Prepared by : H. Mitasova Images:GMSLab University of Illinois: T. Frank, W.Brown, W.Reez, D. Johnston Slide design: A. Mitas

3. Objective
Coastal field measurements and models involve processing, analysis and visualization of large volumes of spatial data, generated in different environments and formats. To support coastal applications GIS needs enhancements to improve support for large, heterogeneous, spatio-temporal data sets at multiple scales.
Goal
Enhance, develop and test Open source GIS GRASS tools for:
multivariate interpolation with analysis of geomorphology
multidimensional dynamic cartography
GIS support for coastal processes simulations

4. Open source GIS: GRASS
General purpose GIS for raster, vector, site and image data processing for all flavors of UNIX: LINUX, MacOS X, Solaris, IRIX and WINDOWS/Cygwin.
Originally developed at US Army CERL.
Released under General Public Licence (GPL) as free to:
run, study, modify, re-distribute, improve, and release
(cannot be improved and released as proprietary)
Lead development coordinator:
Markus Neteler, ITC, Trento, Italy

5. Modeling with GRASS
Raster: map algebra, topographic analysis, line of sight, solar radiation, cost surfaces, covariant analysis, buffers,…
Vector: digitizing, overlay
Imagery: processing of multispectral data, image rectification, principal component analysis, edge detection, …
Sites: spatial interpolation, voronoi polygons, …
Projections, transformation between data models, spatial interpolation, import/export
Visualization: 2D display with zoom and pan, interactive 3D visualization with multiple surfaces, vector and site data
Link to other OS projects: Map Server, OSSIM, R-stats, GSTATS…

6. Digital nearshore data
USGS DEM or NED: insufficient resolution
Traditional surveying and photogrammetry: points-> TINs -> contours (breaklines)
RTK GPS profiles: directional oversampling
LIDAR sweeps (no breaklines, oversampled)
Nearshore bathymetry: LARC profiles (directional oversampling), sonar
Fast, accurate and consistent transformation between the measured data and GIS data models are necessary: grid, contours (vector), sites (points), at various levels of detail
USGS DEM
RTK GPS
LIDAR
SONAR

7. Challenges unlabeled, oversampled data (no defined breaklines)
massive data sets (million points data sets)
unlabeled, oversampled data (no defined breaklines)
surfaces are complex and data are often noisy
anisotropy or directional oversampling is common
spatial coverage and accuracy may be heterogeneous
both statistical and geometrical accuracy is needed
almost all data are spatio-temporal

8. Relaxed Splines
Relaxed spline method (regularized spline with tension - RST) was implemented in GRASS to support spatial interpolation of multivariate data.
Properties:
2D, 3D and 4D implementation,
flexible properties through tension and smoothing,
simultaneous computation of slope, aspect, curvatures,
computation of deviations and cross-validation error,
segmented processing for large data sets.
Examples

9. FRF Duck nearshore profiles: LARC
Interpolation by RST with simultaneous computation of topographic parameters: slope and profile curvature
Elevation
Profile curvature
In normal plane in gradient direction
Slope

10. Jockey’s Ridge LIDAR: methods
1m rasterization, m rasterization
RST interpolation:
1m resolution grid
all points with distance > 0.5m preserved

11. Jockey’s Ridge curvature
Curvatures computed by RST at 3 levels of detail, controlled by tension and smoothing,
resolution is 2m,
red is convex
blue is concave

12. Jockey’s Ridge visualization
LIDAR-based DEM (2m resolution) + IR DOQQ with modified
color, visualized in GRASS5 NVIZ

13. Bald Head Island Data Renourished beach in 2001
Data: LIDAR , RTK GPS, NED
NED
RTK GPS
LIDAR98
Dec. 2001
Jan. 2002
LIDAR2000

14. Bald Head Island change 1998-2000
1998 and 2000 LIDAR: 5m resolution rasterized

15. Bald Head slope and curvature
1999
Computed by RST at 2m resolution with high tension parameter (high level of detail).
2000
legend

16. Bald Head: slope and curvature
1999
2000
slope
Computed by RST
with lower tension
profile curvature

17. Bald Head RTKS: Anisotropic data
RTK GPS data are oversampled in the direction of the vehicle movement. Anisotropic interpolation is needed when distance between profiles is significantly greater than resolution.
RST interpolation with default parameters
RST with anisotropic tension, 1:10 at 160 deg
anisotropy with incorrect angle (135 deg)

18. Bald Head Island change Dec.-Jan.
January 2002
December 2001

19. Bald Head Island: moved volumes
Jan Dec. 2001
RTK GPS
total eroded: 149,000m3
total gained: 43,000m3
Aug – Fall 1999
LIDAR
total eroded: 445,000m3
total gained: 225,000m3

20. Simulation of processes
Path sampling method uses duality between particle and field representation to solve the governing equations
It is mesh free so it is easier to use with GIS than finite element or finite difference methods.
Process can be modeled as evolution of particles or fields.

21. Overland flow sediment transport
sand:high detachment and deposition rates, short distance transport
clay: low detachment and deposition, long distance transport

22. Multiscale simulations
Density of walkers is adjusted to resolution and is controlled by an importance sampling functionW(r)
Entire area: 10m res., ~400x400 grid, Inset: 2m res., 600x400 grid

23. Conclusions
RST method with simultaneous topographic analysis was applied to several types of data used for characterization of nearshore environment:
LIDAR: Jockey's Ridge was interpolated at 1m resolution with analysis of surface geometry at various levels of detail. Improvement of performance for high density data points is being implemented. The 3D map of Jockey's Ridge with draped IR-DOQQ was created using GRASS GIS tool NVIZ.
LIDAR and RTK GPS measurements were used to assess the change of the Bald Head Island shoreline, including in geomorphologic properties. Anisotropic tension was necessary for processing of RTK GPS profile data. The analysis has shown that after renourishment the pattern of erosion and deposition remains the same, possibly at higher rate.
Technology Transfer 
Jockey’s Ridge LIDAR is included in the book on Open source GIS: the GRASS GIS approach, to be published in 2002.
All improvements are tested and immediately released with the development version of GRASS GIS

24. What is next New generation interpolation:
– faster, adaptable, with automatic optimization of parameters
Support for spatio-temporal data beyond timestamp and animations
Finish and enhance the support for volume data
General path sampling simulation tool for GIS

25. GIS and the Future Key challenge:
research, infrastructure and tools for employment of geospatial data for proactive protection of environment.
Free access to spatial environmental data and tools:
rapid development of new technologies for environment
encouragement of environmentally responsible behavior
Integration of monitoring, simulations and optimization:
sustainable land use management,
real-time response to environmental disasters,
prevention unexpected environmental impacts
Images : GMSLab University of Illinois at U-C: W. Brown, H. Mitasova Prepared by : H. Mitasova,