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Applied and Environmental Soil Science
Volume 2013 (2013), Article ID 891534, 13 pages
Research Article

The Use of LiDAR Terrain Data in Characterizing Surface Roughness and Microtopography

1Hobart and William Smith Colleges, 318 Stern Hall Hobart and William Smith Colleges, Geneva, NY 14456, USA
2Department of Ecosystem Science and Management, The Pennsylvania State University, University Park, PA 16802, USA
3Department of Geography, The Pennsylvania State University, University Park, PA 16802, USA

Received 21 December 2012; Accepted 10 March 2013

Academic Editor: Artemi Cerda

Copyright © 2013 Kristen M. Brubaker et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 microtopography over a large geographic area; however, data resolution and processing costs versus resulting accuracy may be too costly. We examined two LiDAR datasets 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 the calculated roughness between both resulting DEMs using standard deviation of slope, standard deviation of curvature, a pit fill index, and the difference between a smoothed splined surface and the original DEM. These results were then compared to field-surveyed plots and transects of microterrain. Using both datasets, patterns of roughness were identified, which were associated with different landforms derived from hydrogeomorphic 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 statewide 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.