Preprints
https://doi.org/10.5194/tc-2021-34
https://doi.org/10.5194/tc-2021-34

  12 Feb 2021

12 Feb 2021

Review status: this discussion paper is a preprint. It has been under review for the journal The Cryosphere (TC). The manuscript was not accepted for further review after discussion.

Mapping snow depth and volume at the alpine watershed scale from aerial imagery using Structure from Motion

Joachim Meyer1, S. McKenzie Skiles1, Jeffrey Deems2, Kat Bormann3, and David Shean4 Joachim Meyer et al.
  • 1Department of Geography, University of Utah, Salt Lake City, UT, USA
  • 2National Snow and Ice Data Center, Boulder, CO, USA
  • 3Airborne Snow Observatories, Inc., Mammoth Lakes, CA, USA
  • 4Dept. of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA

Abstract. Time series mapping of water held as snow in the mountains at global scales is an unsolved challenge to date. In a few locations, lidar-based airborne campaigns have been used to provide valuable data sets that capture snow distribution in near real-time over multiple seasons. Here, an alternative method is presented to map snow depth and quantify snow volume using aerial images and Structure from Motion (SfM) photogrammetry over an alpine watershed (300 km2). The results were compared to the lidar-derived snow depth measurements from the Airborne Snow Observatory, collected simultaneously. Where snow was mapped by both ASO and SfM, the depths compared well, with a mean difference of 0.01 m, NMAD of 0.22 m, and snow volume agreement (difference 1.26 %). ASO though, mapped a larger snow area relative to SfM, with SfM missing ~14 % of total snow volume as a result. Analyzing the SfM reconstruction errors shows that challenges for photogrammetry remain in vegetated areas, over shallow snow (< 1 m), and slope angles over 50 degrees. Our results indicate that capturing large scale snow depth and volume with airborne images and photogrammetry could be an additional viable resource for understanding and monitoring snow water resources in certain environments.

Joachim Meyer et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-34', Anonymous Referee #1, 26 Feb 2021
  • RC2: 'Review of tc-2021-34', Yves Bühler, 26 Feb 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-34', Anonymous Referee #1, 26 Feb 2021
  • RC2: 'Review of tc-2021-34', Yves Bühler, 26 Feb 2021

Joachim Meyer et al.

Joachim Meyer et al.

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Short summary
Snow that accumulates seasonally in mountains forms a natural water reservoir and is difficult to measure in the rugged and remote landscapes. Here, we use modern software that models surface elevations from overlapping aerial images to map snow depth by calculating the difference in surface elevations between two dates, one with snow and one without. Results demonstrate the potential value of aerial images for understanding the amount of water held as snow in remote and inaccessible locations.