Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations

Abstract

Abstract. 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.

Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

“…The comparison between UAS flights and manual sampling show higher RMSEs, in agreement with what reported before by e.g., [49] (0.14 m), [50] (less than 0.15 on rocks and less than 0.3 m on grass), [52] This finding expands existing comparisons between UAS flights and other techniques on snow, which have focused on either manual sampling (see previous paragraph) or terrestrial laser scanners, capable of centrimetric accuracy [28,54]. A MS was used here on snow for the first time, to our knowledge.…”

“…Larger errors are attributed to vegetated areas [28,50,52]. However, the performances of UAS on snow have mostly been quantified using datasets at low density [48][49][50][51][52] or mixed fresh-old snow and bare-ice surface textures [55], whereas only [54] and [28] present comparisons with a TLS under different illumination conditions. Snow tends to form homogeneous surfaces and therefore the identification of homologous points on different images of the photogrammetric block can be highly uncertain [55][56][57][58], especially in case of high-resolution images where each frame covers only a small area.…”

“…Existing works employing UAS on snow or glaciers report an expected Root Mean Square Error (RMSE) for HS below 30 cm (e.g., see [28,[48][49][50][51][52][53][54][55]). Larger errors are attributed to vegetated areas [28,50,52].…”

Geomorphological evolution of landslides near an active normal fault in northern Taiwan, as revealed by lidar and unmanned aircraft system data

“…A UAS, commonly known as a drone, is an aircraft that flies autonomously and has many applications because of its low cost, convenience, and high resolution (Huang and Chang, 2014;Chen et al, 2015;FernandezGalarreta et al, 2015;Giordan et al, 2015;Tokarczyk, 2015;Bühler et al, 2016;Deffontaines et al, 2016). The UAS used in this study is a modified version of the Skywalker X8 deltawing aircraft.…”

Spatiotemporal variability of snow depth across the Eurasian continent from 1966 to 2012

“…To increase the spatial coverage of snow depth, researchers have used different instruments (e.g., lidar, airborne laser scanning, and unmanned aerial systems) (Hopkinson et al, 2004;Grünewald and Lehning, 2013;Bühler et al, 2016) or developed and/or improved passive microwave snow algorithms (Foster et al, 1997;Derksen et al, 2003;Grippaa et al, 2004;Che et al, 2016). Although snow depth and SWE obtained from passive microwave satellite remote sensing could mitigate regional deficiency of in situ snow depth measurements, they have low spatial resolution (25 km × 25 km), and the accuracy is always affected by underlying surface conditions and algorithms.…”