Surface roughness measurements using UAV LiDAR and analysis of uncertainty factors

Abstract Surface root mean square height (SRMSH) is a key parameter characterizing surface roughness; it reflects soil hydrological properties and influences related physical processes. LiDAR offers an effective means for measuring SRMSH over large areas, yet the method involves several uncertainty factors that require further investigation. In this study, we proposed a method based on UAV LiDAR data, utilizing linear sampling in target areas. Three potential sources of uncertainty are examined: the spatial interpolation method, the interpolation interval, and the linear sampling length. By selecting and combining representative values for these factors, SRMSH is computed for multiple sample areas. The effects of each factor are then evaluated by comparing results of different parameter configurations. The main findings are as follows: (1) The linear sampling method is capable of estimating SRMSH but introduces systematic errors that correlate primarily with the sampling length; (2) Sensitivity analysis reveals that the sampling length dominates measurement outcomes (contributing >50% of variation), followed by the interpolation method (<10%), while the interpolation interval has minimal influence (<2%). The quantification of parameter impacts provides valuable methodological references for optimizing measurement protocols and developing robust alternatives.