坡沟系统是黄土丘陵沟壑区主要产沙源地，精确监测其侵蚀过程和形态发育对过程模型构建与土壤侵蚀防治具有重要意义。地面三维激光扫描（Terrestrial laser scanning，TLS）、地基运动恢复结构（Terrestrial structure from motion，TSfM）、无人机激光雷达（Unmanned aerial vehicle laser scanning，ULS）、无人机运动恢复结构（Unmanned aerial vehicle SfM，UAV-SfM）和卫星遥感等技术可高效、快速、非接触监测地形变化，已成为土壤侵蚀过程精细化研究的重要工具。然而，以上方法监测黄土丘陵沟壑区侵蚀过程的精度缺乏系统评估。鉴于此，本文以黄土丘陵沟壑区辛店沟流域自然坡沟系统和桥沟流域自然侵蚀沟道为研究对象，首先于辛店沟流域设置三个自然坡沟系统径流小区各开展五场模拟放水冲刷试验，梁峁坡放水流量分别为85 L min^-1、55 L min^-1和25 L min^-1，沟谷坡放水流量均为10 L min^-1，每场试验前后均利用TLS和TSfM获取地形，基于实测值，评估TLS和TSfM监测坡沟系统土壤侵蚀的精度；其次，基于TLS技术扫描不同区域（坡面、道路、三角堰）并提取侵蚀沟形态，评估ULS和UAV-SfM提取侵蚀沟道形态参数精度，再以TLS扫描不同坡度（0°、15°、30°、45°、60°、75°、90°）聚氯乙烯塑料（Polyvinyl Chloride，PVC）板为“真值”，验证ULS获得点云数据几何精度；最后，基于ULS，评估UAV-SfM和资源3号卫星数据（ZY303）提取侵蚀沟道参数的精度。主要研究结果如下：

      （1）TLS和TSfM监测坡沟系统精度方面。单场次中，不同流量下TLS估算产沙量相对误差的均值介于-27.80%~8.24%，与实测产沙量之间的线性拟合R²介于0.70~0.99，而TSfM估算产沙量相对误差的均值介于-16.20%~73.10%，与实测产沙量之间的线性拟合R²在0.86~0.96之间。累积场次中，基于TLS估算产沙量相对误差的均值介于-20.10%~54.30%，与实测产沙量之间的线性拟合R²在0.88~0.93之间，而TSfM估算产沙量相对误差的均值在-24.10%~84.50%之间，与实测产沙量之间的线性拟合R²在0.69~0.93之间。不同流量下，TLS提取侵蚀细沟长度的绝对误差和相对误差的均值分别介于-0.15 m~ -0.02m和-2.18%~-0.16%，与实测侵蚀细沟长度的线性拟合R²介于0.87~0.99。TSfM提取侵蚀细沟长度绝对误差和相对误差分别介于-0.38 m~-0.15 m和-7.09%~-1.49%，与实测侵蚀细沟长度的线性拟合R²介于0.61~0.96。试验结果表明TLS定量监测坡沟系统土壤侵蚀精度更高，同时TSfM可作为TLS监测土壤侵蚀的替代方案。

      （2）ULS监测精度方面。基于TLS获取桥沟流域不同部位（坡面、道路、三角堰）地形信息，评估ULS和UAV-SfM在不同区域的监测精度。ULS提取侵蚀沟道绝对误差和相对误差分别小于0.02 m和0.55%，而UAV-SfM监测不同区域提取侵蚀沟道绝对误差和相对误差分别小于0.72 m和3.92%。因此，基于ULS数据提取侵蚀沟精度优于UAV-SfM。利用ULS技术监测不同区域不同坡度PVC板的平均绝对误差介于0 m~0.08 m，中值介于0 m~0.09 m。

      （3）UAV-SfM和ZY303监测侵蚀沟道精度方面。UAV-SfM可监测侵蚀沟道17条主沟和16条支沟形态，而ZY303可监测到17条主沟形态，仅监测到11条支沟形态。2020年和2021年UAV-SfM、ZY303与ULS提取主沟二维形态参数（面积、周长、沟长）的百分误差分别小于20%和35%，提取支沟的二维形态参数分别小于25%、40%。2020年和2021年UAV-SfM和ZY303提取主沟三维形态参数（侵蚀沟体积、横截面积、平均沟深）与ULS的百分误差分别小于20%和30%，提取支沟的三维形态参数分别小于20%和50%。结果表明UAV-SfM更适合于监测植被稀疏且地形相对复杂区域的侵蚀沟道，而ZY303更适合监测地形平缓区域，同时ZY303适合区域尺度应用。

