Terrestrial Laser Scanning With Real-Time Data Analytics For Avalanche Forecasting

Examples of this include living by coastlines and face potential seasonal hurricanes or typhoons, living in an area with a volcano close by or in this stories’ case, living in the colder region of Montana in the United State, where avalanches are known to have disastrous aftermaths when they occur.

The team of researchers at Montana State University’s Subzero Research Laboratory (SRL) are exploring the potential for terrestrial laser scanners in order to detect avalanche risk in real-time with their recently acquired RIEGL VZ-6000. Please continuing reading below for more on this fascinating case study.

If the team at the Subzero Research Laboratory can obtain an early warning of an avalanche occurrence, they will be able to successfully save lives and additionally, will be able to help significantly reduce the costs that are associated with shutting down mountain roadways and ski areas. The VZ-6000 terrestrial laser scanner enables the creation of photorealistic virtual models for geological and geotechnical investigations, avalanche research, geomorphology, and geological features through the data captured. If the individuals who live in these areas ae given enough time to be proactive instead of reactive to the disaster, then this becomes a highly valued asset to the research team and all that will be affected. The waveform technology and use of more powerful lasers make it possible to measure snow and ice across expanses many kilometers wide.

Last December, UNAVCO field engineer Keith Williams brought the RIEGL VZ-6000 TLS instrument to SRL and performed two days of training in the lab, as well as in the field to SRL researchers. The VZ-6000 offers an extremely long measurement range of more than 6000 m for topographic (static) applications. Being the Laser Class 3B companion to the VZ-4000, it is, due to its laser wavelength, exceptionally well suited for measuring snowy and icy terrain in glacier mapping and monitoring applications in mountainous regions, which makes it an ideal system to use for avalanche risk detection. The researchers have since been able to the instrument to collect data on various snowpack conditions on-site, as well as at multiple outdoor test sites. “The researchers are testing TLS instruments for accuracy and signal-to-noise ratio as a function of snow depth, density, grain-type, grain-size, surface characteristics, and angle of incidence. These measurements will guide software development for snow remote sensing, data interpretation, and data analytics, as well as being able to provide avalanche forecasters with new insights from real-time TLS data.”

The measurements that the team is able to acquire will help guide software development for the group for snow remote sensing, data interpretation, and data analytics, as well as being able to provide avalanche forecasters with new insights from real-time TLS data.

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