Northeastern (46th Annual) and North-Central (45th Annual) Joint Meeting (20–22 March 2011)

Paper No. 3
Presentation Time: 1:30 PM-5:30 PM

LYMAN RUN LAKE: AN EVALUATION OF SONAR-BASED BATHYMETRY USING A BARE-EARTH DIGITAL ELEVATION MODEL FROM AIRBORNE LIDAR


BEHR, Rose-Anna, Dcnr, Pennsylvania Topographic and Geologic Survey, 3240 Schoolhouse Road, Middletown, PA 17057 and MOORE, Michael E., Dcnr, Bureau of Topographic and Geologic Survey, 3240 Schoolhouse Road, Middletown, PA 17057, rosbehr@state.pa.us

Lyman Run Lake, Potter County, Pennsylvania, offered a unique opportunity to assess the bathymetric data collection and data processing methodologies of the Pennsylvania Geological Survey. When PAMAP lidar data was collected in spring of 2007, Lyman Run Lake, a 42-acre impoundment at Lyman Run State Park, had been drained and major dam renovations were nearly complete. The lidar-derived bare-earth digital elevation model (DEM) provided accurate ( ±4 inches) lake-bottom elevations against which to compare sonar data collected in October 2010. The differences in elevations of the sonar readings compared to the elevations extracted from the DEM varied from zero to 8.5 feet, with a mean difference of 1.06 feet and standard deviation of 0.97 feet. Sonar readings tended to understate the depth in shallow water, and overstate the depth in deeper water.

Datasets of sonar readings and of lidar-derived elevations at the same locations were subsequently used to model the lake bottom with the Topo-to-Raster tool in ESRI’s ArcGIS 10 software. This tool is designed to interpolate a hydrologically correct surface. Elevation points, elevation contours up to 2 feet above the lake edge, and the lake boundary were included in both models. Anomalies between the DEM of the lake bottom and the two modeled surfaces were analyzed. Some anomalies could be explained by insufficient data density necessary to define the terraced areas on the lake bottom at the dam face. Most problematic are the anomalies that appear to be induced by deficiencies in the Topo-to-Raster algorithm.