Performance Testing of Optical Flow Time Series Analyses Based on a Fast, High-Alpine Landslide

Abstract

Accurate remote analyses of high-alpine landslides are a key requirement for future alpine safety. In critical stages of alpine landslide evolution, UAS (unmanned aerial system) data can be employed using image registration to derive ground motion with high temporal and spatial resolution. However, classical area-based algorithms suffer from dynamic surface alterations and their limited velocity range restricts detection, resulting in noise from decorrelation and hindering their application to fast landslides. Here, to reduce these limitations we apply for the first time the optical flow-time series to landslides for the analysis of one of the fastest and most critical debris flow source zones in Austria. The benchmark site Sattelkar (2130–2730 m asl), a steep, high-alpine cirque in Austria, is highly sensitive to rainfall and melt-water events, which led to a 70,000 m³ debris slide event after two days of heavy precipitation in summer 2014. We use a UAS data set of five acquisitions (2018–2020) over a temporal range of three years with 0.16 m spatial resolution. Our new methodology is to employ optical flow for landslide monitoring, which, along with phase correlation, is incorporated into the software IRIS. For performance testing, we compared the two algorithms by applying them to the UAS image stacks to calculate time-series displacement curves and ground motion maps. These maps allow the exact identification of compartments of the complex landslide body and reveal different displacement patterns, with displacement curves reflecting an increased acceleration. Visually traceable boulders in the UAS orthophotos provide independent validation of the methodology applied. Here, we demonstrate that UAS optical flow time series analysis generates a better signal extraction, and thus less noise and a wider observable velocity range—highlighting its applicability for the acceleration of a fast, high-alpine landslide.

Applications of Image-Based Computer Vision for Remote Surveillance of Slope Instability

“…Optical flow is represented as the apparent motion of objects and surfaces between two different timeframes caused by relative motion between the observer (camera) and the scene (e.g., objects, surfaces, and edges) (O'Donovan, 2005;Zach et al, 2007;Wedel et al, 2009;Hermle et al, 2022). The most important assumption in optical flow velocity calculations is constancy in image brightness.…”

“…Numerous monitoring approaches, including ground-based radar (e.g., Rosenblad et al, 2015), InSAR (e.g., Ferretti et al, 2007;Ferretti, 2014), Lidar (e.g., Lin et al, 2013;Tortini et al, 2015;Kromer et al, 2017;Williams et al, 2018;Williams et al, 2019;Holst et al, 2021), seismometers (e.g., Suriñach et al, 2005), and more recently computer vision (O'Donovan, 2005;Zach et al, 2007;Wedel et al, 2009;Javier et al, 2013;Sánchez Pérez et al, 2013;Kim et al, 2020;Hermle et al, 2022), have been used to study landslides and slope instability. However, the elevated cost, complexity, and intrinsic technical limitations of these methods frequently necessitates the development of alternative and complementary techniques.…”

“…The optical flow equation includes:I x + Δx, y + Δy, t + Δt I x, y, t where I(x, y, t) is the intensity of the image at position (x, y) for time t and the change in position is indicated by Δx and Δy at change of time Δt. After applying a Taylor series expansion to the left hand side of the equation, it can be rewritten as follows (O'Donovan,2005;Zach et al, 2007;Wedel et al, 2009;Hermle et al, 2022):I x + Δx, y + Δy, t + Δt I x, y, t + above equation, we obtain the optical flow constraint equation:…”

Die permanente dreidimensionale Datenerfassung geotechnischer Strukturen mittels webbasierter Anwendung terrestrischer LiDAR‐Sensorik

“…Derartige Ereignisse werden durch natürliche Prozesse wie Niederschlag oder Temperaturschwankungen ausgelöst. Durch den Klimawandel werden neue Rahmenbedingungen geschaffen, die ein zunehmendes Risiko solcher Ereignisse in Bezug auf Ausmaß und Häufigkeit darstellen [3].…”

“…Such events are triggered by natural processes such as precipitation or temperature fluctuations. Climate change creates new framework conditions that pose an increasing risk of such events in terms of scale and frequency [3].…”