COMBINING EM AND LIDAR TO MAP COASTAL WETLANDS: AN EXAMPLE FROM MUSTANG ISLAND, TEXAS. Abstract

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1 Paine, J. G., White, W. A., Smyth, R. C., Andrews, J. R., and Gibeaut, J. C., 25, Combining EM and lidar to map coastal wetlands: an example from Mustang Island, Texas, in Proceedings, Symposium on the Application of Geophysics to Engineering and Environmental Problems: Environmental and Engineering Geophysical Society, p (CD-ROM). COMBINING EM AND LIDAR TO MAP COASTAL WETLANDS: AN EXAMPLE FROM MSTANG ISLAND, TEXAS Jeffrey G. Paine, William A. White, Rebecca C. Smyth, John R. Andrews, and James C. Gibeaut Bureau of Economic Geology, Jackson School of Geosciences, The niversity of Texas at Austin Abstract We combined airborne lidar and ground-based EM induction measurements with vegetation surveys along two transects across Mustang Island, a barrier island on the Texas coast, to examine whether these methods can be used to map coastal wetlands and associated geomorphic environments. Conductivity varied inversely with elevation along both transects. Elevation and conductivity profiles correlated reasonably well with habitat mapped in the largely imagery-based 1992 National Wetland Inventory (NWI), but they possessed greater detail and identified misclassified habitat. Detail achievable with elevation and conductivity data was similar to that achieved in on-the-ground vegetation surveys. Lowest elevations and highest conductivities were measured in saline environments (marine and estuarine units, forebeach, salt marsh, and wind-tidal flats). Highest elevations and lowest conductivities were measured in nonsaline environments (upland and palustrine units, dunes, vegetated-barrier flats, and fresh marsh). Elevation and conductivity data allow better discrimination among coastal wetland and geomorphic environments than can be achieved from image interpretation alone. Future work should include evaluating the effect of vegetation density on lidar-beam penetration, quantifying seasonal change in ground conductivity in fresh and saline coastal environments, examining the geographic variability of elevation and conductivity statistics, and evaluating the use of airborne EM sensors to measure ground conductivity at multiple exploration depths. Introduction We explore whether two noninvasive geophysical methods lidar (light detection and ranging) and EM (electromagnetic induction) can improve the accuracy and resolution of wetland mapping that has historically been based chiefly on analysis of aerial photographs and limited field checks. The importance of monitoring the status and trends of coastal wetlands has been increasingly recognized in recent decades as we have become more aware of the critical role wetlands play in the transitional aquatic-terrestrial environment and have become concerned about the rapid change in wetlands resulting from the historic rise in relative sea level. sing Mustang Island on the central Texas coast as an example (Figure 1), we examine the strong relationships among (1) elevation, soil and water salinity, and coastal habitat and (2) conductivity and salinity. We did this by acquiring lidar-derived elevations and EM-derived conductivities and comparing these measurements with coastal habitat and geomorphic environment data across this sandy barrier island. We selected two representative transects across Mustang Island (Figure 1), where we acquired lidar data, surveyed vegetation type, and measured the apparent electrical conductivity of the ground. Conductivity, which is closely correlated to soil and water salinity, was measured along the transects using a ground conductivity meter. We evaluated the traditional approach to wetland mapping by comparing habitat types extracted from the most recent wetland maps with coastal environments directly observed along the transects. We evaluated the lidar and EM approach by examining the relationships along each transect among mapped wetland units, lidar-derived elevation, and measured ground conductivity and vegetation type determined during the ground surveys. We employ terms from two common classification systems to examine the relationship between elevation, conductivity, and coastal vegetation assemblages: the more technical system used by the.s. Fish and Wildlife Service in the National Wetland Inventory (NWI) program and a geomorphic system used in our ground-based 745

