ARO Staff Research Review Meeting

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ARO Staff Research Review Meeting 5-6 October
2009
Terrain/Landscape Monitoring, Dynamics,
Sustainability ARO Staff Research Project
53470-EV 2009-12
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Project Objective
The research objective is to understand the 3-D
dynamics of landscapes undergoing rapid
transition from one state to another.
Research Approach
The research focus is on identifying the most
vulnerable phase(s) in the progression of
metastable landscapes, and investigating the most
important sustainability differences between of
natural and intensively used lands.
Implement a unique monitoring-modeling system
that combines (i) real-time observations using
high- resolution georeferenced webcams
with automated in situ-sensors and samplers at a
small NCSU experimental watershed at
NCSU with (ii) a tangible physical laboratory
model, GIS-based virtual models, and
numerical simulations. Explore the
idea of special configurations, rules, or general
laws of landscape development patterns
that lead to stable landscapes, potentially
leading to new approaches for management of
disturbances and implementation of
control measures at a minimum cost.
Overlay coupled geospatial monitoring and
simulation results onto the laboratory model to
study physical processes and conditions
that can elucidate poorly understood aspects of
landscape dynamics and lead to new
knowledge about response of landscapes to
disturbances.



Funding Profile
FY08 11,500 FY10
47,800 FY09 35,900 FY11
47,900
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ARO Staff Research Team, 2004-09
Co-PI Dr. Helena Mitasova, Department of Marine
Earth, Atmospheric Sciences, NCSU ARO NRC
Postdoctoral Fellow (from 5/09) Dr. Michael
Starek (from University of Florida) Graduate
Research Students Eric Hardin, Physics Paul
Paris, MEAS, Brent Foglemann, GIST Research
Collaborators Dr. Margery Overton, Department
of Civil Engineering, NCSU Dr. Lubos
Mitas, Department of Physics, NCSU David
Kinner, US Geological Survey, 2004-05
Dr. Carlo Ratti, MIT Sensable City Laboratory,
2004-05 Dr. Jon Pelletier, University of
Arizona, 2007-08 Dr. Richard McLaughlin,
Department of Soil Science, NCSU
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ARO Staff Research Publications, 2004-09
Mitasova, H., Drake, T.G., Harmon, R.S.,
Bernstein, D. 2004. Quantifying rapid changes in
coastal topography using modern mapping
techniques and GIS Environmental and Engineering
Geoscience, 101-11. Kinner, D.A., Mitasova, H.,
Stallard, R.F., Harmon, R.S., Toma, L, 2005, GIS
database and stream network analysis for the
Upper Río Chagres basin, Panama in The Río
Chagres, Panama - Multidiscplinary Profile of a
Tropical Watershed (R.S. Harmon, ed.) Springer,
New York 83-96. Mitasova, H., Mitas, L., and
Harmon, R.S., 2005, Simultaneous spline
approximation and topographic analysis for lidar
elevation data -methods for open-source GIS IEEE
Geoscience and Remote Sensing Letters Special
Theme Issue on Frontier Tools and Techniques for
Surficial Mapping, Analysis and Characterization
Relevance to Geosciences, 2 375-379. Mitasova,
H., Overton, M., Harmon, R.S., 2005, Geospatial
analysis of a coastal sand dune field evolution
Jockey's Ridge, North Carolina Geomorphology,
72 204-221. Mitasova, H., Mitas, L., Ratti, C.,
Ishii, H., Alonso, J., and Harmon, R.S., 2006,
Real-time human interaction with landscape models
using a tangible geospatial modeling environment
IEEE Computer Graphics Applications Journal
Special Issue on Exploring Geovisualization,
2655-63. Pelletier, J., Mitasova, H., Harmon,
R.S., and Overton M., 2009, The effects of
interdune vegetation changes on eolian dune field
evolution a numerical-modeling case study at
Jockey's Ridge, North Carolina, USA, Earth
Surface Processes and Landforms, 34(9) p
1245-1254. Mitasova, H., Hardin, E., Overton,
M., and Harmon, R.S., 2009, New spatial measures
of terrain dynamics derived from time series of
lidar data, Proc. 17th Int. Conf. Geoinformatics,
Fairfax, VA. 15 Conference presentations with
published abstracts 2004-09
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Terrain evolution analysis

• Two approaches
• feature-based extract topographic features from
  DEMs (e.g., shorelines, ridges, streams,
  channels) and measure their migration in 3D
  space, developed for 2005 JR paper
• raster-based per cell statistics is used to
  generate new type of maps where each grid cell is
  a function of grid cells in the entire DEM time
  series. These maps are then used to identify and
  quantify
• dynamic layer 3D space where terrain evolved
• continuous changes trend and rate in elevation
  change or vegetation growth
• discrete changes building construction /
  destruction, forest destruction by fire, storm
  impacts

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LIDAR source of multitemporal 3D data

• LiDAR Light Detection and Ranging
• LiDAR data
• 3D point clouds
• multiple returns, captures and penetrates
  vegetation
• Different scales laboratory, ground-based,
  airborne, satellite
• North Carolina is leader in airborne lidar
  mapping
• coast mapped almost annually since 1996,
• first state with complete coverage (2001-2007, NC
  Flood mapping program)

