Landslide Monitoring based on Terrestrial Laser Scanning: A Novel Semi-automatic Workflow

TL;DR

This paper introduces a semi-automatic TLS-based workflow for long-term landslide monitoring, achieving centimeter-level accuracy and enabling effective deformation analysis and early warning.

Contribution

The study presents a novel semi-automatic workflow combining multi-view registration, vegetation removal, and deformation analysis for landslide monitoring using TLS data.

Findings

Achieved centimeter-level deformation monitoring accuracy.

Effectively identified significant deformation areas.

Demonstrated long-term landslide trend analysis.

Abstract

In this paper, we propose a workflow that uses Terrestrial Laser Scanning(TLS) to semi-automatically monitor landslide and then test it in practice. Firstly, several groups of TLS stations are set on different time to collect the raw point cloud of the object mountain. Next, Hierarchical Merging Based Multi-view (HMMR) registration algorithm is adapted to accomplish single-phase multi-view registration.In order to analyze deformation between multiple periods, Iterative Global Similarity Point (IGSP) algorithm is applied to accomplish multiple-phase registration, which outperforms ICP in experiments. Then the cloth simulation filtering (CSF) algorithm was used together with manual post-processing to remove vegetation on the slope. After that, the mountain slope's digital terrain model (DTM) is generated for each period, and the distance between adjacent DTMs are calculated as the landslide deformation mass. Furthermore, average deformation rate of the landslide surface is calculated and analyzed.To validate the effectiveness of proposed workflow, we uses the TLS data of five periods of the landslide in the Shanhou village of northern Changshan Island from 2013 to 2015. The results indicate that the method can obtain centimeter-level deformation monitoring accuracy which can effectively monitor and analyze long-term landslide morphology and trend as well as position the significant deformation area and determine the type of landslide. In addition, the process can be automated to provide end-to-end TLS based long-term landslide monitoring applications, providing reference for monitoring and early warning of potential landslides.