Comparison between image- and surface-derived displacement fields for landslide monitoring using an unmanned aerial vehicle

The traditional particle image velocimetry technique generates a 2D displacement field for landslide monitoring using multi-temporal unmanned aerial vehicle (UAV) orthoimages. As UAV photogrammetry can produce a 2.5D digital surface model (DSM) and 3D point clouds, two different surface-based approaches-DSM-and point-based methods-were developed to provide 3D displacement fields for landslide monitoring. The DSM-based approach utilized the image matching technique via an interpolated surface model, while the point-based approach used the windowed iterative closest point technique via irregular points. Several in-situ real-time ki-nematics measurements were used to analyze the quality of the different approaches. The experimental results showed that the performance of the point-based method was better than the image-and DSM-based approaches and attained 0.1 m accuracy for horizontal and vertical displacement. In the qualitative analysis, the results of the point-based method were similar to the actual surface movement, demonstrating uniform behavior in the landslide region. In summary, the use of point clouds from dense image matching proved beneficial for providing 3D displacement fields for landslide monitoring.