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

Marc S Adams,Yves Bühler,Reinhard Fromm

doi:10.1007/s00024-017-1748-y

Marc S Adams, Yves Bühler + Show 1 more

Open Access

https://doi.org/10.1007/s00024-017-1748-y

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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 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 UASSDMs was 0.04 m for a small, stable area and below 0.33 m for the whole study site (both at 1σ).

Highlights

The spatial distribution of snow depth in alpine environments is highly heterogeneous (Elder et al.1998)
A visual check of the data sets confirmed a large amount of blurry imagery on flight 1/1, possibly due to an error in data acquisition; no apparent deficiencies with regard to image sharpness were detected in the other imagery
To calculate a statistically significant correlation between overlap, quality index and marker/reprojection error, the sample size (n = 13) is too small in the presented case; a visual interpretation of the results points to high overlap ([ 8.9, when excluding outlier 36) leading to low marker error (\ 0.15 m) and vice versa; little or no connection was found between the other parameters

Summary

Introduction

The spatial distribution of snow depth in alpine environments is highly heterogeneous (Elder et al.1998). Detailed information on slope-scale snow depth distribution plays an important role for many applications in snow science and practice, including numerical modelling of snow drift (Durand et al 2005; Beyers et al 2004), ecological studies on alpine flora and fauna (Bilodeau et al 2013; Peng et al 2010), planning avalanche hazard mitigation measures (Margreth and Romang 2010; Fuchs et al 2007), avalanche forecasting and warning (Helbig et al 2015; Vernay et al 2015), avalanche event documentation, e.g., for hazard zone mapping (Holub and Fuchs 2009; Decaulne 2007), prediction and assessment of flood hazard resulting from snow melt (Painter et al 2016; Schober et al 2014) or as an input for the optimisation of numerical simulation models in avalanche dynamics research (Fischer et al 2015; Teich et al 2014).

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