Three-dimensional movement of landslide using mobile laser scanning technique

Landslide is one of the natural disasters which occurs after a flood when the land, rocks and earth debris could no longer be held together. As such, landslides should be monitored periodically to prevent damage and loss of human life. Various technologies can be used to monitor the occurrence of landslides. Recently, geoinformation technology provides a surface-based approach using the Mobile Laser Scanning (MLS) methods. MLS is able to provide fast and accurate data acquisition of the landslide surface. Steep slope conditions at the area of study has caused a limitation for the vehicle-based MLS to cover flat areas. Nevertheless, with a combination of human-based approach MLS have helped in completing the point cloud data of the study area. Besides, using Geographic Information System (GIS) spatial analysis methods has also provided better visualization on the movement at landslide area. Thus, the aim of this study is to investigate landslide surface movement area using MLS and GIS spatial analysis methods. The methodology of this study began with the research planning and later the MLS survey was conducted which involved the use of vehicle and human-based data acquisition approach and the total station survey for the three-dimensional (3D) target measurement. Then, the MLS data went through the point cloud registration methods involving four types of cloud-tocloud (C2C) distance computation methods. Finally, it was determined that the Iterative Closest Point (ICP) was the best registration method and the least square plane as the best C2C distance computation methods for calculating the difference between the clouds. The results from total station survey showed that the area nearest to the target labelled BW110 had greater movement that affected the fractures at the top of the slope area. Surface deviation analysis carried out using CloudCompare software showed that the movement had happened at the same area which was similar to results obtained from the visualization analysis using GIS software. The comparison analysis of MLS output with Unmanned Aerial Vehicle (UAV) photogrammetric data also showed similar movement at the same area. The MLS data was then evaluated using 3D coordinate of measurement targets obtained from total station survey and it achieved a Root Mean Square Error (RMSE) value of about ± 0.639 meter. Through the generation of several landslide movement maps using MLS data, showed there were movements which occurred at the area of study within a period of one month. This proved that the surface deviation analysis and visualization analysis was able to detect the movement of landslides. More significantly, the combination of MLS technology with GIS spatial analysis method were capable in providing useful landslide information and monitoring