2 N New Mexico Texas Oklahoma Corpus Christi Bay Mustang Island Port Aransas transect N Mexico Study area Gulf of Mustang Island State Park transect 2 mi Mexico 5 1 km 2 km 5 mi Figure 1. Mustang Island study area, Texas Gulf Coast. mapping that includes wetlands and other associated coastal environments. Geomorphic units identified along these transects include beach, dune, vegetated-barrier flat (VBF), fresh and salt marsh, and wind-tidal flat (Figure 2), and each have correlative categories within the NWI system (Table 1). Methods We acquired surface elevations using airborne lidar and made ground-based conductivity measurements and vegetation observations along the Mustang Island State Park and Port Aransas transects (Figure 1) across the island. We compared elevation and ground conductivity data with vegetation assemblages as depicted on NWI maps and geomorphic units as determined from on-the-ground observations. Figure 2. Generalized profile of a barrier island showing common geomorphic features that may be present between the bay (left) and gulf (right) shorelines. 746

3 Table 1. Classification system (Cowardin and others, 1979) used by the.s. Fish and Wildlife Service in the 1992 National Wetland Inventory (NWI). This partial list of units includes only those mapped along the Mustang Island transects (Figure 1). NWI code Classification description Common (geomorphic) description pland Not a wetland E2AB1P E2SP E2SN E1BL M2SP M2SN Palustrine emergent persistent wetland, temporarily flooded Palustrine emergent persistent wetland, seasonally flooded Estuarine intertidal persistent emergent wetland, irregularly flooded Estuarine intertidal persistent emergent wetland, regularly flooded Estuarine intertidal aquatic bed, algal, irregularly flooded Estuarine intertidal unconsolidated shore, irregularly flooded Estuarine intertidal unconsolidated shore, regularly flooded Estuarine subtidal unconsolidated bottom, subtidal Marine intertidal unconsolidated shore, irregularly flooded Marine intertidal unconsolidated shore, regularly flooded Fresh or interior marsh, persistent vegetation, topographically high Fresh or interior marsh, persistent vegetation, topographically low Salt- to brackish-water marsh, persistent vegetation, topographically high Salt- to brackish-water marsh, persistent vegetation, topographically low Tidal and wind-tidal flats, with algal mats, topographically high Tidal and wind-tidal flats, topographically high Tidal and wind-tidal flats, topographically low Estuarine open water Backbeach along Gulf shore Forebeach along Gulf shore Lidar Survey The niversity of Texas at Austin Bureau of Economic Geology lidar team acquired and processed airborne, scanning laser terrain (lidar) data in September and October 23. Lidar x, y, and z points representing the ground surface were generated by combining laser range and aircraft attitude data (Wehr and Lohr, 1999) collected using an Optech Airborne Laser Terrain Mapper 1225 with once-per-second aircraft positions collected using geodetic quality airborne and ground-based GPS receivers. The lidar point data have a vertical accuracy of about 15 cm and are spaced at about one per.5 m 2. We used lidar point data to produce digital elevation models (DEM s) along swaths from the gulf to the bay. We also constructed gulf-to-bay elevation profiles by averaging lidar data points located within 1.5 m of a transect station where we also measured ground conductivity and vegetation characteristics. EM Survey We used the frequency-domain EM (FDEM) method to measure apparent electrical conductivity. FDEM employs a changing primary magnetic field created around a transmitter coil to induce current to flow in the ground, which in turn creates a secondary magnetic field that is sensed by the receiver coil (Parasnis, 1986; Frischknecht and others, 1991; West and Macnae, 1991). The strength of the secondary field is a complex function of EM frequency and ground conductivity (McNeill, 198), but it generally increases with ground conductivity at constant frequency. We used a Geonics EM38 ground conductivity meter to measure the apparent conductivity of the ground. This instrument operates at a primary frequency of 15 khz, measuring apparent conductivity to a depth of about.8 m (horizontal dipole [hd] orientation) and 1.5 m (vertical dipole [vd] orientation). The instrument has a range of about 1 millisiemen/m to more than 1, ms/m. We acquired measurements at 234 sites on Mustang 747

