Derivation of ground surface and vegetation in a coastal Florida wetland with airborne laser technology
By Ellen A. Raabe, Melanie S. Harris, Ramesh L. Shrestha, and William E. Carter


















Open-File Report 2008-1125
U.S. Department of the Interior
U.S. Geological Survey


U.S. Department of the Interior
DIRK KEMPTHORNE, Secretary
U.S. Geological Survey
Mark D. Myers, Director 
U.S. Geological Survey, Reston, Virginia 2008
Revised and reprinted: 2008
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Suggested citation:
Raabe, E.A., Harris, M.S., Shrestha, R.L., and Carter, W.E., 2008,  Derivation of ground surface and vegetation in a coastal Florida wetland with airborne laser technology, U.S. Geological Survey, Open-File Report 2008-1125, Saint Petersburg , FL, 41 p.
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Contents
Figures	iii
Photos	iv
Tables	iv
Abstract	1
Introduction	1
Study Area	2
Methods	5
Ground Control	5
ALSM Data Acquisition and Processing	6
Digital-Surface Model	7
Results	8
Bare-Earth DSM	8
Vegetation-Canopy DSM	16
Land-Cover Maps	21
Shoreline	26
Geomorphic-Profile Transects	27
Discussion	32
Acknowledgments	32
References Cited	33
Appendix I	35

Figures
Figure 1. Locations of airborne laser-altimetry flights at Lukens Creek, Bird Creek, and Waccasassa, August, 17, 1999.	3
Figure 2. Example data cloud and break points for functional landscape units.	8
Figure 3a, b. Differences between field-surveyed and surface-model elevations using 2-, 5-, and 10-m filters for a) marsh at Lukens Creek, b) forest and thick marsh.	9
Figure 4. (a) Orthophotoquad, and (b) ground-surface DSM at Lukens Creek.	10
Figure 5. (a) Orthophotoquad, and (b) ground-surface DSM at Bird Creek and Waccasassa.	10
Figure 6. Bare-earth digital-surface model at Lukens Creek derived from ALSM.	12
Figure 7. Bare-earth digital-surface model at Bird Creek derived from ALSM.	13
Figure 8. Bare-earth digital-surface model at Waccasassa River derived from ALSM.	14
Figure 9. Canopy-height digital-surface model at Lukens Creek derived from ALSM.	17
Figure 10. Canopy-height digital-surface model at Bird Creek derived from ALSM.	18
Figure 11. Canopy-height digital-surface model at Waccasassa River derived from ALSM.	19
Figure 12. Lukens Creek land-cover categories derived from ALSM.	22
Figure 13. Bird Creek land-cover categories derived from ALSM.	23
Figure 14. Waccasassa River land-cover categories derived from ALSM.	24
Figure 15. ALSM-derived shoreline compared to 1:24,000 hydrography (USGS) shoreline at Bird Creek.	26
Figure 16. Transect A-A1 across Lukens Creek showing profile derived from bare-earth and canopy-surface models..	28
Figure 17. Transect B-B1 showing profile from Lukens Creek across tidal marsh and forest remnants.	29
Figure 18. Transect C-C1 showing profile from upper reach of Lukens Creek to forest and relict dunes.	30
Figure 19. Transect D-D1 showing profile from Waccasassa River to a meander on Bird Creek.	31
 
Photos
1. Low marsh at Site 1, Lukens Creek.	4
2. Black needlerush at Site 2, Bird Creek	4
3. Coastal forest at Site 3, Waccasassa River	5
4. Differential GPS roving antenna in high marsh at Lukens Creek	6
5. Roving antenna and geodimeter in black-needlerush marsh	6
6. Transition from tidal marsh to forest at Bird Creek	11
7. Morbid forest with standing dead Sabal palms at Lukens Creek	11
8. Bird Creek levee with scrub and coastal-forest vegetation	15
9. Sinkhole and sawgrass at Lukens Creek	15
10. Thick sawgrass along tidal-creek tributary to Waccasassa River	16
11. Tall, thick-canopied hydric hammock in summer	20
12. Open-canopy oak and palmetto scrub in sand hills	20
13. Recovery of sand-hill oak scrub following fire management	21
14. Scrubby flatwood along open sand road at Trail 8	25
15. Game trails in high marsh at Lukens Creek	25

Tables
Table 1. Redundancy in DGPS ground survey.	5
Table 2. Data collection and technical specifications.	7
Table 3. Orthometric heights (O.H.) of ground control points (GCP) and base stations.	35


