Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs:Part 2: Snow processes and snow–canopy interactions
Abstract Detailed information on seasonal snow cover and depth is essential to the understanding of snow processes, to operational forecasting, and as input for hydrological models. Recent advances in uncrewed or unmanned aircraft systems (UASs) and structure from motion (SfM) techniques have enable...
Main Authors: | , , , , , , , |
---|---|
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Copernicus Publications
2023
|
Subjects: | |
Online Access: | http://urn.fi/urn:nbn:fi-fe20231020140784 |
id |
ftunivoulu:oai:oulu.fi:nbnfi-fe20231020140784 |
---|---|
record_format |
openpolar |
spelling |
ftunivoulu:oai:oulu.fi:nbnfi-fe20231020140784 2023-11-12T04:26:57+01:00 Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs:Part 2: Snow processes and snow–canopy interactions Meriö, L.-J. (Leo-Juhani) Rauhala, A. (Anssi) Ala-aho, P. (Pertti) Kuzmin, A. (Anton) Korpelainen, P. (Pasi) Kumpula, T. (Timo) Kløve, B. (Bjørn) Marttila, H. (Hannu) 2023 application/pdf http://urn.fi/urn:nbn:fi-fe20231020140784 eng eng Copernicus Publications info:eu-repo/semantics/altIdentifier/url/https://doi.org/10.23729/43d37797-e8cf-4190-80f1-ff567ec62836 info:eu-repo/semantics/openAccess © Author(s) 2023. This work is distributed under the Creative Commons Attribution 4.0 License. https://creativecommons.org/licenses/by/4.0/ UAS drone info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2023 ftunivoulu https://doi.org/10.23729/43d37797-e8cf-4190-80f1-ff567ec62836 2023-10-25T23:00:15Z Abstract Detailed information on seasonal snow cover and depth is essential to the understanding of snow processes, to operational forecasting, and as input for hydrological models. Recent advances in uncrewed or unmanned aircraft systems (UASs) and structure from motion (SfM) techniques have enabled low-cost monitoring of spatial snow depth distribution in resolutions of up to a few centimeters. Here, we study the spatiotemporal variability in snow depth and interactions between snow and vegetation in different subarctic landscapes consisting of a mosaic of conifer forest, mixed forest, transitional woodland/shrub, and peatland areas. To determine the spatiotemporal variability in snow depth, we used high-resolution (50 cm) snow depth maps generated from repeated UAS–SfM surveys in the winter of 2018/2019 and a snow-free bare-ground survey after snowmelt. Due to poor subcanopy penetration with the UAS–SfM method, tree masks were utilized to remove canopy areas and the area (36 cm) immediately next to the canopy before analysis. Snow depth maps were compared to the in situ snow course and a single-point continuous ultrasonic snow depth measurement. Based on the results, the difference between the UAS–SfM survey median snow depth and single-point measurement increased for all land cover types during the snow season, from +5 cm at the beginning of the accumulation to −16 cm in coniferous forests and −32 cm in peatland during the melt period. This highlights the poor representation of point measurements in selected locations even on the subcatchment scale. The high-resolution snow depth maps agreed well with the snow course measurement, but the spatial extent and resolution of maps were substantially higher. The snow depth range (5th–95th percentiles) within different land cover types increased from 17 to 42 cm in peatlands and from 33 to 49 cm in the coniferous forest from the beginning of the snow accumulation to the melt period. Both the median snow depth and its range were found to increase with canopy density; ... Article in Journal/Newspaper Subarctic Jultika - University of Oulu repository |
institution |
Open Polar |
collection |
Jultika - University of Oulu repository |
op_collection_id |
ftunivoulu |
language |
English |
topic |
UAS drone |
spellingShingle |
