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PublisherThe University of Arizona.
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EmbargoRelease after 06/01/2020
AbstractThe presented three studies attempted to enhance our understanding of the evolution of stony hillslope in drylands. Multi-temporal terrestrial LiDAR scans were conducted at six positions around stony plots (2 m × 6.1 m) with three slope treatments (5%, 12%, and 20%, two replications for each slope treatment) through 22 simulated rainfall applications. The first study examines the potential use of terrestrial LiDAR scanner to measure erosion on small plots at high resolutions. With the aid of fixed reference controls in the form of concrete target surfaces of varying roughness, registration accuracy was better than 1 mm and the mean level of change detection was less than 2.2 mm. Comparisons between the erosion mass estimated from LiDAR scans and from runoff samples suggest that using the LiDAR to monitor soil erosion at the plot scale is feasible, and provides guidance about the level of accuracy one might expect in doing so, and the number of scan positions could be reduced to three while not significantly impacting the volumetric change estimations. The second study investigates the effects of DEM interpolation and interpolation methods on quantification of soil surface roughness. DEMs errors tended to increase after the surfaces evolved to a rougher state and when a coarser resolution was used. Comparisons between the random roughness calculated from LiDAR point cloud directly and from LiDAR-interpolated DEMs showed that DEMs generated from LiDAR underestimated soil surface roughness and are ineffective for tracking changes in soil surface roughness over time and that LiDAR point cloud data must be used instead on the present scale. The third study tracks the spatiotemporal evolution of soil surface roughness on stony plots. Roughness indices including random roughness (RR), fractal dimension and generalized fractal dimension, were calculated from LiDAR point cloud directly. Surface roughness showed an increasing trend as the rainfall simulation proceeded, and the steeper slope resulted in greater surface roughness. Both the increase of surficial exposed rocks and the formations of erosion features, e.g., rills and depressions, contributed to the spatiotemporal variations of soil surface roughness. Results also showed that the fractal dimension was not a good indicator of soil surface roughness, but rather was an index of the form of the surface. Crossover length was a measure of roughness at a scale of a few millimeters, while random roughness was a measure of elevation variation on the scale of the length of the transect measured, and thus encompassed larger morphological features including rills. We also established a new method for analyzing multiple fractals that characterized the heterogeneity of soil surface roughness. These results improve our understanding of the evolution of semiarid stony hillslopes.
Degree ProgramGraduate College