Preprints
https://doi.org/10.5194/nhess-2021-18
https://doi.org/10.5194/nhess-2021-18

  02 Feb 2021

02 Feb 2021

Review status: a revised version of this preprint is currently under review for the journal NHESS.

Challenging the timely prediction of landside early warning systems with multispectral remote sensing: a novel conceptual approach tested in the Sattelkar, Austria

Doris Hermle1, Markus Keuschnig2, Ingo Hartmeyer2, Robert Delleske2, and Michael Krautblatter1 Doris Hermle et al.
  • 1Technical University of Munich, Chair of Landslide Research, Munich, Germany
  • 2GEORESEARCH Forschungsgesellschaft mbH, Puch, Austria

Abstract. While optical remote sensing has demonstrated its capabilities for landslide detection and monitoring, spatial and temporal demands for landslide early warning systems (LEWS) were not met until recently. We introduce a novel conceptual approach for comprehensive lead time assessment and optimisation for LEWS. We analysed time to warning as a sequence; (i) time to collect, (ii) to process and (iii) to evaluate relevant optical data. The difference between 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 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 UAS and PlanetScope totals 31 h/21 h and is comprised of (i) time to 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 lead time for reactive measures. We show optical remote sensing data can support LEWS with a sufficiently fast processing time, demonstrating the feasibility of optical sensors for LEWS.

Doris Hermle et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-18', Jan Henrik Blöthe, 19 Feb 2021
  • RC2: 'Comment on nhess-2021-18', Sigrid Roessner, 13 Apr 2021
    • AC3: 'Reply on RC2', Doris Hermle, 19 Apr 2021
    • AC4: 'Reply on RC2', Doris Hermle, 07 May 2021

Doris Hermle et al.

Doris Hermle et al.

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Short summary
Multispectral remote sensing images enable landslide detection and monitoring, but its applicability for time–critical early–warning is rarely studied. Here we present a concept to operationalise the use for landslide early–warning aiming to extend lead time. We tested PlanetScope and UAS images on a complex mass movement and compared processing times to historic benchmarks. We show that acquired data is within the forecasting window approving the feasibility for landslide early–warning.
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