Direct photogrammetry with multispectral imagery for UAV-based snow depth estimation
More accurate snow quality predictions are needed to economically and socially support communities in a changing Arctic environment. This contrasts with the current availability of affordable and efficient snow monitoring methods. In this study, a novel approach is presented to determine spatial sno...
Published in: | ISPRS Journal of Photogrammetry and Remote Sensing |
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Language: | English |
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Uppsala universitet, Luft-, vatten- och landskapslära
2022
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Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-473802 https://doi.org/10.1016/j.isprsjprs.2022.01.020 |
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ftuppsalauniv:oai:DiVA.org:uu-473802 2024-02-11T10:01:43+01:00 Direct photogrammetry with multispectral imagery for UAV-based snow depth estimation Maier, Kathrin Nascetti, Andrea Van Pelt, Ward Rosqvist, Gunhild 2022 application/pdf http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-473802 https://doi.org/10.1016/j.isprsjprs.2022.01.020 eng eng Uppsala universitet, Luft-, vatten- och landskapslära KTH Royal Inst Technol, Geoinformat Div, Stockholm, Sweden. KTH Royal Inst Technol, Geoinformat Div, Stockholm, Sweden.;Polytech Univ Bari, Dept DICATECh, Bari, Italy. Stockholm Univ, Dept Phys Geog, Tarfala Res Stn, Stockholm, Sweden. Elsevier BV ISPRS journal of photogrammetry and remote sensing (Print), 0924-2716, 2022, 186, s. 1-18 orcid:0000-0003-4839-7900 http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-473802 doi:10.1016/j.isprsjprs.2022.01.020 ISI:000782587800001 info:eu-repo/semantics/openAccess Unmanned aerial vehicle Photogrammetry Direct georeferencing Snow depth Cryosphere Principal component analysis Remote Sensing Fjärranalysteknik Article in journal info:eu-repo/semantics/article text 2022 ftuppsalauniv https://doi.org/10.1016/j.isprsjprs.2022.01.020 2024-01-17T23:32:18Z More accurate snow quality predictions are needed to economically and socially support communities in a changing Arctic environment. This contrasts with the current availability of affordable and efficient snow monitoring methods. In this study, a novel approach is presented to determine spatial snow depth distribution in challenging alpine terrain that was tested during a field campaign performed in the Tarfala valley, Kebnekaise mountains, northern Sweden, in April 2019. The combination of a multispectral camera and an Unmanned Aerial Vehicle (UAV) was used to derive three-dimensional (3D) snow surface models via Structure from Motion (SfM) with direct georeferencing. The main advantage over conventional photogrammetric surveys is the utilization of accurate Real-Time Kinematic (RTK) positioning which enables direct georeferencing of the images, and therefore eliminates the need for ground control points. The proposed method is capable of producing high -resolution 3D snow-covered surface models (<7 cm/pixel) of alpine areas up to eight hectares in a fast, reli-able and affordable way. The test sites' average snow depth was 160 cm with an average standard deviation of 78 cm. The overall Root-Mean-Square Errors (RMSE) of the snow depth range from 11.52 cm for data acquired in ideal surveying conditions to 41.03 cm in aggravated light and wind conditions. Results of this study suggest that the red components in the electromagnetic spectrum, i.e., the red, red edge, and near-infrared (NIR) band, contain the majority of information used in photogrammetric processing. The experiments highlighted a sig-nificant influence of the multi-spectral imagery on the quality of the final snow depth estimation as well as a strong potential to reduce processing times and computational resources by limiting the dimensionality of the imagery through the application of a Principal Component Analysis (PCA) before the photogrammetric 3D reconstruction. The proposed method is part of closing the scale gap between discrete point ... Article in Journal/Newspaper Arctic Northern Sweden Tarfala Uppsala University: Publications (DiVA) Arctic Tarfala ENVELOPE(18.608,18.608,67.914,67.914) ISPRS Journal of Photogrammetry and Remote Sensing 186 1 18 |
institution |
Open Polar |
collection |
Uppsala University: Publications (DiVA) |
op_collection_id |
ftuppsalauniv |
language |
English |
topic |
