Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques
Vegetation has a tremendous influence on snow processes and snowpack dynamics, yet remote sensing techniques to resolve the spatial variability of sub-canopy snow depth are not always available and are difficult from space-based platforms. Unmanned aerial vehicles (UAVs) have had recent widespread a...
Published in: | The Cryosphere |
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2020
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00051783 2023-05-15T18:32:33+02:00 Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques Harder, Phillip Pomeroy, John W. Helgason, Warren D. 2020-06 electronic https://doi.org/10.5194/tc-14-1919-2020 https://noa.gwlb.de/receive/cop_mods_00051783 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00051439/tc-14-1919-2020.pdf https://tc.copernicus.org/articles/14/1919/2020/tc-14-1919-2020.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-14-1919-2020 https://noa.gwlb.de/receive/cop_mods_00051783 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00051439/tc-14-1919-2020.pdf https://tc.copernicus.org/articles/14/1919/2020/tc-14-1919-2020.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess CC-BY article Verlagsveröffentlichung article Text doc-type:article 2020 ftnonlinearchiv https://doi.org/10.5194/tc-14-1919-2020 2022-02-08T22:36:16Z Vegetation has a tremendous influence on snow processes and snowpack dynamics, yet remote sensing techniques to resolve the spatial variability of sub-canopy snow depth are not always available and are difficult from space-based platforms. Unmanned aerial vehicles (UAVs) have had recent widespread application to capture high-resolution information on snow processes and are herein applied to the sub-canopy snow depth challenge. Previous demonstrations of snow depth mapping with UAV structure from motion (SfM) and airborne lidar have focussed on non-vegetated surfaces or reported large errors in the presence of vegetation. In contrast, UAV-lidar systems have high-density point clouds and measure returns from a wide range of scan angles, increasing the likelihood of successfully sensing the sub-canopy snow depth. The effectiveness of UAV lidar and UAV SfM in mapping snow depth in both open and forested terrain was tested in a 2019 field campaign at the Canadian Rockies Hydrological Observatory, Alberta, and at Canadian prairie sites near Saskatoon, Saskatchewan, Canada. Only UAV lidar could successfully measure the sub-canopy snow surface with reliable sub-canopy point coverage and consistent error metrics (root mean square error (RMSE) <0.17 m and bias −0.03 to −0.13 m). Relative to UAV lidar, UAV SfM did not consistently sense the sub-canopy snow surface, the interpolation needed to account for point cloud gaps introduced interpolation artefacts, and error metrics demonstrated relatively large variability (RMSE<0.33 m and bias 0.08 to −0.14 m). With the demonstration of sub-canopy snow depth mapping capabilities, a number of early applications are presented to showcase the ability of UAV lidar to effectively quantify the many multiscale snow processes defining snowpack dynamics in mountain and prairie environments. Article in Journal/Newspaper The Cryosphere Niedersächsisches Online-Archiv NOA Canada The Cryosphere 14 6 1919 1935 |
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Niedersächsisches Online-Archiv NOA |
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ftnonlinearchiv |
language |
English |
topic |
article Verlagsveröffentlichung |
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article Verlagsveröffentlichung Harder, Phillip Pomeroy, John W. Helgason, Warren D. Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques |
topic_facet |
article Verlagsveröffentlichung |
description |
Vegetation has a tremendous influence on snow processes and snowpack dynamics, yet remote sensing techniques to resolve the spatial variability of sub-canopy snow depth are not always available and are difficult from space-based platforms. Unmanned aerial vehicles (UAVs) have had recent widespread application to capture high-resolution information on snow processes and are herein applied to the sub-canopy snow depth challenge. Previous demonstrations of snow depth mapping with UAV structure from motion (SfM) and airborne lidar have focussed on non-vegetated surfaces or reported large errors in the presence of vegetation. In contrast, UAV-lidar systems have high-density point clouds and measure returns from a wide range of scan angles, increasing the likelihood of successfully sensing the sub-canopy snow depth. The effectiveness of UAV lidar and UAV SfM in mapping snow depth in both open and forested terrain was tested in a 2019 field campaign at the Canadian Rockies Hydrological Observatory, Alberta, and at Canadian prairie sites near Saskatoon, Saskatchewan, Canada. Only UAV lidar could successfully measure the sub-canopy snow surface with reliable sub-canopy point coverage and consistent error metrics (root mean square error (RMSE) <0.17 m and bias −0.03 to −0.13 m). Relative to UAV lidar, UAV SfM did not consistently sense the sub-canopy snow surface, the interpolation needed to account for point cloud gaps introduced interpolation artefacts, and error metrics demonstrated relatively large variability (RMSE<0.33 m and bias 0.08 to −0.14 m). With the demonstration of sub-canopy snow depth mapping capabilities, a number of early applications are presented to showcase the ability of UAV lidar to effectively quantify the many multiscale snow processes defining snowpack dynamics in mountain and prairie environments. |
format |
Article in Journal/Newspaper |
author |
Harder, Phillip Pomeroy, John W. Helgason, Warren D. |
author_facet |
Harder, Phillip Pomeroy, John W. Helgason, Warren D. |
author_sort |
Harder, Phillip |
title |
Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques |
title_short |
Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques |
title_full |
Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques |
title_fullStr |
Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques |
title_full_unstemmed |
Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques |
title_sort |
improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques |
publisher |
Copernicus Publications |
publishDate |
2020 |
url |
https://doi.org/10.5194/tc-14-1919-2020 https://noa.gwlb.de/receive/cop_mods_00051783 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00051439/tc-14-1919-2020.pdf https://tc.copernicus.org/articles/14/1919/2020/tc-14-1919-2020.pdf |
geographic |
Canada |
geographic_facet |
Canada |
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-14-1919-2020 https://noa.gwlb.de/receive/cop_mods_00051783 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00051439/tc-14-1919-2020.pdf https://tc.copernicus.org/articles/14/1919/2020/tc-14-1919-2020.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-14-1919-2020 |
container_title |
The Cryosphere |
container_volume |
14 |
container_issue |
6 |
container_start_page |
1919 |
op_container_end_page |
1935 |
_version_ |
1766216743572733952 |