A landslide monitoring method using data from unmanned aerial vehicle and terrestrial laser scanning with insufficient and inaccurate ground control points

Abstract

Non-contact remote sensing techniques, such as terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) photogrammetry, have been globally applied for landslide monitoring in high and steep mountainous areas. These techniques acquire terrain data and enable ground deformation monitoring. However, practical application of these technologies still faces many difficulties due to complex terrain, limited access and dense vegetation. For instance, monitoring high and steep slopes can obstruct the TLS sightline, and the accuracy of the UAV model may be compromised by absence of ground control points (GCPs). This paper proposes a TLS- and UAV-based method for monitoring landslide deformation in high mountain valleys using traditional real-time kinematics (RTK)-based control points (RCPs), low-precision TLS-based control points (TCPs) and assumed control points (ACPs) to achieve high-precision surface deformation analysis under obstructed vision and impassable conditions. The effects of GCP accuracy, GCP quantity and automatic tie point (ATP) quantity on the accuracy of UAV modeling and surface deformation analysis were comprehensively analyzed. The results show that, the proposed method allows for the monitoring accuracy of landslides to exceed the accuracy of the GCPs themselves by adding additional low-accuracy GCPs. The proposed method was implemented for monitoring the Xinhua landslide in Baoxing County, China, and was validated against data from multiple sources.

Keywords

• Landslide monitoring
• Data fusion
• Terrestrial laser scanning (TLS)
• Unmanned aerial vehicle (UAV)
• Model reconstruction