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

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/m2spacing) 1 m DEM available statewide in Pennsylvania and a high point density (10.28 points/m2spacing) 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.

Detecting Terrain Stoniness From Airborne Laser Scanning Data †

“…They reported ground return ratio is 40% with the ground sample density ρ = 4/ m 2 , which is much higher than ρ ≈ 0.8/ m 2 in our study. The distribution of the local ground sample density was not reported in [19] but is probably much higher than in our case. DEM in Figure 1) is a standard data type used by geographic information systems (GIS).…”

“…Their data has the sample density ρ = 5/ m 2 which produces geometric error of size 0.3 m which is larger than the observed shapes (curbstones) and thus not practical. Effects of foliage and woody debris are discussed in [19]. They mention that even a high-density ALS campaign is not able to get a dense sampling of the ground surface in a non-boreal forest (Pennsylvania, U.S.).…”

“…One reference [19] lists several alternative LiDAR based DEM features, which could be used in stone detection, too. These include fractal dimension, curvature eigenvectors, and analyzing variograms generated locally over multiple scales.…”

“…• various roughness measures, e.g., rugosity (related trigonometrically to the average slope), local curvature, standard deviation of slope, standard deviation of curvature, mount leveling metric (opposite to a pit fill metric mentioned in [19]). …”

Synergistic Use of LiDAR and APEX Hyperspectral Data for High-Resolution Urban Land Cover Mapping

“…As such, LiDAR is a powerful tool for altimetric modelling of nearly any terrain [9]. The advent of LiDAR marked a major step forward in urban remote sensing since it allowed to move beyond the limits dictated by multi-and hyperspectral imagery by adding new components to the equation such as height, intensity and multiple return/texture features [9,[12][13][14][15].…”