Discussion to: ‘Guidelines on the use of inverse velocity method as a tool for setting alarm thresholds and forecasting landslides and structure collapses’ by T. Carlà, E. Intrieri, F. Di Traglia, T. Nolesini, G. Gigli and N. Casagli

Bozzano, Francesca; Moretto, Serena

doi:10.1007/s10346-018-0976-2

Cited by 14 publications

(15 citation statements)

References 5 publications

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“…The monitoring sampling frequency also affects the accuracy of the forecasting methods, so that the higher the sampling frequency, the higher the accuracy of predictions [87]. If a landslide process is monitored with suitable techniques (high spatial and temporal resolution), forecasting methods can be a powerful tool in risk management strategies and for early warning systems [81].…”

Section: Discussionmentioning

confidence: 99%

“…The analyses highlight that forecasting the landslide occurrence from space is currently possible. However, at present, the revisit time still represents the principal constraint for operational services in a priori analysis, limiting the applicability of the forecasting analysis to landslide phenomena characterized by long tertiary creep phase and affecting the accuracy of predictions [81].…”

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confidence: 99%

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The Role of Satellite InSAR for Landslide Forecasting: Limitations and Openings

Moretto

Bozzano

2021

Remote Sensing

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The paper explores the potential of the satellite advanced differential synthetic aperture radar interferometry (A-DInSAR) technique for the identification of impending slope failure. The advantages and limitations of satellite InSAR in monitoring pre-failure landslide behaviour are addressed in five different case histories back-analysed using data acquired by different satellite missions: Montescaglioso landslide (2013, Italy), Scillato landslide (2015, Italy), Bingham Canyon Mine landslide (2013, UT, USA), Big Sur landslide (2017, CA, USA) and Xinmo landslide (2017, China). This paper aimed at providing a contribution to improve the knowledge within the subject area of landslide forecasting using monitoring data, in particular exploring the suitability of satellite InSAR for spatial and temporal prediction of large landslides. The study confirmed that satellite InSAR can be successful in the early detection of slopes prone to collapse; its limitations due to phase aliasing and low sampling frequency are also underlined. According to the results, we propose a novel landslide predictability classification discerning five different levels of predictability by satellite InSAR. Finally, the big step forward made for landslide forecasting applications since the beginning of the first SAR systems (ERS and Envisat) is shown, highlighting that future perspectives are encouraging thanks to the expected improvement of upcoming satellite missions that could highly increase the capability to monitor landslides’ pre-failure behaviour.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

The Role of Satellite InSAR for Landslide Forecasting: Limitations and Openings

Moretto

Bozzano

2021

Remote Sensing

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“…Identification of hazardous locations within the monitoring area by OOA and TOA based on this method is demonstrated above. In addition, the linear correlation between the inverse velocity and time is found to be weak after OOA in the initial stage (Bozzano et al 2018). The traditional inverse velocity method is improved, and it can be used to predict the time of occurrence of the landslide when the deformation is slow (Zhou et al 2020).…”

Section: Automatic Warning Of Areas At Riskmentioning

confidence: 97%

Application of the method for prediction of the failure location and time based on monitoring of a slope using synthetic aperture radar

Yihai

2021

Environ Earth Sci

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Synthetic aperture radar (SAR) technology has been widely used in landslide deformation monitoring in the past decade. It has the advantages of high monitoring accuracy, a wide range, and flexibility allowing all-weather continuous monitoring. The self-developed S-SAR synthetic aperture radar (slope radar) is the first completely domestic-made radar used in slope deformation monitoring equipment in China, and its performance and technical parameters are equal to or better than similar products made abroad. The characteristics of deformation data collected by S-SAR slope radar deployed in the front open pit mine are analyzed to further develop a spatio-temporal landslide prediction method which is applicable to the massive monitoring data within the monitoring range of slope radar. The intersection points of short-term moving average velocity curve and long-term moving average velocity curve of slope deformation, which are onset of acceleration (OOA) and termination of acceleration (TOA). When OOA occurs, the deformation will accelerate, and when TOA occurs, the deformation will tend to stabilize. The OOA can identify areas at risk in the monitored area before failure, so that the spatial position prediction of landslide early warning can be realized. Based on the assumptions of the inverse velocity method, a T-log (t) logarithmic model is established, and the updated monitoring data are corrected to approximate the time of failure, thus improving the accuracy of landslide location and time prediction. In an open-pit copper mine in Serbia, the accurate prediction of landslide location and time has been successfully applied, guaranteeing safe mining.

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“…High-resolution remote sensing data were long restricted due to high costs and data volume (Goodchild, 2011;Westoby et al, 2012). Today commercial very high resolution (VHR) optical satellites exist, but tasked acquisitions make them inflexible and very cost intensive, thus limiting research (Butler, 2014;Lucieer et al, 2014). There is a vast spectrum of available remote sensing data with a high spatiotemporal resolution (Table 1).…”

Section: Introductionmentioning

confidence: 99%

“…The latest developments in Earth observation programmes include both the new Copernicus Sentinel fleet operated by ESA and a new generation of micro cube satellites, sent into orbit in large numbers by Planet Labs, Inc. These micro cube satellites, known as "Doves" as part of PlanetScope (from now on referred to as PlanetScope satellites), and Sentinel-2A and Sentinel-2B offer very high revisit rates of 1-5 d and high spatial resolutions of 3 and 10 m, respectively (Table 1), for multispectral imagery (Drusch et al, 2012;Butler, 2014;Breger, 2017). These high spatiotemporal resolutions open up unprecedented possibilities of studying a wide range of landslide velocities and natural hazards through remote sensing.…”

Section: Introductionmentioning

confidence: 99%

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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Discussion to: ‘Guidelines on the use of inverse velocity method as a tool for setting alarm thresholds and forecasting landslides and structure collapses’ by T. Carlà, E. Intrieri, F. Di Traglia, T. Nolesini, G. Gigli and N. Casagli

Cited by 14 publications

References 5 publications

The Role of Satellite InSAR for Landslide Forecasting: Limitations and Openings

The Role of Satellite InSAR for Landslide Forecasting: Limitations and Openings

Application of the method for prediction of the failure location and time based on monitoring of a slope using synthetic aperture radar

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

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