The effects of DEM interpolation on quantifying soil surface roughness using terrestrial LiDAR

Authors: LI, LI - University Of Arizona; Nearing, Mark; Nichols, Mary; Polyakov, Viktor; GUERTIN, D.P. - University Of Arizona; Cavanaugh, Michelle

Submitted to: Soil and Tillage Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/23/2019
Publication Date: 1/13/2020
Citation: Li, L., Nearing, M.A., Nichols, M.H., Polyakov, V.O., Guertin, D., Cavanaugh, M.L. 2020. The effects of DEM interpolation on quantifying soil surface roughness using terrestrial LiDAR. Soil and Tillage Research. 198. https://doi.org/10.1016/j.still.2019.104520.
DOI: https://doi.org/10.1016/j.still.2019.104520

Interpretive Summary: Scientists often measure the roughness of soil surfaces for a variety of reasons, including when attempting to understand how water moves over a soil surface and how much soil is eroded during a rainstorm. A common method for quantifying roughness from point cloud data as measured by LiDAR or photogrammetry utilizes Digital Elevation Models (DEM), which consist of regularly spaced point values of elevation derived using interpolation from the irregularly spaced measured points. In this experiment we applied artificial rainfall to a 6 by 20-foot soil plot and measured finely spaced surface elevation values with a Terrestrial LiDAR scanner, which is a very accurate instrument for measuring surface elevations utilizing a laser. We found that the process of interpolation, commonly used when processing this type of data, introduced significant error and bias into the quantification of surface roughness, because the DEMs were smoothed out. This resulted in some cases of contradictory results in tracking changes in roughness over time on the experimental plots. Errors associated with the smoothing were greater on the rougher surfaces. We did find that the errors could be somewhat reduced when the DEM grid size was made finer (i.e., 5 mm as opposed to 10 mm spacing between points), but the best overall method was to just use the LiDAR points directly in the calculations of roughness. The DEMs, though commonly used without a great deal of thought by scientists for this purpose, were basically ineffective when used to track changes in the surface roughness,

Technical Abstract: Soil surface roughness is often calculated based on the Digital Elevation Models (DEMs) obtained by interpolating point cloud data from terrestrial LiDAR measurements. This study investigates the effects of DEM interpolation and interpolation methods on quantification of soil surface roughness on 2 by 6.1 m plots. LiDAR scans were conducted correspondent to rainfall applications at six positions around rainfall simulation plots with 12% and 20% slope treatments. DEMs with resolutions of 5 and 10 mm were generated from LiDAR point clouds using Inverse Distance Weighting, Natural Neighbors and Universal Kriging methods implemented in ESRI ArcGIS 10.5. Random roughness index was calculated based on the LiDAR-interpolated DEMs and LiDAR points directly and then compared. Results showed: 1) the random roughness values calculated from the interpolated DEMs were consistently lower than those estimated from the LiDAR points directly, indicating smoothing of the modeled surface during the interpolation process; 2) DEM errors increased as the surfaces evolved to rougher states; 3) DEM errors tended to be greater when coarser (10 mm) resolution was used; 4) the resultant increase in DEM errors as the surface became rougher masked the true changes in soil surface roughness over time using all three interpolation methods. This study shows that at plot scale, DEMs generated through interpolations from LiDAR points underestimate soil surface roughness and are ineffective at tracking changes in soil surface roughness over time, and that LiDAR point data must be used instead.