Derivation of ground surface and vegetation in a coastal Florida wetland with airborne laser technology
By Ellen A. Raabe, Melanie S. Harris, Ramesh L. Shrestha, and William E. Carter
Abstract 
The geomorphology and vegetation of marsh-dominated coastal lowlands were mapped from airborne laser data points collected on the Gulf Coast of Florida near Cedar Key. Surface models were developed using low- and high-point filters to separate ground-surface and vegetation-canopy intercepts. In a non-automated process, the landscape was partitioned into functional landscape units to manage the modeling of key landscape features in discrete processing steps.  The final digital ground surface-elevation model offers a faithful representation of topographic relief beneath canopies of tidal marsh and coastal forest. Bare-earth models approximate field-surveyed heights by + 0.17 m in the open marsh and + 0.22 m under thick marsh or forest canopy. The laser-derived digital surface models effectively delineate surface features of relatively inaccessible coastal habitats with a geographic coverage and vertical detail previously unavailable.
Coastal topographic details include tidal-creek tributaries, levees, modest topographic undulations in the intertidal zone, karst features, silviculture, and relict sand dunes under coastal-forest canopy.  A combination of laser-derived ground-surface and canopy-height models and intensity values provided additional mapping capabilities to differentiate between tidal-marsh zones and forest types such as mesic flatwood, hydric hammock, and oak scrub. Additional derived products include fine-scale shoreline and topographic profiles. The derived products demonstrate the capability to identify areas of concern to resource managers and unique components of the coastal system from laser altimetry.
Because the very nature of a wetland system presents difficulties for access and data collection, airborne coverage from remote sensors has become an accepted alternative for monitoring wetland regions.  Data acquisition with airborne laser represents a viable option for mapping coastal topography and for evaluating habitats and coastal change on marsh-dominated coasts. Such datasets can be instrumental in effective coastal-resource management.
Introduction
Airborne laser-swath mapping (ALSM) systems collect range data in the form of timed pulse returns from an airborne laser.  The two-way travel time (TWTT) of the laser pulse represents an active measurement of the distance from the ground surface to the sensor (Fowler et al., 2001).  The laser-ranging vectors can be processed with information from Differential Global Positioning System (DGPS) and aircraft position to derive three-dimensional coordinates (x, y, z values) of each ground position (Shrestha et al., 1999). Objects on the Earth surface, such as buildings and trees, may reflect the laser pulse before it reaches the Earth surface (Kraus and Pfeifer, 1998).  A series of post-processing steps is therefore required to extract a bare-earth digital-surface model (DSM) or vegetation characteristics from the cloud of points identified by the laser returns (Axelsson, 1999; Blair et al., 1999; Vosselman, 2000).  The acquisition of distance measurements with an airborne laser altimeter will be referred to as laser altimetry in this report. It is also known as Light Detection and Ranging (LIDAR).
Since the very nature of a wetland presents difficulties for access, navigation, and data collection, airborne coverage from remote sensors has become a widely accepted alternative for monitoring wetland regions.  Extensive work has been conducted in wetlands using aerial photography, satellite imagery, and airborne sensors, but the earlier body of work focuses on classification and change detection using spectral reflectance of the vegetation and land surface (Lee and Lunetta, 1995; Raabe and Stumpf, 1997).  An essential but missing component is an accurate representation of the surface topography that dictates submergence, exposure, salinity, and habitats in coastal lowlands (Lyon and McCarthy, 1995; Mitsch and Gosselink, 2000).
Wetlands are responsive to minor differences in surface-drainage network, flooding regime, topography, and sediment movement (Stoddard et al., 1989; Montague and Odum, 1997; Day et al., 1998).  Currently available digital elevation along the marsh-dominated Gulf Coast of Florida has a horizontal resolution of 30 m and vertical resolution of 1.5 m, or 5 ft (USGS, 2007). This level of topographic detail is inadequate for understanding tidal processes in a coastal region, where elevation differences of centimeters to decimeters correspond to distinct tidal-flood zones and habitats (Clewell, 1997; Stumpf and Haines, 1998; Williams et al., 1999). Morphological characteristics for hydrodynamic modeling have been derived from airborne laser data (Gomes Pereira and Wicherson, 1999; Sallenger et al., 2003), but Baltsavias (1999) argued that the geomorphology derived from most laser-altimetry processing algorithms is 'non-intelligent' and eliminates important topographic features.
Extraction of the ground surface to + 0.10 m under vegetation canopy has been achieved in some studies but required extensive mathematical modeling of the surface (Kraus and Pfeifer, 1998; Ackerman, 1999). A 10-m grid has been used to determine the ground surface under a tree canopy but without field verification (Means et al., 2000). The typical vegetation cover of tidal marshes of Florida's Gulf Coast is 0.3 to 1.4 m tall (Clewell, 1997).  Earlier laser-altimetry data on this marsh coast presented vertical errors on the order of + 0.40 m, attributable to laser-pulse detection errors and thick vegetation (Carter et al., 1999).  Test flights with improved calibration methods over a bare road surface produced a vertical accuracy of + 0.10 m or better (Shrestha et al., 1999).  
Knowing the instrument accuracy that was achievable over a non-vegetated surface, a method was developed to derive a representation of ground surface in the coastal marsh and forest with more detail than the currently available digital models (Iavarone, 2002). In this non-automated approach, the coastal region was partitioned into unique landscape elements, and each element was processed separately. Low- and high-point filters were used to extract ground-intercept and vegetation-intercept data points for bare-earth and vegetation-canopy surface models. Filter search space was adjusted to determine the optimal balance between generalizing elevation under canopy and preservation of topographic detail. We compared ground-surface DSM derived from 2-, 5-, and 10-m filters to field-collected orthometric heights (O.H.) NAVD88.
This report covers several objectives:
* to separate ground-surface intercepts from vegetation intercepts for each landscape element,
* to model the ground surface from laser-derived O.H. to within + 0.2 m of ground-control O.H. with a horizontal resolution of 20 m or less,
* to model the vegetation canopy across marsh and coastal forest,
* to derive profiles, shoreline, habitat, and land-cover parameters.
Study Area
Tidal marshes stretch virtually unbroken along Florida's Big Bend coast (Fig. 1).  The coastal landscape is a mosaic of tidal creeks, tidal marsh, elevated coastal hammocks, and coastal forest inland of the marsh (Raabe and Stumpf, 1997). Underlying karst limestone is near surface or exposed, and the Floridan aquifer flows through seeps and springs into the tidal marshes (Rupert and Arthur, 1997; Raabe and Bialkowska-Jelinska, 2007). Relict sand deposits provide occasional relief in the coastal lowlands, and surface features reflect underlying limestone-fracture patterns at some locations (Hine et al., 1988; Vernon, 1951). The tide range is 1 m, and local topographic gradients are 1:1000 or less (Clewell, 1997; Raabe and Stumpf, 1997).  Narrow elevation ranges of less than 0.5 m separate tidal submerged, emergent, and upland habitats (Clewell, 1997; Williams et al., 1999). Coastal-forest morbidity and expansion of the tidal marsh inland have been attributed to the relatively flat terrain and recent sea-level rise on this coast (Raabe et al., 2004; Stumpf and Haines, 1998; Williams et al., 1999).


