Landslide Deformation Extraction from Terrestrial Laser Scanning Data with Weighted Least Squares Regularization Iteration Solution

Abstract

The extraction of landslide deformation using terrestrial laser scanning (TLS) has many important applications. The landslide deformation can be extracted based on a digital terrain model (DTM). However, such methods usually suffer from the ill-posed problem of a multiplicative error model as illustrated in previous studies. Moreover, the edge drift of commonly used spherical targets for point cloud registration (PCR) is ignored in the existing method, which will result in the unstable precision of the PCR. In response to these problems, we propose a method for extracting landslide deformations from TLS data. To archive the PCR of different period point clouds, a new triangular pyramid target is designed to eliminate the edge drift. If a fixed target is inconvenient, we also propose a PCR method based on total station orientation. Then, the use of the Tikhonov regularization method to derive the weighted least squares regularization solution is presented. Finally, the landslide deformation is extracted by DTM deference. The experiments are conducted on two datasets with more than 1.5 billion points. The first dataset takes Lashagou NO. 3 landslide in Gansu Province, China, as the research object; the point cloud data were collected on 26 February 2021 and 3 May 2021. The registration accuracy was 0.003 m based on the permanent triangular pyramid target and 0.005 m based on the total station orientation. The landslide deforms within 3 cm due to the ablation of the frozen soil. The second dataset is TLS data from the Lihua landslide in Chongqing, China, collected on 20 April 2021 and 1 May 2021. The overall deformation of the Lihua landslide is small, with a maximum value of 0.011 m. The result shows that the proposed method achieves a better performance than previous sphere-based registration and that the weighted least square regularization iterative solution can effectively reduce the ill-condition of the model.

Formative Period Tracing and Driving Factors Analysis of the Lashagou Landslide Group in Jishishan County, China

“…Following the analysis presented in Section 4.2, it was determined that the landslides within the Lashagou landslide group, with the exception of L8, were formed either during the construction of highway G310 (L3, L4, L5, and L6) or after its completion (L1, L2, L7, R1, and R2). Excavation conducted during the construction of the highway is widely acknowledged as a significant factor and an essential condition for the formation of the Lashagou landslide group (e.g., [10,24]). This excavation activity altered the original state of the stress equilibrium in the slope, thereby directly inducing slope deformation, as exemplified by the occurrence of landslide L6.…”

“…The Lashagou landslide group is situated in the transitional zone between the Qinghai-Tibet Plateau and the Loess Plateau (Figure 1a). Its primary cause of formation is considered to be the hillslope excavation activities conducted during the construction of the highway G310 from 2011 to 2013 [10,24]. However, no studies have yet demonstrated the impact of the construction of the highway G310 on the formation and evolution of the Lashagou landslide group.…”

“…However, no studies have yet demonstrated the impact of the construction of the highway G310 on the formation and evolution of the Lashagou landslide group. The latest research indicates that the Lashagou landslide group remained in a state of continuous downward movement from 2020 to 2021 [24], posing a significant threat to the safety of both vehicles and pedestrians on highway G310. To exacerbate the situation, a magnitude 6.2 earthquake transpired on 18 December 2023, at a distance of approximately 18 km from the Lashagou landslide group (Figure 1a).…”

3D Landslide Monitoring in High Spatial Resolution by Feature Tracking and Histogram Analyses Using Laser Scanners

“…It also does not need to touch the landslide body [42]. Consequently, numerous studies these days focus on using TLS for monitoring landslides [43][44][45][46][47][48]. However, this method also has some disadvantages, including a high volume of generated point clouds, especially for large areas, and the problem of accurately registering these point clouds.…”

Establishment and Extension of a Fast Descriptor for Point Cloud Registration

“…The iterative nearest point (ICP) algorithm is the most commonly applied PCR method because the iteration is simple, and the convergence speed is fast [11][12][13][14]. It iteratively calculates the optimum transformation until the function criterion converges to the specified value.…”