Abstract
In the GEOSFAIR (Geohazard Survey from Air) innovation project for the Norwegian public
sector, several UAV (Uncrewed Aerial Vehicles)-borne remote sensing payloads (LiDAR scanners; RGB, infrared, and multispectral cameras) were tested to gather information on the snow surface and its changes over
time. For example, by comparing bare-earth and snow surface digital elevation models over time, it is possible
to derive estimates of snow height and snow height changes. However, in addition to resolution limitations for
snow height (which is dependent on the quality of baseline terrain models), none of these remote sensing
methods can give information on the snow layering and snowpack properties. During the last three years, we
tested UAV-borne GPR (Ground Penetrating Radar) sensors to get information on the subsurface of the snow,
i.e., on snow height, snow layering and snow properties (snow density and liquid water content). Numerous
field tests in real mountain conditions including mapping flat areas and BVLOS flights in avalanche starting
zones enabled us to determine optimal operational flight guidelines. To optimize data quality, the GPR sensors
should record data at an altitude less than 5 m above the snow surface, which requires altimeter and UAV
terrain-following capabilities. Flying downslope at a speed up to 2-3 m/s and following the surface at 2-4 m
have been shown to be the best compromise in terms of flight safety and data quality. In dry snowpacks, we
use a shielded antenna with a 1 GHz central frequency, that penetrates up to 8-10 m of snow and can detect
changes in layering down to a couple of centimeters. We show good correlations between snow pits and
interpreted layers in the GPR data, especially when mapping melt-freeze crusts. By converting snow density
to GPR wave velocity at a snow pit location, and by mapping snow surface and snow-ground interfaces, we
are also able to derive high-resolution snow height maps which correlate well with snow height derived from
LiDAR surveys with lower spatial resolution. UAV-borne GPR is a promising tool to provide remote snowpack
information including high resolution snow height and snow layering and may be used to support local avalanche forecasting. Additional work is on-going to derive snow properties without the need for local snow pit
information
sector, several UAV (Uncrewed Aerial Vehicles)-borne remote sensing payloads (LiDAR scanners; RGB, infrared, and multispectral cameras) were tested to gather information on the snow surface and its changes over
time. For example, by comparing bare-earth and snow surface digital elevation models over time, it is possible
to derive estimates of snow height and snow height changes. However, in addition to resolution limitations for
snow height (which is dependent on the quality of baseline terrain models), none of these remote sensing
methods can give information on the snow layering and snowpack properties. During the last three years, we
tested UAV-borne GPR (Ground Penetrating Radar) sensors to get information on the subsurface of the snow,
i.e., on snow height, snow layering and snow properties (snow density and liquid water content). Numerous
field tests in real mountain conditions including mapping flat areas and BVLOS flights in avalanche starting
zones enabled us to determine optimal operational flight guidelines. To optimize data quality, the GPR sensors
should record data at an altitude less than 5 m above the snow surface, which requires altimeter and UAV
terrain-following capabilities. Flying downslope at a speed up to 2-3 m/s and following the surface at 2-4 m
have been shown to be the best compromise in terms of flight safety and data quality. In dry snowpacks, we
use a shielded antenna with a 1 GHz central frequency, that penetrates up to 8-10 m of snow and can detect
changes in layering down to a couple of centimeters. We show good correlations between snow pits and
interpreted layers in the GPR data, especially when mapping melt-freeze crusts. By converting snow density
to GPR wave velocity at a snow pit location, and by mapping snow surface and snow-ground interfaces, we
are also able to derive high-resolution snow height maps which correlate well with snow height derived from
LiDAR surveys with lower spatial resolution. UAV-borne GPR is a promising tool to provide remote snowpack
information including high resolution snow height and snow layering and may be used to support local avalanche forecasting. Additional work is on-going to derive snow properties without the need for local snow pit
information
