Remote Sensing ¶

Up-to-date information on triggered avalanches is essential for the avalanche warning services and for avalanche research purposes. Following extreme events, such as those that occurred in the winter of 1999, remote sensing devices can be used to map avalanches over a large area. The primary focal points are the starting zones, runout distances and depths of deposited snow. Enormous quantities of data can be evaluated efficiently and quickly using recently developed techniques for detecting avalanches automatically. The collected information is essential for validating avalanche dynamics models and other purposes.

During the winters 2018 and 2019 the avalanche danger level "very high" (level 5) was predicted for large regions in Switzerland for the first time since 1999. We covered these avalanche periods in collaboration with FOEN and swisstopo with optical stallite data (SPOT6) over large parts of the Swiss Alps (Bühler et al. 2019). Weather independent Radar sensors (TerraSAR-x & Sentinel-1) were also tested (Leinss et al. 2020). Such data analysis proofed to be very helpful for various cantonal authoroties and will play a more important role for avalanche documentation in the future.

Radar sensors can be used to monitor critical slopes, e.g. those above roads and settlements. These offer the advantage of being able to supply data even when visibility is poor (in fog, during snowfall, at night). The researchers are testing ground-based radar sensors on the Dorfberg in Davos and have discovered that even the snowpack acceleration which precedes the actual release of a gliding avalanche can be measured (Caduff et al. 2016).

During the very snow rich winters 2018 and 2019 many avalanche defence structures were filled with snow. To monitor the effectiveness of defence structures, for example wind fences, and to detect potential problems with overfilled structures, we measured the snow depth distribution with drones to acquire information within otherwise inacessible areas (Bühler et al. 2018).

At the SLF test site in Vallée de la Sionne (town of Arbaz), large-sized avalanches can be artificially triggered. In order to obtain the greatest possible benefit from these rare events, which are of great value to science, the researchers' tool kit includes remote sensing technology. The snow surface is accurately surveyed via laser scanning before and after the avalanche is released. During the avalanche, the scientists employ photogrammetry to determine the volume of the powder cloud and the frontal velocity. A thermal imaging camera measures the snow temperatures in the avalanche. These data are subsequently used for validating and calibrating the avalanche dynamics model RAMMS​​​​​​​.

Data on snow depth and its spatial variability are important for numerous applications not only for snow and avalanche research, but also in the field of hydrology. Snow depth information is collected predominantly by means of point measurements recorded at observation stations nowadays, but this method is unable to record the spatial variations which occur in high alpine terrain. SLF researchers are testing remote sensing methods to obtain seamless snow depth data over a large area and thus evaluate the potential of these techniques for operational use. Aircraft-based measurements, when surveying large areas such as the entire Dischma valley in Davos, can achieve an accuracy of 30 cm (Bühler et al. 2015a). Although laser scanning and drones cover only relatively small areas, the measuring accuracy of these techniques is 5 to 10 cm – as compared to the absolute accuracy of manual reference measurements (Bühler et al 2016 & 2017). The recorded detail and high precision broaden the scope of numerous research and practical applications.

Near-infrared solar radiation depends to a large extent on snow properties such as grain size and the liquid water content. For example, old snow composed of large grains and moist snow, in particular, appear much darker than drifted snow composed of small grains. Optical remote sensing instruments can thus be used to differentiate various snow types. The SLF is testing the suitability of drone, aircraft and satellite-mounted instruments for snow type mapping (Bühler et al. 2015b).