4 Island in December 23, recording apparent conductivity in the hd and vd orientations at stations spaced 2-m apart from the gulf beach to the bay shore. Where the apparent conductivity of the ground was within the instrument s range, we recorded measurements with the instrument on the ground. In areas where apparent conductivity approached or exceeded the upper limit of the instrument s range, we made one set of measurements with the instrument on the ground and another set with the instrument.6 m above the ground. We then corrected the out-of-range values by extrapolating the lower apparent conductivities recorded with the instrument at a fixed height according to the relationship observed between the ground-level and fixed-height measurements made over ground having lower apparent conductivities. In the vd orientation, we determined the relationship between ground-level and raised-instrument measurements using 24 data pairs that had apparent conductivities at ground level of less than 1,3 ms/m (Figure 3). The relationship, σ g = 1.89 σ r , where σ g is the apparent conductivity at the ground surface and σ r is the apparent conductivity with the instrument.6 m above the ground surface, has an r 2 value of.95. We then extrapolated a ground-level apparent conductivity from the raised-instrument conductivity for stations where the measured conductivity at ground level exceeded 1,3 ms/m, the instrument's maximum linear limit in the vd orientation. In the hd orientation, the 22 data pairs having apparent conductivities less than 1,4 ms/m produced a similar relationship, σ g = 4.3 σ r 85.5, that yields an r 2 value of s g : apparent conductivity at m s g = 1.89 x s r r 2 =.95 n = ms/m Measured Corrected s r : apparent conductivity at.6 m Figure 3. Relationship between apparent conductivity measured in the vd orientation at an instrument height of.6 m above ground (σ r ) and apparent conductivity measured at ground level (σ g ) determined using only ground-level measurements below 1,3 ms/m. 748

5 Vegetation Survey At each transect station, we recorded plant species, percent cover, vegetation height, and soil wetness. We combined aerial photograph signatures and field observations to classify the locations into the following geomorphic environments: beach, dune, VBF, fresh marsh, salt or brackish marsh, and wind-tidal flat. Mustang Island State Park Transect The Mustang Island State Park (MISP) transect is located on the southwest part of Mustang Island (Figures 1 and 4), extending 2.2 km from the gulf beach to the Corpus Christi Bay shore. We surveyed vegetation and measured apparent conductivity at 112 stations along this transect and obtained elevations at these stations from the lidar point data. Elevation and Vegetation Elevations range from.1 to 5.5 m above the 1988 North American Vertical Datum (NAVD88) (Figures 5 and 6). We found the highest elevations (2 m or more) across the fore-island dunes within about 3 m of the gulf shoreline and midisland dunes between about 8 and 15 m from the gulf shoreline. We found lowest elevations (.3 m or less) on the beach and bayward of the midisland dunes to the bay shoreline. At one-third (38) of the locations, vegetation was sufficiently dense for us to question whether the lidarderived elevation represented the ground surface or the top of the vegetation mass. At these locations, height of massed vegetation averaged.5 m, ranging from.1 to 1.4 m. These heights can be subtracted from the lidarderived elevation profile to produce a corrected ground-surface elevation profile, assuming lidar was unable to penetrate the vegetation. In densely vegetated areas, vegetation mass might cause significant overestimation of land-surface elevation and potential misclassification of environments on the basis of lidar data alone. E1BL E2SP E2SP PEM1F SH 361 MISP Transect PEM1F M2SN M1BL Gulf of Mexico N.3 mi.5 km Figure 4. Aerial photomosaic of the MISP transect showing habitats (Table 1) identified on the 1992 NWI map. 749

6 E1BL E2SP E2SP PEM1F SH 361 MISP Transect PEM1F M2SN M1BL Gulf of Mexico Elevation (m) > 5 N.3 mi.5 km Figure 5. Lidar-derived DEM swath along the MISP transect showing habitats depicted on the 1992 NWI map. Transect locations with the highest elevations generally correlated with upland or high palustrine units and locations with the lowest elevations generally coincided with estuarine units (Figure 6a). Average elevation was highest for upland () locations (Table 2), but elevation for this unit overlapped with elevation ranges for other NWI units. The highest of the palustrine units () had the next highest average elevation. nit, topographically lower than, had a slightly lower average elevation. Estuarine units,, and E2SP have similar average elevations that are considerably lower than those for the upland and palustrine units. Elevation limits for the