The monitoring of river runoff is of great significance to the management and utilization of water resources， but how to obtain river runoff flexibly and accurately is still a difficult problem. Due to the limited resolution of satellite remote sensing， it is difficult to accurately invert the runoff of small and medium-sized rivers. The traditional river flow monitoring technology is complex and expensive， and its application is limited in the areas without data and in the emergency monitoring of sudden disasters. Therefore， this study takes advantage of the fast and flexible characteristics of UAVs and the advantages of LiDAR to obtain terrain information with high accuracy. Based on the 3D model of UAVs LiDAR point cloud data， combined with the Particle Tracking Velocity （PTV） method， This paper presents a method of runoff monitoring for small and medium-sized rivers. In this method， the boundary line between water body and land is extracted by using the strong absorption characteristics of the near infrared band of LiDAR， and the cross section is obtained by matching and merging with the original profile of the river. Based on low-altitude UAV optical remote sensing images， the particle tracking velocity measurement method is used to calculate the river velocity， and then the river runoff is estimated by the velocity area method. After 24 UAV runoff monitoring experiments in the reach of Liancheng Hydrology Station， the following conclusions are reached： The average relative Error between the flow monitored by LiDAR and the measured flow is 8.67%， the minimum relative error is 0.46%， and the Root Mean Squared Error （RMSE） is 0.09 m³/s. MPE （Mean Percentage Error） is 0.02， Pbias （Percent bias） is 1.95%， the Nash-Sutcliffe efficiency coefficient （NSE） was 0.94， which could meet the monitoring accuracy requirements of small and medium-sized rivers in areas without data. By comparison， the monitoring accuracy of runoff using this method is significantly higher than that of Manning formula runoff estimation （RMSE， NSE）. This study demonstrates the feasibility and reliability of the unmanned aerial vehicle Lidar point cloud data runoff monitoring， and provides a new idea for the emergency monitoring of sudden disasters in areas without data and the runoff monitoring of small and medium-sized rivers.