小流域是黄土高原主要的侵蚀产沙单元，深入理解其泥沙生成、输移和沉积过程与影响因素对黄土高原水土流失治理具有重要意义。已有侵蚀过程主要基于坡面小尺度或区域大尺度开展，鲜有研究小流域侵蚀产沙、泥沙输移和沉积过程的精细化研究，尤其对地形、土地利用、植被覆盖等变化过程的驱动因素认识不足。鉴于此，本文以黄土丘陵沟壑区桥沟流域为研究对象，借助无人机激光雷达技术（Unmanned Aerial Vehicle Laser Scanning，ULS）与无人机摄影测量技术(Unmanned Aerial Vehicle-Photogrammetry，UAV-P)，于黄土丘陵沟壑区桥沟流域进行了6次ULS、UAV-P监测试验，利用获取的精细地表信息，研究适用于桥沟流域泥沙连通性指数（Index of Connectivity，IC_c）的最佳分辨率。并在此基础上，计算6种泥沙连通性指数Roughness Index of Connectivity（IC_w）、IC_c、Joint Index of Connectivity（IC_j）、Revised Version of the IC（IC_revised ）、Rainfall Index of Connectivity（IC_r ）和Revised Sediment Connectivity Index（RIC），分析其时空变化特征，结合野外调查数据遴选出适用于桥沟流域的泥沙连通性指数。最后，探究所选泥沙连通性指数与影响因素（降雨量、植被覆盖度、坡度、地表粗糙度、曲率和沟壑密度）之间的关系。主要研究结果如下：

（1）泥沙连通性最佳分辨率研究表明，0.25 m、0.5 m、0.75 m、1 m、2 m、5 m分辨率数字高程模型（Digital Elevation Model，DEM）对地表微地形参数表达有明显影响，且不同分辨率和不同土地利用类型的泥沙连通性也具有明显差异。其中，随着DEM分辨率的降低，桥沟流域整体IC_c指数的平均值呈先递增后递减趋势，且分辨率越低变化越大，而不同土地利用类型的IC_c指数在分辨率为0.25m-0.5m时整体波动最小。结合野外调查发现，分辨率为0.5 m时，IC_c指数的空间分布能清晰合理反映流域尺度泥沙输移痕迹，可以用来分析桥沟流域泥沙输移的过程。

（2）六种泥沙连通性指数适用性评价结果显示，RIC与野外调查获取的Filed Index of Connectivity（FIC），之间的显著性最高（R²=0.85，p < 0.05），与沉积量之间的关系最为显著（R²=0.69，p < 0.01）。表明RIC指数在黄土丘陵沟壑区桥沟流域有较高的监测精度，可以用来探究桥沟流域泥沙连通性的变化。

（3）RIC指数与影响因素间的关系分析显示，植被被覆盖度（R²=0.86，p < 0.01）和坡度（R²=0.84，p < 0.01）是影响泥沙连通性RIC指数的关键因素，地表粗糙度（R²=0.58，p < 0.05）和沟壑密度（R²=0.50，p < 0.05）是影响泥沙连通性RIC指数的次要因素。不同土地利用类型下的RIC指数存在明显差异，且水土保持工程措施（坡面治理工程）会对RIC指数产生重要影响。

The small catchment is the major source of erosion and sediment production on the Loess Plateau, and an in-depth understanding of erosion, sediment transport and deposition processes and influencing factors is important for regional soil and water conservation. Previous studies mainly focused on soil erosion processes of small-scale slopes or large-scale regions. Few research was conducted to figure out the erosion-transport-deposition processes at the catchment scale and their influencing factors such as topography, land use, and vegetation cover etc. This study investigated the sediment connectivity in the Qiaogou catchment of the hilly and gully Loess Plateau based on the high-resolution terrain information acquired by six aerial LiDAR and unmanned aerial vehicle (UAV) photogrammetry flights. Firstly, the optimal pixel size of the sediment connectivity index IC_c for the Qiaogou watershed was determined through a comparison with field-surveyed results. Then, six sediment connectivity indices, namely, the roughness index of connectivity (IC_w), joint index of connectivity (IC_j), index of connectivity (IC_c), revised version of the IC model (IC_revised), joint index of connectivity (IC_r), and revised sediment connectivity index (RIC) were derived. Then the spatio-temporal features were analyzed and the optimal sediment connectivity metrics were evaluated and compared with field surveys. Finally, the relationship between the optimal sediment connectivity index and driving factors such as rainfall, vegetation cover, slope, surface roughness, curvature, and gully density was explored. Main findings are as follows:  

(1) the investigations into the optimal resolution for the sediment connectivity index showed that the resolutions of 0.25 m, 0.5 m, 0.75 m, 1 m, 2 m, and 5 m digital elevation model (DEM) significantly affected the expression of surface microtopographic parameters, and sediment connectivity varied notably across different resolutions and land use types. As the resolution of the IC_c index decreased, the average IC_c index of the entire catchment exhibited an initially increasing and then decreasing trend, with greater fluctuations observed at lower resolutions. The overall fluctuation of IC_c indices for different land use types was minimized at resolutions of 0.25-0.5 m. At the 0.5 m resolution, the spatial distribution of IC_c indices was able to clearly and reasonably reflect basin-scale sediment transport patterns, and hence the 0.5m resolution was selected as the optimal resolution for the Qiaogou catchment.

(2) The comparison of six sediment connectivity indices showed that RIC showed the highest agreement with filed index of connectivity (FIC) (R²=0.85, p<0.05), and its relationship with sediment deposition was strongest (R²=0.69, p<0.01). This indicated that the RIC was the optimal sediment connectivity index for the study area and could be used to explore the changes in sediment connectivity.

(3) The relationship between RIC and influencing factors demonstrated that vegetation cover (R²=0.86, p<0.01) and slope (R²=0.84, p<0.01) were the key factors affecting the RIC, while surface roughness (R²=0.58, p<0.05) and gully density (R²=0.50, p<0.05) were secondary influencing factors. The RIC varied considerably across land use types, while soil and water conservation engineering measures exerted an important impact on the RIC.