Detailed information on the spatiotemporal snow depth distribution is a crucial input for numerous applications in hydrology, climatology, ecology and avalanche research. Today, snow depth distribution is usually estimated by combining point measurements from weather stations or observers in the field with spatial interpolation algorithms. However, even a dense measurement network like the one in Switzerland, with more than one measurement station per 10 km² on average, is not able to capture the large spatial variability of snow depth present in alpine terrain.Remote sensing methods, such as laser scanning or digital photogrammetry, have recently been successfully applied to map snow depth variability at local and regional scales. However, in most countries such data acquisition is costly if manned airplanes are involved. The effectiveness of ground-based measurements on the other hand is often hindered by occlusions, due to the complex terrain or acute viewing angles. In this paper, we investigate the application of unmanned aerial systems (UASs), in combination with structure-from-motion photogrammetry, to map snow depth distribution. Compared to manual measurements, such systems are relatively cost-effective and can be applied very flexibly to cover terrain not accessible from the ground. In this study, we map snow depth at two different locations: (a) a sheltered location at the bottom of the Flüela valley (1900 m a.s.l.) and (b) an exposed location on a peak (2500 m a.s.l.) in the ski resort Jakobshorn, both in the vicinity of Davos, Switzerland. At the first test site, we monitor the ablation on three different dates. We validate the photogrammetric snow depth maps using simultaneously acquired manual snow depth measurements. The resulting snow depth values have a root mean square error (RMSE) of less than 0.07 to 0.15 m on meadows and rocks and a RMSE of less than 0.30 m on sections covered by bushes or tall grass, compared to manual probe measurements. This new measurement technology opens the door for efficient, flexible, repeatable and cost-effective snow depth monitoring over areas of several hectares for various applications, if the national and regional regulations permit the application of UASs.

Reliable and timely information on the spatio-temporal distribution of snow in alpine terrain plays an important role for a wide range of applications. Unmanned aerial system (UAS) photogrammetry is increasingly applied to cost-efficiently map the snow depth at very high resolution with flexible applicability. However, crucial questions regarding quality and repeatability of this technique are still under discussion. Here we present a multitemporal accuracy and precision assessment of UAS photogrammetry for snow depth mapping on the slope-scale. We mapped a 0.12 km² large snow-covered study site, located in a high-alpine valley in Western Austria. 12 UAS flights were performed to acquire imagery at 0.05 m ground sampling distance in visible (VIS) and near-infrared (NIR) wavelengths with a modified commercial, off-the-shelf sensor mounted on a custom-built fixed-wing UAS. The imagery was processed with structure-from-motion photogrammetry software to generate orthophotos, digital surface models (DSMs) and snow depth maps (SDMs). Accuracy of DSMs and SDMs were assessed with terrestrial laser scanning and manual snow depth probing, respectively. The results show that under good illumination conditions (study site in full sunlight), the DSMs and SDMs were acquired with an accuracy of ≤ 0.25 and ≤ 0.29 m (both at 1σ), respectively. In case of poorly illuminated snow surfaces (study site shadowed), the NIR imagery provided higher accuracy (0.19 m; 0.23 m) than VIS imagery (0.49 m; 0.37 m). The precision of the UAS_SDMs was 0.04 m for a small, stable area and below 0.33 m for the whole study site (both at 1σ).

The European Space Agency SnowSAR instrument is a side-looking, dual-polarised (VV/VH), X/Ku band synthetic aperture radar (SAR), operable from various sizes of aircraft. Between 2010 and 2013, the instrument was deployed at several sites in Northern Finland, Austrian Alps and northern Canada. The purpose of the airborne campaigns was to measure the backscattering properties of snow-covered terrain to support the development of snow water equivalent retrieval techniques using SAR. SnowSAR was deployed in Sodankylä, Northern Finland, for a single flight mission in March 2011 and 12 missions at two sites (tundra and boreal forest) in the winter of 2011–2012. Over the Austrian Alps, three flight missions were performed between November 2012 and February 2013 over three sites located in different elevation zones representing a montane valley, Alpine tundra and a glacier environment. In Canada, a total of two missions were flown in March and April 2013 over sites in the Trail Valley Creek watershed, Northwest Territories, representative of the tundra snow regime. This paper introduces the airborne SAR data and coincident in situ information on land cover, vegetation and snow properties. To facilitate easy access to the data record, the datasets described here are deposited in a permanent data repository (https://doi.org/10.1594/PANGAEA.933255, Lemmetyinen et al., 2021).

Debris flows cause an average € 30 million damages and 1-2 fatalities every year in Austria. Detailed documentation of their extent and magnitude is essential for understanding, preventing and mitigating these natural hazard events. The recent development of unmanned aerial systems (UAS) has provided a new possibility for on-demand high-resolution monitoring and mapping. Here, we present a study, where the spatial extent and volume of a large debris flow event were mapped with different UAS, fitted with commercial off-the-shelf sensors. Orthophotos and digital terrain models (DTM) were calculated using structure-from-motion photogrammetry software. Terrain height differences caused by the debris flow in the catchment and valley floor were derived by subtracting the pre-event airborne laser scanning (ALS) DTM from a post-event UAS-DTM. The analysis of the volumetric sediment budget showed, that approximately 265,000 m³ material was mobilised in the catchment, of which 45,000 m³ settled there; of the material, which reached the valley floor, 120,000 m³ was deposited, while another 10,000 m³ was eroded from there. The UAS-results were validated against ALS data and imagery from a traditional manned-aircraft photogrammetry campaign. In conclusion, the UAS-data can reach an accuracy and precision comparable to manned aircraft data, but with the added benefits of higher flexibility, easier repeatability, less operational constraints and higher spatial resolution.

Debris flows cause an average € 30 million damages and 1-2 fatalities every year in Austria. Detailed documentation of their extent and magnitude is essential for understanding, preventing and mitigating these natural hazard events. The recent development of unmanned aerial systems (UAS) has provided a new possibility for on-demand high-resolution monitoring and mapping. Here, we present a study, where the spatial extent and volume of a large debris flow event were mapped with different UAS, fitted with commercial off-the-shelf sensors. Orthophotos and digital terrain models (DTM) were calculated using structure-from-motion photogrammetry software. Terrain height differences caused by the debris flow in the catchment and valley floor were derived by subtracting the pre-event airborne laser scanning (ALS) DTM from a post-event UAS-DTM. The analysis of the volumetric sediment budget showed, that approximately 265,000 m³ material was mobilised in the catchment, of which 45,000 m³ settled there; of the material, which reached the valley floor, 120,000 m³ was deposited, while another 10,000 m³ was eroded from there. The UAS-results were validated against ALS data and imagery from a traditional manned-aircraft photogrammetry campaign. In conclusion, the UAS-data can reach an accuracy and precision comparable to manned aircraft data, but with the added benefits of higher flexibility, easier repeatability, less operational constraints and higher spatial resolution.