Journal of Coastal Research
24
6
1555–1564
West Palm Beach, Florida
November 2008
Developing Terrestrial-LIDAR-Based Digital Elevation Models for Monitoring Beach Nourishment Performance Lisa S. Pietro†, Michael A. O’Neal†*, and Jack A. Puleo‡ † University of Delaware Department of Geography 125 Academy Street Newark, DE 19716, U.S.A.
[email protected]
‡ Center for Applied Coastal Research Department of Civil and Environmental Engineering University of Delaware Newark, DE 19716, U.S.A.
ABSTRACT PIETRO, L.S.; O’NEAL, M.A., and PULEO, J.A., 2008. Developing terrestrial-LIDAR-based digital elevation models for monitoring beach nourishment performance. Journal of Coastal Research, 24(6), 1555–1564. West Palm Beach (Florida), ISSN 0749-0208. Since the completion of a 398,000 m3 nourishment project along 2 km of Rehoboth Beach, Delaware, in August 2005, the subaerial volume and area of the northern 25% of the beach has been monitored monthly through the use of terrestrial-based light detection and ranging (LIDAR) surveys. Traditionally, the Delaware Department of Natural Resources and Environmental Control and the U.S. Army Corps of Engineers use analyses of beach width from aerial imagery and volumes estimated from widely spaced profile surveys to assess nourishment performance and assist in determining renourishment quantities. However, these survey methods lack the spatial and temporal resolution needed for short-term management strategies. Recent efforts at monitoring Atlantic Coast beaches using airborne LIDAR show the potential of this technology for providing more detailed representations of beach volumetric change over time, but the operational costs still limit the frequency of surveys. Alternatively, our terrestrial LIDAR study allows for the development of models of subaerial beach topography with both high temporal and spatial resolution. Although geographically less extensive than airborne surveys, the digital elevation models from our data (1) allow for a better understanding of the range in variation in beach area and volume, especially that due to storm events, (2) provide more accurate volume estimates than traditional profile surveys by as much as 8%, and (3) indicate that the area and volume do not covary, limiting the usefulness of using aerial imagery in estimating volume. ADDITIONAL INDEX WORDS: LIDAR, beach nourishment, digital elevation models, Delaware.
INTRODUCTION In the summer of 2005, the Delaware Department of Natural Resources and Environmental Control (DNREC) emplaced approximately 398,000 m3 of sand along 2 km of Rehoboth Beach, Delaware, at a cost of $3.21/m3. This beach nourishment project was designed primarily to provide storm protection to local infrastructure with the added benefit of stimulating local commerce through increased recreational areas (e.g., PHILLIPS and JONES, 2006). Based on initial construction costs, storm-erosion modeling, historical shorelinechanges, and past experience with shore protection projects, DNREC and the U.S. Army Corps of Engineers (USACE) anticipate a 50-year life span for the Rehoboth Beach project allowing for 99,500 m3 of renourishment every 3 years (ASSISTANT SECRETARY OF THE ARMY, 1997). However, analyses of nourishment projects along U.S. shorelines indicate that sediments emplaced in nourishment projects are often eroded faster than projected by feasibility studies (DIXON and PILKEY, 1991; GARES, WANG, and WHITE, 2006; LEONARD, CLAYTON, and PILKEY, 1990; PILKEY and CLAYTON, 1989). Identifying key factors that affect the performance of a nourDOI: 10.2112/07-0904.1 received 27 June 2007; accepted in revision 30 October 2007. * Corresponding author.
