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Detailed information on seasonal snow cover and depth is essential to the understanding of snow processes, operational forecasting, and as input for hydrological models. Recent advances in unmanned aircraft systems (UASs) and structure from motion (SfM) techniques have enabled low-cost monitoring of spatial snow depth distribution in resolutions up to a few centimeters. Here, we study the spatiotemporal variability of 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 of 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 sub-canopy 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 even on the sub-catchment 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 variability (5–95 percentiles) within different land cover types increased from 17 cm to 42 cm in peatlands and from 33 cm 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 variability 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), then a decline of snow depth near the canopy with increasing distance (from 1 m to 2.5 m) of the peak value through the snow season. This study highlights the potential 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 is not available.

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.

The European Union (EU) regulations require the pilots of Unmanned Aircraft System (UAS) to register and pass a basic theory exam from 2021. Therefore, it is essential to systematically gather information for pilots for utilizing UAS in a safe and meaningful manner especially in Nordic weather conditions. This book chapter describes the Nordic challenges for UAS operations. These can be categorized into two main categories: technological and operational. Based on the extensive literature review and practical experience of the authors, both categories of challenges especially with relevance to the severe Arctic weather conditions are presented. The relevant weather conditions include changing the density of the air, dust and solid particles clouds, extreme light conditions, freezing rain, heavy and gusty wind, heavy clouds, ice fog, rain, mist and fog combinations, rapid temperature changes, snow, storm and hailstorm, temperature crossing 0 C several times within 24 hours, wind shear and whirlwind. Many technical and operational challenges (e.g., weather-related phenomena) are overlapping on both categories and can be mitigated partly by technological advances and partly by operational preparedness. Finally, the future challenges and needs in UAS research are also discussed.

Subarctic ecohydrological processes are changing rapidly, but detailed and integrated ecohydrological investigations are not as widespread as necessary. We introduce an integrated research catchment site (Pallas) for atmosphere, ecosystems, and ecohydrology studies in subarctic conditions in Finland that can be used for a new set of comparative catchment investigations. The Pallas site provides unique observational data and high-intensity field measurement datasets over long periods. The infrastructure for atmosphere- to landscape-scale research in ecosystem processes in a subarctic landscape has recently been complemented with detailed ecohydrological measurements. We identify three dominant processes in subarctic ecohydrology: (a) strong seasonality drives ecohydrological regimes, (b) limited dynamic storage causes rapid stream response to water inputs (snowmelt and intensive storms), and (c) hydrological state of the system regulates catchment-scale dissolved carbon dynamics and greenhouse (GHG) fluxes. Surface water and groundwater interactions play an important role in regulating catchment-scale carbon balances and ecosystem respiration within subarctic peatlands, particularly their spatial variability in the landscape. Based on our observations from Pallas, we highlight key research gaps in subarctic ecohydrology and propose several ways forward. We also demonstrate that the Pallas catchment meets the need for sustaining and pushing the boundaries of critical long-term integrated ecohydrological research in high-latitude environments.

Treatment peatlands are water purification systems located on existing mires. They are commonly used to treat different types of waters, ranging from municipal wastewaters to mine effluent. This study evaluated the capacity of unmanned aerial vehicle (UAV)-based thermal infrared (TIR) imaging, color infrared imaging, and stable water isotopes as a combined method for monitoring the functioning of a treatment peatland purifying mine process effluent water under boreal conditions in northern Finland. The results showed that TIR was an efficient tool for pinpointing cold groundwater seepage points in the peatland area that were not otherwise visible. Color infrared imaging was used to define Normalized Difference Vegetation Index (NDVI), as an indicator of plant health in the treatment area. A NDVI map of the area, measured on a day representing the main growing season (summer, +12 °C day temperature), revealed areas with stressed coniferous trees. This was probably due to excess water in these areas, resulting from successful spread of the process effluent water to the treatment peatland. Stable water isotopes were able to spatially differentiate the treated process effluent water, surface waters, and groundwater in different parts of the treatment peatland. This first attempt at combining these methods in monitoring of treatment peatlands was promising, as the results obtained with different methods complemented each other. While they produce only a snapshot of prevailing conditions, all three methods, singly and in combination, could be valuable tools in treatment peatland management.

