基于无人机摄影测量的地形变化检测方法与小流域输沙模型研究

流域输沙过程是地貌学和地表动力学的重要研究内容,但传统的输沙过程监测方法仅能得到某个区域的总输沙率,无法推算其空间分布。论文以黄土高原绥德县窑家湾小流域为例,利用无人机摄影测量技术得到其2006年和2019年2期数字高程模型(DEM)并计算地形变化量;然后,根据质量守恒原理和多流向算法建立泥沙在空间上的输送模型,进而计算小流域输沙率的空间分布。实验结果表明,该模型能有效模拟泥沙在空间上的输送情况,输沙率出现质量不守恒的区域面积占比小于4%,且不守恒区域多为人类活动影响区。同时,论文讨论了DEM的选择和不同地形变化检测水平对模型结果的影响。当使用第一期DEM进行泥沙搬运路径推算时,质量不守恒区域的面积显著降低。使用误差空间分布图进行地形变化检测得到的输沙率结果鲁棒性更强。使用中误差进行地形检测得到的结果在不同置信度下变化较大。基于无人机地形变化检测的空间输沙模型能方便、快捷地提供详尽的输沙率空间分布,为地表过程研究带来了新的机遇。

Modelling sediment transport in space in a watershed based on topographic change detection by UAV survey

Sediment transport rate, as an important indicator for studying Earth surface processes and for planning soil and water conservation, is a spatial variable. However, traditional monitoring methods can only obtain the global sediment transport rate in a certain area and are unable to map the spatial distribution of sediment transport rate. In this study, unmanned aerial vehicle (UAV) photogrammetry was used to obtain a two-phase digital elevation model (DEM) and calculate the amount of topographic change. In accordance with the principle of mass conservation and using a multi-flow direction algorithm, a spatial sediment transport model was established, by which the spatial sediment transport rate from a loess watershed was obtained. The results show that this method can effectively simulate the transportation of sediment in space. The area where the mass is not conserved is less than 4%, and the non-conserved areas are mostly those that are affected by human activities. The effects of DEM choice and the level of topographic change detection on the proposed model are also discussed. When DEM in the first phase is used to calculate the sediment transportation path, the area where the mass is not conserved is significantly reduced. The results of the spatial sediment transport model are more robust when a precision map is used to detect topographic changes, whereas the results from topographic changes detected by root mean square error (RMSE) greatly varied under different confidence levels. The proposed model can conveniently and quickly provide a detailed spatial distribution of sediment transport rate through UAV-based topographic change detection, which provides new perspectives for research of Earth surface processes.