mapped upland and palustrine units overlap, as do ranges for the estuarine units (Table 2). Nevertheless, there is a distinct difference in average elevation (and little overlap in elevation range) between the palustrine and estuarine units. During the ground-based survey, we classified each transect station into a coastal geomorphic unit (Figure 2) on the basis of field characteristics such as vegetation (Figure 6b and Table 3). Most common were dune, VBF, and wind-tidal flat. These ground-based surveys produced a classification with higher spatial resolution than that depicted on the NWI maps and one that better represents the variability evident from the topographic profile (Figure 6b). The dune environment has the highest average elevation and the largest elevation range, overlapping at the low end with the VBF, fresh marsh, and beach environments (Table 3). Relatively high elevation averages are associated with VBF, high fresh marsh, low fresh marsh, and beach environments, which all have some degree of overlap in elevation ranges. Distinctly lower elevation averages are associated with high and low salt marsh and high and low wind-tidal flat environments. Elevation ranges for these estuarine environments overlap with each other, but not with fresh marsh, VBF, or dune environments. Conductivity and Vegetation Apparent ground conductivity varies over three orders of magnitude, ranging from resistive ground at a few ms/m to relatively conductive ground at more than 2, ms/m (Figure 6 and Table 2). We found high apparent conductivities (greater than 1 ms/m) within a few tens of meters of the gulf shoreline, along two 75

7 (a) 8 M2SN E2SP E2SP 1, Elevation (m) Conductivity Elevation Apparent conductivity Distance (m) (b) 8 B D V D D V D V D M VBF BF B BF FH F M FH M W V MFL V D VBF M M V FL B BF FL FH BF F D VBF DV D M W W BF SH TF TF M WTFH M WTFH W SL SH TF W TF W TF B M SL 1, V V H L L H L Elevation (m) B F B F Conductivity Elevation M SL D Apparent conductivity Distance (m) Figure 6. Elevation and conductivity (vd orientation) profiles superimposed on (a) 1992 NWI units and (b) surveyed coastal environments along the MISP transect. Distances are measured from the gulf shoreline. B = beach or berm, D = dune, other units as described in Tables 1 and 3. 1 midisland segments, and near the bay shoreline (Figure 6). We measured lowest apparent conductivities (about 1 ms/m or less) just inland from the gulf shoreline and along two midisland segments. Conductivity correlates reasonably well with NWI units (Figure 6 and Table 2). pland () and high palustrine () units tend to occur where apparent conductivity is low (less than about 1 ms/m), whereas lower palustrine (), estuarine (,, and E2SP), and marine (M2SN) units are mapped where apparent conductivity is relatively high (greater than 1 ms/m). Among the more conductive NWI units, average apparent conductivity is highest for the topographically lowest estuarine unit (E2SP), decreases slightly for the next lowest estuarine unit (), and decreases further for the highest of the mapped estuarine units (). Marine-influenced (M2SN) and lowest palustrine () units have lower conductivities that do not overlap with those measured for the more conductive estuarine units. Among the relatively nonconductive NWI units, the lowest average conductivity is associated with upland () locations. Slightly higher average conductivity is associated with the highest palustrine unit (). The conductivity range measured for locations within 751

8 Table 2. Elevation and apparent conductivity ranges measured at 112 locations for 1992 NWI units mapped along the MISP transect (Figure 4). Elevations are relative to NAVD88. Apparent conductivities were measured using a Geonics EM38 in the vd and hd orientations. NWI unit n Elev. avg. (m) Elev. range (m) avg., vd range, vd avg., hd range, hd E2SP M2SN Table 3. Elevation and apparent conductivity ranges measured at 112 locations for geomorphic units (Figure 2) along the MISP transect. VBF = vegetated-barrier flat, MFH = high fresh marsh, MFL = low fresh marsh, MSH = high salt marsh, MSL = low salt marsh, WTFH = high wind-tidal flat, WTFL = low wind-tidal flat. Environment n Elev. avg. (m) Elev. range (m) avg., vd range, vd avg., hd range, hd Dune VBF MFH MFL MF (all) MSH MSL MS (all) WTFH WTFL WTF (all) Beach, berm Water upland () units overlapped with ranges measured for locations within palustrine units, but not with marine or estuarine units. Geomorphic environments also correlate well with measured apparent conductivity (Figure 6b and Table 3). Highest apparent conductivities measured in the vd orientation occur in beach, low and high salt marsh, and low and high wind-tidal flat environments. Lowest apparent conductivities occur in dune, VBF, and low and high fresh marsh environments. Dune locations have the lowest average conductivity, but their measured range extends above the average values observed for low and high fresh marshes. Low average conductivities are also found in VBF environments. Gulf beach and bay berm environments have higher average apparent conductivities than are found in dune and fresh marsh environments. Salt marsh and wind-tidal flats have the highest apparent conductivities. 752

