Abstract
Performing two independent surveys in 2016 and 2017 over a flat sample plot (6700 m 2 ), we compare snow-depth measurements from Unmanned-Aerial-System (UAS) photogrammetry and from a new high-resolution laser-scanning device (MultiStation) with manual probing, the standard technique used by operational services around the world. While previous comparisons already used laser scanners, we tested for the first time a MultiStation, which has a different measurement principle and is thus capable of millimetric accuracy. Both remote-sensing techniques measured point clouds with centimetric resolution, while we manually collected a relatively dense amount of manual data (135 pt in 2016 and 115 pt in 2017). UAS photogrammetry and the MultiStation showed repeatable, centimetric agreement in measuring the spatial distribution of seasonal, dense snowpack under optimal illumination and topographic conditions (maximum RMSE of 0.036 m between point clouds on snow). A large fraction of this difference could be due to simultaneous snowmelt, as the RMSE between UAS photogrammetry and the MultiStation on bare soil is equal to 0.02 m. The RMSE between UAS data and manual probing is in the order of 0.20–0.30 m, but decreases to 0.06–0.17 m when areas of potential outliers like vegetation or river beds are excluded. Compact and portable remote-sensing devices like UASs or a MultiStation can thus be successfully deployed during operational manual snow courses to capture spatial snapshots of snow-depth distribution with a repeatable, vertical centimetric accuracy.
Highlights
Monitoring snow distribution has important implications for both water resources management and risk prevention [1]
By comparing Unmanned Aerial Systems (UASs)-based photogrammetric maps of snow depth with point clouds acquired with a Leica Nova MultiStation (MS) [59,60], we show that these instruments return highly consistent and repeatable results in measuring snow depth over a flat sample plot, a well-established scenario for operational services
While the first is necessary for image matching algorithms, the latter guarantees a satisfactory intersection between homologous rays
Summary
Monitoring snow distribution has important implications for both water resources management and risk prevention [1]. The amount of snow can be quantified indirectly as snow depth (HS, in m), or directly as snow water equivalent (SWE, in mm w.e. or kg/m2, see [2]) Both variables are often measured with snow pits and manual probing [2], which are both time consuming and risky in avalanche-prone, remote areas. The significance of local measurements has been often debated [6,7,8,9,10], especially in view of the marked spatial variability of snow processes [11,12,13,14] To partially take this variability into account, snow manual measurements are often performed along snow courses and averaged to provide a more representative estimation of available SWE and snow depth [15]
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