Preprints
https://doi.org/10.5194/tc-2022-239
https://doi.org/10.5194/tc-2022-239
 
20 Dec 2022
20 Dec 2022
Status: this preprint is currently under review for the journal TC.

Measuring the spatiotemporal variability of snow depth in subarctic environments using unmanned aircraft systems (UAS) – Part 1: Measurements, processing, and accuracy assessment

Anssi Rauhala1, Leo-Juhani Meriö2, Anton Kuzmin3, Pasi Korpelainen3, Pertti Ala-aho2, Timo Kumpula3, Bjørn Kløve2, and Hannu Marttila2 Anssi Rauhala et al.
  • 1Civil Engineering, Faculty of Technology, University of Oulu, Oulu, FI-90570, Finland
  • 2Water, Energy and Environmental Engineering, Faculty of Technology, University of Oulu, Oulu, FI-90570, Finland
  • 3Department of Geographical and Historical Studies, University of Eastern Finland, Joensuu, FI-80101, Finland

Abstract. Snow conditions in the northern hemisphere are rapidly changing, and information on snow depth is critical for decision-making and other societal needs. Unmanned aircraft systems (UASs) can offer data resolutions of a few centimeters at a catchment-scale, and thus provide a low-cost solution to bridge the gap between sparse manual probing and low-resolution satellite data. In this study, we present a series of snow depth measurements using different UAS platforms throughout the winter in the Finnish subarctic site Pallas, which has a heterogeneous landscape. We discuss the different platforms, the methods utilized, difficulties working in the harsh northern environment, and the results and their accuracy compared to in situ measurements. Generally, all UASs produced spatially representative estimates of snow depth in open areas after reliable georeferencing by using the Structure from Motion (SfM) photogrammetry technique. However, significant differences were observed in the accuracies produced by the different UASs compared to manual snow depth measurements, with overall RMSEs varying between 13.0 to 25.2 cm, depending on the UAS. Additionally, a reduction in accuracy was observed when moving from an open mire area to forest covered areas. We demonstrate the potential of low-cost UASs to efficiently map snow surface conditions, and we give some recommendations on UAS platform selection and operation in a harsh subarctic environment with variable canopy cover.

Anssi Rauhala et al.

Status: open (until 08 Mar 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Anssi Rauhala et al.

Data sets

Unmanned aircraft system (UAS) snow depth mapping at the Pallas Atmosphere-Ecosystem Supersite Rauhala, A., Meriö, L. J., Korpelainen, P. and Kuzmin, A. https://doi.org/10.23729/43d37797-e8cf-4190-80f1-ff567ec62836

Anssi Rauhala et al.

Viewed

Total article views: 288 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
232 51 5 288 18 2 2
  • HTML: 232
  • PDF: 51
  • XML: 5
  • Total: 288
  • Supplement: 18
  • BibTeX: 2
  • EndNote: 2
Views and downloads (calculated since 20 Dec 2022)
Cumulative views and downloads (calculated since 20 Dec 2022)

Viewed (geographical distribution)

Total article views: 286 (including HTML, PDF, and XML) Thereof 286 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Feb 2023
Download
Short summary
Snow conditions in the northern hemisphere are rapidly changing and information on snow depth is important for decision-making. We present snow depth measurements using different drones throughout the winter in a subarctic site. Generally, all drones produced good estimates of snow depth in open areas. However, differences were observed in the accuracies produced by the different drones and a reduction in accuracy was observed when moving from an open mire area to forest covered areas.