ishment project is difficult and requires detailed observational data that are often unavailable at the spatial and temporal resolution preferred for management strategies. Traditionally, the state of Delaware and the USACE evaluate nourishment project performance based on volume losses interpolated from semiannual profile-surveys spaced more than 150 m apart (U.S. ARMY CORPS OF ENGINEERS, 2004). A video system spanning 6 km of Delaware coastline centered at Rehoboth Beach was implemented to complement ground surveys by evaluating shoreline changes with hourly temporal resolution (PEARRE et al., unpubl. data). However, the video system was implemented for two-dimensional planform analyses and is not designed to yield volumetric measurements. Because the short-term nourishment performance is difficult to assess with geometrically, spatially, or temporally limited data sets, surveys that can overcome these limitations are preferred. In recent years, digital elevation models (DEM) developed from airborne light detection and ranging (LIDAR) systems have been used to assess volumetric changes to beaches along the Atlantic coast (e.g., GARES, WANG, and WHITE, 2006; WHITE and WANG, 2003; WOOLARD and COLBY, 2002). Although the geographic extent and detail that can be obtained from airborne LIDAR data sets are much improved over profile surveys, collection efforts are typically infrequent and ex-
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pensive. Analyses based on widely spaced beach profiles or airborne LIDAR data collected a few times each year may be useful for long-term trends in beach volume or area. However, they fail to portray accurately the dynamic changes in beach volume resulting from more short-term forcing (i.e., storms, eolian processes, and seasonal changes in wave climate). These short-term trends are important if making crucial decisions about the timing and volume of renourishment. A high-resolution alternative to airborne LIDAR is terrestrial-based LIDAR. These systems operate from survey tripods and produce large point clouds of the surrounding topography, typically within the range of a few hundred meters of the instrument. Although the geographic extent of the individual data sets that can be collected over a few days is much smaller than can be obtained from airborne platforms, spatially accurate surveys of large areas can be obtained by combining data from adjacent stations. Moreover, this method is less expensive to perform on a periodic basis, providing a feasible mechanism for developing time-series data sets. In this study, a terrestrial LIDAR system was used to collect topographic data along a 500- ⫻ 70-m stretch of Rehoboth Beach, Delaware, on a monthly basis between January 2006 and April 2007. The objectives of this study are as follows: (1) to investigate the utility of using a terrestrial LIDAR system for collecting a time series of high-resolution topographic data, (2) to identify an efficient process for reducing these data into DEMs, (3) to use these DEMs to analyze temporal and spatial patterns of deposition and erosion, and (4) to assess area and volume changes in the context of management strategies.
STUDY AREA The study area is a 500 m long, straight, shore-parallel stretch of northern Rehoboth Beach (Figure 1). This area represents the northern 25% of the 2 km length of Rehoboth Beach nourished in 2005. Of the 398,000 m3 emplaced on Rehoboth Beach in 2005, the portion of sediment added to the study area was approximately 95,000 m3. This fill volume of 48 m3 per meter of shoreline increased the overall subaerial beach volume to no less than 119,000 m3 and increased the average beach width to 90 m (unpublished engineering plans, USACE). This design allowed for an 82 m wide berm and back beach, and an 8 m wide dune with a crest height of 4.3 m. The dune areas were separated from recreational areas by fences, and grasses were added to the dunes for stabilization (Figure 2). The study area contains three littoral barriers: a storm drain located at Maryland Avenue, a storm drain between Virginia Avenue and Grenoble Place, and a rock groin extending from Lake Avenue (Figure 1). The dune north of the rock groin is restricted by Lake Avenue, which lies in the dune’s natural area of development. A recreational boardwalk aligned with commercial properties extends from the landward side of the dunes along the majority of the study area. Rehoboth Beach is part of a headland shoreline. Tides are semidiurnal with a mean range of 1.2 m. Waves approach the coast with greatest frequency from east and southeast directions resulting in a regional net littoral drift to the north. Estimates of the net alongshore transport rate and long-term
erosion rates for the entirety of Rehoboth Beach are 258.2 m3/y and 16,065 m3/y, respectively (ASSISTANT SECRETARY OF THE ARMY, 1997). The prevailing wind direction is offshore out of the northwest and southwest. Northwesterly winds are more common during the winter months, southwesterly during the summer, and variable directions during the spring and fall. Winds blowing from northeast and southeast have the greatest influence on the direction of storm attack. Highest velocity winds are associated with tropical storms and hurricanes, and extratropical storms (nor’easters).
