3D Modeling Laser Scanning Lidar safety Surveying Technology

Landslide Deformation Modeling Improved with Targets

Graph of DTM Surface Landslide Deformation Modeling Error
Landslide Deformation Modeling Error

The extraction of landslide deformation is important for many applications [1,2,3,4], such as disaster management and deformation detection. Laser scanning can be used to acquire accurate and dense 3D points from a target surface and has unique advantages when it comes to landslide monitoring, deformation extraction, and disaster management.

From a paper in MDPI by Zhao, et al.

As the most direct manifestation of landslide stability, landslide surface deformation has received special attention from scholars [5,6,7]. Conventional landslide monitoring methods, such as using the GNSS (Global Navigation Satellite System) [8,9,10], leveling [11,12], crack gauges [13,14], etc., can only obtain sparse measurements at a few locations or within the affected slopes. Therefore, it is difficult to interpret the overall deformation characteristics of a landslide [15,16].

Terrestrial laser scanning (TLS) is a ground-based active imaging method that rapidly acquires precise and dense 3D point clouds on the surface of objects through laser ranging. Guo et al. [17] used point cloud data and the digital elevation model visualization method capable of a sky view to carry out geological disaster identification research and verified the reliability of airborne LiDAR identification results through field investigation. Abellan et al. [18] used TLS technology to monitor a dangerous rock mass in Spain, and discussed the feasibility of millimeter-level high-precision monitoring. Kayen et al. [19] used TLS to monitor nearly 400 large landslides induced by the Chuetsu earthquake in Niigata Prefecture, Japan, and greatly improved the efficiency of post-earthquake disaster assessment. Liu et al. [20] proposed a landslide displacement monitoring method based on point cloud density characteristics; the method identified the slope variation area and directly reflected the landslide surface deformation.

PCR can merge these individually scanned period point clouds. The basic idea of PCR is to seek the best transformation parameters to transform a point cloud with a local coordinate system to the same reference system [21]. PCR is divided into real-time registration and accurate registration of point clouds in different periods. Real-time registration refers to the detection of the surrounding environment while scanning and registering the real-time scanning point cloud using mobile laser scanning with the acquired point cloud. It is widely used for fast modeling, indoor navigation, and simultaneous localization and mapping (SLAM). It is a low-precision PCR method. The point cloud data of different periods is typically obtained using fixed scanners (such as TLS). The main PCR methods for different period data are marker-based registration and data-based registration.

However, there are still many problems with the application of landslide monitoring using TLS technology [22,23]. When using TLS to monitor landslide deformation, it is necessary to collect a multi-period landslide point cloud and calculate the landslide deformation by comparing the spatial position of the point cloud. Unfortunately, each period point cloud is based on an independent coordinate system, and the reference data are not unified, resulting in the low accuracy and unreliable results of landslide deformation detection [24].

For the complete paper on landslide deformation modeling CLICK HERE.

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