Meriö, L.-J., Rauhala, A., Ala-aho, P., Kuzmin, A., Korpelainen, P., Kumpula, T., Kløve, B., and Marttila, H.: Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 2: Snow processes and snow–canopy interactions, The Cryosphere, 17, 4363–4380, https://doi.org/10.5194/tc-17-4363-2023, 2023
Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs : Part 2: Snow processes and snow–canopy interactions
|Author:||Meriö, Leo-Juhani1,2; Rauhala, Anssi3; Ala-aho, Pertti1;|
1Water, Energy and Environmental Engineering, Faculty of Technology, University of Oulu, Oulu, 90014, Finland
2Water Resources, Finnish Environment Institute (Syke), 90014, Oulu, Finland
3Civil Engineering, Faculty of Technology, University of Oulu, Oulu, 90014, Finland
4Department of Geographical and Historical Studies, University of Eastern Finland, Joensuu, 80101, Finland
|Online Access:||PDF Full Text (PDF, 11.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20231020140784
|Publish Date:|| 2023-10-20
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; this increase was greatest in the conifer forest area, followed by mixed forest, transitional woodland/shrub, and open peatlands. Using the high-spatial-resolution data, we found a systematic increase (2–20 cm) and then a decline in snow depth near the canopy with increasing distance (from 1 to 2.5 m) of the peak value through the snow season. This study highlights the applicability of the UAS–SfM in high-resolution monitoring of snow depth in multiple land cover types and snow–vegetation interactions in subarctic and remote areas where field data are not available or where the available data are collected using classic point measurements or snow courses.
|Pages:||4363 - 4380|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
218 Environmental engineering
This study was supported by the Maa-ja vesitekniikan tuki ry, K. H. Renlund Foundation, Academy of Finland (projects 316349, 330319, and ArcI Profi 4); the Strategic Research Council (SRC) decision no. 312636 (IBC-Carbon); EU Horizon 2020 Research and Innovation Programme grant agreement no. 869471; and the Kvantum Institute at the University of Oulu.
|Academy of Finland Grant Number:||
316349 (Academy of Finland Funding decision)
330319 (Academy of Finland Funding decision)
The data underlying this analysis and its documentation are available at https://doi.org/10.23729/43d37797-e8cf-4190-80f1-ff567ec62836 (Rauhala et al., 2022) under a Creative Commons CC-BY-4.0 license.
© Author(s) 2023. This work is distributed under the Creative Commons Attribution 4.0 License.