Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle
Quantifying the spatial distribution of snow is crucial to predict and assess its water resource potential and understand land–atmosphere interactions. High-resolution remote sensing of snow depth has been limited to terrestrial and airborne laser scanning and more recently with application of struc...
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ftdoajarticles:oai:doaj.org/article:22284c5b0ece44c5bba2e3afa369bbca 2023-05-15T18:32:31+02:00 Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle P. Harder M. Schirmer J. Pomeroy W. Helgason 2016-11-01T00:00:00Z https://doi.org/10.5194/tc-10-2559-2016 https://doaj.org/article/22284c5b0ece44c5bba2e3afa369bbca EN eng Copernicus Publications http://www.the-cryosphere.net/10/2559/2016/tc-10-2559-2016.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 1994-0416 1994-0424 doi:10.5194/tc-10-2559-2016 https://doaj.org/article/22284c5b0ece44c5bba2e3afa369bbca The Cryosphere, Vol 10, Iss 6, Pp 2559-2571 (2016) Environmental sciences GE1-350 Geology QE1-996.5 article 2016 ftdoajarticles https://doi.org/10.5194/tc-10-2559-2016 2022-12-31T12:34:58Z Quantifying the spatial distribution of snow is crucial to predict and assess its water resource potential and understand land–atmosphere interactions. High-resolution remote sensing of snow depth has been limited to terrestrial and airborne laser scanning and more recently with application of structure from motion (SfM) techniques to airborne (manned and unmanned) imagery. In this study, photography from a small unmanned aerial vehicle (UAV) was used to generate digital surface models (DSMs) and orthomosaics for snow cover at a cultivated agricultural Canadian prairie and a sparsely vegetated Rocky Mountain alpine ridgetop site using SfM. The accuracy and repeatability of this method to quantify snow depth, changes in depth and its spatial variability was assessed for different terrain types over time. Root mean square errors in snow depth estimation from differencing snow-covered and non-snow-covered DSMs were 8.8 cm for a short prairie grain stubble surface, 13.7 cm for a tall prairie grain stubble surface and 8.5 cm for an alpine mountain surface. This technique provided useful information on maximum snow accumulation and snow-covered area depletion at all sites, while temporal changes in snow depth could also be quantified at the alpine site due to the deeper snowpack and consequent higher signal-to-noise ratio. The application of SfM to UAV photographs returns meaningful information in areas with mean snow depth > 30 cm, but the direct observation of snow depth depletion of shallow snowpacks with this method is not feasible. Accuracy varied with surface characteristics, sunlight and wind speed during the flight, with the most consistent performance found for wind speeds < 10 m s −1 , clear skies, high sun angles and surfaces with negligible vegetation cover. Article in Journal/Newspaper The Cryosphere Directory of Open Access Journals: DOAJ Articles The Cryosphere 10 6 2559 2571 |
institution |
Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
spellingShingle |
Environmental sciences GE1-350 Geology QE1-996.5 P. Harder M. Schirmer J. Pomeroy W. Helgason Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
description |
Quantifying the spatial distribution of snow is crucial to predict and assess its water resource potential and understand land–atmosphere interactions. High-resolution remote sensing of snow depth has been limited to terrestrial and airborne laser scanning and more recently with application of structure from motion (SfM) techniques to airborne (manned and unmanned) imagery. In this study, photography from a small unmanned aerial vehicle (UAV) was used to generate digital surface models (DSMs) and orthomosaics for snow cover at a cultivated agricultural Canadian prairie and a sparsely vegetated Rocky Mountain alpine ridgetop site using SfM. The accuracy and repeatability of this method to quantify snow depth, changes in depth and its spatial variability was assessed for different terrain types over time. Root mean square errors in snow depth estimation from differencing snow-covered and non-snow-covered DSMs were 8.8 cm for a short prairie grain stubble surface, 13.7 cm for a tall prairie grain stubble surface and 8.5 cm for an alpine mountain surface. This technique provided useful information on maximum snow accumulation and snow-covered area depletion at all sites, while temporal changes in snow depth could also be quantified at the alpine site due to the deeper snowpack and consequent higher signal-to-noise ratio. The application of SfM to UAV photographs returns meaningful information in areas with mean snow depth > 30 cm, but the direct observation of snow depth depletion of shallow snowpacks with this method is not feasible. Accuracy varied with surface characteristics, sunlight and wind speed during the flight, with the most consistent performance found for wind speeds < 10 m s −1 , clear skies, high sun angles and surfaces with negligible vegetation cover. |
format |
Article in Journal/Newspaper |
author |
P. Harder M. Schirmer J. Pomeroy W. Helgason |
author_facet |
P. Harder M. Schirmer J. Pomeroy W. Helgason |
author_sort |
P. Harder |
title |
Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle |
title_short |
Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle |
title_full |
Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle |
title_fullStr |
Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle |
title_full_unstemmed |
Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle |
title_sort |
accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle |
publisher |
Copernicus Publications |
publishDate |
2016 |
url |
https://doi.org/10.5194/tc-10-2559-2016 https://doaj.org/article/22284c5b0ece44c5bba2e3afa369bbca |
genre |
The Cryosphere |
genre_facet |
The Cryosphere |
op_source |
The Cryosphere, Vol 10, Iss 6, Pp 2559-2571 (2016) |
op_relation |
http://www.the-cryosphere.net/10/2559/2016/tc-10-2559-2016.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 1994-0416 1994-0424 doi:10.5194/tc-10-2559-2016 https://doaj.org/article/22284c5b0ece44c5bba2e3afa369bbca |
op_doi |
https://doi.org/10.5194/tc-10-2559-2016 |
container_title |
The Cryosphere |
container_volume |
10 |
container_issue |
6 |
container_start_page |
2559 |
op_container_end_page |
2571 |
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