Figure 1. Locations of airborne laser-altimetry flights at Lukens Creek, Bird Creek, and Waccasassa, August, 17, 1999.
Airborne laser flights were undertaken in Levy County, Florida, near Cedar Key and Waccasassa River (Fig. 1). Flight paths included Lukens Creek (1), Bird Creek (2) and Waccasassa (3). Site 1 was selected for shallow tidal creeks, extensive low marsh, a recent burn, and relict sand dunes in the adjoining uplands (Photo 1). Site 2 was selected as representative of a deep, spring-fed tidal creek, near-surface limestone, and a thick cover of the high marsh, black needlerush, Juncus romerianus (Photo 2). Site 3 was selected to cover the transition zone between tidal marsh and coastal forest along the Waccasassa River (Photo 3). The sites lie within the Cedar Key Scrub State Reserve and the Waccasassa Bay State Preserve and were accessed via boat, airboat, and dirt road to obtain field-based ground elevations and reconnaissance.


Photo 1. Low marsh at Site 1, Lukens Creek.


Photo 2. Black needlerush at Site 2, Bird Creek.

Photo 3. Coastal forest at Site 3, Waccasassa River.
Methods
Ground Control
Field surveys with DGPS and geodimeter were conducted in the salt marsh and adjoining forest to collect ground-control elevation measurements. Sites were selected to represent elevations from tidal wetlands to elevated uplands and from sparsely vegetated to thick-canopied habitats. DGPS data were collected with a roving kinematic antenna and a stationary base station linked to local vertical control (Raabe et al., 1996). The DGPS antennas were mounted on 2-m poles for unobstructed data acquisition above marsh vegetation (Photo 4). The geodimeter was employed to survey a closed-level loop from DGPS ground control in the open marsh to ground-control sites in the closed-canopy forest (Photo 5). Eighty-five horizontal, x, y-value (UTM 17R NAD83) and vertical, z-value (NAVD88) ground measurements were collected. Four positions were reoccupied for redundancy (Table 1). Position 001 was re-occupied twice. Overall DGPS survey vertical accuracy was 0.008 + 0.005 m. Surveyed field elevations ranged from 0.28 to 1.7 m NAVD88. Vertical ground control was used to evaluate and to select an effective method for deriving a ground-surface model in a vegetated environment from laser altimetry (Appendix I).
Table 1. Redundancy in DGPS ground survey.
Position
Northing Difference (m)
Easting Difference (m)
Vertical Difference (m)
001
0.000
 0.000
0.0170
012
0.002
 0.111
0.0010
023
0.097
-0.020
0.0130
037
0.000
 0.001
0.0330
Mean/Std
0.02/0.04
 0.018/0.05
0.019/0.014



Photo 4 Differential GPS roving antenna in high marsh at Lukens Creek.


