An Airborne Laser Topographic Mapping Study of Eastern Broward County, Florida With Applications to Hurricane Storm Surge Hazard

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
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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.

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