黄土高原土壤侵蚀严重。近半个世纪以来，学者们采用不同方法监测该区域的土壤侵蚀，但已有研究多基于径流小区开展，大范围（如流域尺度）的土壤侵蚀监测严重缺乏。近年来，新兴的测绘遥感技术，如机载LiDAR，为大范围土壤侵蚀的高效监测提供了可能，但其监测土壤侵蚀的能力尚不清楚。鉴于此，本文以黄土高塬沟壑区董庄沟流域为研究区，探索了机载LiDAR监测地形变化的不确定性（误差），进而基于文献分析获取的土壤侵蚀数据，评估了其监测不同土壤侵蚀过程的能力。首先获取了研究区的无人机LiDAR数据，对比了MCC、ETEW、ATIN、PM、SBF等5种常用滤波算法的精度，确定了适合于研究区的滤波算法；其次，分析IDW、LINE、NN和TIN等4种插值算法DEM求差 (DEM of Difference, DoD）不确定性的空间分布、大小及其与地形和点云密度等关键影响因素的关系，明确了适用于研究区的插值算法，并建立了DoD误差与地形和点云密度之间的关系。最后，通过比较最小DoD误差与不同土壤侵蚀过程导致的地形变化，评估机载LiDAR技术可监测的土壤侵蚀类型。同时，基于构建的高精度DEM，建立了侵蚀沟道与沟长和面积的统计模型。可为黄土高原土壤侵蚀的大尺度、高效率监测提供理论与方法支撑。主要研究结果如下： （1）滤波算法比较方面。MCC和PM的I类（地面点云错分比例）误差通常最低，ETEW和SBF最高，ATIN居中，各算法的II类误差（非地面点云错分比例）和总误差（总体点云错分比例）大小顺序与I类误差大致相反，PM在陡峭区域的点云滤波效果最优。坡度和植被覆盖度显著影响各算法的I类误差。MCC和PM的I类误差随坡度上升和植被覆盖度下降的速率最小，ETEW最大，SBF和ATIN居中。随着点云密度增加，MCC、PM、ATIN的I类误差无明显变化，II类误差和总误差缓慢下降，SBF和ETEW的I类误差增加，II类误差和总误差下降。 （2）DoD误差空间分布及插值算法对比方面。各插值算法DoD不确定性空间分布相似，仅在局部存在明显差异，梁峁坡区域DoD误差绝对值的均值和标准差明显小于沟谷坡区域，沟道处DoD误差极值均位于地形陡峭的沟壁上。基于TIN的DoD误差绝对值小于其他三种插值算法（IDW、NN和LINE）。DoD误差绝对值与地形粗糙度显著正相关（R2>0.99，p<0.001），与点云密度显著负相关（R2>0.97，p<0.001)。经多元回归，建立了DoD误差绝对值与地形粗糙度和点云密度的回归方程（R2>0.95，p<0.001)，其可用于估算DoD误差的空间分布，为提升机载LiDAR的地形变化监测精度提供了依据。 （3）土壤侵蚀监测与评估方面。通过对比DoD误差绝对值与不同土壤侵蚀过程侵蚀量，发现采用DoD方法，机载LiDAR可监测由次暴雨引起的切/冲沟类型的土壤侵蚀以及浅层滑坡和深层滑坡，有可能监测浅沟侵蚀，但不能监测细沟侵蚀。基于机载LiDAR点云所生成高精度DEM提取的沟道体积（V）、长度（L）和面积（A_g），得到体积与沟长拟合公式为V=0.015L^2.917（R2=0.541，p<0.001），与面积拟合公式为V=0.0005〖A_g〗^2.338（R2=0.98, p<0.001），面积与体积拟合结果明显优于沟长与体积的拟合关系，建议采用面积与体积的关系预测沟道体积。

The Chinese Loess Plateau is one of the world’s mostly eroded areas. During the past decades, scholars have used various methods to monitor soil erosion on the Loess Plateau. However, due to the limitation of monitoring methods, soil erosion has been primarily monitored at the erosion plot scale, while the large-scale (e.g. watershed scale) monitoring was seriously lacking. In recent years, emerging remote sensing technologies, such as airborne LIDAR, have made it possible to effectively monitor soil erosion process over a large area, but their ability to monitor soil erosion remains unclear. Hence, this study, taking Dongzhuanggou, a small watershed in the gullied loess plateau, as the study area, explored the uncertainty (error) of airborne LiDAR in terrain change detections and evaluated its ability to monitor different soil erosion processes based on soil erosion measurements obtained from literatures. Firstly, five commonly-used filtering algorithms (MCC, ETEW, ATIN, PM and SBF) were assessed to determine the suitable algorithms for the study area. Secondly, four interpolation methods (IDW, LINE, NN and TIN) were used to generate DEM of difference (DoD). The suitable interpolation method was identified for the study area. The spatial distribution and magnitude (DoD_ua) of DoD uncertainty (error) was studied and the relationship between DoD_ua and key influencing factors such as terrain and point cloud density were investigated. Mutltivirable relationships were then established between DoD_ua and local roughness and point cloud density. Finally, the ability of airborne LiDAR technology to monitor soil erosion was evaluated by comparing the minimum DoD_ua with the terrain changes