UAS drone Meriö, L.-J. (Leo-Juhani) Rauhala, A. (Anssi) Ala-aho, P. (Pertti) Kuzmin, A. (Anton) Korpelainen, P. (Pasi) Kumpula, T. (Timo) Kløve, B. (Bjørn) Marttila, H. (Hannu) Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs:Part 2: Snow processes and snow–canopy interactions |
topic_facet |
UAS drone |
description |
Abstract Detailed information on seasonal snow cover and depth is essential to the understanding of snow processes, to operational forecasting, and as input for hydrological models. Recent advances in uncrewed or unmanned aircraft systems (UASs) and structure from motion (SfM) techniques have enabled low-cost monitoring of spatial snow depth distribution in resolutions of up to a few centimeters. Here, we study the spatiotemporal variability in snow depth and interactions between snow and vegetation in different subarctic landscapes consisting of a mosaic of conifer forest, mixed forest, transitional woodland/shrub, and peatland areas. To determine the spatiotemporal variability in snow depth, we used high-resolution (50 cm) snow depth maps generated from repeated UAS–SfM surveys in the winter of 2018/2019 and a snow-free bare-ground survey after snowmelt. Due to poor subcanopy penetration with the UAS–SfM method, tree masks were utilized to remove canopy areas and the area (36 cm) immediately next to the canopy before analysis. Snow depth maps were compared to the in situ snow course and a single-point continuous ultrasonic snow depth measurement. Based on the results, the difference between the UAS–SfM survey median snow depth and single-point measurement increased for all land cover types during the snow season, from +5 cm at the beginning of the accumulation to −16 cm in coniferous forests and −32 cm in peatland during the melt period. This highlights the poor representation of point measurements in selected locations even on the subcatchment scale. The high-resolution snow depth maps agreed well with the snow course measurement, but the spatial extent and resolution of maps were substantially higher. The snow depth range (5th–95th percentiles) within different land cover types increased from 17 to 42 cm in peatlands and from 33 to 49 cm in the coniferous forest from the beginning of the snow accumulation to the melt period. Both the median snow depth and its range were found to increase with canopy density; ... |
format |
Article in Journal/Newspaper |
author |
Meriö, L.-J. (Leo-Juhani) Rauhala, A. (Anssi) Ala-aho, P. (Pertti) Kuzmin, A. (Anton) Korpelainen, P. (Pasi) Kumpula, T. (Timo) Kløve, B. (Bjørn) Marttila, H. (Hannu) |
author_facet |
Meriö, L.-J. (Leo-Juhani) Rauhala, A. (Anssi) Ala-aho, P. (Pertti) Kuzmin, A. (Anton) Korpelainen, P. (Pasi) Kumpula, T. (Timo) Kløve, B. (Bjørn) Marttila, H. (Hannu) |
author_sort |
Meriö, L.-J. (Leo-Juhani) |
title |
Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs:Part 2: Snow processes and snow–canopy interactions |
title_short |
Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs:Part 2: Snow processes and snow–canopy interactions |
title_full |
Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs:Part 2: Snow processes and snow–canopy interactions |
title_fullStr |
Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs:Part 2: Snow processes and snow–canopy interactions |
title_full_unstemmed |
Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs:Part 2: Snow processes and snow–canopy interactions |
title_sort |
measuring the spatiotemporal variability in snow depth in subarctic environments using uass:part 2: snow processes and snow–canopy interactions |
publisher |
Copernicus Publications |
publishDate |
2023 |
url |
http://urn.fi/urn:nbn:fi-fe20231020140784 |
genre |
Subarctic |
genre_facet |
Subarctic |
op_relation |
info:eu-repo/semantics/altIdentifier/url/https://doi.org/10.23729/43d37797-e8cf-4190-80f1-ff567ec62836 |
op_rights |
info:eu-repo/semantics/openAccess © Author(s) 2023. This work is distributed under the Creative Commons Attribution 4.0 License. https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.23729/43d37797-e8cf-4190-80f1-ff567ec62836 |
_version_ |
1782340743669481472 |