World of Science | Review ISSW2018: Operational remote sensing

This time: Operational remote sensing - application for snow and avalanches (Session 4).

Remote sensing means that something is explored from a distance - practical remote sensing for skiers would be, for example, spotting a possible line and observing the snow conditions in it with binoculars. You could also remove the near-infrared filter in your digital camera and use the red channel of the images to interpret the near-infrared spectrum of the snow surface for its composition (see image, P4.4). Or simply count tracks in the images, as a research team observed a slope to show the terrain preferences of freeriders depending on the avalanche danger level (P4.5). One interesting result is the preference for skiing alone depending on the danger level: 66% of the descents at danger level 3, 85% at a 2 and only 93% at a 1 took place in groups - just say "no friends on powder days" isn't true ...

Remote sensing encompasses much more, however: for example, it can be categorized based on the "distance" - the distance to the object - or based on the measurement method. In the current session, remote sensing applications are represented that are based on data and observations recorded from the ground (terrestrial laser scanning, time-lapse photography), from aircraft (currently drones, traditionally manned flights) and above all - increasingly important - from satellites. Optical methods are always used as measurement methods, but optical here does not only mean radiation in the visible spectrum: lasers, near-infrared, visible wavelengths and radar are used from short to long wavelengths. Furthermore, any measurement method can be used actively or passively: Active means that an object is illuminated and then the reflections are measured. Passive means that only the passively emitted radiation is recorded.

Avalanche deposits look different from powder

Avalanche forecasting is based on various spatial information such as spatial and temporal changes in the snowpack - e.g. the amount of (new) snow or the distribution of spring melting and freezing cycles. On the other hand, the number of avalanches during a storm is a major unknown and avalanche detection has its own thematic block at the ISSW (see Session 7: "Avalanche detection: Industry and Research").

Satelite-based radar measurements as a discipline of remote sensing can, in principle, detect the contrast between untouched snow and coarse deposits of, for example, wet snow avalanches (P4.9). However, only deposits from sufficiently large avalanches produce a radar signal that differs from the already very noisy microwave signal. Small and dry avalanches, those that are most relevant for skiers, can therefore not (yet) be detected by radar.

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A model tells you quickly

A second important topic is data assimilation. Assi what? It is about how the spatial information from the satellite missions can be incorporated into spatial (snowpack) models and used to re-initialize the initial and boundary conditions. Traditionally, such snowpack models are fed with results from weather models and point meteostation data. The variability and heterogeneity of the snow cover should be added from the remote sensing data.

The models can thus be restarted again and again on the basis of the data, and continuous errors are reduced.

In this way, information can be modeled quasi physically-correct from the existing snow cover, the current weather conditions and the additional spatial information from satellite data. Such model chains not only have the potential to generate better information for avalanche forecasting (O4.10), but they are also useful for hydrology, where it is often necessary to quantify the water content of the entire snowpack (O4.5). The water content (snow depth x density), its change over time and the corresponding melt runoff are often index-based forecast variables, i.e. the current situation is extrapolated from comparable events to a larger area. The article (O4.3) questions whether such estimates from "historical events" are still correct and sufficient for future situations in the course of climate change.

One vision of the future, which was not explicitly mentioned in the session, but which might not be too far off, is that the drones we carry with us in our free time could provide us with precisely such snow maps in real time. Then we could visualize every little shark on our head-up display in our ski goggles. If the goggles were also equipped with near-infrared lenses, we would be able to distinguish even the smallest powder residue from wind and melt crusts - freeriding with science fiction, that would be something...

Conclusion

In conclusion, it can be said that remote sensing is an important interdisciplinary measurement and analysis method. Remote sensing has always been an integral part of geography, but many other sciences have also been using the techniques for some time. The state of snow and avalanche research shows very clearly that remote sensing is important in many respects and will only increase in importance in the future. Satellite data in particular will provide us with many helpful insights into the snow cover in the coming years.