From Bureau of Economic Geology, The University of Texas at Austin (www.beg.utexas.edu).
For more information, please contact the author.

AGU Fall Meeting, San Francisco, California, December 5–9, 2005

Mapping Coastal Wetlands Using EM and Airborne Lidar:
a Texas Example

J. G. Paine, W. A. White, R. C. Smyth, J. R. Andrews, and J. C. Gibeaut

Abstract:

We combined EM induction and airborne lidar measurements with vegetation surveys along two transects across Mustang Island, a Texas barrier island, to examine whether EM and lidar can be used to map coastal wetlands and associated geomorphic environments. Lidar-derived elevations correlate well with National Wetland Inventory (NWI) upland, palustrine, estuarine, and marine units. Lidar can be used to map wetland habitat more accurately and in greater detail than is feasible from aerial photographs and limited field checks, approaching that achievable on the ground. 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 also correlate well with NWI habitat and geomorphic unit. High conductivities are measured within marine and estuarine NWI units and in salt marsh, wind-tidal flat, and forebeach environments. Low conductivities are measured within upland and palustrine NWI habitats and in dune, VBF, and fresh marsh environments. Conductivity profiles possess more mappable detail than is present on NWI maps. Tests of an airborne EM sensor towed 30 m above the ground yielded insufficient resolution. Elevation and conductivity are inversely correlated along the transects. EM and lidar readily discern saline- and fresh-water environments and complement traditional wetland classification by helping distinguish environments that have similar signatures on aerial photographs. There is some overlap in elevation and conductivity among similar habitats and environments, but a statistical classification based on integrated data from lidar, EM, and aerial photographs can achieve greater resolution and accuracy than current remote-sensing methods. Future work should include evaluating the effect of vegetation density on lidar-beam penetration, quantifying seasonal change in ground conductivity in fresh and saline environments, examining the geographic variability of elevation and conductivity, and further evaluating the use of airborne EM sensors to measure conductivity