Photo 5. Roving antenna and geodimeter in black-needlerush marsh.
ALSM Data Acquisition and Processing
ALSM data were collected at three pre-specified locations on August 17, 1999 (Fig. 1).  Tide range for the day was -0.312 to 0.550 m, and the timing of the flights coincided with the lowest tides of the day in an effort to capture the details of the tidal-marsh-drainage network (Table 2). First and last return with intensity were recorded for each pulse. Base stations, part of a previously established DGPS vertical network (Raabe et al., 1996), were occupied near Cedar Key and Waccasassa River. Accuracy, precision, and calibration of laser-instrument measurements are discussed by Shrestha et al. (1999).
Laser-altimetry data were processed with proprietary software from Operational Technologies Corporation (Carter et al., 1999). Horizontal coordinates (x,y) were converted to Universal Transverse Mercator (UTM) 17R NAD83. Vector lengths were converted to elevation (z values) as NAVD88 orthometric heights (O.H.). Intensities (I) for each laser pulse were recorded and included in the dataset.
Table 2. Data collection and technical specifications.
Data Collection
Technical Specifications
ALSM Specifications
August 17, 1999
ALTM Model 1210, Optech Inc.
10kHz pulse repetition rate
11:30 am to 2:10 pm
Mounted on Cessna 337
12 Hz scan rate
Lukens Creek (6 km2)
Ashtech GPS and Aplanix INS
+/-19 degree scan angle
Bird Creek (4 km2)
Class IV Laser
500 m flying altitude
Waccasassa (4.5 km2)
1064 nm Laser wavelength
60 m/s average speed over ground
Tide level -0.312 to -0.024 m MSL
10,000 pulse per second
344 m swath width; 65% overlap
2 DGPS Base Stations
Single return & intensity
30 cm diameter footprint
Digital-Surface Model
A bare-earth Digital-Surface Model (DSM) was developed in phases. Flight paths were pieced together to create a mosaic for each site. The team developed an interactive filter program to extract lowest or highest points (z values) from each mosaic with adjustable parameters specified by the analyst. Parameters included filter cell size, number of lowest or highest points per cell, and the option of defining an elevation minimum and/or maximum. Definition of a lower and upper limit for each pass allowed the analyst to develop layers representing functionally different portions of the landscape and to separate ground intercepts from vegetation intercepts.
The choice of planar-grid spacing from fine (1 m) to coarse (20 m) in post-processing was fundamental to retaining detail where possible while modeling actual ground surface under vegetation canopy. Where true ground returns were closely spaced, fine-cell spacing could be used to model the ground surface without interference from vegetation canopy. Where suitable ground returns were sparsely distributed, a coarse-cell spacing optimized extraction of true ground returns and minimized interference from vegetation intercepts. By partitioning the landscape into functional units, the analyst could retain details of topographic features without modeling vegetation artifacts under thick canopy.
An initial surface was created with ordinary kriging to interpolate low-point-filter (LPF) results to a 1-m grid. Preliminary break points for landscape elements were identified in the z-value histogram and corroborated with field measurements of O.H. and vegetation height from previous elevation surveys (Raabe et al., 1996; Raabe et al., 2000; Williams et al., 1999).
Break points for three zones were established, and a mask was created for each layer. Data points below -0.5 m were determined to be in error and were eliminated by setting -0.5 as the minimum elevation. Z values between -0.5 and 0.2 m represent the un-vegetated and submerged portions of tidal creeks, tributaries, and water bodies. The first mask was created to partition this tidal-drainage portion of the landscape.  The initial 1-m DSM under this mask was retained to preserve maximum detail of tidal-creek morphology.
The second mask was derived from a 2-m high-point-filter (HPF) where z values exceeded 1.9 m. This mask separated elevated ground and thickly forested areas from low-elevation features, marsh, and single-storied scrub habitat.  The final mask was created to include landscape elements with elevations greater than 0.2 m and excluded from the 1.9-m HPF partition.  This third partition included tidal marsh, grassland, and scrubland along the coast. An example of a data cloud and the three landscape partitions are shown in Figure 2.


Figure 2. Example data cloud and break points for functional landscape units.
Subsequent surface models for each zone were derived from LPF series and evaluated against field-surveyed ground control. Parameters were varied one at a time to facilitate comparisons between interpolated surfaces and ground-control O.H.  Filter cell size was varied sequentially to isolate laser-pulse returns that fully penetrated the vegetation, while maintaining as much detail of the topography as possible. Low-point filters of 2, 5, and 10 m were applied iteratively to each set of data points. Only one point per cell was selected for each 2-m pass. One, three, and five points per cell were selected and averaged for each 5- and 10-m pass. Local topographic charts show no elevation in the area exceeded 4.6 m (15 ft). A corresponding ceiling of 5 m was set in the LPF extraction process.
Results of each iteration of the LPF were interpolated to a 2-m grid using point kriging and a search ellipse of 15 (2 and 5 m) or 30 m (10 m). A surface was created by interpolating between the data points and by re-combining the layers to create a single elevation model. A comparison was made between modeled z values and corresponding field-survey elevations. The absolute difference between each set of points was calculated and summary statistics on the differences were obtained.
Results
Bare-earth and canopy-height models, land-cover categories, shoreline, and topographic profiles were derived from the laser-altimetry data. The development and selection of a bare-earth or ground-surface DSM was based on a "best-fit" approach to minimize differences between modeled z values and field-survey O.H. The data were further explored for the potential of identifying land-cover categories, geomorphology from profiles, and shoreline.
Bare-Earth DSM
Z values in the laser-acquired ground-surface DSM were compared with field-collected O.H. to assess correspondence of the DSM to ground-control elevations (Fig. 3). The 2-m LPF provides the "best-fit" model for the marsh ground surface at Lukens Creek with a correspondence of 0.11 + 0.06 m (Fig. 3a). This filter resolution retains considerable detail of the marsh surface with little interference from vegetation intercepts. The variability in standard deviation is attributable to three factors: 1) the variability of true surface elevation within a 2-m plot, 2) the limited probability that a field-surveyed point corresponds precisely to the laser-ground intercept, and 3) interpolation and model artifacts.
In thickly vegetated areas, the coarser 10-m filter achieved a 0.14 + 0.08-m correspondence with field-surveyed O.H. (Fig. 3b). The 10-m filter provided the "best-fit" ground-surface model under forest canopy at all sites, and thick marsh grasses at Bird Creek and Waccasassa. LPF extractions at 2 m and 5 m retained too many vegetation intercepts to represent the ground surface accurately. The DSM from the 10-m LPF retains the general topographic elements of the landscape. Although it lacks the detail obtained in the marsh at Lukens Creek, the model is an improvement over existing surface models (USGS, 2007).


