Digital elevation model (DEM) of difference (DoD) produced by unmanned aerial vehicle (UAV)-borne laser scanning (ULS) data has been one of the important methods for monitoring catchment-scale landscape change processes, while its accuracy has been limited by the lack of understanding for the spatially variable uncertainties from systematic errors and random errors included in the DoD. In this study, the DoD uncertainty derivation (DUD) method was improved by undertaking an exhaustive error analysis, estimation of residual systematic errors, and incorporating different DoD uncertainty elimination strategies, based on multitemporal ULS data acquired from a small catchment of the Chinese Loess Plateau. The adapted method was employed to estimate the soil erosion and deposition of the catchment, while the reliability of the method was verified by the volume of mass movement and the depths of gullies measured through field surveys. Results showed that mean systematic biases were 0.025, 0.008, -0.074, and 0.051 m for multitemporal point clouds, respectively. After coregistration, the corresponding systematic bias were 0.001, 0.008, -0.016, and -0.021 m, respectively. The change results showed a significant relationship with the results of mass movement and gully depths (R-2 > 0.8, p < 0.01). The adapted DUD method was able to capture different erosion processes, including gully headcut retreat, gully development, mass movement, and localized deposition, while it also achieved an underestimation of the changes compared to field survey results. In the catchment, the area of human activity contributed the highest percentage of the volumetric changes, followed by the gully slope and gully bottom, and the hillslope normally contributed the lowest. Overall, the adapted DUD method provided a reliable way for estimating geomorphic changes at the catchment scale.