Automatic monitoring of landslides from satellite optical images

5 mois, février-juin 2022

The monitoring of landslides relies on the installation of sensors on sites (extensometers, GNSS, RFID,….) that measures the displacement of the landslides through time. This displacement time-series is analyzed in near-real time to provide an alert. These instrumentations provide mostly punctual informations and require regular maintenance. The use of remote-sensing techniques from either satellites or aircrafts allows to overcome these limitations and is therefore complementary to in situ monitoring techniques.
The launch over the last 10 years of high-frequency satellites has shown the interest of optical satellites to monitor landslides at a daily scale with a spatially distributed information. This monitoring is the base for the detection of unusual ground surface displacement, that can be precursor of catastrophic failures (Lacroix et al., 2018).

All the methods to monitor landslides from optical satellite data exist (some of which have been developed in ISTerre), but their applications for an operational purpose is still lacking. During this internship the student will implement an automatic procedure that will combine the existing methods of landslide monitoring from optical satellite images (from the automatic image selection to the visualization of the data for a landslide expert). The student will test the procedure on Sentinel-2 images on an area in Southern Peru, affected by many large slow-moving landslides that present episodic accelerations (Lacroix et al., 2019, 2020). The validation of the outputs will be done by comparison with in situ measurements on the Siguas, Pachaqui and Punillo landslides, that affect agricultural fields, destroy villages, and regularly dam the downslope valleys, creating lake impoundments. The outputs are dedicated to be used by the Geological Survey of Peru for an operational monitoring goal.

Related Publications :

P. Lacroix, G. Bievre, E. Pathier, U. Kniess, D. Jongmans (2018), Use of Sentinel-2 images for the detection of precursory motions before landslide failures, Remote Sensing of Environment, 215, 507-516.
Lacroix, P., Araujo, G., Hollingsworth, J., & Taipe, E. (2019). Self entrainment motion of a slow‐moving landslide inferred from Landsat‐8 time‐series. Journal of Geophysical Research : Earth Surface.
Lacroix, P., Dehecq, A. & Taipe (2020), E. Irrigation-triggered landslides in a Peruvian desert caused by modern intensive farming. Nature Geosciences. 13, 56–60

Prerequisites : Interest for landslide monitoring. Good knowledges of statistical data processing (Matlab or Python). Knowledges of GIS can also be valued.