Monitoring Landslide Displacements Through Maximum Cross-Correlation of Satellite Images

Abstract

Abstract. Landslides are one of the most dangerous and disastrous geological hazard worldwide, posing threats to human life, infrastructures and to the natural environment. Consequently, monitoring active landslides is crucial in order to reduce the risk of damages and casualties. With this aim, this work proposes a way to compute landslide displacements through time, by exploiting the great availability of high quality multispectral satellite images. The developed procedure produces maps of displacement magnitude and direction by means of local cross-correlation of Earth Observation data from the Sentinel-2 and PlanetScope missions. The Ruinon landslide, an active landslide in Northern Lombardy, Italy, was selected as a case study. The workflow of the developed procedure is described, and the results are presented and discussed. Moreover, a validation of the analysis by comparison with UAV surveys of the landslide is reported, along with a discussion on future developments and improvements of this technique. This work was designed to be entirely based on free and open-source GIS software and to rely mainly on open data. These characteristics allow the proposed analysis to be easily replicated, customized, and empowered.

Estimating Landslide Surface Displacement by Combining Low-Cost UAV Setup, Topographic Visualization and Computer Vision Techniques

“…By changing the spatial and intensity scale, the approach succeeded in precisely estimating velocity vectors through the epochs of the most severe reactivations of the central part of the landslide body, which obviously cannot be considered for estimating volumetric changes and more abrupt land cover changes (e.g., loss of forested areas). Some of our results derived from the 2019 surveys (Figure 10a-c) are consistent with the results obtained from another approach we implemented for estimating Ruinon reactivations through Sentinel-2 imagery [37]. Furthermore, comparable outputs for the Ruinon landslide are reported by Carla et al [36], with the difference that their work utilized a GBInSAR monitoring technique.…”

“…Aside from the GBInSAR, the landslide has been monitored since 1997 through a manual and automatic geotechnical network [33]. In addition to the data from permanent ground stations during the last two decades, the Ruinon landslide was also monitored through spaceborne optical and radar missions [36,37], which recorded the most disastrous reactivations during the summers of 2016, 2018 and 2019. During the year 2021, no significant critical activities on the landslide body were reported [38].…”

Application of Lucas-Kanade Dense Flow for Terrain Motion in Landslide Monitoring Application

“…The number of wraps was kept the same, two, as the experimental setups. Overall, the results depict accurate estimated displacement fields and movement directions which are in line with the ones reported in previous studies (Amici et al, 2022). The estimated magnitudes range from a few meters to more than 20 meters between two acquisitions.…”

“…To validate our approach to a realistic scenario, we evaluated it on optical satellite images, especially from the ESA's Sentinel-2 mission. The adopted landslide case study is the Ruinon landslide in Northern Italy, which was active in the period 2019-2021 and thoroughly studied by using ground-, air-and spaceborne means (Del Ventisette et al, 2012, Carlà et al, 2021, Amici et al, 2022, Yordanov et al, 2023. This application offers a more challenging and practical setting, as Sentinel-2 (SE2) images have a much lower spatial resolution (10 m/pix) compared to the submillimetre level of the simulator setup.…”

“…Moreover, external factors such as cloud cover, vegetation seasonality and uncontrolled image acquisition can introduce more variability and complexity in the data. This case study follows the already defined SE2 yearly application scheme used by (Amici et al, 2022) for the application of local Maximum Cross-Correlation (MCC) analysis, i.e. the applicability of LK dense flow for displacement estimation was tested on one image per year for the period 2015 to 2020.…”