  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.