Seasonal snow accumulation and melt dominates the hydrology in high latitude areas, providing water storages for both ecological and human needs. However, until recent years there has been a lack of cost-efficient way to measure the spatiotemporal variability of the snow depth and cover in high resolution. Unmanned aircraft systems (UAS) can offer spatial resolutions up to few centimeters, depending on the weather and light conditions, camera quality and drone specification. We used multiple different quadcopters and a fixed wing UAS to determine and analyze the spatiotemporal variability of snow depth and cover in three test plots with different land-cover types (forested slope, open peatland, and peatland-forest) in subarctic northern Finland, where weather and light conditions are challenging. Five measurement campaigns were conducted during winter 2018/2019 and a snow-free bare ground survey after snowmelt. Snow depth maps were constructed using Structure from Motion (SfM) photogrammetry technique and by differentiating the acquired models from snow-covered and snow-free surveys. Due to poor sub-canopy penetration with UAS-SfM method, tree masks were utilized to remove canopy effects prior to analysis. The snow depth maps produced with different UAS were compared to in situ snow course and an automatic ultrasonic measurement data. We highlight the difficulties of working in subarctic winter conditions and discuss the accuracy of UAS-derived snow depth maps. We show that the UAS-derived snow depth measurements agree well with manual snow survey measurements and UAS are suitable method for extending the spatial snow data coverage, whereas a continuous point snow depth measurement is unable to accurately present sub-catchment scale snow depth variability. Furthermore, the spatiotemporal variability of snow accumulation and melt between and within different land cover types is presented.

Seasonally frozen ground (SFG) occurs on ~25% of the Northern Hemisphere’s land surface, and the influence of SFG on water, energy, and solute fluxes is important in cold climate regions. The hydrological role of permafrost is now being actively researched, but the influence of SFG has been receiving less attention. Intuitively, water movement in frozen ground is blocked by ice forming in soil pores that were open to water flow prior to freezing. However, it has been shown that the hydrological influence of SFG is insignificant in some cases, with soil remaining permeable to water even when frozen. There is a clear knowledge gap concerning (1) how intensively and (2) under what physiographical and climate conditions SFG influences hydrological fluxes. We conducted a systematic literature review examining the hydrological importance of SFG we found reported in 143 publications. We found a clear hydrological influence of frozen ground in small-scale laboratory measurements, but a more ambiguous effect when the spatial scale under study increased to hillslopes, catchments, or watersheds. We also found that SFG may be hydrologically less important in forested areas or in regions with deep snow cover. Our systematic review suggests that hydrological influence of SFG may become more important in a future warmer climate with less snow and intensified land use in high-latitude areas.

A study of Multi-Dimensional information modelling of underground tunnel spaces is introduced. As a reference model an international standard of Building Information Modelling (BIM) supported by Building Smart is used. Specific Finnish guidelines for infrastructures including tunnels are used. As experimental case underground Pyhäsalmi Mine on North Finland was used. Three selected tunnel at level of 660 meter were used. The tunnels were measured using advanced 3D laser scanning technologies as well as photogrammetric imaging. Different examples of tunnel information models were created and analysed. Recommendations for future work how to develop tunnel information modelling towards more and more information rich Multi-Dimensional information models are suggested.

The mining industry produces significant amounts of waste materials, with the greatest volumes generated during enrichment. These waste materials or by-products, tailings, may be used as alternative civil engineering materials, reducing the need for natural resources. In cold climates, the frost behaviour of materials is one critical functional requirement in civil engineering applications. The frost behaviour of tailings classified as sand, silty sand, or sandy clayey silt (EN ISO), from four metal mines located in northern Finland and Sweden, was evaluated in this study using basic laboratory characterisation and one-dimensional freezing tests. The laboratory results were compared with existing guidelines on estimating the frost susceptibility of natural soils. Based on grain size distribution, the tailings samples were characterised as non-susceptible or frost-susceptible. Capillary rise values indicated that the frost susceptibility of the samples ranged from negligible to strong. In the one-dimensional frost heave test, measured frost heave after 96 h was 0.1–23.1 mm and calculated segregation potential (SP) was 0–9.7 mm²/Kh. Based on the frost heave test, the frost susceptibility of the samples ranged from low to strong. The results indicated that the finest samples classified as sandy clayey silt (L1−L3) generated the majority measured amount of frost heave and were classified as frost-susceptible material. The coarser tailings samples (K1-K3 and AK1) classified as sand or silty sand were classified non-susceptible, and measured frost heave amounts were low

Mining typically involves extensive areas where environmental monitoring is spatially sporadic. New remote sensing techniques and platforms such as Structure from Motion (SfM) and unmanned aerial vehicles (UAVs) may offer one solution for more comprehensive and spatially continuous measurements. We conducted UAV campaigns in three consecutive summers (2015–2017) at a sub-Arctic mining site where production was temporarily suspended. The aim was to monitor a 0.5 km² tailings impoundment and measure potential subsidence of tailings. SfM photogrammetry was used to produce yearly topographical models of the tailings surface, which allowed the amount of surface displacement between years to be tracked. Ground checkpoints surveyed in stable areas of the impoundment were utilized in assessing the vertical accuracy of the models. Observed surface displacements were linked to a combination of erosion, tailings settlement, and possible compaction of the peat layer underlying the tailings. The accuracy obtained indicated that UAV-assisted monitoring of tailings impoundments is sufficiently accurate for supporting impoundment management operations and for tracking surface displacements in the decimeter range.