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Introduction
Accurate topographic information is essential for predicting
storm surge damage and flooding. This data is an important component
for the construction of evacuation maps based on hurricane storm
surge models such as the NOAA SLOSH model. In many areas, the best
existing topographic data consists of USGS contour maps produced
at 5 to 10 foot contour intervals. The absolute vertical accuracy
of these maps is also limited due to poor sampling and the analog
techniques used to produce the contours. In low relief areas such
as South Florida, this poor accuracy and resolution can result in
large errors in the determination of flooded areas due to storms.
As a result, the detail of most flood hazard maps is limited. Recent
advances in microcomputers, laser ranging technology, and GPS positioning
have resulted in the development of this compact and lightweight
Airborne Laser Terrain Mapping (ALTM) system which can inexpensively
acquire topographic data of unprecedented detail and accuracy. We
present preliminary results of an ALTM survey of eastern Broward
County, FL and the application of this data for the revision of storm
surge evacuation maps.
Figure 1. Color shaded relief map of the Florida Peninsula. Data
source: USGS 3" DEMs. Study area is shown by green box. Data Acquisition
LIDAR data was collected in eastern Broward County over 4 days
in December, 1999 to March. 2000. Over 240 km2 of the county
were surveyed with an average point spacing of 2.5 m. The survey
consisted of 25 N-S trending 600-m-wide swaths spaced every 500
m and 2 E-W trending cross lines (Figure 2). Data was measured
from elevations ranging from 700 - 1200 m. Over 140 million irregularly
spaced ground surface elevations were measured (Figure 3). Ground
control was provided by 2 Ashtech Z-12 GPS receivers positioned
over National Geodetic Survey (NGS) benchmarks.
Figure 2. Index map of eastern Broward County showing locations
of individual data swaths Figure
3. Schematic
diagram showing data acquisition parameters used for Broward
County ALTM survey. Data
Processing
After each flight, LIDAR and GPS data are downloaded to a computer
and processed by proprietary Optech software to produce UTM X,Y
coordinates and ellipsoidal heights of each laser return. Positional
accuracy was improved by calculating a precise aircraft trajectory
using the KARS software provided by Dr. Gerry Mader of NGS (Mader,
1986; 1992). Elevations were converted from GPS ellipsoidal heights
to NAVD88 orthometric heights with the NGS GEOID99 model. Data
from overlapping swaths were checked for internal consistency,
combined and subdivided into over 300 1-km©˜ tiles. Each
tile was then gridded using nearest neighbor interpolation to produce
2m resolution DEMs (Figures 4 and 5).
Figure 4. Color shaded relief map of 2 m resolution DEM gridded
from point elevations in a 1 km©˜ tile. Over 500,000
irregularly spaced measurements were gridded to produce this
DEM. Location of the data shown in Figure 5 is shown by the
white box. Figure
5. Color shaded relief map
of DEM gridded from filtered point elevations for tile shown
in Figure 6. Note the different vertical scale which has been
expanded to emphasize the ground topography. Terrain Filtering
The ALTM system returns the elevation of the first reflective body
that is scanned beneath the flight path. Often, these returns
correspond to reflections from vegetation, vehicles, or buildings
rather than the "actual" ground surface. For flood
studies, additional filtering is required to remove this "ground
clutter". Ground clutter was removed with an iterative expanding
window threshold algorithm. First data outside a specified vertical
range was excluded. Each tile was then subdivided into a series
of 2 m square blocks and all points except the minimum elevation
were discarded. The blocks were then doubled in size and the
minimum elevation in each block was determined. Finally, all
points with elevations greater than a threshold above the minimum
were discarded. The process was repeated with the blocks doubling
in size until the block size was 128 m or no points were discarded
from the previous iteration. A 1:20 ratio of block width to elevation
threshold was used for each iteration. After filtering, data
for each tile was gridded into a 2 m resolution DEM using kriging
with a linear variogram model.