FIELD METHODS The full extent of the study area was surveyed using a Trimble GS200 LIDAR system each month between January 2006 and April 2007, with the exception of a failed survey in August 2006. The GS200 calculates the distance to surfaces in the survey domain by measuring the time of flight of emitted pulses of green light (532 nm) with a factory-tested accuracy of ⫾1.3 mm at a distance of 100 m. The distance measurements are coupled with data regarding the azimuth and zenith of the emitted pulse to place each point in a local Cartesian coordinate system that originates at the instrument. Each data point measured in the survey domain represents the three-dimensional coordinates of the first surface reflection along any vector; full waveform data are not returned by this instrument. Each LIDAR survey was completed by scanning the beach from approximately 9 stations spaced between 50 and 75 m apart along a north–south transect (Figure 3). This spacing was selected to ensure substantial overlap given the scanner’s maximum range of 375 m (note—375 m returns are difficult to achieve with the relatively large incidence angles between the instrument and the subtle beach topography). The overlap between adjacent stations also reduces any shadow effects that would occur in the lee of topographic high points. To facilitate the transfer of equipment between survey stations, we mounted the LIDAR on the roof rack of a fourwheel drive vehicle. To ensure that vehicle motions did not interfere with the scanner precision, we used hydraulic jacks to stabilize the vehicle when parked at a survey station. The LIDAR used for this study rotates on its base (around a vertical axis) at user-prescribed increments. This present increment, and the radial pattern of data collection, influence the resolution of the surface model developed from the point clouds and determines the size of small features that can be detected in the far field areas. There are substantially greater numbers of observations closer to the instrument than in the far field area (i.e., tens of thousands vs. a single, or no observation). For this study we set the step angle to 0.001⬚ (e.g., 0.20-m spacing at a distance of 100 m). The laser footprint is 1 cm at 100 m, but the actual size of the footprint will vary as a function of the vertical scan angle, laser beam divergence, and the targeted topography. All LIDAR data were georeferenced to real-world coordinates using L1 global positioning systems (GPS). A stationary GPS unit was set up over a local benchmark, and four reference tripod-mounted GPS units, placed over survey targets,
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Figure 1. Maps displaying the state of Delaware (left) and the 2001 air photo of the location of the Rehoboth Beach study site (right).
were scanned at each station. All GPS data were processed for differential correction to obtain centimeter-accurate coordinates for the survey targets. Using the processed GPS data, targets from each station were individually georeferenced to avoid errors from point cloud matching procedures
that rely on targets common to each survey station (KERSTEN et al., 2004). All elevation data are based on the 2003 geoidal model, and coordinates were converted into the Universal Transverse Mercator (UTM) coordinate system, zone 18 north, relative to the North American Datum 1983.
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Figure 2. Photo looking south at the Rehoboth Beach study site showing the dune fences and grasses.
DATA REDUCTION The natural topography of the beach that we are attempting to model can be obscured by a variety of natural and artificial features (i.e., driftwood, trash cans, fences, umbrellas, debris, people, etc.) that should be removed, if possible, before analysis. Many features that are common throughout the study area, like dune fences, are difficult to remove manually because they blend into rougher parts of the topography. Manual filtering of obvious unwanted features is possible using a variety of software applications. However, the time involved in visually manipulating the large number of points typical of LIDAR surveys limits the usefulness of this approach. Therefore, a minimal amount of manual filtering was completed to remove large nonterrain features (e.g., buildings and vehicles) as an initial phase of data set reduction. A variety of automated data reduction techniques can be used to remove points from LIDAR data that do not represent the beach surface (SILVAN-CARDENAS and WANG, 2006; ZHANG et al., 2003). There is no single filtering method that is appropriate for all data sets, and researchers commonly apply a combination of several techniques to specific areas depending on the types of information to be omitted. The two
techniques used in this study are based on simple geometric characteristics of points in relation to their nearest neighbors by first separating the nonground features using slope–elevation relationships and then eliminating groups of points via multivariate statistical techniques (e.g., ROGGERO, 2001; VOSSELMAN, 2000). Geomorphic features within the study area are typically smooth and subtle, with a slope angle of less than 30⬚, representing the angle of repose for dry sand. Many points collected from nonground features (e.g., fences, beach umbrellas, people) exceed the elevation expected of the natural terrain. Therefore, after segmenting each data set into 1 m by 1 m subsets, unwanted points were first filtered by comparing the elevation of each point with the highest elevation expected for the natural terrain in each subset. Finally, remaining smaller nonground features were identified using a cluster analysis. With this technique, the Euclidean distances between all points are calculated within each 1 m by 1 m subset, and clusters are formed based on minimum distances between points. To maximize the potential for separating small objects protruding from the beach surface, we exaggerated the vertical coordinates by a factor of 3. Once all points are assigned to a cluster, the slope of a least-squares plane fit
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Figure 3. Depiction of the field survey stations with circles representing the extent of individual point clouds.
