Detecting Recent Dynamics in Large-Scale Landslides via the Digital Image Correlation of Airborne Optic and LiDAR Datasets: Test Sites in South Tyrol (Italy)

Abstract

Large-scale slow-moving deep-seated landslides are complex and potentially highly damaging phenomena. The detection of their dynamics in terms of displacement rate distribution is therefore a key point to achieve a better understanding of their behavior and support risk management. Due to their large dimensions, ranging from 1.5 to almost 4 km2, in situ monitoring is generally integrated using satellite and airborne remote sensing techniques. In the framework of the EFRE-FESR SoLoMon project, three test-sites located in the Autonomous Province of Bolzano (Italy) were selected for testing the possibility of retrieving significant slope displacement data from the analysis of multi-temporal airborne optic and light detection and ranging (LiDAR) surveys with digital image correlation (DIC) algorithms such as normalized cross-correlation (NCC) and phase correlation (PC). The test-sites were selected for a number of reasons: they are relevant in terms of hazard and risk; they are representative of different type of slope movements (earth-slides, deep seated gravitational slope Deformation and rockslides), and different rates of displacement (from few cm/years to some m/years); and they have been mapped and monitored with ground-based systems for many years (DIC results can be validated both qualitatively and quantitatively). Specifically, NCC and PC algorithms were applied to high-resolution (5 to 25 cm/px) airborne optic and LiDAR-derived datasets (such as hillshade and slope maps computed from digital terrain models) acquired during the 2019–2021 period. Qualitative and quantitative validation was performed based on periodic GNSS surveys as well as on manual homologous point tracking. The displacement maps highlight that both DIC algorithms succeed in identifying and quantifying slope movements of multi-pixel magnitude in non-densely vegetated areas, while they struggle to quantify displacement patterns in areas characterized by movements of sub-pixel magnitude, especially if densely vegetated. Nonetheless, in all three landslides, they proved to be able to differentiate stable and active parts at the slope scale, thus representing a useful integration of punctual ground-based monitoring systems.

Laser Scanning for Terrain Analysis and Route Design for Electrified Public Transport in Urban Areas

“…For this research, multi-resolution DEMs (10 m, 1 m, 0.5 m, 0.25 m, and 0.10 m) based on photogrammetric techniques and LiDAR data were used. Digital image correlation from airborne optic and LiDAR datasets detected recent large-scale landslide dynamics in South Tyrol (Italy) [31]. In the forest environment, [32] explores the potential of LiDAR and Sentinel-2 data to model the post-fire structural characteristics of gorse shrublands in northwest Spain.…”

The Contribution of Digital Image Correlation for the Knowledge, Control and Emergency Monitoring of Earth Flows

“…Although imaging techniques possess considerable potential benefits, the monitoring of earth flows near linear infrastructures remains a relatively nascent and evolving field, even in common practice. In recent years, some analyses based on digital image correlation (DIC) algorithms have been successfully employed for landslide assessment and monitoring [18][19][20][21][22][23][24][25][26][27][28][29][30], as well as for many other applications in geosciences [31]. Applying these techniques requires careful consideration of several factors, including selecting appropriate processing algorithms, data calibration and validation, and integrating different data types.…”