Figure 6. Color coded point elevations (in meters NAVD88)
of irregularly spaced ALTM data for a portion of a data tile
in Hollywood, FL. Horizontal coordinates are in UTM meters.
Figure 7. Color coded point elevations (in meters NAVD88)
of data shown in Figure 5. after terrain filtering. Storm Surge Models
A storm surge is the abnormal rise of water levels along a coastline
caused by wind and pressure forces of an approaching hurricane
or other intense storm. Storm surge heights can exceed 5 m with
inundation in low relief areas extending several 10s of kilometers
inland. The height of a storm surges at a given location depends
on several factors including hurricane size, intensity and forward
speed, the orientation of winds relative to the coast, coastline
shape, and near shore bathymetry. Computer models estimate storm
surge height based on numerical approximations to fluid equations
of motion and continuity equations. Data provided to these models
includes offshore bathymetry, onshore topography, and storm parameters
such as storm size, wind speed, wind direction, and atmospheric
pressure. Output from these models can then be used to determine
the areas inundated by a storm surge. In the U. S., the most
widely used storm surge model is the National Weather Service
SLOSH (sea, lake, and overland surges form hurricanes) model
(Jarvinen and Lawrence, 1985). The SLOSH model computes water
height at a network of grid points in a pie-shaped geographical
area known as a basin (Figure 8). The size of each grid cell
varies from 0.5 km near the center or pole of the basin to over
7 km at the outer boundaries of the basin. Typically, a basin
is oriented such that the highest density of points is over land
where surge heights are of greatest interests. Bathymetry or
topography relative to sea level is specified at each grid point.
The model can also incorporate sub-grid cellfeatures such as
barriers, levees, rivers, and channels. A series of overlapping
basins provide coverage for most of the Gulf and Atlantic coastlines.
In order to integrate the ALTM data with SLOSH, the 2 m resolution
DEM was subaveraged to 30 m resolution and converted to NGVD29
feet. This model was subtracted from the SHOSH storm surge heights
to produce a model of Storm surge flooding depth for a particular
westward moving Saffir-Simpson category 5 hurricane (Figure 9).
This procedure was also conducted with storm composites known
as MOMs (Figure 10).
Figure 8. Composite SLOSH storm surge heights for a westward
moving category 5 hurricane hitting the east coast of Florida
10 miles north of the Miami harbor entrance.
Figure 9. Estimated storm surge depth in eastern Broward
Co. for the SLOSH run shown in Figure 10.
Figure 10. Part A B C. Storm surge depths for Cat 1, 3,
and 5 MOMs. Output from a composite of numerous SLOSH runs
are
used to
define flood prone areas for evacuation planning. Typically,
surge height values from 200 - 300 hypothetical storms impacting
the basin at various locations and from various directions
are calculated. The output from these model runs are composited
to construct a map of maximum potential storm surge height
for a given Saffir-Simpson category. These maps are referred
to as MOMs (maximum of maximum) and the indicate the worst
case scenario for a given storm strength. References
Cuddy, M, Flying Higher, Working Faster, EOM, Tech notes, July,
1999
Jarvinen B. J. and C.J. Neumann, Am evaluation of the SLOSH storm
surge model, Bull. Amer. Meteor. Soc., 66, 1408-1411, 1985.
Gutelius, G, W.E. Carter, R.L. Shrestha, E. Medvedev, R. Gutierez,
and J.G. Gibeaut, Engineering Applications of Airborne Scanning
Lasers: Reports from the Field, PE&RS, The Journal of American
Society for Photogrammetry and Remote Sensing, Vol. LXIV, No. 4,
pp. 246-253, 1998.
Mader, G.L.,Dynamic Positioning Using GPS Carrier Phase Measurements,
Manuscripta Geodaetica, 11(4); 272-277, 1986.
Mader, G.L.; "Rapid Static and Kinematic Global Positioning
System Solutions Using the Ambiguity Function Technique," Journal
of Geophysical Research, 97, 3271-3283, 1992.
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