Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos

Differencing of digital surface models derived from structure from motion (SfM) processing of airborne imagery has been used to produce snow depth (SD) maps with between ∼2 and ∼15 cm horizontal resolution and accuracies of ±10 cm over relatively flat surfaces with little or no vegetation and over a...

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Published in:The Cryosphere
Main Authors: Fernandes, Richard, Prevost, Christian, Canisius, Francis, Leblanc, Sylvain G., Maloley, Matt, Oakes, Sarah, Holman, Kiyomi, Knudby, Anders
Format: Article in Journal/Newspaper
Language:English
Published: Copernicus Publications 2018
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Online Access:https://doi.org/10.5194/tc-12-3535-2018
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spelling ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00004119 2023-05-15T18:32:32+02:00 Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos Fernandes, Richard Prevost, Christian Canisius, Francis Leblanc, Sylvain G. Maloley, Matt Oakes, Sarah Holman, Kiyomi Knudby, Anders 2018-11 electronic https://doi.org/10.5194/tc-12-3535-2018 https://noa.gwlb.de/receive/cop_mods_00004119 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00004076/tc-12-3535-2018.pdf https://tc.copernicus.org/articles/12/3535/2018/tc-12-3535-2018.pdf eng eng Copernicus Publications The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-12-3535-2018 https://noa.gwlb.de/receive/cop_mods_00004119 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00004076/tc-12-3535-2018.pdf https://tc.copernicus.org/articles/12/3535/2018/tc-12-3535-2018.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess CC-BY article Verlagsveröffentlichung article Text doc-type:article 2018 ftnonlinearchiv https://doi.org/10.5194/tc-12-3535-2018 2022-02-08T23:00:16Z Differencing of digital surface models derived from structure from motion (SfM) processing of airborne imagery has been used to produce snow depth (SD) maps with between ∼2 and ∼15 cm horizontal resolution and accuracies of ±10 cm over relatively flat surfaces with little or no vegetation and over alpine regions. This study builds on these findings by testing two hypotheses across a broader range of conditions: (i) that the vertical accuracy of SfM processing of imagery acquired by commercial low-cost unmanned aerial vehicle (UAV) systems can be adequately modelled using conventional photogrammetric theory and (ii) that SD change can be more accurately estimated by differencing snow-covered elevation surfaces rather than differencing a snow-covered and snow-free surface. A total of 71 UAV missions were flown over five sites, ranging from short grass to a regenerating forest, with ephemeral snowpacks. Point cloud geolocation performance agreed with photogrammetric theory that predicts uncertainty is proportional to UAV altitude and linearly related to horizontal uncertainty. The root-mean-square difference (RMSD) over the observation period, in comparison to the average of in situ measurements along ∼50 m transects, ranged from 1.58 to 10.56 cm for weekly SD and from 2.54 to 8.68 cm for weekly SD change. RMSD was not related to microtopography as quantified by the snow-free surface roughness. SD change uncertainty was unrelated to vegetation cover but was dominated by outliers corresponding to rapid in situ melt or onset; the median absolute difference of SD change ranged from 0.65 to 2.71 cm. These results indicate that the accuracy of UAV-based estimates of weekly snow depth change was, excepting conditions with deep fresh snow, substantially better than for snow depth and was comparable to in situ methods. Article in Journal/Newspaper The Cryosphere Niedersächsisches Online-Archiv NOA The Cryosphere 12 11 3535 3550
institution Open Polar
collection Niedersächsisches Online-Archiv NOA
op_collection_id ftnonlinearchiv
language English
topic article
Verlagsveröffentlichung
spellingShingle article
Verlagsveröffentlichung
Fernandes, Richard
Prevost, Christian
Canisius, Francis
Leblanc, Sylvain G.
Maloley, Matt
Oakes, Sarah
Holman, Kiyomi
Knudby, Anders
Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos
topic_facet article
Verlagsveröffentlichung
description Differencing of digital surface models derived from structure from motion (SfM) processing of airborne imagery has been used to produce snow depth (SD) maps with between ∼2 and ∼15 cm horizontal resolution and accuracies of ±10 cm over relatively flat surfaces with little or no vegetation and over alpine regions. This study builds on these findings by testing two hypotheses across a broader range of conditions: (i) that the vertical accuracy of SfM processing of imagery acquired by commercial low-cost unmanned aerial vehicle (UAV) systems can be adequately modelled using conventional photogrammetric theory and (ii) that SD change can be more accurately estimated by differencing snow-covered elevation surfaces rather than differencing a snow-covered and snow-free surface. A total of 71 UAV missions were flown over five sites, ranging from short grass to a regenerating forest, with ephemeral snowpacks. Point cloud geolocation performance agreed with photogrammetric theory that predicts uncertainty is proportional to UAV altitude and linearly related to horizontal uncertainty. The root-mean-square difference (RMSD) over the observation period, in comparison to the average of in situ measurements along ∼50 m transects, ranged from 1.58 to 10.56 cm for weekly SD and from 2.54 to 8.68 cm for weekly SD change. RMSD was not related to microtopography as quantified by the snow-free surface roughness. SD change uncertainty was unrelated to vegetation cover but was dominated by outliers corresponding to rapid in situ melt or onset; the median absolute difference of SD change ranged from 0.65 to 2.71 cm. These results indicate that the accuracy of UAV-based estimates of weekly snow depth change was, excepting conditions with deep fresh snow, substantially better than for snow depth and was comparable to in situ methods.
format Article in Journal/Newspaper
author Fernandes, Richard
Prevost, Christian
Canisius, Francis
Leblanc, Sylvain G.
Maloley, Matt
Oakes, Sarah
Holman, Kiyomi
Knudby, Anders
author_facet Fernandes, Richard
Prevost, Christian
Canisius, Francis
Leblanc, Sylvain G.
Maloley, Matt
Oakes, Sarah
Holman, Kiyomi
Knudby, Anders
author_sort Fernandes, Richard
title Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos
title_short Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos
title_full Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos
title_fullStr Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos
title_full_unstemmed Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos
title_sort monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos
publisher Copernicus Publications
publishDate 2018
url https://doi.org/10.5194/tc-12-3535-2018
https://noa.gwlb.de/receive/cop_mods_00004119
https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00004076/tc-12-3535-2018.pdf
https://tc.copernicus.org/articles/12/3535/2018/tc-12-3535-2018.pdf
genre The Cryosphere
genre_facet The Cryosphere
op_relation The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424
https://doi.org/10.5194/tc-12-3535-2018
https://noa.gwlb.de/receive/cop_mods_00004119
https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00004076/tc-12-3535-2018.pdf
https://tc.copernicus.org/articles/12/3535/2018/tc-12-3535-2018.pdf
op_rights https://creativecommons.org/licenses/by/4.0/
uneingeschränkt
info:eu-repo/semantics/openAccess
op_rightsnorm CC-BY
op_doi https://doi.org/10.5194/tc-12-3535-2018
container_title The Cryosphere
container_volume 12
container_issue 11
container_start_page 3535
op_container_end_page 3550
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