Lesson learned from the pre-collapse time series of displacement of the Preonzo landslide (Switzerland)

Moretto, Serena; Bozzano, Francesca; Esposito, Carlo

doi:10.3301/rol.2016.140

Cited by 4 publications

(6 citation statements)

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“…In Figure 9c, an example is shown where the prediction accuracy increases when approaching the actual failure based on the Preonzo landslide monitoring data. [66]). The predictions have been updated by simulating the data acquisition over time.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Landslide Pre-Failure Monitoring and Forecasting Using Satellite SAR Interferometry

et al. 2017

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Abstract:In this work, the ability of advanced satellite interferometry to monitor pre-failure landslide behaviours and the potential application of this technique to Failure Forecasting Methods (FFMs) are analysed. Several limits affect the ability of the technique to monitor a landslide process, especially during the pre-failure phase (tertiary creep). In this study, two of the major limitations affecting the technique have been explored: (1) the low data sampling frequency and (2) the phase ambiguity constraints. We explored the time series of displacements for 56 monitored landslides inferred from the scientific literature and from different in situ and remote monitoring instruments (i.e., extensometers, inclinometers, distometers, Ground Base InSAR, and total station). Furthermore, four different forecasting techniques have been applied to the monitoring data of the selected landslides. To analyse the reliability of the FFMs based on the InSAR satellite data, the 56 time series have been sampled based on different satellite features, simulating the satellite revisit time and the phase ambiguity constraints. Our analysis shows that the satellite InSAR technique could be successful in monitoring the landslide's tertiary creep phase and, in some cases, for forecasting the corresponding time of failure using FFMs. However, the low data sampling frequency of the present satellite systems do not capture the necessary detail for the application of FFMs in actual risk management problems or for early warning purposes.

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Section: Discussionmentioning

confidence: 99%

Assessment of Landslide Pre-Failure Monitoring and Forecasting Using Satellite SAR Interferometry

et al. 2017

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“…Its application is restricted to relatively slow motions (≤1 m/yr), i.e., remaining below a quarter of the SAR sensors' wavelength λ (≥λ/4), with some exceptions of λ/2 [4]. Although active radar sensors are relatively independent of atmospheric constraints and of shadows, further limiting high-alpine factors include snow cover, slope exposition, layover effects and foreshadowing [52,53]. Nevertheless, using optical remote sensing for research on high-alpine sites is often difficult due to meteorological constraints such as snow cover, clouds or cloud shadows and mountain ridge topographic shadowing effects for certain seasons and times of day.…”

Section: Introductionmentioning

confidence: 99%

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

Hermle¹,

Gaeta

Krautblatter³

et al. 2022

Remote Sensing

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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.

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“…As landslides tend to accelerate beyond the deformation rate observable with radar systems before failure, we concentrate on optical image analysis (Moretto et al, 2016). One advantage of optical imagery is its temporally dense data (Table 1) compared to open data radar systems with a sensor repeat frequency of 6 d and revisit frequency of 3 d at the Equator, about 2 d over Europe and less than 1 d at high latitudes (Sentinel-1, ESA).…”

Section: Introductionmentioning

confidence: 99%

“…Optical data allow direct visual impressions from the multispectral representation of the acquisition target and the option to employ these data for further complementary and expert analyses. While active radar systems overcome constraints posed by clouds and do not require daylight, data voids can be significant due to layover or shadowing effects in steep mountainous areas (Mazzanti et al, 2012;Plank et al, 2015;Moretto et al, 2016). Moreover, north-/south-facing slopes are less suitable and thus limit the range of investigation (Darvishi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Timely prediction potential of landslide early warning systems with multispectral remote sensing: a conceptual approach tested in the Sattelkar, Austria

Hermle

Keuschnig²,

Hartmeyer³

et al. 2021

Nat. Hazards Earth Syst. Sci.

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Abstract. While optical remote sensing has demonstrated its capabilities for landslide detection and monitoring, spatial and temporal demands for landslide early warning systems (LEWSs) had not been met until recently. We introduce a novel conceptual approach to structure and quantitatively assess lead time for LEWSs. We analysed “time to warning” as a sequence: (i) time to collect, (ii) time to process and (iii) time to evaluate relevant optical data. The difference between the time to warning and “forecasting window” (i.e. time from hazard becoming predictable until event) is the lead time for reactive measures. We tested digital image correlation (DIC) of best-suited spatiotemporal techniques, i.e. 3 m resolution PlanetScope daily imagery and 0.16 m resolution unmanned aerial system (UAS)-derived orthophotos to reveal fast ground displacement and acceleration of a deep-seated, complex alpine mass movement leading to massive debris flow events. The time to warning for the UAS/PlanetScope totals 31/21 h and is comprised of time to (i) collect – 12/14 h, (ii) process – 17/5 h and (iii) evaluate – 2/2 h, which is well below the forecasting window for recent benchmarks and facilitates a lead time for reactive measures. We show optical remote sensing data can support LEWSs with a sufficiently fast processing time, demonstrating the feasibility of optical sensors for LEWSs.

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Lesson learned from the pre-collapse time series of displacement of the Preonzo landslide (Switzerland)

Cited by 4 publications

References 0 publications

Assessment of Landslide Pre-Failure Monitoring and Forecasting Using Satellite SAR Interferometry

Assessment of Landslide Pre-Failure Monitoring and Forecasting Using Satellite SAR Interferometry

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

Timely prediction potential of landslide early warning systems with multispectral remote sensing: a conceptual approach tested in the Sattelkar, Austria

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