无人机以其机动灵活、受天气影响小、快速获取目标区域影像的优势已成为矿区地表形变监测的重要方法，如何提升无人机测量精度是亟需解决的关键问题。在系统分析无人机监测地表点精度影响因素基础上，重点研究了像控点质量对空三点误差分布、精度的影响规律。首先采用大量的重复观测实验的方法，研究揭示了在四周均匀布设像控点条件下，监测点平面位置、高程的误差特性及其分布特征；在此基础上，通过对像控点平面坐标、高程坐标施加随机误差，研究了像控点平面、高程精度与监测点平面、高程精度的对应关系。最后，依据前述研究成果，给出了提升无人机地表沉陷监测精度的策略。研究结果表明，在四周均匀布设像控点条件下，无人机测量点位平面、高程误差符合正态分布规律，但存在一定的系统误差影响；像控点平面坐标、高程坐标对空三解算平面坐标、高程坐标影响具有独立性；通过对多次测量结果进行数字平均可以有效提升测量精度。研究成果为采煤塌陷区无人机高精度监测方案设计提供理论与技术支撑。

Unmanned aerial vehicles (UAVs) have been an important means of monitoring the surface deformation in mining areas since they are flexible, are insensitive to weather, and can swiftly acquire images of target areas. However, there is an urgent need to improve UAV-based measurement accuracy. Based on a systematic analysis of factors affecting the accuracy of surface measurement points, this study focuses on the influence of the quality of ground control points on the point error distribution and accuracy of aerial triangulation. Firstly, through a large number of repeated observations and experiments, this study investigated the error distributions of the planar locations and elevations of monitoring points under the condition of evenly arranged ground control points around. Accordingly, by applying random errors to the planar coordinates and elevations of ground control points, this study determined the corresponding relationship between the planar and elevation accuracy of ground control points and monitoring points. Finally, this study proposes a strategy for improving the UAV-based monitoring accuracy of surface subsidence. The results are as follows: (1) Under the condition of evenly arranged ground control points around, the planar and elevation errors of UAV-based measurement points showed normal distributions but were affected by systematic errors to some extent. (2) The planar coordinates and elevations of the ground control points had independent effects on the planar coordinates and elevations of points in the aerial triangulation. (3) The measurement accuracy can be effectively improved by conducting digital averaging for multiple measurement results. The results of this study will provide theoretical and technical support for the design of UAV-based high-precision monitoring schemes for areas with surface collapse in coal mines.