The use of LiDAR terrain data in characterizing surface roughness and microtopography

Kristen M. Brubaker, Wayne L. Myers, Patrick J. Drohan, Douglas A. Miller, Elizabeth W. Boyer

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Article number891534
JournalApplied and Environmental Soil Science
Volume2013
DOIs
StatePublished - May 13 2013

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lidar
microtopography
microrelief
surface roughness
roughness
digital elevation model
spatial distribution
spacing
landforms
stream channels
detection
lowlands
stream channel
gully
modeling
landform
curvature
fill
transect
methodology

All Science Journal Classification (ASJC) codes

  • Soil Science
  • Earth-Surface Processes

Cite this

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abstract = "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.",
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The use of LiDAR terrain data in characterizing surface roughness and microtopography. / Brubaker, Kristen M.; Myers, Wayne L.; Drohan, Patrick J.; Miller, Douglas A.; Boyer, Elizabeth W.

In: Applied and Environmental Soil Science, Vol. 2013, 891534, 13.05.2013.

Research output: Contribution to journalArticle

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