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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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...
»