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

Abstract

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.

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

“…As the debris consisted of boulders up to 10 m, continuous visual block tracking could be employed to estimate the displacement within the active area based on aerial orthophotos. Recent studies conducted with COSI-Corr confirmed this ongoing increase of mass wasting processes, with variable displacement rates ranging from 1-14 m in only 42 days [25].…”

“…Image registration, also known as image matching and image correlation, geometrically aligns images and allows tracking for accurate 2D change measurements in optical images. It has been used on a local to regional scale to assess motion fields for glaciological [8][9][10], earthquake [11][12][13], dune migration [14,15], rock glacier [16] and landslide studies [5,[17][18][19][20][21][22][23][24][25].…”

“…Geometrical inaccuracies arise from co-registration, with additional problems resulting from vegetation changes and dynamically significant surface processes, leading to mismatches [11,18]. While less sensitive to illumination differences, which are particularly important and challenging for high-alpine environments (arising from low contrast, moving cast shadows and cloud shadows), these factors can still result in erroneous displacement results [11,18,25]. The most important problems for their application to fast landslides are velocities exceeding the search window-related matching limit, which causes decorrelation and ambiguous signals [5,41,42].…”

“…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. Even though satellite revisit rates have significantly increased (e.g., Sentinel-2, five days, PlanetScope, daily), their actual net revisit rate for high-alpine sites is restricted to a few images per month [25,54]. Therefore, UAS campaigns offer the highest temporal flexibility to overcome these obstacles [17], but good quality data depends on GCPs (ground control points) with reproducible flight plans [55], which requires significantly more time, and hence, higher costs.…”

Rainfall-Induced Landslide Early Warning System based on corrected mesoscale numerical models: an application for the Southern Andes

“…infrastructure damages (Guzzetti et al, 2020;Chikalamo et al, 2020;Hermle et al, 2021). The present work evaluates the design of a RILEWS using a mesoscale atmospheric model coupled to a logistic discriminator in the Southern Andes.…”