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 hmitaso@unity.ncsu.edu, drake@ncsu.edu Advisor: R.S. Harmon, ARO

GIS and the Environment Monitoring processing imagery and site environmental data, spatial access to data through Internet (USGS, EPA,NOAA) 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 H. Mitasova Images:GMSLab University of Illinois: T. Frank, W.Brown, W.Reez, D. Johnston Slide design: A. Mitas

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 H. Mitasova

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 http://grass.itc.it/ H. Mitasova

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… H. Mitasova

Digital nearshore data USGS DEM or NED: 30m resolution Traditional surveying and photogrammetry RTK GPS profiles LIDAR Nearshore bathymetry: CRAB and LARC profiles, multibeam surveys, and conventional sonar Fast, accurate and consistent transformation between the measured data and GIS data models is necessary: grid, contours (vector), sites USGS DEM RTK GPS LIDAR H. Mitasova

Challenges unlabeled, oversampled data (no defined breaklines) massive data sets (over million points) 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 H. Mitasova

Regularized Spline with Tension Regularized Spline with Tension – RST, (Mitasova and Mitas 1993) 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 H. Mitasova

Jockey’s Ridge N H. Mitasova

Jockey’s Ridge LIDAR: methods Points assigned directly to raster 1m resolution 3m resolution RST interpolation: 1m resolution grid all points with distance > 0.5m are preserved H. Mitasova

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 H. Mitasova

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

Bald Head Island: data Spatio-temporal elevation: USGS NED (30m), NOAA-USGS LIDAR 1997-2000, 2001-02 RTK GPS (MEAS, D. Bernstein) NED RTK GPS shoreline LIDAR98 Dec. 2001 Jan. 2002 LIDAR2000 H. Mitasova

Bald Head Island: beach re-nourishment 1998: LIDAR shoreline 1998 2000: LIDAR shoreline 2000 2001, Dec.: RTK GPS shoreline surface is 1998 LIDAR H. Mitasova

Bald Head Island change 1998-2000 Overlayed 1998 and 2000 LIDAR surfaces 5m resolution rasterized Interactive cutting planes support slicing through the overlayed surfaces H. Mitasova

Bald Head: slope and curvature Hot spot: 1998 Interpolated by RST at 2m resolution with high tension parameter (high level of detail). Slope 2000 concave convex Curvature H. Mitasova

Bald Head: slope and curvature Reducing the impact of noise by changing tension and smoothing Slope Profile curvature 1998 1998 2000 2000 H. Mitasova

RTK GPS: 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) H. Mitasova

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

Bald Head Island: moved volumes Jan. 2002 - Dec. 2001 RTK GPS total eroded: 149,000m3 total gained: 43,000m3 Aug. 2000 – Fall 1999 LIDAR total eroded: 445,000m3 total gained: 225,000m3 H. Mitasova

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. H. Mitasova

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

Multiscale simulations Multiscale simulation of overland water flow: density of walkers is adjusted to resolution and is controlled by an importance sampling function W(r) Entire area: 10m res., ~400x400 grid, Inset: 2m res., 600x400 grid Possible application to modeling of hot spots within a given coastal area H. Mitasova

Conclusions Technology Transfer 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. LIDAR and RTK GPS measurements were used to assess the change of the Bald Head Island shoreline, including its geomorphologic properties. Anisotropic tension was necessary for processing of RTK GPS data. The analysis has shown that after renourishment the pattern of erosion and deposition remains the same, possibly at higher rate. Technology Transfer Processing of LIDAR data 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 H. Mitasova

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 H. Mitasova,

What is next Next generation interpolation: automatic optimization of parameters and adaptation to data and computational resources Extend support for spatio-temporal data beyond recording time (time stamp) and animations Finish and enhance the support for volume data General path sampling simulation tool for GIS H. Mitasova

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 H. Mitasova