Multi-temporal UAV data for assessing rapid rill erosion in typical gully heads on the largest tableland of the Loess Plateau, China

Quantitative estimation of rill erosion research plays a significant role in the understanding of rill formation and their development mechanisms, which is lacking presently. In this paper, rill erosion is quantified to analyze and evaluate the spatial and temporal distribution in typical gully heads of the Loess Plateau of China. Although several studies have carried rill erosion quantification, the variability analysis of rill erosion rates at different slope position is limited primarily because of the unavailability of high resolution elevation models. Using the low-altitude photography with Unmanned Aerial Vehicles (UAVs), digital surface models (DSMs) with centimeter accuracy were generated, and then geometrical parameters of a total of 1,129 rills at tableland-slope (T-S), hillslope (S), and slopes affected by artificial vegetation (SV) in the study were counted. Then, spatial and temporal development as well as erosion rates of the rills at the three sites were compared. Although many studies revealed the erosion law at the S position, few studies have linked the development of rills at the T-S position, and artificial afforestation at the SV position exacerbates the rill erosion. Furthermore, based on the centimeter-resolution DSMs obtained by the two-stage UAV photography, we found that the slope gradient which was the most susceptible to rill erosion were 5-10°, 35-50°, and 65-80° at T-S, S and SV sites, respectively. At the SV, rill incision was the most sensitive to slope changes, and rill incision potential proved to be the greatest. Generally, the higher the rill erosion depth, the smaller the frequency of occurrence, but this trend was mitigated at the SV position. Therefore, urgent action is needed to control rill erosion caused by artificial afforestation on hillslopes to alleviate and eliminate the adverse effects of vegetation planting on rill erosion on the Loess Plateau of China.