Using an unmanned aerial vehicle for topography mapping of the fault zone based on structure from motion photogrammetry

Abstract

High-precision and high-resolution topography is the basis of the quantitative study of active faults. Light detection and ranging (lidar) is currently the most popular method for obtaining such data, but its relatively high cost greatly limits its use in many geoscience applications. Recently, with the rapid development of computer vision science and the growing application of small unmanned aerial vehicles (UAVs), Structure from Motion (SfM) photogrammetry shows great potential for providing topographic information of comparable resolution and precision to lidar surveys, but at significantly lower cost. In this study, we examined the applicability of SfM photogrammetry in modelling the topography of the fault zone using images acquired with a low-cost digital camera mounted on a UAV over the Haiyuan fault. The resolution and accuracy of the SfM-derived topography were evaluated in detail using existing airborne lidar data as a benchmark. The results show that the density of the point cloud generated by SfM photogrammetry is nearly 70 times higher than that from the airborne lidar. Furthermore, considering the errors in the lidar data itself, the precision of the SfM point cloud is comparable to that of the lidar point cloud. Overall, our results demonstrate that the UAV-based SfM photogrammetry method can provide an inexpensive, effective, and flexible alternative to airborne lidar for the topography mapping of the fault zone.