Each year landslides, debris-flows (mud-flows, earth-flows) cause a lot of disasters in mountainous areas all over the world. Periodically thousands of populated areas, dams, roads, oil and natural gas pipeline routes, high voltage electric lines and agricultural lands are under the heavy influence, sometimes with catastrophic impact of hazardous geological processes. Catastrophic mass-movements not only periodically strongly damage the environment, but they also are followed by human losses. Thus it is of great importance to create reliable and cost-effective early warning systems for monitoring mass-movements in potentially dangerous areas. There are a lot of methods in monitoring mass-movements, but the cost of such systems is very high and it is practically impossible for developing countries to purchase them. Besides, the number of mass-movement dangerous sources is huge. Thus, the multitude of dangerous sources, growing number of exposed vulnerable objects and limited resources of developing countries, which are most prone to mentioned hazards call for developing cost-effective and the same time accurate monitoring /early warning telemetric systems. A fusion of these apparently conflicting concepts (cost-effectiveness and accuracy) is possible using the modern high-tech systems.
This project aims to develop a cost-effective complex telemetric geophysical monitoring and with autonomous power source (solar batteries) for signalling debris flow/landslide initiation using radio signals or Internet connection.The complex includes monitoring of two main categories of parameters leading to mass-movements: soil humidity and deformation (e.g. displacement/acceleration/micro-seismicity) caused by the hydro-mechanical loading of the slope. Two types of approaches will be pursued: the development and test of 1) ground-based modern sensors, and of 2) remote sensors (such as terrestrial cameras and very-high resolution satellite imagery).
Selection of optimal set of modern cost-effective and accurate sensors for monitoring of the soil humidity and deformation (displacement, acceleration, strain, micro-seismic activity) as well as multi-channel data acquisition modules. Test of the system in laboratory conditions. Elaboration of data processing chains for image time series and seismic/acoustic signals analysis.
Construction of working monitoring prototype module including sensors and data acquisition system as well as modern cost-effective autonomous power sources and transmission systems and field test of the prototype module for establishing optimal frequency/amplitude range of EWS.
Development and evaluation of an automated processing chain for the analysis of satellite image time series
Surface motion measurements from optical data on large computing infrastructures
Implementation and testing of an image processing chain for Automatic Landslide Detection and Inventory Mapping (ALADIM)
Martina Wilde, Andreas Günther, Paola Reichenbach, Jean-Philippe Malet & Javier Hervás (2018) Pan-European landslide susceptibility mapping: ELSUS Version 2, Journal of Maps, 14:2, 97-104, DOI: 10.1080/17445647.2018.1432511.
André Stumpf, Jean-Philippe Malet, Christophe Delacourt, Correlation of satellite image time-series for the detection and monitoring of slow-moving landslides, Remote Sensing of Environment, Volume 189, 2017, Pages 40-55, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2016.11.007.
Provost, F., Hibert, C., Malet, J.-P. 2016. Automatic classification of endogenous landslide seismicity using the Random Forest supervised classifier. Geophysical Research Letters, (accepted, in press). https://doi.org/10.1002/2016GL070709.
Clément Hibert, Floriane Provost, Jean-Philippe Malet, Alessia Maggi, André Stumpf, Valérie Ferrazzini, Automatic identification of rockfalls and volcano-tectonic earthquakes at the Piton de la Fournaise volcano using a Random Forest algorithm, Journal of Volcanology and Geothermal Research, Volume 340, 2017, Pages 130-142, ISSN 0377-0273, https://doi.org/10.1016/j.jvolgeores.2017.04.015.