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

Abstract

Soil surface roughness (SSR) is often calculated based on the Digital Elevation Models (DEMs) obtained by interpolating points from terrestrial LiDAR measurements. This study aimed to investigate the effects of DEM interpolation and interpolation methods on quantification of SSR. A series of rainfall at the intensity of 59 and 178 mm hr−1 were simulated on a 2 by 6.1 m stony soil plot under 12 % and 20 % slope treatments. LiDAR measurements were conducted after each rainfall simulation at six positions around the soil plot. DEMs gridded at 5 and 10 mm were generated from LiDAR points using Inverse Distance Weighting, Natural Neighbors and Universal Kriging methods implemented in ESRI ArcGIS 10.5. Random roughness index (RR) was calculated based on the LiDAR-interpolated DEMs and LiDAR points directly and then compared. Results showed: 1) average values of RR from the DEMs were under-predicted compared to those directly calculated from LiDAR points by 7%–20%, indicating smoothing of the modeled surface during the interpolation process; 2) RR from LiDAR points indicated that soil surface became rougher as rainfall was progressed; 3) DEM errors increased as the surfaces evolved to rougher states and when 10 mm resolution was used; 4) the temporal SSR variations using the DEMs were -0.27 and -0.19 mm at 20 % slope and 0.30 and 0.36 mm at 12 % slope for the resolution 10 and 5 mm, respectively, while they were 0.22 mm at 20 % slope and 0.65 mm at 12 % slope when LiDAR points were directly used, the differences are due to the resulting increased DEM errors as the surface became rougher that masked the true changes in soil surface roughness. 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 at present scale.