High resolution spatial measurement of snow properties in a mountain environment: A multi-instrument snow properties assessment experiment
Abstract
Snow science is in transition from interpolation of point samples to high resolution spatial measurement of snow properties from airborne and ground-based Lidar (TLS), digital photogrammetry and ground penetrating radar (GPR). New methodology has provided time-series detailed high-resolution (sub-meter scale) of snow depth and distribution over complex mountain regions from Lidar and photogrammetrically-derived structure from motion (SFM). Density and SWE can be derived from GPR, however GPR is more difficult to apply from an airborne or drone platform. In March of 2018 we undertook a field experiment over a 3-hectare snow-covered region near the headwaters of the San Joaquin basin in the southern Sierra Nevada, California, to compare carefully collected manual snow measurements to airborne and TLS Lidar, airborne and drone SFM, and a series of detailed GPR transects. In September 2018 we returned to the site for snow-off Lidar and SFM data collection to complete the experiment. We present preliminary results from the experiment.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.C13G1213M
- Keywords:
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- 0736 Snow;
- CRYOSPHEREDE: 0740 Snowmelt;
- CRYOSPHEREDE: 0764 Energy balance;
- CRYOSPHEREDE: 0798 Modeling;
- CRYOSPHERE