Remote sensing sensors on the ground, on drones, aircraft or satellites are increasingly becoming an important tool in natural hazard research. In this way, disaster-related data can quickly be used to collect area-wide, spatially-high-resolution data, even in areas that are difficult or impossible to access from the ground.
The SLF is researching how such sensor systems can optimally be used in high alpine terrain in the future. The following works are in focus:
- detailed surveying and mapping of avalanches, debris flows and rockfall events
- Collect current and accurate digital terrain models in high alpine areas
- Detecting the small-scale snow depth variability
- large area mapping of snow types
- Characterizing protection forest properties
etection and mapping of avalanches
Up-to-date information on avalanche decline is essential for avalanche warning and avalanche research. After extreme events such as the winter of 1999, remote sensing sensors can be used to map the avalanches over a large area. In particular, the cracking zones, the discharge distances and the deposit heights are of great interest. Newly developed methods for the automated detection of avalanche emissions allow the huge amount of data to be efficiently and quickly evaluated. This information is also essential to validate avalanche dynamics models.
Radar sensors can be used to monitor critical slopes, such as roads or settlements. These have the advantage that they provide data even in poor visibility (fog, snowfall, night). The researchers are testing ground-based radar sensors at the Dorfberg in Davos and have discovered that the acceleration of the snowpack can already be measured before the departure of a gliding snow avalanche.
Digital terrain models
Digital terrain models (DEM) are the basis for the numerical simulation of mass movements such as avalanches, debris flows or rockfalls with RAMMS and for many other research applications. SLF scientists are researching various technologies for producing high-precision terrain models such as LiDAR or photogrammetric image correlation (see below). In selected test areas with extreme terrain properties, they quantify the achieved accuracies and identify systematic errors. They also examine how terrain model quality and resolution affect the simulation results.
Surveying avalanche experiments
In the SLF test area Vallée de la Sionne (municipality of Arbaz), large avalanches can be triggered artificially. In order to benefit optimally from these rare and highly valuable events, the researchers also use remote sensing technology: the snow surface is precisely measured before and after the avalanche with laser scanning. During descent, the scientists use photogrammetry to determine the volume of the dust cloud and the frontal speed. A thermal camera measures the snow temperatures in the avalanche. These data are then used for the validation and calibration of the avalanche dynamics model RAMMS.read more on wikipedia