Landslides and geo-hydrological hazards affect every region of the world each year, and their impacts are being reshaped by climate change, urban expansion and land-use pressure. The rising toll of damaging events has pushed authorities, practitioners and scientists towards monitoring strategies that are reliable, operational and, where possible, low-cost. A well-designed monitoring system for landslides and ground deformation is today fundamental for both long- and short-term risk reduction.
The temporal evolution of a slope can be captured in many ways: conventional and novel in situ instrumentation (inclinometers, sensors arrays, distributed fibre-optic sensing, low-cost IoT networks), ground-based platforms (GB-InSAR, terrestrial laser scanner, robotic total stations), UAV surveys, and satellite data (both optical and radar), including the opportunities opened by recent and forthcoming SAR missions and constellations (Sentinel-1C/D, SAOCOM, LuTan-1, NISAR). All these approaches share one purpose: measuring displacement through time in order to anticipate future behaviour or, at least, to reconstruct the recent past. Real-time, near-real-time and deferred-time strategies can each be effective, depending on the process under study, the instrument selected and the level of risk considered acceptable.
The session is aligned with the aims of the International Consortium on Landslides, namely: i) promote landslide research for the benefit of society; ii) integrate geosciences and technology within cultural and social contexts in order to evaluate landslide risk; and iii) combine and coordinate international expertise.
We welcome contributions on: i) innovative in situ, ground-based, airborne and satellite monitoring applications; ii) multi-temporal, multi-sensor and multi-platform case studies supporting risk management; iii)integration and synergistic use of different techniques, including cross-validation between sensors; iv) displacement-based early warning, velocity thresholds and failure forecasting; v) new tools and processing chains, from big-data management to AI and machine-learning approaches; vi) lessons learned from operational monitoring systems, including their limitations and failures.
Case studies from data-scarce regions and low-cost, transferable solutions are particularly encouraged.