through each cluster is compared with the same angle-of-repose limitation used earlier and any clusters exceeding the threshold are removed from the data set. The statistically filtered data sets became the final bare-earth point files for this study area. Three 1 m by 1 m subsets of data, which included a dune fence, beach umbrella, and person, were selected from different monthly surveys to evaluate the performance of the filtering techniques (Figure 4). All nonground points were manually removed from a control version of each 1 m by 1 m subset for comparison. Then, the automated filtering techniques were applied to the original subsets and the average
Figure 4. Three subsets of the LIDAR data used to test the statistical filtering techniques: (A) a dune fence, (B) beach umbrella, and (C) a person. Gray points represent those automatically removed by filtering.
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Figure 6. Mean absolute deviation of all elevation points within each DEM grid cell from January 2006 through April 2007.
lation of cells that lack any data was completed using nearest neighbor averaging of the surrounding cells.
RESULTS
Figure 5. Digital elevation models of the Rehoboth Beach, Delaware, study area, based on the January 2006 through April 2007 terrestrial LIDAR surveys. Grid cells are 1 m2, and grayscale indicates elevations from 0 (black) to 6 m (white). The dashed line represents the boundary of the study area.
elevation was calculated from all remaining points. The elevation differences between the average elevation of the control subsets and the statistically filtered subsets are used as a measure of the success of the filtering techniques. DEMs developed in this study are based on a raster data model (e.g., WOOLARD and COLBY, 2002) because the characteristics of a grid facilitate measurement of area and elevation change and result in data comparable to other remotely sensed data available for the study area (i.e., digital orthophotos, airborne LIDAR, high-resolution satellite imagery, and the aforementioned video). Although we produce 1-m cell DEMs for this study, the large number of observations that can be collected using LIDAR allow for model development at larger scales. Each 1 m by 1 m cell of the DEMs is given the average value of all the elevation points within that cell after removing unwanted features (there are as many as 32,000 observations per cell). The zero-elevation grid cells, representing mean sea level, are depicted by manually creating the shoreline boundary using elevation points that were captured during backwash or during low tide. Interpo-
Each monthly survey yielded between 3 million and 8 million raw data points that were reduced to those representing only bare earth by applying the slope–elevation and multivariate statistical filtering techniques. The robustness of the techniques was underscored by the fact that three test subsets—a dune fence, a beach umbrella, and a person—yielded differences in average elevation of 0.5, 1, and 0.6 cm, respectively, when compared with the average elevation of the control subsets. The DEMs created by averaging the bare earth points within each 1 m by 1 m cell are presented in Figure 5. The cumulative percentages of mean absolute deviations (MAD) for the data used to develop each DEM are presented in Figure 6. The MAD values throughout the berm areas are very low, typically less than 2 cm, and higher MAD values are from topographically rough parts of the beach (i.e., sand waves in the dunes and walkways, vegetation in the dunes, tire tracks in the foredunes). For all 15 surveys, over 90% of the MAD values for each 1 m by 1 m cell are within 10 cm of the average value for that cell, emphasizing the appropriateness of the average elevation as the statistic for our DEMs. DEM-based calculations of the beach area for each monthly survey are presented in Table 1. The beach area fluctuated between 32,417 and 41,605 m2 over the study period with an average area of 36,300 m2. The maximum and minimum changes in beach area that occurred between any consecutive surveys were ⫹2409 m2 and ⫺6223 m2, respectively. The average beach width calculated for each of the 15 DEMs ranges from 65 to 83 m with a mean of 73 m and a standard deviation of 5 m. The April 2007 data indicate an average shoreline retreat of 19 m since the 2005 nourishment. Although this retreat is much greater than the 0.5 to 1 m/y rate estimated for this area (ASSISTANT SECRETARY OF THE ARMY, 1997), it is expected given that such nourishment construction usually results in a steep profile that is out of equilibrium so that cross-shore losses are expected. Volumes estimated using profile data extracted from the
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Table 1. Volume of sediment, area of subaerial beach above mean sea level, and the volumetric error calculated for each survey during the study (January 2006 through April 2007).
