Multitemporal Accuracy and Precision Assessment of Unmanned Aerial System Photogrammetry for Slope-Scale Snow Depth Maps in Alpine Terrain

Abstract

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 km 2 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 fixedwing 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 B 0.25 and B 0.29 m (both at 1r), 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 1r).

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.…”

“…[28] expanded on this comparison involving 12 flights with visible and near-infrared cameras (accuracy below 0.29 m in full sunlight). On the one hand, our results show the expected increased accuracy of a MultiStation compared to a TLS and further support the high agreement between scanning and photogrammetric techniques on snow.…”

“…Existing methods include Terrestrial Laser Scanner (TLS, [7,13,[18][19][20][21][22]), digital photogrammetry [23,24], tachymetry [20], Ground Penetrating Radar (GPR) [25], time-lapse photography [26,27], or satellite-based sensors [16]. Among these alternatives, TLS is the commonest choice in most applications [28].…”

“…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].…”

“…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.…”