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 at multiple resolutions to the lidar-derived snow depth measurements from the Airborne Snow Observatory (ASO), collected simultaneously. Where snow was mapped by both ASO and SfM, the depths compared well, with a mean difference between −0.02 m and 0.03 m, NMAD of 0.22 m, and close snow volume agreement (+/−5 %). ASO mapped a larger snow area relative to SfM, with SfM missing ~14 % of total snow volume as a result. Analyzing the differences shows that challenges for SfM 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.
Highlights
Snow depth and snow water equivalent are essential monitored quantities and applied to many water resource applications
We show that Structure from Motion (SfM) DEMs can be used to differentially calculate snow depths and corresponding snow volume over a 65 relatively large alpine watershed (300 km2) at scales commensurate with airborne lidar-based applications
The normalized median absolute deviation (NMAD) can be used as a measure for uncertainty in the snow depth values calculated from the two SfM DEMs
Summary
Snow depth and snow water equivalent are essential monitored quantities and applied to many water resource applications. Smaller in spatial extent and periodic, it has been shown that Structure from Motion (SfM) photogrammetry using imagery from Remotely Piloted Aircraft System (RPAS) can map snow depth at sub-decimeter resolution, while maintaining centimeter accuracy for areas up to alpine catchments size (< 1km2) 45 (Bühler et al, 2016; Harder et al, 2016; Schirmer & Pomeroy, 2020). We show that SfM DEMs can be used to differentially calculate snow depths and corresponding snow volume over a 65 relatively large alpine watershed (300 km2) at scales commensurate with airborne lidar-based applications This comparison demonstrates that SfM is a reliable remote sensing technique for large-scale DEM reconstruction and differential volume mapping in complex terrain, and will further expand our understanding of the strengths and weaknesses of applying photogrammetric-based techniques to automate areal snow observations
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