The global aim of this project is to improve our understanding on the kinematics of active slope movements, either in space and time, through the development of new algorithms for the treatment of massive 3D datasets. Although the use of new remote sensing techniques, either terrestrial, aerial or satellite based, is shedding light into how landslides behave and evolve, still many questions need to be solved regarding the treatment of these datasets, more specifically LiDAR point clouds and its application to a better modelling and forecasting of landslides in 4D (X,Y,Z and Time).
Our research is focused in the development of new algorithms for the modelling and quantification of the geometrical variation of different failure mechanisms (e.g. toppling, falls, slides, etc) along time. The project is conceived through a threefold strategy: in a first step, we are simulating mass movements at analogue scale using a sandbox, in order to acquire high resolution 3D temporal data. Then, we are exploiting these datasets for the development of new algorithms aiming to better modelling and quantify the landslide geometrical variation along the different phases of the rupture. In the third and final step, we are applying these algorithms to the study of more complex landslides in well instrumented pilot study areas, aiming to a better modelling and understanding of the 3D evolution suffered by complex mass movements during the pre-failure and failure stages.This project is conceived as the logical continuation of the one year FNS project 138015 “Understanding landslide precursory deformation from superficial 3D data”. The outputs of the project will improve future implementation of 3D remote sensing techniques in early warning systems, a great challenge in current risk management strategies.