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Discussion papers | Copyright
https://doi.org/10.5194/tc-2018-82
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 15 May 2018

Research article | 15 May 2018

Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal The Cryosphere (TC) and is expected to appear here in due course.

Monitoring snow depth change across a range of landscapes with ephemeral snow packs using Structure from Motion applied to lightweight unmanned aerial vehicle videos

Richard Fernandes1, Christian Prevost1, Francis Canisius1, Sylvain G. Leblanc1, Matt Maloley1, Sarah Oakes1, Kyomi Holman2, and Anders Knudby2 Richard Fernandes et al.
  • 1Canada Centre for Remote Sensing, Natural Resources Canada, Ottawa, K1A 0Y7, Canada
  • 2Department of Geography, Environment and Geomatics, University of Ottawa, Ottawa, K1N 6Y5, Canada

Abstract. Snow depth (SD) can vary by more than an order of magnitude over length scales of metres due to topography, vegetation and microclimate. Differencing of digital surface models derived from Structure from Motion (SfM) processing of airborne imagery has been used to produce SD maps with between ∼2cm to ∼15cm horizontal resolution and accuracies on the order of ±10cm over both relatively flat surfaces with little or no vegetation and over alpine regions. Studies indicate that accuracy is lower in the presence of vegetation above or below the snowpack and in rough topography; suggesting that some biases may be temporally persistent. Moreover, flight and image parameters vary across studies but they are typically not related a priori to an expected uncertainty in SD. This study tests two hypotheses: i) that SD change can be more accurately estimated when differencing snow covered elevation surfaces rather than the absolute snow depth based on differencing a snow covered and snow free surface and ii) the vertical accuracy of SfM processing of imagery acquired by commercial light weight unmanned aerial vehicle (UAV) systems can be adequately modelled using conventional photogrammetric theory. Moreover, these hypotheses are tested over areas with ephemeral snow pack conditions and across a range of micro-topography and vegetation cover. Weekly SD maps with <3cm horizontal resolution are derived for a period spanning peak snowpack to snow free condition for five sites with differing micro-topography and vegetation cover. Across the sites, the root mean square difference (RMSD) over the observation period, in-comparison to the average of in-situ measurements along ∼50m transects, ranged from 1.58cm to 10.56cm for SD and from 2.54cm to 8.68cm for weekly SD change. RMSD was not related to micro-topography as quantified by the snow free surface roughness. Biases in SD due to vegetation in the snow covered or snow free image contributed to over 85% of the observed difference at sites where the RMSD of SD exceed 5cm. RMSD of weekly SD change was dominated by outliers corresponding to rapid in-situ melt or onset and low point cloud density. In contrast to the RMSD, the median absolute difference of SD change ranged from 0.65cm to 2.71cm. Validation results agree with photogrammetric theory that predicts uncertainty is proportional to UAV altitude and linearly related to horizontal uncertainty. These results indicate that while the accuracy of UAV based estimates of snow depth is similar to other studies with different snow pack and terrain conditions, the accuracy of UAV based estimates of weekly snow depth change was, excepting conditions with low point cloud densities, substantially better and is comparable to in-situ methods.

Richard Fernandes et al.
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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Richard Fernandes et al.
Richard Fernandes et al.
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Latest update: 18 Oct 2018
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Short summary
The use of lightweight unmanned aerial vehicle based surveys of surface elevation tomap snow depth and weekly snow depth change was evaluated over 5 study areas spanning a range of topography and vegetation cover. Snow depth was estimated with an accuracy of better than 10 cm vertical and 3 cm horizontal. Vegetation in the snow free elevation map was a major source of error. As a result, the snow depth change between two dates with snow cover was estimated with an accuracy of better than 4 cm.
The use of lightweight unmanned aerial vehicle based surveys of surface elevation tomap snow...
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