9 Average apparent conductivity increases from high to low salt marsh and from low to high wind-tidal flat. Ranges of measured conductivities overlap for the salt marsh and wind-tidal flats and for the dunes, VBFs, and fresh marshes, but there is little or no overlap in observed conductivity range between these two groups of relatively saline and nonsaline environments. Elevation, Conductivity, and Vegetation In general, elevation and apparent conductivity vary inversely (Figure 6), reflecting the strong negative correlation between elevation and salinity in coastal environments. As elevation decreases, the frequency of flooding by saline water increases. At higher elevations, infrequent saline flooding, infiltrating fresh precipitation, and relatively dry soil combine to produce less electrically conductive soil. Conductivity values vary over a greater range than do elevations, but both vary significantly and measurably across the island. We can attempt to better discriminate NWI and geomorphic units that may have overlapping elevation or conductivity ranges by combining elevation and apparent conductivity. For example, locations within the NWI upland () unit generally have both low apparent conductivities and high elevations, whereas the highest palustrine unit () generally has lower elevations and higher conductivities (Figure 7a). High and low palustrine units and have minor differences in elevation but more distinct differences in apparent conductivity. Estuarine and marine units have both very low elevations and very high apparent conductivities. Among the geomorphic units, dunes have high and highly variable elevations but have low conductivities that vary over a relatively small range (Figure 7b). VBF environments generally have lower elevations than dune environments and higher and more variable conductivity values. High fresh marshes have elevations that are indistinguishable from VBF environments but have apparent conductivities that tend to be higher than those measured in the VBFs. Salt marshes and wind-tidal flats have very low elevations and very high apparent conductivities. Advantages and Limitations Airborne lidar offers detailed and accurate elevation measurements that can be used to help classify wetlands and associated habitats more accurately than classifications based on aerial photographs alone. Comparisons of mapped NWI units with elevation profiles across Mustang Island show that elevation detail achieved with lidar allows more precise discrimination of wetland and upland units than appears on NWI maps (Figure 6a). Furthermore, some NWI units on both island transects are misclassified; some units mapped as wetland contain upland habitat, and some units mapped as upland contain wetland habitat. Comparisons of lidar-derived elevations with geomorphic units delineated during the field survey show similar levels of detail (Figure 6b). This suggests that lidar can be used to map coastal environments at the same level achievable with labor-intensive ground-based surveys, which are impractical over large areas. Lidar-derived elevations can complement aerial photographic analysis by helping to distinguish coastal environments, as well as upland, palustrine, estuarine, and marine habitats that may have ambiguous photographic signatures. Most NWI habitats and coastal geomorphic environments have statistically distinct average elevations but rather wide elevation ranges that overlap to varying degrees with other habitats and environments. Furthermore, lidar pulses may not reach the ground surface in densely vegetated areas, producing anomalously high elevations at those points and leading to potential misclassification of habitat or environment on the basis of elevation alone. Conductivity is highly inversely correlated to lidar-derived elevation on the Mustang Island transects. EMderived conductivities correlate well with both mapped NWI wetland and upland habitat and coastal geomorphic units identified in the field. EM and lidar achieve similar levels of detail exceeding that depicted on the NWI maps. Conductivities closely track changes in coastal environment, suggesting that EM data can be used to distinguish habitats and geomorphic units to the same level achievable with ground-based vegetation surveys. Comparisons of mapped NWI units with conductivity data reveal apparent misclassifications in the NWI maps, both where mapped 753

