Abstract
In recent years, low-cost unmanned aerial vehicles (UAVs) photogrammetry and terrestrial laser scanner (TLS) techniques have become very important non-contact measurement methods for obtaining topographic data about landslides. However, owing to the differences in the types of UAVs and whether the ground control points (GCPs) are set in the measurement, the obtained topographic data for landslides often have large precision differences. In this study, two types of UAVs (DJI Mavic Pro and DJI Phantom 4 RTK) with and without GCPs were used to survey a loess landslide. UAVs point clouds and digital surface model (DSM) data for the landslide were obtained. Based on this, we used the Geomorphic Change Detection software (GCD 7.0) and the Multiscale Model-To-Model Cloud Comparison (M3C2) algorithm in the Cloud Compare software for comparative analysis and accuracy evaluation of the different point clouds and DSM data obtained using the same and different UAVs. The experimental results show that the DJI Phantom 4 RTK obtained the highest accuracy landslide terrain data when the GCPs were set. In addition, we also used the Maptek I-Site 8,820 terrestrial laser scanner to obtain higher precision topographic point cloud data for the Beiguo landslide. However, owing to the terrain limitations, some of the point cloud data were missing in the blind area of the TLS measurement. To make up for the scanning defect of the TLS, we used the iterative closest point (ICP) algorithm in the Cloud Compare software to conduct data fusion between the point clouds obtained using the DJI Phantom 4 RTK with GCPs and the point clouds obtained using TLS. The results demonstrate that after the data fusion, the point clouds not only retained the high-precision characteristics of the original point clouds of the TLS, but also filled in the blind area of the TLS data. This study introduces a novel perspective and technical scheme for the precision evaluation of UAVs surveys and the fusion of point clouds data based on different sensors in geological hazard surveys.
Highlights
Landslides are an extensively studied natural phenomenon, and they can sometimes cause serious economic losses and casualties (Lindner et al, 2015; Peppa et al, 2017; Hu et al, 2018; Godone et al, 2020; Zhang et al, 2021)
To evaluate the impact of the ground control points (GCPs) on the different types of unmanned aerial vehicles (UAVs), we sat up three testing schemes—Project 1: Mavic Pro UAVs-Mavic Pro UAVs (Figure 5); Project 2: Phantom 4 real-time kinematic (RTK) UAVs- Phantom 4 RTK UAVs (Figure 6); and Project 3: Phantom 4 RTK UAVs- Mavic Pro UAVs (Figure 7)—and used
It should be noted that the M3C2 distance calculated using the three schemes is the threedimensional straight-line distance between two points, while the digital surface model (DSM) of the difference (DOD) results show the elevation changes during the two periods of the digital elevation model (DEM), which may cause differences between the M3C2 distance statistics table and the landform detection change results
Summary
Landslides are an extensively studied natural phenomenon, and they can sometimes cause serious economic losses and casualties (Lindner et al, 2015; Peppa et al, 2017; Hu et al, 2018; Godone et al, 2020; Zhang et al, 2021). The two most commonly used remote sensing methods used for this fine three-dimensional (3D) mapping are the TLS and UAVs methods (Mancini et al, 2013; Neugirg et al, 2016; Chatzistamatis et al, 2018; Guisado-Pintado et al, 2019) The former uses active laser emission to record the 3D coordinates and reflectivity of the scanned object, with a sub-centimeter level accuracy (Neugirg et al, 2016; Brede et al, 2019; Sasak et al, 2019; Wijesingha et al, 2019). Carey et al (2019) used a global positioning system (GPS)-supported drone to obtain aerial images of the upper Scenic Drive landslide over 2 days and used the images to generate a digital elevation model (DEM) with a high spatial resolution (3–10 cm/pixel). Godone et al (2020) resumed monitoring of farmland, houses, and infrastructure through UAVs flights and assessed the remaining risks caused by large-scale landslides. Hendrickx et al (2020) used the UAVs and TLS methods to survey a talus slope on the Col du Sanetsch (Swiss Alps) for three consecutive summers and successfully detected debris flows, snow push, and rill erosion
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