Unmanned aerial vehicle Photogrammetry Direct georeferencing Snow depth Cryosphere Principal component analysis Remote Sensing Fjärranalysteknik |
spellingShingle |
Unmanned aerial vehicle Photogrammetry Direct georeferencing Snow depth Cryosphere Principal component analysis Remote Sensing Fjärranalysteknik Maier, Kathrin Nascetti, Andrea Van Pelt, Ward Rosqvist, Gunhild Direct photogrammetry with multispectral imagery for UAV-based snow depth estimation |
topic_facet |
Unmanned aerial vehicle Photogrammetry Direct georeferencing Snow depth Cryosphere Principal component analysis Remote Sensing Fjärranalysteknik |
description |
More accurate snow quality predictions are needed to economically and socially support communities in a changing Arctic environment. This contrasts with the current availability of affordable and efficient snow monitoring methods. In this study, a novel approach is presented to determine spatial snow depth distribution in challenging alpine terrain that was tested during a field campaign performed in the Tarfala valley, Kebnekaise mountains, northern Sweden, in April 2019. The combination of a multispectral camera and an Unmanned Aerial Vehicle (UAV) was used to derive three-dimensional (3D) snow surface models via Structure from Motion (SfM) with direct georeferencing. The main advantage over conventional photogrammetric surveys is the utilization of accurate Real-Time Kinematic (RTK) positioning which enables direct georeferencing of the images, and therefore eliminates the need for ground control points. The proposed method is capable of producing high -resolution 3D snow-covered surface models (<7 cm/pixel) of alpine areas up to eight hectares in a fast, reli-able and affordable way. The test sites' average snow depth was 160 cm with an average standard deviation of 78 cm. The overall Root-Mean-Square Errors (RMSE) of the snow depth range from 11.52 cm for data acquired in ideal surveying conditions to 41.03 cm in aggravated light and wind conditions. Results of this study suggest that the red components in the electromagnetic spectrum, i.e., the red, red edge, and near-infrared (NIR) band, contain the majority of information used in photogrammetric processing. The experiments highlighted a sig-nificant influence of the multi-spectral imagery on the quality of the final snow depth estimation as well as a strong potential to reduce processing times and computational resources by limiting the dimensionality of the imagery through the application of a Principal Component Analysis (PCA) before the photogrammetric 3D reconstruction. The proposed method is part of closing the scale gap between discrete point ... |
format |
Article in Journal/Newspaper |
author |
Maier, Kathrin Nascetti, Andrea Van Pelt, Ward Rosqvist, Gunhild |
author_facet |
Maier, Kathrin Nascetti, Andrea Van Pelt, Ward Rosqvist, Gunhild |
author_sort |
Maier, Kathrin |
title |
Direct photogrammetry with multispectral imagery for UAV-based snow depth estimation |
title_short |
Direct photogrammetry with multispectral imagery for UAV-based snow depth estimation |
title_full |
Direct photogrammetry with multispectral imagery for UAV-based snow depth estimation |
title_fullStr |
Direct photogrammetry with multispectral imagery for UAV-based snow depth estimation |
title_full_unstemmed |
Direct photogrammetry with multispectral imagery for UAV-based snow depth estimation |
title_sort |
direct photogrammetry with multispectral imagery for uav-based snow depth estimation |
publisher |
Uppsala universitet, Luft-, vatten- och landskapslära |
publishDate |
2022 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-473802 https://doi.org/10.1016/j.isprsjprs.2022.01.020 |
long_lat |
ENVELOPE(18.608,18.608,67.914,67.914) |
geographic |
Arctic Tarfala |
geographic_facet |
Arctic Tarfala |
genre |
Arctic Northern Sweden Tarfala |
genre_facet |
Arctic Northern Sweden Tarfala |
op_relation |
ISPRS journal of photogrammetry and remote sensing (Print), 0924-2716, 2022, 186, s. 1-18 orcid:0000-0003-4839-7900 http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-473802 doi:10.1016/j.isprsjprs.2022.01.020 ISI:000782587800001 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1016/j.isprsjprs.2022.01.020 |
container_title |
ISPRS Journal of Photogrammetry and Remote Sensing |
container_volume |
186 |
container_start_page |
1 |
op_container_end_page |
18 |
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1790597514349510656 |