AbstractTillage and slope will influence soil surface roughness that changes during rainfall events. This study tests this effect under controlled conditions quantified by geostatistical and fractal indices. When four commonly adopted tillage practices, namely, artificial backhoe (AB), artificial digging (AD), contour tillage (CT), and linear slope (CK), were prepared on soil surfaces at 2 × 1 × 0.5 m soil pans at 5°, 10°, or 20° slope gradients, artificial rainfall with an intensity of 60 or 90 mm h−1 was applied to it. Measurements of the difference in elevation points of the surface profiles were taken before rainfall and after rainfall events for sheet erosion. Tillage practices had a relationship with fractal indices that the surface treated with CT exhibited the biggest fractal dimension D value, followed by the surfaces AD, AB, and CK. Surfaces under a stronger rainfall tended to have a greater D value. Tillage treatments affected anisotropy differently and the surface CT had the strongest effect on anisotropy, followed by the surfaces AD, AB, and CK. A steeper surface would have less effect on anisotropy. Since the surface CT had the strongest effect on spatial variability or the weakest spatial autocorrelation, it had the smallest effect on runoff and sediment yield. Therefore, tillage CT could make a better tillage practice of conserving water and soil. Simultaneously, changes in semivariogram and fractal parameters for surface roughness were examined and evaluated. Fractal parameter – crossover length l – is more sensitive than fractal dimension D to rainfall action to describe vertical differences in soil surface roughness evolution.
Potential and accurate evaluation of unmanned aerial vehicle (UAV) remote sensing for soil surface roughness measurement
