Digital Terrain Modeling, Mapping and Distribution of Geospatial Data in USA

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Digital Terrain Modeling, Mapping and
Distribution of Geospatial Data in USA
Helena Mitasova, Jaroslav
Hofierka Department of Marine, Earth and
Atmospheric Sciences, North Carolina State
University, Raleigh, NC hmitaso_at_unity.ncsu.edu ht
tp//skagit.meas.ncsu.edu/helena/ Supported by
Army Research Office and National Research Council
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GIS and the Environment
Monitoring keep an eye on the state of earth
systems using satellites and monitoring stations
(water, ecosystems, urban development,security),
data are often available in near-real-time on
Internet Analysis and risk assessment find the
problem areas and analyse the possible causes
(soil erosion, groundwater pollution, traffic
jams) Modeling, simulation predict
consequences of human actions and natural
processes Planning and decision support
provide information and tools for better
management of natural and socio-economic resources
H. Mitasova
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National Geospatial Data Clearinghouse
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Data Distribution Internet
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Geospatial Data Portals
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Town of Cary GIS
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Town of Cary and Wake County GIS
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Commercial Distribution MapQuest and GlobeXplorer
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Wake County DEM from National Elevation Dataset
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Thematic Data Real Time Environmental Monitoring
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Revolution in 3D Mapping Technology  
Examples

• Digital Photogrammetry
• Real Time Kinematic GPS
• LIDAR
• Airborne Laser Terrain Mapping
• IFSARE
• Interferometric Synthetic Aperture Radar

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Processing Digital Elevation Data

• Challenges
• massive data sets, oversampling, (1 square km of
  LIDAR data has over 1 million points) unsorted,
  unlabeled, (no defined breaklines)
• noisy data and complex surfaces
• anisotropic and heterogeneous coverage
• almost all data are spatio-temporal
• To integrate and analyse these data, accurate and
  consistent transformation between the measured
  data and GIS data models is necessary.

H. Mitasova
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Spatial interpolation  methods
scattered points  ----gt   raster Voronoi
polygons                 TIN    
          IDW Kriging      
        Topogrid     
             RST      
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Jockeys Ridge Sand Dune
Combining 1998 IR-DOQQ, 1999 LIDAR and 2002 RTK
GPS to assess the change in topography
decreasing elevation, movement towards homes, road
C
B
D
A
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Jockeys Ridge LIDAR data area A
Given x,y,z points what can we gain by high
quality interpolation ?
a
b
points assigned directly to a grid a) 1m and
b) 3m resolution
c
c) RST interpolation 1m resolution grid all
points with distance gt 0.5m are preserved
H. Mitasova
100m
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Identification of dune crests using profile
curvature
b
a
Computing curvature using RST at 1m resolution
at a) high, b) low level of detail.
Measuring dune movement 40m horizontal, -2.6m
vertical
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Jockeys Ridge Change
Area B
a 40m horizontally needs 40m to reach the road
a
b 3m vertically
Area C
b
2000 2002
Minigolf castle
Fences
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Jockeys Ridge 1999
LIDAR-based DEM (1m resolution) IR DOQQ with
modified color, visualized in GRASS5 NVIZ
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Wilmington Harbor Navigation Project
Multiscale characterization (30m 0.5m
resolution) for monitoring beach and nearshore
response to anthropogenic changes (beach
renourishment, channel deepening, mounds)
Data DEM, LIDAR (annually), RTK-GPS (monthly),
Sonar (annually)
Bald Head Island
Wilmington
Cape Fear River
channel
offshore mound
30m resolution
10m resolution
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Bald Head Island South Beach Evolution
Time series elevation data NOAA-USGS LIDAR
1997-2000, 2001-02 RTK GPS (MEAS, D. Bernstein)
LIDAR 2000
LIDAR 1998
Beach renourishment 2001
Photo Zalewski
RTK-GPS 2001
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Bald Head Island change 1998-2000
Overlayed 1998 and 2000 LIDAR surfaces 5m
resolution rasterized
25m
Interactive cutting planes support slicing
through the overlayed surfaces
40m
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Bald Head slope and curvature
Reducing the impact of noise by changing tension
and smoothing
1m resolution interpolated, smoothed and
topographic parameters computed by RST
1998
1998
Slope
Profile curvature
2000
2000
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Bald Head Island South Beach 2002
Area A
stable or growing
Area B eroding
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Bald Head Island Moved Volumes
Jan. 2002 - Dec. 2001
RTK GPS
total eroded 149,000m3 total gained 43,000m3
Aug. 2000 Fall 1998
LIDAR
total eroded 445,000m3 total gained 225,000m3
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Off-shore Sediment Disposal Mound
2m resolution model interpolated from sonar data
by RST
0.5m resolution model smoothed by RST depth
15-7m, size 250x300m
Original gridded data RST interp.,
low smoothing Overlayed with smoothed


