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World of Science | Review ISSW2018: Operational remote sensing

What's happening in snow science?

by Anselm Köhler 11/28/2019
Every two years, the International Snow Science Workshop (ISSW) brings together scientists and practitioners from a wide range of different, but always snow-related, subject areas. New findings and research results are presented in various thematic blocks - so-called sessions. We break the whole thing down into more or less digestible morsels and summarize the sessions of the ISSW2018 for you every two weeks.

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.

Some ground, aircraft and satellite-based remote sensing products have become an integral part of our daily lives and, above all, of the daily work of avalanche warning services. Satellite data provides an overview of the current weather situation and incoming storms. Data from a weather radar shows the current status of a snowstorm. And after the powder has fallen, we go and enjoy the snow, but the avalanche warning services often call in a helicopter flight to assess the extent of the avalanches. Wouldn't it be desirable to extract all this information from satellite observations alone, about the snow depth and snow surface characteristics distributed over a wide area, about the number and size of avalanches per storm period, about the amount of water stored in the winter snowpack waiting to drive the hydroelectric power plants in summer?

I'm gonna send from outta space ...

One of the most remarkable large satellite research programs for snow and avalanche research is the Sentinel satellite series of the EU's Copernicus Earth observation program. A total of three Sentinel satellites were sent into orbit by the ESA. They are equipped with different sensors and the data is freely accessible to everyone . Sentinel 1 is equipped with a radar that reacts sensitively to melting snow. Sentinel 2 and 3 have sensors in the near-infrared to visible wavelength range, but both satellites provide their data in different spatial and temporal resolutions. In addition to the Sentinel series, there are other relevant satellites, e.g. the LandSat series, EnviSat, RadarSat etc.

Possible problems with satellite data arise from the spatial and temporal resolution. Sentinel 3, for example, can provide daily data, but only with a pixel resolution of several hundred meters. Sentinel 2, on the other hand, manages to fly over the same area with a resolution of at least several tens of meters every 6 days. Nevertheless, optical sensors and, above all, radar sensors can provide parameters such as snow depth, density and melting rates on a regional scale (O4.1).

Many of the papers in the session deal with the analysis of satellite data. In some cases, operational products are presented that are regularly generated from the data. An Innsbruck-based company, for example, offers snow cover maps or melt maps (O4.12). A Norwegian research team operationally detects avalanche deposits (O4.9). Other articles on satellite data tend to be more cautious and discuss as scientific papers what is not yet really possible, but of course has a lot of potential.

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.

The www.mysnowmaps.com project, presented in the article (O4.7), is a combined product of remote sensing data, local data and observations and snow cover models. In addition to the detailed, alpine-wide snow depth map (see image), the website also contains a portal where users can enter their own measurements. The aim behind this is to calculate the snow depth map even better and more accurately the more local absolute values are known. As far as I can see, there are a few good prizes to be won and only a few data-providing users so far - their current values are more or less complementary to the forecast values in our snow depth prediction game. I can't say exactly how the map differs from the SnowGRID data provided on PowderGuide (ISSW 2013), but with the information from both maps you should have a good basis for planning.

"Lasers and drones" sounds like a science fiction B-movie

A final major topic block of the remote sensing session dealt with the use of terrestrial laser scanning - a laser-based distance measurement as used on construction sites - and photogrammetry with drones, i.e. the mapping of spatial structures from photos taken from different viewing directions ("structure from motion"). Both methods are quasi local remote sensing. Lasers can be used up to a distance of around 5 km and drones may only be flown within visual range. After some data processing, both methods produce a digital terrain model, i.e. a height value for each coordinate. Subtracting the snow-free condition directly results in the absolute snow depth in the terrain with accuracy in the centimeter range. A comparison of snow depth measurement by drone, laser scan, satellite and interpolated manual measurements is discussed in article (P4.15). Other contributions are of a more methodical nature, either examining data processing (P4.14) or discussing the weather dependency of laser measurements.

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.

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This article has been automatically translated by DeepL with subsequent editing. If you notice any spelling or grammatical errors or if the translation has lost its meaning, please write an e-mail to the editors.

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