Figure 3a, b. Differences between field-surveyed and surface-model elevations using 2-, 5-, and 10-m filters for a) marsh at Lukens Creek, b) forest and thick marsh.
Final model results were based on filters using one data point per filter cell. Filtered points were interpolated and combined in a 2-m grid to create a final DSM. Three grids, tidal creeks, marsh, and forest, were combined into one DSM for each area, showing details of tidal creeks, marsh-surface features, and the topography beneath the forest (Figs. 4, 5).


Figure 4. (a) Orthophotoquad, and (b) ground-surface DSM at Lukens Creek.

Figure 5. (a) Orthophotoquad, and (b) ground-surface DSM at Bird Creek and Waccasassa.
Color-coded versions of ground-surface DSM are shown in Figures 6, 7, and 8. The majority of tidal marsh occurs at elevations of 0.2 to 0.6 m. The transition from tidal marsh to forest begins at approximately 0.6 to 0.7 m (Photo 6). Morbid forested areas show as individual spikes scattered across the interior tidal marsh in the surface model (Fig. 6 and Photo 7). Where trees are still alive, soil and root mounds, approximately 0.5 m high, contrast with the surrounding lowered land surface (Raabe et al., 2007; Raabe et al., 2004). Forest floor below ~0.9 m indicates low-elevation swamp areas susceptible to flooding and storm-surge influence. Relict sand dunes at Lukens Creek occupy the 1.7 to 4.6-m range (Fig. 6). Elevations in the Bird Creek and Waccasassa area do not exceed 1.6 m (Figs. 7, 8). Other important features include high levees along Bird Creek and Waccasassa River (Photo 8), silviculture activities, karst-controlled tidal creeks, and sinkholes (Photo 9). An August acquisition date resulted in intercepts with thick canopy forest and interfered with ground intercepts. Artifacts of the LPF process and intercepts with thick forest canopies are indicated in the forested areas as no data. 


Photo 6. Transition from tidal marsh to forest at Bird Creek.











Photo 7. Morbid forest with standing dead Sabal palms at Lukens Creek.



Figure 6. Bare-earth digital-surface model at Lukens Creek derived from ALSM.


Figure 7. Bare-earth digital-surface model at Bird Creek derived from ALSM.


Figure 8. Bare-earth digital-surface model at Waccasassa River derived from ALSM.



Photo 8. Bird Creek levee with scrub and coastal-forest vegetation.


Photo 9. Sinkhole and sawgrass at Lukens Creek.

Vegetation-Canopy DSM
A vegetation-canopy DSM was derived from a maximum-intercept DSM and the bare-earth DSM to obtain a surface model of canopy height in two steps. The results of a 2-m HPF in marsh zones and a 5-m HPF in forested zones were interpolated to a 2-m grid to derive a surface model of maximum-intercept values. Next, the bare-earth DSM was subtracted from the maximum-intercept DSM to obtain a surface model of vegetation-canopy heights, as normally observed from ground level.
Figures 9 - 11 show color-coded vegetation-canopy DSM. The boundary between the tidal marsh and the coastal forest is demarcated at canopy heights of ~1.4 m. Tall marsh types near this canopy height include black needlerush and sawgrass (Photo 10) and correspond to estimates of tidal-marsh canopy height made by Clewell (1997). Tall trees in hydric hammock exceeded 18 m in height (Photo 11). At Lukens Creek, considerable coastal-forest canopy is less than 7 m in height.  This includes palmetto scrub, oak scrub, and scrubby flatwood on the sand hills (Photo 12). Data acquisition during August, with full-leaf cover, facilitated the extraction of the canopy-surface model from laser altimetry. Canopy-height characteristics can be obtained during summer when trees are fully leafed. However, winter-data acquisition would be preferable for a bare-earth model.



Photo 10. Thick sawgrass along a tidal-creek tributary to Waccasassa River.


Figure 9. Canopy-height digital-surface model at Lukens Creek derived from ALSM.


Figure 10. Canopy-height digital-surface model at Bird Creek derived from ALSM.


Figure 11. Canopy-height digital-surface model at Waccasassa River derived from ALSM.



Photo 11. Tall, thick-canopied hydric hammock in summer.


Photo 12. Open-canopy oak and palmetto scrub in sand hills.