Date (m/d/y)
Volume (m3)
Area (m2)
Volumetric Error (⫾m3)
01/19/06 02/17/06 03/24/06 04/26/06 05/30/06 06/29/06 07/19/06 09/27/06 10/18/06 11/15/06 12/15/06 01/31/07 02/28/07 03/21/07 04/30/07
110,780 111,121 94,614 97,836 95,088 95,070 107,863 104,651 87,985 85,739 83,964 86,292 82,687 77,466 89,677
40,475 41,605 35,382 36,070 34,812 36,062 38,207 35,189 35,896 32,417 34,826 35,949 34,966 37,306 35,342
1214 1248 1061 1082 1044 1082 1146 1056 1077 973 1044 1078 1049 1119 1060
Figure 7. Subaerial volumes with associated error calculated using the full extent of the DEM (circle) and subaerial volumes calculated using the profile method (square).
DISCUSSION DEMs, based on DNREC/USACE survey locations, differ from those derived from all cells of the DEMs by as much as 8369 m3, which is 8% of the initial fill volume (Figure 7). Subaerial beach volumes calculated using the DEMs (Figure 5) range from 77,466 ⫾ 1119 m3 to 111,121 ⫾ 1248 m3 (Table 1). Errors for these data are based on the cumulative error of the LIDAR, the GPS, and subsequent georeferencing processes, and are ⱕ3 cm. The volume calculated from the January 2006 DEM at the beginning of this study was 110,780 ⫾ 1214 m3, with a width of 81 m. The plot of beach volumes over time indicates a nonuniform decreasing trend in beach volume from 110,780 ⫾ 1214 m3 to 89,677 ⫾ 1060 m3 for the entire study period (Figure 7). Raster data sets representing the difference in elevation for each cell between each consecutive survey were calculated and are presented in Figure 8. The largest one-way changes between consecutive surveys are to the dune at the northern end of the study area and around the storm drain in the middle of the study area. The dune erosion is likely due to the seaward extent of Lake Avenue causing the dune and berm to reside in close proximity during stormy conditions. The shoreline anomaly around the storm drain in January 2006 is the result of the construction of a coffer dam for an outflow pipe. The largest localized fluctuations in elevation, on the order of 2 m, are along the beach face and occur as a result of accretion and erosion in response to changing shoreline positions and intermittent buildup of a prominent berm. Smaller localized changes of less than 0.5 m occur within the dune complexes between consecutive surveys. The predominant volumetric changes between surveys are between the berm crest and the foredune (i.e., the largest geographic area of the study beach). Although elevation changes in the backbeach between surveys are generally less than 1 m, substantial erosion and deposition of subtle shore-parallel ridges and troughs, with as much as 2.5 m of relief, regularly affects this area. As an indicator of gross change, the sum of the absolute change in elevation for each cell calculated using the 15 data sets collected in this study is presented in Figure 9.