10 (a) 8 pland 6 Elevation (m) 4 E2SP M2SN , Apparent conductivity (vd; ms/m) (b) Elevation (m) Dune VBF MFH MFL MSH MSL WTFH WTFL Beach/Berm Water , Apparent conductivity (vd; ms/m) Figure 7. Elevation and apparent conductivity of (a) 1992 NWI units and (b) coastal geomorphic units along the MISP transect. wetland units enclose areas having conductivities that indicate an upland habitat and where mapped upland habitats enclose areas having conductivities that indicate wetlands. Average conductivities for each NWI and coastal geomorphic unit are distinct (Tables 2 and 3), but the ranges of conductivities measured within these units overlap to varying degrees. pland and fresh-water environments are most easily distinguished from estuarine and marine environments because conductivity strongly responds to changes in salinity. Overlap in ranges could lead to misclassification of units if the classification is based on conductivity alone. 754

11 Classifying Wetland and Coastal Environments Correlations among wetland habitat, geomorphic unit, elevation, and conductivity suggest that lidar and EM data can be used to improve the accuracy of coastal habitat classification and perhaps partly automate the process. One approach would be to combine photographic, elevation, and conductivity data in a common spatial environment, using elevation and conductivity as a supplement to aid classification of ambiguous habitat signatures on aerial photographs. A more quantitative and automatable approach would be to establish statistical elevation and conductivity characteristics for all habitat and geomorphic types. One could then use measured elevations and conductivities to classify locations according to proximity of each measurement to average elevation and conductivity for each habitat or environment. Because the statistical characteristics (such as average, range, and standard deviation) could be calculated for each habitat or environment type, probabilities of accurate classification could be assigned for each point. Because elevations and conductivities are easily distinguished between upland and fresh-water habitats and estuarine and marine environments, likelihood of misclassification at this level would be low. Likelihood of misclassification among habitats with more elevation and conductivity overlap, such as between some estuarine and marine units and saline environments, would be higher. Future Work Preliminary results are encouraging, but many uncertainties remain to be investigated before lidar and EM can be used routinely and accurately in coastal habitat classification. For example, further work is needed to determine where vegetation density is great enough to prevent lidar from detecting the top of vegetation rather than the ground surface. In coastal areas, where errors of less than one meter can lead to significant habitat misclassification, methods of correcting for vegetation height become critical. We measured ground conductivity late in the fall and examined the relationship with habitat and geomorphic units on the basis of those measurements. It is likely that conductivities within the uppermost meter of the subsurface will change seasonally with precipitation and ambient temperature. We hope to reoccupy the same sites in different seasons, especially following precipitation or flooding events, to examine the magnitude of these changes and to identify the environments that are most susceptible to seasonal change. We made our conductivity measurements using a ground-based instrument that explores.8 to 1.5 m in the subsurface. Ground surveys are adequate for limited field investigations but are too labor-intensive to map larger areas. Similar instruments can be towed beneath low-flying helicopters to rapidly and remotely acquire conductivity data. Measurements from airborne EM instruments can be made simultaneously at multiple explorations depths, enabling shallow data to be used for vegetation mapping and deeper data to be used for complementary purposes such as monitoring saltwater intrusion into coastal aquifers and characterizing fresh-water lenses underlying many coastal