Land-Cover Maps
Preliminary habitat maps were created from unsupervised classifications of bare-earth and canopy-height surface models and intensity values. The land-cover and habitat categories were selected to correspond with management programs for Cedar Key Scrub Reserve and Waccasassa Bay State Preserve (FDEP, 2005). An unsupervised classification was conducted using the bare-earth DSM, canopy-height DSM, and intensity values from the laser-altimetry dataset. Preliminary maps were created for each area (Figs. 12 - 14). Categories were validated through field reconnaissance, comparison with existing maps, and aerial photography. Categories for tidal marsh at four different flooding zones correspond to the single management category of estuarine tidal marsh. Sawgrass or depression wetlands occur in low-elevation swales between sand hills. Categories of sand-hill oak scrub and palmetto scrub were added to mesic and scrubby-flatwood categories to delineate distinct elevation and canopy-cover differences at high points on the sand hills and the low palmetto cover in open areas. Hydric hammock and mesic flatwood were more difficult to separate due to similarities in canopy height and canopy composition. Canopy height plays a greater role in this type of classification. Thus, differences between old-growth stands and fire-management recovery may be monitored (Photo 13). Burn areas in the marsh at Lukens Creek, sinkholes, limestone highs, silviculture, roads (Photo 14), and trails (Photo 15) can be seen in this map product. 




Photo 13. Recovery of sand-hill oak scrub following fire management.


Figure 12. Lukens Creek land-cover categories derived from ALSM.


Figure 13. Bird Creek land-cover categories derived from ALSM.


Figure 14. Waccasassa River land-cover categories derived from ALSM.



Photo 14. Scrubby flatwood along open sand road at Trail 8.


Photo 15. Game trails in high marsh at Lukens Creek.


Shoreline
Detailed shorelines were also extracted from the bare-earth DSM at the 0.2-m contour. This shoreline is more comprehensive than existing digital shoreline as shown in Figure 15 (FGDL, 2008; USGS, 2008). In an environment susceptible to flooding, storm impact, and sea-level rise, the morphology of tidal-creek drainages and changes over time are critical.



Figure 15. ALSM-derived shoreline compared to 1:24,000 hydrography (USGS) shoreline at Bird Creek.

Topographic-Profile Transects
Topographic profiles across bare-earth and canopy DSM were prepared as illustrations of marsh and coastal-forest geomorphology. Transects A-A1, B-B1, and C-C1 are located at Lukens Creek (Fig. 6). Transect D-D1 is located at Bird Creek (Fig. 7). Elevation point data for each transect were extracted from ground- and canopy-surface models and filtered with a 6-m-centered moving average (Figs. 16-19). The vertical axis is exaggerated (VE) in each profile to highlight subtle topographic relief. Notable characteristics are indicated by red arrows on Figures 16-19 and are listed below.

* Water level is depicted at 0.3 m for illustration purposes. 
* Tidal-marsh elevations range from 0.3 to 0.9 m MSL and are marked for reference. 
* The upper-elevation range of the tidal marsh, ~0.06 - 0.09 m, represents a transition from marine to terrestrial influence. Elevations below 0.9 m in the coastal-forest canopy indicate areas susceptible to flooding from both tidal and freshwater-surface flow.
* Lukens Creek has distinct tidal-creek levees (Fig. 16), but they are considerably lower than levees on Bird Creek (Fig. 19).
* Relict sand dunes are locally important, supporting an endemic oak-scrub habitat and associated faunal species (Figs. 16, 18).
* Sinkholes (circular features by air) appear as water-filled depressions in the transects (Figs. 16, 17).
* Forest remnants within the marsh: usually salt scrub and living or dead trees (Figs. 17, 18; Photos 7, 15).
* The absence of clearly defined surface drainage is characteristic of areas with thin sediment and near-surface limestone (Fig. 17).
* Where the transect crosses an historic shoreline or forest boundary, the historic-feature zone is marked (all figures; Raabe et al., 2004). 
* Notches just to the interior edge of tree stands occur in the landward forest stands. Forest remnants closer to the coast appear to be missing the feature.
* Recent headward erosion of tidal creek is highlighted by low topography at the marsh interior (Fig. 18). Loss of forest soils and slow-sedimentation processes create a temporary topographic low during marine transgression (Raabe et al., 2004; Raabe et al., 2007).

The profiles show variations between high- and low-marsh canopies, and different creek and shore-edge forms, such as sloping or steep banks. Two characteristics, a canopy height less than 1.5 m and an elevation range of 0.3 to 0.9 m MSL (NAVD88), are key parameters of the tidal marsh. Higher elevations in the marsh tend to be toward the gulf, along levees, and high-marsh inland. 
The coastal-forest elevation in these profiles ranges from about 0.5 to 2.5 m MSL (NAVD88). Forest-canopy height near the tidal marsh does not exceed 9 m. Game trails (Photo 15) and feral-hog ruts are common along this coast. Regardless of whether the forest-edge notches are the result of animal activity, a geomorphic process, or both, the coastal forest is vulnerable to flooding at these low points. Such notches are missing in forest remnants close to the coast, although a relict notch may be indicated where the forest has already converted to tidal influence. 

Figure 16. Transect A-A1 across Lukens Creek showing profile derived from bare-earth and canopy-surface models.

Figure 17. Transect B-B1 showing profile from Lukens Creek across tidal marsh and forest remnants.

Figure 18. Transect C-C1 showing profile across upper reach of Lukens Creek to forest and relict dunes.

Figure 19. Transect D-D1 showing profile from Waccasassa River to a meander on Bird Creek.

