Cormons landslide characterization using Lidar and remote sensed data.

Landslides are a global phenomenon occurring in several climatic and geomorphologic contexts, generating billions in economic losses and causing thousand of causalities each year. This phenomenon is often characterized as a local problem, but its effect and cost frequently cross local jurisdiction and may become a national problem [1]. Landslides, resulting from disturbance in slope equilibrium induced by the movement of a mass of rock, debris or earth down a slope and pose a significant threat to landscapes, infrastructure and human life [2]. Landslides can be labelled into different categories depending on the type of movement and the type of material involved. They may be triggered by several phenomena; the primary are seismic activities and heavy rainfall. More precisely, rainfall-induced landslides typically occur in regions prone to heavy precipitation, with steep slopes and poorly consolidated soil or rock [2]. In Italy, the most recent case study, is the Cormons (Gorizia, Italy) landslide occurred on November 17^th 2025. Here, intense rainfalls caused a mud-flow inducing the collapse of several buildings and two casualties. In this area, landslides are the most frequent type of instability. These are mostly small and medium-sized landslides, located on flyschoid hills, affecting vineyards and only locally affecting roads and rural settlements [3]. Identifying these phenomena through satellite-based remote sensing techniques offers essential data and insight for landslide studies. Information regarding timing, location and spatial extent of detected landslides, along with changes in surface materials, plays a key role in risk and susceptibility assessments as well as in effective disaster management, monitoring and response activities. For the purpose of this work, optical satellite images provided by Sentinel-2, together with the Lidar provided by the Italian Civil Defense have been used with the aim to identifying the Cormons landslide and its characteristics in terms of dimensions, shape and amount of material moved during the event. The use of optical imagery from Sentinel-2 it’s been used to evaluate spectral indices like Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Bare Soil Index (BSI). Instead Lidar and DEM have been used to define the ground changes in terms of elevation and also the amount of material involved in the event. From the GIS analysis, the results confirm the presence of a mudflow within a watershed located in the Cormons area. Additionally, from the Lidar other small collapse features have been highlighted in the surrounding area.

 

REFERENCES:

• Highland, L. M., & Bobrowsky, P. (2008). The landslide handbook-A guide to understanding landslides(No. 1325). US Geological Survey.
• Peters, S., Liu, J., Keppel, G., Wendleder, A., & Xu, P. (2024). Detecting coseismic landslides in GEE using machine learning algorithms on combined optical and radar imagery. Remote Sensing, 16(10), 1722.
• https://www.isprambiente.gov.it/files/pubblicazioni/rapporti/rapporto-frane-2007/Capitolo_11_Friuli_Venezia_Giulia.pdf