Bureau of Economic Geology, The University of Texas at Austin (www.beg.utexas.edu).
National Ground Water Association’s 2005 Ground Water Summit, San Antonio, April 17-20
Use of Lidar to Improve Estimates of Hydrologic Modeling Parameters
Terrain and vegetation information obtained from lidar (light detection and ranging) datasets can be used to help generate more realistic input to surface and groundwater hydrologic models (e.g. enhanced identification of recharge features and refinement of evapotranspiration (ET) estimates). We wish to demonstrate how highly accurate, densely-spaced lidar data can be used as an innovative tool to improve estimates of these and other significant components of hydrologic budgets.
Researchers with the University of Texas at Austin (UT) Bureau of Economic Geology (BEG) and Center for Space Research (CSR) use an Optech Inc. airborne laser terrain mapping system (ALTM1225) to create lidar all-points and bare-earth point data sets and digital elevation models (DEM). Bare-earth or terrain-only DEM’s are generated after separating ground from non-ground lidar x, y, and z points using lidar data classification algorithms developed at UT CSR. Non-ground points can also be distinguished as vegetation or buildings. BEG and CSR researchers are presently working with Optech Inc. to develop a waveform digitizer addition to the ALTM1225 system to more fully characterize returned laser pulses.
Among the parameters that can be enhanced using lidar bare-earth data are (1) recharge features such as faults and sinkholes, which are often obscured by vegetation, (2) topographically low areas that focus surface runoff, (3) detailed stream channel morphology, (4) surface area of lakes and rivers for estimates of evaporation, and (5) sediment transport by identifying areas of erosion and aggradation. Detailed surface topography might also be used to infer water table characteristics.
By being able to view vegetation separately from ground, hydrologic modelers will be able to estimate vegetation density and height, and improve calculations of ET. Specific parameters that will be available to increase accuracy of ET estimation are (1) surface roughness and its effect on wind velocity, (2) leaf area index (LAI), (3) zero-displacement height, and (4) vegetation type. In addition, identification of vegetation type may allow inferences to be made about soil moisture conditions. Repeat lidar surveys might allow estimation of increase and decrease in rates of soil moisture fluctuation.