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. While optical remote sensing has demonstrated its capabilities for landslidedetection and monitoring, spatial and temporal demands for landslide earlywarning systems (LEWSs) had not been met until recently. We introduce a novelconceptual approach to structure and quantitatively assess lead time forLEWSs. 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 differencebetween the time to warning and “forecasting window” (i.e. time fromhazard becoming predictable until event) is the lead time for reactivemeasures. We tested digital image correlation (DIC) of best-suitedspatiotemporal techniques, i.e. 3 m resolution PlanetScope daily imageryand 0.16 m resolution unmanned aerial system (UAS)-derived orthophotos to reveal fast grounddisplacement and acceleration of a deep-seated, complex alpinemass movement leading to massive debris flow events. The time to warning forthe 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 theforecasting window for recent benchmarks and facilitates a lead time forreactive measures. We show optical remote sensing data can support LEWSs witha sufficiently fast processing time, demonstrating the feasibility ofoptical sensors for LEWSs.

natural hazards and earth system sciences

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