      The slope-gully system is the main source of sediment in the hilly and gully Loess Plateau, and monitoring accurate of soil erosion and gully erosion is of great significance for soil and water conservation, process-based erosion model development, and soil erosion control. Remote sensing technologies, such as terrestrial laser scanning (TLS), unmanned aerial vehicle laser scanning (ULS), terrestrial structure from motion (TSfM) photogrammetry, unmanned aerial vehicle SfM (UAV-SfM) and satellite remote sensing, are capable of monitoring terrain changes efficiently in a non-contact manner. These technologies provide an important tool for soil erosion study. However, the accuracy of the techniques for erosion monitoring remained unclear. Thus, this study focused on the natural slope-gully system and the natural gully (i.e. Xindiangou catchment and Qiaogou catchment ) in the hilly and gully Loess Plateau. Firstly, five souring experiments were implemented on each of the three plots, consisting of hillslope and gully area, established on field slopes. The input flow rates of three hillslopes were 25, 55 and 85 L min^-1, respectively, while that for the gully area was 10 L min^-1. The terrain of the plots before and after the experiments was recorded by TLS and TSfM, while the accuracy of TLS and TSfM in monitoring soil erosion in the gully-slope system was evaluated based on measured values. Secondly, TLS was used to scan different areas (slope, road, and triangular weir) to extract the gully erosion as the validation value and evaluated the accuracy of UAV-SfM and ULS in monitoring gully erosion. The Polyvinyl Chloride (PVC) panels with different slopes (0°, 15°, 30°, 45°, 60°, 75°, 90°) scanned by TLS were used as the validation value to verify the accuracy of the point cloud data obtained by ULS. Finally, the accuracy of gully parameters and erosion volume monitored by UAV-SfM and satellite images (ZY303) were further assessed by ULS results. Main findings are as follows:

      (1) In terms of the results for individual experiments, the average values of relative errors for sediment yield estimated by TLS under different flow rates were -27.80% - 8.24%, and the R² of the linear relationships between the calculated and measured consecutive sediment yield were 0.70 - 0.99. The average values of relative errors for sediment yield estimated by TSfM were -16.20% - 73.10%, and the R² of the linear relationships between the calculated and measured consecutive sediment yield were 0.86 - 0.96. In terms of cumulative results for the experiments, the average values of relative errors for sediment yield estimated by TLS were -20.10% - 54.30%, and the R² of the linear relationships between the calculated and measured sediment yield were 0.88 - 0.93. The average values of relative errors for sediment yield estimated by TSfM were -24.10% - 84.50%, and the R² of the linear relationships between the calculated and measured sediment yield were 0.69 - 0.93. Under different flow rates, the mean values of absolute errors and relative errors for erosion rill length extracted by TLS were -0.15 m - -0.02 m and -2.18% - -0.16%, respectively, and the R² of the linear relationships between the calculated and measured rill length were 0.87 - 0.99. The mean values of absolute errors and relative errors for rill length extracted by TSfM were -0.38 m - -0.15 m and -7.09% - -1.49%, respectively, and the R² of the linear relationships between the calculated and measured rill length were 0.61 - 0.96. The experimental results showed that TLS had higher accuracy in quantitative monitoring of soil erosion in slope-gully system, and TSfM could be used as an alternative solution to TLS for monitoring soil erosion.

      (2) The average values of absolute errors and relative errors for extracting gullies from different areas (slope, road, and check dam) using ULS were lower than 0.02 m and 0.55%, respectively, while those using UAV-SfM monitoring were lower than 0.72 m and 3.92%, respectively. Therefore, the accuracy of extracting gullies using ULS was superior to that of UAV-SfM. The average values of absolute errors for monitoring PVC boards with different slopes using ULS  were 0 m - 0.082 m, with a median value were 0 m - 0.09 m.

      (3) UAV-SfM was able to monitor the morphology of 17 main gullies and 16 tributary gullies, while ZY303 monitored the morphology of 17 main gullies and 11 tributary gullies. The percent errors of UAV-SfM, ZY303, compared with ULS in obtaining two-dimensional morphological parameters (i.eg. area, perimeter and gully length) of the main and tributary gullies in 2020 and 2021 were all lower than 20%, 34% and 25%, 40%, respectively. The percent errors of UAV-SfM, ZY303, compared with ULS in obtaining three-dimensional morphological parameters, such as cross-sectional area, average gully depth, and erosion volume, were all lower than 20%, 30% and 20%, 50%, respectively. The results suggested that UAV-SfM had a higher accuracy for monitoring erosion gullies in areas with sparse vegetation and relatively complex terrain, while ZY303 was more suitable for monitoring areas with gentle terrain, while ZY303 was appropriate for a regional scale application.

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