Presence and absence of levees along tidal creeks can help distinguish areas with higher sediment supply. The Waccasassa River is situated at the head of an embayment that can serve to focus storm surge and suspended sediments at the river mouth. Higher river and creek levees near the mouth of the river could be attributed to sediment pulses focused in the embayment. This process may be exaggerated by logging activities in Gulf Hammock that have altered sediment supply and forest cover over the last 150 years (Raabe et al., 2004). The position of the historic shoreline at Bird Creek (Fig. 19) coincides with a topographic low. The apparent expansion of tidal marsh seaward may be attributable to the same process. Limestone control of the relatively deeper incised channels at Bird Creek and Waccasassa River also contributes to differing morphology from that at Lukens Creek.
Discussion
Previously unavailable topographic detail for this coastal environment was modeled successfully by sequential processing for three functionally unique landscape elements, using non-automated processing steps. The preservation of topographic features, such as tidal drainage, was feasible by partitioning the landscape into sections. Parameters for each feature were selected based on scale of feature, vegetation cover, and frequency of laser penetration to ground surface. Familiarity with local topography and landscape elements was key to modeling the surface in this way.
At Lukens Creek, the shorter canopy of the low marsh and the thin plant cover in burn areas provided consistent penetration of the laser and the extraction of ground intercepts with a 2-m LPF.  However, the limited laser returns with this early instrument resulted in infrequent ground penetration in thick vegetation, particularly at Bird Creek and Waccasassa. A 10-m LPF was used to model the surface under thick forest canopy and marsh vegetation.  Some micro-topographic features in the marsh are smoothed away with the use of a 10-m filter, but the final DSM adds considerable detail to existing digital-elevation models for the region.
Assessment of a DSM from field-survey O.H. is complicated by elevation variability across the area represented by a planar grid cell and the point represented by a field-surveyed O.H. We are confident that with new laser-altimeter instrumentation, multiple returns per pulse, and full-waveform systems (Brock et al., 2004; Lefsky et al., 1999), bare-earth models and canopy characteristics can be developed for coastal wetland and forested areas with ALSM.
The scale of features and processes may be deceptively small in a low-lying coastal system. Coastal wetlands are responsive to minor differences in the surface-drainage network, tidal flow, topography, and sediment supply. Currently available topography shows little or no geomorphic detail for this low-lying coastal zone and is too coarse to depict morphology where vertical difference between adjacent features is less than 0.5 m.
Laser-altimetry data were used to derive surface elevations for three areas of the coastal marsh in Levy County, Florida, where a combination of sediment deposition and erosion, limestone control, and human activities have helped to shape the coast.  Processing of the ALSM data resulted in ground-surface models, detailed delineation of tidal-drainage patterns, vegetation-canopy heights, and preliminary land-cover maps. Topographic profiles extracted from both the bare-earth and canopy-height DSMs, show details of the coastal landscape. The geomorphic characteristics are important to understanding the evolution, stability, and vulnerability of this coastal system. Management efforts, such as control burn and recovery monitoring, can be enhanced with subsequent data acquisition. The results demonstrate the use of airborne laser-altimetry data to model the ground surface beneath vegetated coastal marsh and forest. The results further demonstrate the capacity to derive additional vegetative and geomorphic characteristics from ALSM data.
Acknowledgments
Jeffery DiMaggio, manager at Cedar Key Scrub Preserve, provided invaluable field assistance and expert knowledge. Elzbieta Bialkowska-Jelinska provided editorial and mapping support. Michael Sartori, a graduate student with University of Florida Civil Engineering Department, provided technical support.
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Appendix I
Table 3. Orthometric heights (O.H.) of ground control points (GCP) and base stations.
GCP#
Eastings
Northings
O.H. (m) MSL NAVD88
Re-occupy1   
O.H. (m)
Description
Plant height (m), if given
Base 1
302055.826
3231090.068
0.531
0.531/0.498
rebar

1
302031.132
3231096.593
0.452

sawgrass 
1.1
2
302022.295
3231103.066
0.448

Juncus 
0.9
3
302018.445
3231116.658
0.449

Distichlis 
0.3
4
302013.666
3231127.162
0.446

S. alterniflora
0.2
5
302011.809
3231132.294
0.436

sawgrass
1.1
6
302010.656
3231138.745
0.442

sawgrass
1.1
7
302008.626
3231145.099
0.436

Juncus
1.2
8
301999.216
3231146.593
0.441

Juncus
1.0
9
301991.783
3231144.283
0.447

Juncus
1.1
10
301986.098
3231141.831
0.444

Batis maritima
0.2
11
301976.551
3231135.456
0.549
0.562
Borrichia/B. maritima
0.2
12
301971.283
3231134.549
0.615

Batis maritima
0.1
13
301969.698
3231132.914
0.654

J. coriaceae
0.5
14
301968.322
3231126.508
0.746

Iva/Baccharus salt scrub
1.4
15
301965.411
3231115.693
0.739

Iva/Baccharus salt scrub and Sabal
1.4
16
301953.474
3231117.342
0.516

Batis maritima
0.2
17
301950.712
3231154.419
0.459

Distichlis/B. maritima
0.3
18
301956.363
3231171.726
0.462

S. alterniflora
0.2
19
301952.754
3231187.975
0.433

Distichlis
0.4
20
301941.592
3231211.894
0.435

Distichlis/B. maritima
0.3
21
301937.022
3231228.825
0.443

Batis maritima/Distichlis
0.3
22
301932.097
3231242.103
0.469
0.470
Dead Sabal/B. maritima/S. alterniflora
0.3
23
301929.396
3231249.024
0.447