barriers. Conclusions Elevations measured using airborne lidar correlate well with NWI upland, palustrine, estuarine, and marine units. Lidar-derived elevation profiles provide greater detail than is present in NWI maps produced from aerial photographs and can be used to map wetland habitat more accurately and in greater detail than is feasible from aerial photographs and limited field checks. Mapping detail achievable with lidar approaches that of ground-based investigations. Where vegetation is dense, lidar-derived elevations may represent the top of massed vegetation rather than the ground surface, leading to potential habitat misclassification. Measurements of shallow electrical conductivity using a ground-based EM instrument range over three orders of magnitude and correlate well with both NWI habitats and coastal geomorphic units. Highest conductivi- 755

12 ties are measured within marine and estuarine NWI units and in salt marsh, wind-tidal flat, and forebeach environments. Lowest conductivities are measured within upland and palustrine NWI habitats and in dune, VBF, and fresh marsh environments. Conductivity changes are consistent with, but more detailed than, changes depicted on NWI maps. Lidar-derived elevation and EM-derived conductivities are inversely correlated, and each method has advantages and disadvantages. Both methods readily discern saline- and fresh-water environments and complement traditional, photograph-based wetland classification by helping classify distinct coastal environments that have similar signatures on aerial photographs. Overlap in elevation and conductivity among some habitats and environments suggests that a statistical approach to wetland classification based on integrated data from lidar, EM, and aerial photographs could achieve greater detail and accuracy than methods based on limited field checks of boundaries mapped on aerial photographs or remotely sensed images. Further evaluation of lidar and EM in coastal environment classification should include (1) characterizing and minimizing land-surface elevation error where vegetation is dense; (2) determining the variation in measured conductivity with seasonal changes in ambient temperature and precipitation; (3) evaluating whether elevation and conductivity statistics measured and calculated in one area can be applied to classifying similar environments in other, geographically distinct areas; and (4) migrating conductivity measurements to an airborne platform where large areas can be surveyed rapidly and multiple depths can be explored simultaneously. Acknowledgments This project was partly funded under contract number 3-5 from the Texas General Land Office to The niversity of Texas at Austin, Jeffrey G. Paine, principal investigator. The Texas Coastal Coordination Council funded the project under the Texas Coastal Management Program (CMP). The Army Research Office partly supported lidar data acquisition. This article is a publication of the Coastal Coordination Council pursuant to National Oceanic and Atmospheric Administration (NOAA) award number NA17OZ2353. The views expressed herein are those of the authors and do not necessarily reflect the views of NOAA or any of its subagencies. Lidar data were acquired and processed by John R. Andrews, James C. Gibeaut, Roberto Gutierrez, Tiffany L. Hepner, and Rebecca C. Smyth. Rachel L. Waldinger assisted with vegetation analysis. Publication approved by the Director, Bureau of Economic Geology. References Cowardin, L. M., Carter, V., Golet, F. C., and LaRoe, E. T., 1979, Classification of wetlands and deepwater habitats of the nited States: Washington, D.C., SA,.S. Department of Interior, Fish and Wildlife Service, 131 p. Frischknecht, F. C., Labson, V. F., Spies, B. R., and Anderson, W. L., 1991, Profiling using small sources, in Nabighian, M. N., ed., Electromagnetic methods in applied geophysics applications, part A and part B: Tulsa, Society of Exploration Geophysicists, p McNeill, J. D., 198, Electromagnetic terrain conductivity measurement at low induction numbers: Mississauga, Ont.,Geonics Ltd., Technical Note TN-6, 15 p. Parasnis, D. S., 1986, Principles of applied geophysics: London, Chapman and Hall, 42 p. Wehr, A. and. Lohr, 1999, Airborne laser scanning an introduction and overview: ISPRS Journal of Photogrammetry and Remote Sensing, vol. 54, no. 2-3, p West, G. F., and Macnae, J. C., 1991, Physics of the electromagnetic induction exploration method, in Nabighian, M. N., ed., Electromagnetic methods in applied geophysics applications, part A and part B: Tulsa, Society of Exploration Geophysicists, p