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Understanding landslide precursory deformation from superficial 3D data

English title Understanding landslide precursory deformation from superficial 3D data
Applicant Jaboyedoff Michel
Number 138015
Funding scheme Project funding (Div. I-III)
Research institution Inst. de Géomatique & d'Analyse du Risque Fac. des Géosciences & de l'Environnement Université de Lausanne
Institution of higher education University of Lausanne - LA
Main discipline Geology
Start/End 01.12.2011 - 30.11.2012
Approved amount 78'389.00
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All Disciplines (3)

Discipline
Geology
Other disciplines of Earth Sciences
Geomorphology

Keywords (4)

landslide; remote sensing; 3D deformation; precursory indicators

Lay Summary (English)

Lead
Lay summary

Landslides represent a major threat to human life, buildings and infrastructures around the world. An increase in landslide risk is expected in future climatic scenarios, including global warming and population growth in mountainous areas. Early detection of landslide precursory deformation has become a great challenge for the scientific community during last decade, as these indicators have proved to be of great importance in landslide forecasting and the development of early warning systems.

In our study, we will focus on the quantification of 3D deformation suffered by complex landslide bodies and precursory landslides leading to larger failures using 3D remote sensing techniques, i.e. LIDAR. Presently several technical and methodological limitations are still controlling our global understanding of the landslide phenomena. In order to overcome these limitations, we will carry out a series of experimental tests that will be the basis for the development of new methodologies for the quantification of 3D deformation. Newly developed algorithms will be applied to real landslides, in order to fill the gap between the theoretical models of temporal failure and the spatial variability of the pre-failure indicators.

The desired outputs of the project will help in the understanding of 3D deformation during the pre-failure stage and of failure mechanisms, which are fundamental aspects for future implementation of 3D remote sensing techniques in early warning systems and landslide risk management.

Direct link to Lay Summary Last update: 21.02.2013

Responsible applicant and co-applicants

Employees

Publications

Publication
Using 3D surface datasets to understand landslide evolution: From analogue models to real case study
D. Carrea A. Abellán M.-H. Derron N. Gauvin & M. Jaboyedoff (2012), Using 3D surface datasets to understand landslide evolution: From analogue models to real case study, in Erik Eberhardt Corey Froese Keith Turner (ed.), CRC Press - Taylor & Francis, Banff, Canada, 575-579.

Collaboration

Group / person Country
Types of collaboration
NGU: Geological Survey of Norway Norway (Europe)
- Publication
RISKNAT group: Faculty of Geology, University of Barcelona Spain (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
- Research Infrastructure
- Exchange of personnel
DURHAM University: Department of Geography Great Britain and Northern Ireland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
UPC: Technical University of Catalonia Spain (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication

Scientific events

Active participation

Title Type of contribution Title of article or contribution Date Place Persons involved
VIII Spanish Geological Conference 17.07.2012 Oviedo (Spain)
11th International Symposium on Landslides 03.06.2012 Banff, Alberta (Canada)
European Geosciences Union 2012 22.04.2012 Vienna (Austria)


Self-organised

Title Date Place

Associated projects

Number Title Start Funding scheme
144040 Characterizing and analyzing 3D temporal slope evolution 01.12.2012 Project funding (Div. I-III)

Abstract

Landslides represent a major threat to human life, buildings and infrastructures around the world. An increase in landslide risk is expected in future climatic scenarios, including global warming and population growth in mountainous areas. Early detection of landslide precursory deformation has become a great challenge for the scientific community during last decade, as these indicators have proved to be of great importance in landslide forecasting and the development of early warning systems. In our study, we will focus on the quantification of 3D deformation suffered by complex landslide bodies and precursory landslides leading to larger failures using 3D remote sensing techniques, i.e. LIDAR. Presently several technical and methodological limitations are still controlling our global understanding of the landslide phenomena: biases and occlusions in data acquisition, insufficient accuracy of the unprocessed datasets, insufficient temporal or spatial resolution, limitations in current methods for point cloud processing, etc. In order to overcome these limitations, we will carry out a series of experimental tests that will be the basis for the development of new methodologies for the quantification of 3D deformation. Newly developed algorithms will be applied to real landslides, in order to fill the gap between the theoretical models of temporal failure and the spatial variability of the pre-failure indicators, as follows:(a) EXPERIMENTAL TESTS: a series of analogue experiments over rigid and non-rigid bodies will be carried out under controlled conditions of deformation rates, water level, slope angle inclination, etc. These tests will be analysed using different 3D remote sensing techniques (short and long range scanners) under different conditions (range, resolution, angle of incidence, etc). The influence of triggering factors (rainfall and shaking) on the acceleration of the gravitational processes will be also investigated. The outputs of these experimental tests will be used for the development of point cloud processing techniques.(b) NEW DEVELOPMENTS: a great challenge remains in the development of new algorithms for the better understanding of the precursory phenomena using 3D remote sensing techniques. Our aim is quantifying the real 3D deformation of rigid and non-rigid bodies using automatic tracking techniques at different parts of the slope. Furthermore, the role of precursory landslides leading to larger failures will be studied using clustering techniques. Complementary developments will be carried out in order to filter out the intrinsic instrumental noise studying the case of small scale deformation.(c) APPLICATION TO LANDSLIDES: The previous findings will be applied to the selected study areas that we will monitor during the project. In order to study the activity (deformation) of the mass movements during a longer time span, we have selected areas that have already been monitored by our group for the last 5 years. Results will be validated with other available landslide displacement data (total station, GB-Radar, DGPS, extensometers, etc). Finally, we will couple spatial and temporal predictions of landslides by analyzing the acceleration in the deformation rates not only in 1 or 2D, but also in real 3D.The quality of the project through the 2 years process will be ensured by a close collaboration with external researchers from leading European institutions. The desired outputs of the project will help in the understanding of 3D deformation during the pre-failure stage and of failure mechanisms, which are fundamental aspects for future implementation of 3D remote sensing techniques in early warning systems and landslide risk management.
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