THE USE OF LIDAR TERRAIN DATA IN CHARACTERIZING SURFACE ROUGHNESS AND MICROTOPOGRAPHY

The availability of light detection and ranging data (LiDAR) has resulted in a new era of landscape analysis. For example, improvements in LiDAR data resolution may make it possible to accurately model micro topography over a large geographic area, however data resolution and processing costs versus resulting accuracy may be too costly. We examined two LiDAR data sets of differing resolutions, a low point-density (0.714 points/m² spacing) 1 m DEM available statewide in Pennsylvania and a high point density (10.28 points/m² spacing) 1 m DEM research grade DEM, and compared calculated roughness between both resulting DEMs using standard deviation of slope, standard deviation of curvature, a pit fill index, and as the difference between a smoothed splined surface and the original DEM. These results were then compared to field surveyed plots and transects of micro-terrain. Using both data sets, patterns of roughness were identified which were associated with different landforms derived from hydro-geomorphic features such as stream channels, gullies, and depressions. Lowland areas tended to have the highest roughness values for all methods, with other areas showing distinctive patterns of roughness values across metrics. However, our results suggest that the high-resolution research-grade LiDAR did not improve roughness modeling in comparison to the coarser state-wide LiDAR. We conclude that resolution and initial point density may not be as important as the algorithm and methodology used to generate a LiDAR-derived DEM for roughness modeling purposes.