Our study shows that integrating terrestrial LIDAR surveying, GPS, statistical filtering, and surface modeling allows for the production of time series data sets of beach topography with high levels of spatial and temporal resolution. These data can be used to quantify short-term changes in area, volume, and geomorphology of a nourished beach with spatial and temporal advantages over traditional surveying techniques or more expensive high-resolution techniques. Although the extent of surveying for this study was limited to a length of 500 m, terrestrial LIDAR systems can be used to survey the full extent of similar projects of ⬃2 km in length within a period of a few days. Because of the variety of computationally efficient tools available for filtering LIDAR data, the large quantity of survey points collected can be easily manipulated to produce DEMs that accurately represent the beach topography. One of the primary advantages of the LIDAR-based DEMs is that they are derived from a substantially larger number of observations than can be obtained by a total station or any other manual survey techniques. This limits the impact of volume estimates from interpolation between widely spaced observations and/or profiles. The geomorphic changes apparent in our DEMs (Figure 5) and the derivative difference-inelevation data sets (Figure 8) illustrate the variation in geomorphic details that are excluded from traditional profiles. Because of these variations, volume calculations based on profile measurements underestimate the volume for 14 of the 15 surveys by as much as 8%. This miscalculation suggests a poorer performance of the nourishment project than actually occurs as quantified by the DEMs (Figure 7). Moreover, our monthly surveying approach shows that the typical biannual timing of profile surveys can lead to further underor overestimates in long-term trends by surveying during short-lived changes in beach area and volume. Although monitoring beach nourishment is a long-term observation, our technique and data allow for a better understanding of the short-term variability that is required for determining the appropriate renourishment amounts and intervals.
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Figure 8. Models of the elevation changes between consecutive surveys of the Rehoboth Beach study area based on the January 2006 through April 2007 terrestrial LIDAR surveys. Areas of maximum erosion are depicted in black grading to areas of maximum accretion in white.
Another advantage of our extensive time series data is that we can evaluate changes in volume and area in relation to storm activity and seasonal wave variability. Six major storms with mean daily wave heights ⬎4 m and mean daily wind speeds ⬎10 m/s affected the study area over the 16month study period (i.e., 2 tropical storms, 1 extratropical storm, 2 nor’easters, and 1 unnamed storm). There are three substantial volumetric decreases in the subaerial beach volume following the February 2006 nor’easter, tropical storm Ernesto in August 2006, and a nor’easter in February 2007 (net losses of 16,507, 16,666, and 5221 m3, respectively). The corresponding area changes for these storms are ⫺6223, ⫹707, and ⫹2340 m2, respectively. In the weeks following tropical storm Alberto and a subsequent storm during June 2006, and the nor’easter in April 2007, volumes were larger than their previous month’s surveys (gains of 12,793 and 12,211 m3). The corresponding area changes for these storms are ⫹2145 and ⫺1964 m2, respectively. This juxtaposed volume and area change is not typical (BROWDER and DEAN, 2000). However, the deposition in the subaerial beach between June and July 2006 and erosion between September and October 2006 are anticipated because
of transitions in seasonal wave climates (DEAN and DALRYMPLE, 2002). Although this paper is primarily concerned with the results of the techniques applied for measuring beach volume and area, a valuable insight gained from the data collected for this study, which may only be applicable for nourished systems, is that area is a poor proxy for volume. Although public opinion of nourished beach performance is usually associated with the dry beach width for recreational purposes, it is the estimate of volume that ultimately affects interpretations of project performance and plans for renourishment intervals. Many studies have relied on estimates of volumetric changes by assuming a linear relationship with planform area (MILLER and FLETCHER, 2003; NORCROSS, FLETCHER, and MERRIFIELD, 2002). However, the planform area to volume relationship based on the DEMs developed in this study (Figure 10) indicates these metrics do not covary (r2 value of 0.43), a result similar to that observed by GARES, WANG, and WHITE, (2006) on the North Carolina coast. These findings suggest that the use of shoreline positions from aerial imagery for interpreting nourishment performance may have limited value. The convergence of area and volume remaining after the
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Figure 10. Scatterplot of planform area vs. volume for the 15 surveys from January 2006 through April 2007.
