Geospatial Analysis of Coastal Topography and its Dynamics

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Geospatial Analysis of Coastal Topography and its
Dynamics

• Helena Mitasova
• North Carolina State University
• and collaborators

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Outline

- Topobathy project RENCI, Lisa Stillwell,
Scott Madry, John McCormick - Jockeys Ridge
dune system update - Terrain dynamics from time
series of lidar data General workflow Pea
island application example - Applications
supported by Sea Grant and ARO Eric Hardin
Nags Head - Jockeys Ridge, Rodanthe Onur Kurum
Hatteras Qiang Jin Oregon Inlet
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Data SourcesBeach ProfilesWrightsville Beach,
Topsail, Topsail Island, Pea Island, Ocean
Isle-Shallotte-Holden Beach, Oak Island-Bald
Head, FRF Duck, Fort Fisher, Figure 8 Island,
Dare County, Carolina Kure, Bear Island,
Bogue-Shackleford BanksInletsTopsail,
Shallotte, Rich-Nixon Green Channel, Oregon, New
River, Mason, Masonboro, Lockwoods Folly, Cape
Fear, Bogue, Beaufort, Barden

• North Carolina Floodplain Mapping Project

Lisa Stillwell, RENCI
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CRM and profile data

• North Carolina Floodplain Mapping Project

Longshore profiles improve accuracy of
interpolated DEMs (Mitasova, Bernstein
papers)New method for data thinning should
preserve more detail
Cape Fear
Mason
New River
Topsail
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CRM and channel data - inadequate, new lidar
mapping

• North Carolina Floodplain Mapping Project

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Digital Elevation Modelrelease 2.3

• North Carolina Floodplain Mapping Project

• 10 meter resolution
• 54,000 x 54,000 cells
• 2,916,000,000 total cells

Lisa Stillwell, RENCI
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• new lidar surveys 2007 (NCALM), 2008 (NOAA) -
  high density, multiple return point clouds
• new analysis started
• from beach to sound,
• identify stable core,
• explore vegetation dynamics in addition to
  sand,
• assess state of sand from the South dune
• dune simulations by Jon Pelletier
• Pelletier, J., Mitasova, H., Overton M., and
  Harmon, R.S., 2009, Numerical modeling of recent
  eolian dune field evolution at Jockey's Ridge,
  North Carolina, Earth Surface Processes and
  Landforms, accepted

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1974 108 ft. 2001 72 ft.
The main dune rotates clockwise while its peak
moves southeast. Volume and area are relatively
stable The most important discovery came from
old maps - the dune was a short term phenomenon
and is going back to its ridge form
rate of horizontal migration
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Year 1915 1950 1974 1995 1998 1999
2001 2002 2004 2007 2008 --------------------
--------------------------------------------------
-------- Main m 20 42 33 26 24
26 25 24 22 21 22 East m
nd 22 21 18 17 18 18 nd
19 18 19 West1m nd nd 23
20 20 21 21 nd 22 22
22 West2m nd nd 20 20 20 20
20 nd 19 21 21
West 1 and West2 may be getting some sand from
the disposed South dune and/or the Main duned
Dune field flattens 1999 - 2008 0.40m/y
elevation loss at the main peak, 0.10m/y gain at
smaller peaks small acceleration from 0.37m/y.
Total loss 20m in 58 years.
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2001-2007 - bare earth 1999-2008 - first
return with vegetation and homes
new home and moved home check for permits in
county database
same pattern as 1974-95 and 2007-2008
no transport from disposal area to dune in
2007-2008
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1999 - grey (first return) 2007 -
green-yellow-orange (bare ground)
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Workflow topographic change

• Data integration coordinate system
  transformation cs2cs, ogr2ogr
• Point density and noise analysis selection of
  common resolution and gridding method using per
  cell statistics r.in.xyz
• Detection of systematic error and its
  elimination for all DEMs
• Simultaneous spatial approximation (gridding),
  smoothing of random noise and computation of
  topographic surface parameters v.surf.rst
• per cell statistics

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Workflow topographic change

• Extraction of features to measure the change
  (shoreline, ridges, streams, peaks) r.mapcalc,
  r.watershed, r.terraflow, r.param.scale
• Quantification of change, generation of
  topographic change maps