surface
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Mound evolution April 2001-January 2002
Surfaces interpolated by RST and compared in
NVIZ close to original data
smoothed
m
Mound is slowly flattening with short distance
transport, and a small net gain
H. Mitasova
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Dynamic Cartography, Simulations, and GIS
Helena Mitasova, Jaroslav Hofierka Department of
Marine, Earth and Atmospheric Sciences, North
Carolina State University, Raleigh, NC Bill
Brown University of Illinois at Urbana
Champaign, IL
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Monthly Precipitation in Tropical South America
1 year cycle
Lots of rain Dry
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Soil horizons
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Groundwater pollutioncreated from 10 year well
data
High pollutant concentrations
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Monthly Nitrogen in Chesapeake Bay one year cycle
High nitrogen concentrations
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Areas Reached by Sunlight summer and winter
solstice
Hofierka 1993
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Water flow over complex terrain
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Sediment flow
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Simulation of landscape processes
Important for analysis and prediction of
landscape evolution, decision support in land
management Path sampling method Based on
duality between particle and field representation
to solve the governing equations, processes can
be modeled as evolution of particles or fields.
Approach is mesh free easier to use with GIS
than finite element or finite difference methods.
GISC00
link
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Impact of development on water and sediment flow
Centennial Campus
1993
1998-2001
future golf
C B A
6ft resolution DEM interpolated from 2ft Wake
county contours by RST
1998, 2001 change in elevation from RTK GPS
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Centennial Campus Development
1993
1998-2001
School
grass
Golf
C B A
C B A
Forest
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Overland flow for future topography
work in progress
constructed wetland
detention area
check dam/spreader
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Hydrologic simulations
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Water flow as particles and fields
4x4km area at Hohenfels, modeled at 10m
resolution
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Sediment flow
Sand
Clay
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Multiscale simulations
Density of walkers is adjusted to resolution and
is controlled by an importance sampling function
W(r)
10m res., 400x400 grid, 2m res.,
600x400 grid

• Modeling critical areas
• beach erosion hot spots
• best management practices

movie
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Open source GIS GRASS
http//grass.itc.it/
General purpose GIS for raster, vector, site and
image data processing for all flavors of UNIX
LINUX, Solaris, IRIX, MacOS X and WINDOWS/Cygwin.
Originally developed at US Army CERL (1982
1995). Released under General Public License in
1999 as free to run, study, modify,
re-distribute, improve, and release (but it
cannot be improved and released as
proprietary) Neteler and Mitasova Open source
GIS the GRASS GIS approach, Kluwer, 460p.
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Conclusion

• New generation mapping technologies and computer
  simulations of landscape processes allow us to
  capture the landscape dynamics.
• Dynamic cartography becomes an effective tool to
  communicate this change.
• There is lack of theory and standards for 3D
  dynamic cartography

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Future
Development of Open source GIS GRASS as 3D
dynamic GIS, portable from supercomputers to
handheld devices. New generation implementation
of computationally intensive modules supporting
efficient, self-adapting processing of massive
data sets (in collaboration with Duke Comp.
Science) Implementation of path sampling modules
to support development of water, sediment and
pollutant transport in terrestrial and coastal
environment

• Acknowledgment
• NRC/ARO fellowship for research on GIS for
  nearshore topography
• NC WRRI grant for erosion and sediment control
  study

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DEM from Photogrammetric Data
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