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

Abstract

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.

Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 1: Measurements, processing, and accuracy assessment

“…Data availability. The data underlying this analysis and their 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.…”

Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 2: Snow processes and snow–canopy interactions

“…The aerial surveys were done using four drones: DJI Phantom 4 RTK (real-time kinematic) quadcopter (P4RTK), DJI Mavic Pro, DJI Phantom 4, and eBee Plus RTK. We selected data from P4RTK because it provided the highest accuracy for snow and ground surface maps created using the SfM photogrammetry technique (see Rauhala et al, 2023). The flight height target was 110 m, which provided ∼ 3 cm ground resolution.…”

“…The Corine classification of Kenttärova snow depth sensor location is coniferous forest, the distance to the canopies is approximately 5 m, and the understory in the sensor location is replaced with an artificial green grass mat. More detailed information for the UAS campaigns and flight parameter selection can be found in the accompanying article (Rauhala et al, 2023).…”

“…Airborne lidar from crewed aircraft provides high-resolution snow depth for relatively large areas but with high cost (Deems and Painter, 2006;Deems et al, 2013). The recently popularized uncrewed or unmanned aircraft systems (UASs), together with structure from motion (SfM) techniques, have shown the potential for cost-efficient solutions for high-resolution snow depth mapping (see accompanying article Part 1, Rauhala et al, 2023;Vander Jagt et al, 2015) enabling new methods of snow process research.…”

“…Previous UAS-SfM studies have focused on testing the accuracy of the method on mountainous regions or snow on tundra, glaciers, prairies, and meadows, with mostly low (grassland, shrub, bushes) or non-vegetated surfaces. A more comprehensive review of UAS-SfM studies is included in the accompanying paper (Rauhala et al, 2023). Snow process observations in UAS-SfM studies have mostly been related to topographic features such as aspect, cornices, gullies, exposed ridgelines, and broad elevated slopes (Bühler et al, 2016;Redpath et al, 2018;Niedzielski et al, 2019), as well as wind redistribution, snow erosion, and tree wells (Harder et al, 2020).…”