S. alterniflora
0.2
24
301916.816
3231275.295
0.359

S. alterniflora flooded
0.2
25
301909.965
3231289.716
0.344

S. alterniflora flooded
0.4
26
301888.210
3231319.805
0.385

S. alterniflora flooded
0.3
27
301877.378
3231334.627
0.399

S. alterniflora flooded
0.4
28
301872.716
3231346.238
0.399

Distichlis
0.3
29
301862.988
3231363.037
0.392

Juncus
1.5
30
301847.966
3231377.488
0.346

S. alterniflora
0.3
31
301822.329
3231396.891
0.419

Distichlis
0.3
32
301873.255
3231422.171
0.404

S. alterniflora/Juncus
1.1
33
301878.926
3231418.193
0.894

Dead Sabal trunk; root base 0.6 m above surrounding ground surface
6.0
34
301905.180
3231411.972
0.466
 
Distichlis
0.3
35
301939.941
3231447.511
0.551
 
Forest edge/transition with Distichlis
0.3
36
301937.492
3231446.555
0.554
0.537
Forest edge/transition with dry Distichlis
0.3
37
302073.243
3231099.418
0.285

Sawgrass edge

38
302080.383
3231103.660
0.488

High marsh mix

39
302085.899
3231106.574
0.549

Palmetto

40
302086.827
3231107.441
0.819

50% Canopy mixed wood

41
302098.045
3231113.550
0.846

Pine mixed forest

42
302129.326
3231131.164
1.302

Thick oak and mix canopy

43
302159.966
3231148.283
1.654

Thick scrub oak

44
302061.522
3231093.215
0.457

Juncus marsh

45
302068.842
3231097.356
0.482

Juncus marsh

46
302076.347
3231101.123
0.478

Juncus marsh

47
302084.951
3231105.621
0.532

Open palmetto and pine

48
302092.682
3231111.146
0.842

Hydric hammock mixed

49
302100.925
3231114.804
0.862

Pine/palmetto

50
302108.784
3231118.941
0.997

Pine scrub

51
302116.453
3231124.144
1.208

Palmetto oak scrub

52
302124.847
3231128.864
1.193

Pine and oak scrub

53
302133.589
3231133.741
1.375

Palmetto oak scrub

54
302142.147
3231138.495
1.509

Oak scrub

55
302149.817
3231142.728
1.580

Oak palmetto scrub

56
302157.447
3231147.133
1.700

Thick scrub oak

57
302162.512
3231149.897
1.701

Thick scrub oak

Base 2
324093.641
3227528.102
0.861
0.861/0.861
Juncus/S. alterniflora boundary - base station on Lone Cedar Island

58
324310.805
3228342.773
0.736
0.736
levee: Juncus/Distichlis

59
324312.117
3228329.977
0.593

levee: Juncus boundary

60
324313.852
3228315.391
0.494

wrack barren

61
323951.578
3229001.766
0.542

wrack barren

62
323965.188
3229001.234
0.501

Juncus clump

63
323970.203
3229003.328
0.612

Distichlis at tree edge

64
323708.531
3229160.438
0.493
0.496
levee: Juncus edge

65
323711.906
3229160.078
0.566

levee: Iva/sedge

66
323714.234
3229163.352
0.493

levee edge: S. patens/Juncus

66
323720.836
3229167.977
0.373

thick Juncus

67
323262.781
3229394.141
0.363
0.362
Juncus

68
323257.805
3229394.195
0.341

wetter Juncus

69
323253.625
3229394.648
0.322

wetter Juncus

70
323105.828
3229964.828
0.252

Juncus

71
323107.891
3229950.461
0.247

Juncus

72
327205.930
3228549.805
0.322
0.325
wet Juncus

73
327214.359
3228536.367
0.311

wet Juncus - last of drainage

74
327229.508
3228503.094
0.447

short dry Juncus spp.

75
327146.328
3228617.359
0.373

Sawgrass

76
324176.688
3228436.555
0.581
0.582
levee edge - dry mud

77
324180.648
3228443.438
0.792

levee hammock/mud boundary

78
324189.445
3228450.383
0.761

levee hammock

79
324196.430
3228471.992
0.549

back edge of levee hammock

80
324200.617
3228475.984
0.574

high marsh salt scrub

81
324210.672
3228482.125
0.536

Juncus

82
324213.844
3228487.242
0.500

Juncus

83
324094.654
3227542.190
0.629

Distichlis/Juncus/Salicornia/Iva/dead palm on Cedar Island.

84
323992.809
3227752.155
0.791

Sabal/cedar/Iva no seedlings

85
323997.911
3227767.596
0.868

Hammock interior/Sabal seedlings/Cedar/wrack

1Repeat occupation of a ground control site or base station
??

??

??

??




30