2005 nourishment indicates the time scale required to attain an equilibrated profile likely occurred before our first survey in January 2006 (Table 1). Assuming changes along Rehoboth Beach were roughly uniform (except in the direct vicinity of the littoral barriers), the 29,000 m3 of material eroded from the study area extrapolates to 116,000 m3 eroded from the entirety of the subaerial portion of Rehoboth Beach as of April 2007. This net erosion has already surpassed the 98,000 m3 allocated for nourishment in 2008, a deficit that will likely increase given the trends in Figure 7. If limited to the prescribed renourishment interval and volume, the level of storm protection provided by the beach is uncertain. Because the methods and models, not the nourishment performance, are the primary focus of this paper, the data provided are only used to illustrate the importance of the variation in volume and area that can be observed with high temporal and spatial resolution. This study shows that utilizing a terrestrial-based LIDAR to collect topographic data, and the efficiency of reducing those data, is a feasible mechanism in observing the short-term variability of a beach system. We anticipate that such data will allow for improved estimates of the appropriate renourishment volume and interval required to maintain the level of storm protection of the original nourishment design.
ACKNOWLEDGMENTS The authors would like to thank Tony Pratt and the Delaware Department of Natural Resources and Environmental Control for supporting this research, and Dr. Claire O’Neal and Dr. Brian Hanson for their comments and suggestions for improving the manuscript.
LITERATURE CITED Figure 9. Sum of the absolute elevation changes over the study period (January 2006 through April 2007).
ASSISTANT SECRETARY OF THE ARMY, C.W. 1997. Beach Nourishment Project: Communication from the Acting Assistant Secretary (Civil Works). House document, 105th Congress, 1st session; pp. 105–144. The Department of the Army, transmitting a report on the storm damage reduction and shoreline protection project for Rehoboth Beach and Dewey Beach, Delaware, pursuant to section 101(b)(6) of the Water Resources Development Act of 1996. BROWDER, A.E. and DEAN, R.G., 2000. Monitoring and comparison to predictive models of the Perdido Key beach nourishment project, Florida, USA. Coastal Engineering, 39, 173–191.
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structure in the coastal zone: problems, consequences and management. Tourism Management, 27, 517–524. PILKEY, O.H. and CLAYTON, T.D., 1989. Summary of beach replenishment experience on United-States East Coast barrier islands. Journal of Coastal Research, 5, 147–159. ROGGERO, M., 2001. Airborne laser scanning: clustering in raw data. International Archives of Photogrammetry and Remote Sensing, XXXIV, 227–232. SILVAN-CARDENAS, J.L. and WANG, L., 2006. A multi-resolution approach for filtering LiDAR altimetry data. Isprs Journal of Photogrammetry and Remote Sensing, 61, 11–22. U.S. ARMY CORPS OF ENGINEERS, P.D., 2004. Beachfill, Delaware Coast from Cape Henlopen to Fenwick Island, Rehoboth Beach and Dewey Beach. DE.IFB W912BU-04-B-0014. VOSSELMAN, G., 2000. Slope based filtering of laser altimetry data. International Archives of Photogrammetry and Remote Sensing, XXXII, 153–159. WHITE, S.A. and WANG, Y., 2003. Utilizing DEMs derived from LIDAR data to analyze morphologic change in the North Carolina coastline. Remote Sensing of Environment, 85, 39–47. WOOLARD, J.W. and COLBY, J.D., 2002. Spatial characterization, resolution, and volumetric change of coastal dunes using airborne LIDAR: Cape Hatteras, North Carolina. Geomorphology, 48, 269– 287. ZHANG, K.Q.; CHEN, S.C.; WHITMAN, D.; SHYU, M.L.; YAN, J.H., and ZHANG, C.C., 2003. A progressive morphological filter for removing nonground measurements from airborne LIDAR data. IEEE Transactions on Geoscience and Remote Sensing, 41, 872–882.
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