induced by different soil erosion processes. At the same time, based on the high-resolution DEM derived from the airborne point clouds, regressions were also established between gully volume and gully length as well as gully area. The results provide a theoretical and practical reference for the large-scale and efficient soil erosion monitoring on the Loess Plateau. The main findings are as follows: (1) Type I errors (misclassification ratio of ground points) of MCC and PM were generally lowest, type I error of the ETEW and SBF were highest, and those of ATIN were in the middle. The sequence of type II (misclassification ratio of non-ground points) and total errors (misclassification ratio of total points) of the algorithms were roughly opposite to that of the type I error. The PM, compared to other algorithms, yielded the best filtering results for steep-sloping areas. Slope gradients and vegetation coverage significantly affected type I errors of the employed algorithms rather than type II and total errors. The increase (decrease) rate of type I error with slope gradients (vegetation coverage) was lowest for MCC and PM, and highest for ETEW, with that of SBF and ATIN being in the middle. With the increase of the point density, the type I errors of MCC, ATIN and PM did not change apparently, and the type II and total errors slowly decreased, while the type I errors of SBF and ETEW increased, and the type II errors and total errors decreased. (2) The spatial pattern of DoD uncertainty derived using different interpolation methods were similar. The magnitude of DoD uncertainty (DoD_ua) on hillslopes is much lower than that of gully areas, with peak values emerging on gully walls. Among the four interpolation methods, TIN usually achieved the lowest (DoD_ua), and NN produced less fluctuating (DoD_ua), while LINE and IDW usually produced highest and / or most fluctuating DoD_ua, particularly for gully areas. The DoD_ua significantly increased with local roughness (R2>0.99, p<0.001), while it decreased exponentially with point density (R2>0.97, p<0.001). Multivariable regressions were established between DoD_ua derived using different interpolation methods and local roughness and point density, providing an alternative method for the derivation of spatial DoD uncertainty, particularly for ALS data source. (3) In terms of a comparison of DoD_ua and meansured soil erosion rates, LiDAR is able to detect soil erosion of permanent gullies at an event scale and deep-seated / shallow landslides. It is possible to monitor erosion of shallow (ephemeral) gullies, and may not be able to monitor rill erosion. Based on the gully volume (V), gully length (L) and gully area (A_g) derived based on the high-resolution DEM, regressions were established between V and L (V=0.015L^2.917(R2=0.541, p<0.001)) as well as V and A_g (V=0.0005〖A_g〗^2.338(R2=0.98, p<0.001)). The relationship between V and A_g is stronger than that between V and L, suggesting that a prediction of gully erosion based on the A_g relationship should be preferred.

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