Continuous geodetic measurements in landslide prone regions are necessary to avoid disasters and better understand the spatiotemporal and kinematic evolution of landslides. The detection and characterization of landslides in high alpine environments remains a challenge associated with difficult accessibility, extensive coverage, limitations of available techniques, and the complex nature of landslide process. Recent studies using space-based observations and especially Persistent Scatterer Interferometry (PSI) techniques with the integration of in-situ monitoring instrumentation are providing vital information for an actual landslide monitoring. In the present study, the Stanford Method for Persistent Scatterers InSAR package (StaMPS) is employed to process the series of Sentinel 1-A and 1-B Synthetic Aperture Radar (SAR) images acquired between 2015 and 2019 along ascending and descending orbits for the selected area in the French Alps. We applied the proposed approach, based on extraction of Active Deformation Areas (ADA), to automatically detect and assess the state of activity and the intensity of the suspected slow-moving landslides in the study area. We illustrated the potential of Sentinel-1 data with the aim of detecting regions of relatively low motion rates that be can attributed to activate landslide and updated pre-existing national landslide inventory maps on a regional scale in terms of slow moving landslides. Our results are compared to pre-existing landslide inventories. More than 100 unknown slow-moving landslides, their spatial pattern, deformation rate, state of activity, as well as orientation are successfully identified over an area of 4000 km2 located in the French Alps. We also address the current limitations due the nature of PSI and geometric characteristic of InSAR data for measuring slope movements in mountainous environments like Alps.

International audienceThis work focuses on the development of a combined statistical-mechanical approach to predict changes in landslide displacement rates from observed changes in rainfall amounts. The forecasting tool FLAME (Forecasting Landslides Accelerations induced by Meteorological Events) associates (1) a statistical impulse response (IR) model to simulate the changes in landslide rates by computing a transfer function between the input signal (e.g. rainfall) and the output signal (e.g. displacement) and (2) a simple 1D mechanical (MA) model (e.g. viscoplastic rheology) to take into account changes in pore water pressure. The models have been applied to forecast the displacement rates at the Super-Sauze landslide (South East France). The performance of different combinations of models (IR model alone, MA model alone and a combination of the IR and MA models) is evaluated against observed changes in pore water pressures and displacement rates at the study site. Results indicate that the three models are able to reproduce the displacement pattern in the general kinematic regime (succession of acceleration and deceleration phases); conversely, extreme kinematic regimes such as fluidization of part of the landslide mass are not being reproduced. The approach constitutes however a robust tool to predict changes in displacement rates from rainfall or groundwater time series

International audienceThe prediction of landslide movement acceleration is a complex problem, among others identified for deep-seated landslides, and represents a crucial step for risk assessment. Within the scope of this problem, the objective of this paper is to explore a modelling method that enables the study of landslide function and facilitates displacement predictions based on a limited data set. An inverse modelling approach is proposed for predicting the temporal evolution of landslide movement based on rainfall and displacement velocities. Initially, the hydrogeology of the studied landslides was conceptualised based on correlative analyses. Subsequently, we applied an inverse model with a Gaussian-exponential transfer function to reproduce the displacements. This method was tested on the Grand Ilet (GI) and Mare-à-Poule-d'Eau (HB) landslides on Reunion Island in the Indian Ocean. We show that the behaviour of landslides can be modelled by inverse models with a bimodal transfer function using a Gaussian-exponential impulse response. The cumulative displacements over 7 years of modelling (2 years of calibration period for GI, and 4 years for HB) were reproduced with an RMSE above 0.9. The characteristics of the bimodal transfer function are directly related to the hydrogeological functioning demonstrated by the correlative analyses: the rapid reaction of a landslide can be associated with the effect of a preferential flow path on groundwater level variations. Thus, this study shows that the inverse model using a Gaussian-exponential transfer function is a powerful tool for predicting deep-seated landslide movements and for studying how they function. Beyond modelling displacements, our approach effectively demonstrates its ability to contribute relevant data for conceptualising the sliding mechanisms and hydrogeology of landslides

Physically based model may be used to assess landslide susceptibility over large areas. However, majority of case studies are applied for complex phenomena for a one event, a little site or over large areas when landslides have simple geometry and environmental conditions are homogeneous. Thus, assessing landslide prone areas for different type of landslides with several geometries and for large areas needs some specific strategies. This work presents an application of a specific procedure based on a physically based model for one complex area with several landslide types. By different steps, it is demonstrated that it is possible to improve susceptibility map and to take into account different slope failure with different depths. This first attempt encourages us to continue on this path in order to improve the existing susceptibility maps in this area.

To cite this version:Thomas Houet, Marine Gremont, Laure Vacquié, Yann Forget, Apolline Marriotti, et al.. Downscaling scenarios of future land use and land cover changes using a participatory approach: an application to mountain risk assessment in the Pyrenees (France) . Regional Environnemental Change, Springer, 2017, 17 (8) Better understanding the pathways through which future socio-economic changes might influence land 19 use and land cover changes (LULCC) is a crucial step in accurately assessing the resilience of 20 societies to mountain hazards. Participatory foresight involving local stakeholders may help building 21 fine-scale LULCC scenarios that are consistent with the likely evolution of mountain communities. 22This paper develops a methodology that combines participatory approaches in downscaling socio-23 economic scenarios with LULCC modelling to assess future changes in mountain hazards, applied to a 24 case study located in the French Pyrenees. Four spatially-explicit local scenarios are built each 25 including a narrative, two future land cover maps up to 2040 and 2100, and a set of quantified 26 LULCC. Scenarios are then used to identify areas likely to encounter land cover changes 27 (deforestation, reforestation and encroachment) prone to affect gravitational hazards. In order to 28 demonstrate their interest for decision-making, future land cover maps are used as input to a landslide 29 hazard assessment model. Results highlight that reforestation will continue to be a major trend in all 30 scenarios and confirm that the approach improves the accuracy of landslide hazard computations. This 31 validates the interest of developing fine-scale LULCC models that account for the local knowledge of 32 stakeholders. 33