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Increasing LIDAR point density
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
substantially improved representation of
structures but much larger data sets
2004 lidar, 0.5m resolution DEM binned and
computed by RST (smoothes out the noise and
fills in the gaps
1m res. DEM, computed by RST, 1998 lidar data
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USACE SHOALS LIDAR topo mapping
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Mapping LIDAR point density
pt / 2m cell
Study area NC barrier island RTKGPS survey
and NCDOT benchmarks along NC12 used for lidar
data assessment
Point density maps created by binning
(r.in.xyz)? draped over 2m res DEM (2001) are
used to select common resolution
NC12
2001 NC Flood
2004 SHOALS
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Analysis of systematic error
RTK-GPS 2001 lidar mean diff -0.23m
elevation m
Elevation difference between RTK-GPS survey
(0.03m RMSE)? and lidar data along centerline of
a stable road (NC12).
0 0.7 1.3 2.0
2.6 3.2
distance km
RTK-GPS 2004 lidar mean diff -0.06m
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Impact of shifts in Lidar data
Do we have high erosion rate? Is the road
sinking?
original blue 1999 black 2001 A erosion 12m B
accretion 2m corrected red 1999 violet
2001 A erosion 4m (!) B accretion 8m
A
elevation difference m
B
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Reducing systematic error
Improved data consistency elevation at NCDOT
benchmarks derived from original and corrected
DEMs
sand overwash after hurricane Dennis
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Raster-based time series analysis

• Current approach
• elevation change between two surveys
• volume change for sections (limited spatial
  variability)
• shoreline change
• spatially averaged shoreline erosion / accretion
• feature extraction and measurement of its
  horizontal migration and vertical change
• Spatial indicators derived from longer time
  series of data (10) that preserve the spatial
  detail captured by lidar data coastal terrain
  evolution based on per grid cell statistics using

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Spatial coastal change indicators

• New, spatial indicators representing coastal
  terrain evolution based on per grid cell
  statistics using r.series
• core surface below which elevation never
  decreased
• outer envelope above which elevation never
  increased (core is 67 the envelope volume)?
• standard deviation map shows areas with most
  elevation change in red
• Mitasova, Overton, Recalde, Bernstein, Freeman,
  2009, JCR 25(2)
• Wegmann and Clements, 2004, GRASS Newsletter

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Spatial and temporal indicators
a) time at minimum and b) time at maximum
maps represent timeyear when the grid cell was
at its minimum and its maximum elevation c)
regression slope maps show spatial pattern of
elevation trends, inset transparency added as
function of correlation coefficient, white areas
have r2lt0.3
increase decrease
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Coastal change indicators structures
Beach and dunes near Oregon Inlet

• In areas with homes core surface and outer
  envelope can be used to map new and old homes and
  their relation to core
• Some new homes built with exception from
  regulations do not have any core surface

Beach and homes in Rodanthe
0 100m
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Coastal change indicators structures

• In areas with homes core surface and outer
  envelope can be used to map new and old homes and
  their relation to core
• Some new homes built with exception from
  regulations do not have any core surface

Beach and homes in Rodanthe
0 100m
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Shoreline dynamics band
the distance between the core and outer
envelope surface shorelines ranges between
30-64m (30-90m for the entire study area) and
defines an area within which the shoreline has
been migrating over the years - 1997-2008
0 m contours outer envelope core surface 2008
surface (white)
0 m contours outer envelope core surface 2008
surface (white)
accretion erosion 2005-2008
1997 - yellow 1998 - orange 1999 - red 2001 -
magenta 2003 - violet (june) 2005 - blue 2008 -
white
in the area with no recovery, 2008 shoreline
overlaps with core shoreline - thin white line
over wider black line
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Elevation and shoreline difference
accretion erosion 2005-2008
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Surface evolution as volume
Shorelines
1997 1998 1999 2001 2003 2004
New approach Evolution of terrain surface is
represented as a volume with time used for 3rd
dimension. Evolution of a contour is then
represented as an isosurface.
0 200m
Isosurface representing shorelines 1997-2005
2005 2003 2001 1999 1997
Time year
isosurface
Ym
Xm
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Surface evolution as volume
stable back-shore beach
z1.5m
2005 2003 2001 1999 1997
dynamic shoreline
z0.3m
sand disposal
2005 2003 2001 1999 1997
Time year
Ym
0 200m
Xm
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Surface evolution as volume
highly dynamic ridges of foredunes
stable dune peaks
z7.6m
2005 2003 2001 1999 1997
z4.6m
2005 dune rebuilt 2003 dune overwash
2005 2003 2001 1999 1997
Time year
Ym
0 200m
Xm
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Surface evolution as volume
New approach Evolution of terrain surface is
represented as a volume with time used for 3rd
dimension. Evolution of a contour is then
represented as an isosurface. The approach
reveals often neglected high dynamics of
foredunes (zgt 4m)? and stability of backshore
beach (z1.5m)?
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Current projects

Current projects Sea grant, ARO staff
research and mathematics, GIST
class applied improving methods for topobathy -
class adding structures and vegetation to
multitemporal analysis - SeaGrant and ARO
basic mutivariate function analysis of terrain
evolution using time as 3rd or 4th variable -
gradient fields, critical points, acceleration
-stable-decceleration through hypersurface
curvatures ARO
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Lidar and terrain change

Lidar mapping at high spatial and temporal
resolutions allows us to study terrain as a
dynamic phenomenon. Challenges massive data
sets and rapidly evolving technology increasing
point densities