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Terrain modeling lidar

• Multiple return lidar point cloud data

first return second or last return
surface with vegetation and structures
bare ground
100m
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Density of Data
no. of points/2m grid cell 1996 0.2 1997
0.9 1998 0.4 1999 1.4 2001 0.2 NCflood 2003
2.0 2004 15.0 2005 6.0 2007 3.0 2008 3.0
substantially improved representation of
structures but much larger data sets
1m res. DEM, computed by RST, 1998 lidar data
2004 lidar, 0.5m resolution DEM computed by RST
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USACE SHOALS LIDAR example data
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Processing lidar point time series

• Data integration coordinate system
  transformation
• Point density and noise analysis
• selection of common resolution using binning (per
  cell statistics) points per cell, mean, range
  and stddv within cell
• Spatial approximation using splines
• computation of elevation grid
• derivation of topographic parameters
• smoothing of random noise
• Detection of systematic error and its
  elimination
• time invariant features, e.g., roads, NCDOT
  benchmarks are used to asses the DEM accuracy and
  eliminate shifts in data

Result is a time series of corrected, smoothed,
high resolution (0.3-1.0m) DEMs
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DEM time series analysis barrier island
New, spatial indicators for coastal terrain
evolution based on per grid cell statistics from
DEM time series tk, k1, , m
core surface below which elevation never
decreased zCORE(i,j) min z(i,j,tk)
k outer envelope above which elevation never
increased zENV(i,j) max z(i,j,tk) k
standard deviation map shows areas with most
elevation change in red
tk 1996 - 2008, k1,,13
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core surface zCORE(i,j) min z(i,j,tk)
k outer
envelope zENV(i,j) max z(i,j,tk)
k
The core and envelope surfaces bound terrain
evolution within the study period
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Shoreline Evolution Band
Maps area within which the elevation contour
representing the shoreline evolves over the
study period
Shoreline Band is defined by
Envelope (maximum) shoreline
Core (minimum) shoreline
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Temporal maps
time at minimum map represents timeyear when
the grid cell was at its minimum tMAX(i,j)tl
where z(i,j,tl) zCORE(i,j) time
at maximum maps time year when the surface was
at its maximum elevation tMAX(i,j)tk where
z(i,j,tk) zENV(i,j)
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Dynamic layer for Jockey's Ridge dunes
a
a b c
b
300m
c
Jockey's Ridge sand dunes lidar-based DEM,
2008 I need to add CORE contour here red points
are peaks at 1953-2008
N
500m
Core surface min elevation 1974-2008 Envelope
max elevation 1974-2008
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Discrete changes buildings
Core, envelope, time of minimum and time of
maximum raster maps are used to identify new or
lost structures and the time of change
0 100m
Difference between the core surface and outer
envelope maps new and lost homes zENV(ic,jc)
- zCORE(ic,jc) gt hb lost home tMAX(il,jl) lt
tMIN(il,jl) new home tMAX(in,jn) gt
tMIN(in,jn) time of change tp, p1,...,n-1
?z(ic,jc,td) z(ic,jc,tp) - z(ic,jc,tp1) ?z(
ic,jc,td) gt hb
old home no change, coreenvelope
homes built or lost over the past 10 years
envelope gtgt core
new home built in area with no core
Derived from lidar-based DEM time series
1996-2008, 0.5m resolution
Helena Mitasova, NCSU
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Nags Head homes changes 1997-2008
H1a lost in 2004 H1b rebuilt in 2005 H2 old,
no change
H1 H2
H5 new, built in 2007
H3a lost in 2005 H3b rebuilt 2007 H4a lost
in 1997 H4b rebuilt 1998
H1ab H2
m
envelope core
Core surface min elevation 1997-2008 Envelope
max elevation 1997-2008
Time is determined by automated query of DEM time
series at identified new or lost home centroids
lost built
200m
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Rodanthe homes change and vulnerability
2008 DEM
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Rodanthe homes change and vulnerability
Shown as color draped over 2008 DEM
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Rodanthe homes change and vulnerability
Shown as color draped over 2008 DEM
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Rodanthe homes change and vulnerability
Shown as color draped over 2008 DEM
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Trends rate of elevation change
linear regression slope maps show spatial pattern
of elevation trends Inset white areas have r2 lt
0.3, no linear trend, e.g., transitioned
from growth to loss
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Spatial pattern of evolution trend Jockey's
Ridge
m/y 1.0 0.5 0.0 -0.5 -1.0
1.00 0.75 0.50 0.25 0.00
300m
Elevation trend 1995-2008 linear regression
slope map, rate of loss(orange), rate of gain
(blue)
Coefficient of determination r2
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Integrated dynamic virtual model
Real-time data from terrestrial sensors ISCO
samplers, Econet weather station StarDot
webcams, Leica laser scanner imagery
GIS OSGEO software stack integration analysis mod
eling

• Tangible GIS
• Overlay and analyze
• - real-time data and
• simulation results
• over the physical model
• Create
• new development scenarios
• new BMP configurations

Multitemporal geospatial data multiple return
airborne lidar, high resolution orthophoto,
multispectral imagery
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Conclusion

• New concept for representing terrain dynamics
• core, envelope, 3D dynamic layer
• Methodology for time series analysis of
  lidar-based DEMs
• Raster-Based
• Efficient and highly automated
• Versatile (Not just for coastal environment)

TanGIS slides are here http//courses.ncsu.edu/me
a592a/common/GIS_anal_lecture/Mitasova_TanGIS_MEA5
92b.ppt
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Future

• Integrate laboratory, ground-based, airborne and
  satellite lidar data to capture critical change

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Lidarpoint clouds, DEMs, DSMs
Multiple return point cloud
10m DEM derived from lidar
Bare ground DEM and first return DSM
GRASS GIS
Open Source Geospatial
Foundation