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

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.

Monitoring of Snow Cover Ablation Using Very High Spatial Resolution Remote Sensing Datasets

“…In addition, airborne laser scanning (ALS) and terrestrial laser scanning (TLS) technologies have been applied as the preferred methods to obtain HS data [3,[26][27][28][29][30][31][32][33]. Moreover, tachymetry [28], ground-penetrating radar (GPR) [34,35], and time-lapse photography [36,37] have been used.The use of unmanned aerial system (UAS) technology in snow and avalanche studies has been recently reported in the literature [10,19,33,35,[38][39][40][41][42][43][44]. While the first studies on the use of a UAS in HS mapping investigated its potential and limitations by using manual HS probing for accuracy assessment, more recent studies have used time series of a UAS and compared it with other techniques, such as airborne sensors, including the ADS100 [45], TLS [33,44], and tri-stereoscopic Pléiades satellite images [46].…”

“…Moreover, tachymetry [28], ground-penetrating radar (GPR) [34,35], and time-lapse photography [36,37] have been used.The use of unmanned aerial system (UAS) technology in snow and avalanche studies has been recently reported in the literature [10,19,33,35,[38][39][40][41][42][43][44]. While the first studies on the use of a UAS in HS mapping investigated its potential and limitations by using manual HS probing for accuracy assessment, more recent studies have used time series of a UAS and compared it with other techniques, such as airborne sensors, including the ADS100 [45], TLS [33,44], and tri-stereoscopic Pléiades satellite images [46]. Also, different camera sensors that record data in various parts of the electromagnetic spectrum, such as visible (350-680 nm) and near infrared (NIR) (in different ranges (>700 and >830 nm)), have been evaluated [10,43,44].…”

Quantifying Uncertainties in Snow Depth Mapping From Structure From Motion Photogrammetry in an Alpine Area

“…Like lidar and digital photogrammetry methods, SFM‐derived snow depth maps are computed by differencing two coregistered elevation models acquired for snow‐covered (snow‐on) and snow‐free (snow‐off) conditions. Reported snow depth root mean squared errors (RMSEs) using UAVs in alpine areas typically range between 7 and 30 cm (Adams et al, ; Avanzi et al, ; Bühler et al, ; de Michele et al, ; Harder et al, ; Vander Jagt et al, ).…”

“…The quality of the SFM‐derived elevation models for snow depth mapping generally depends on field site conditions, survey design, and the SFM software. These aspects can include the snowpack conditions (e.g., the presence of fresh snow or ice; Gindraux et al, ; Fernandes et al, ; Bühler et al, ; Cimoli et al, ; Vander Jagt et al, ; de Michele et al, ), terrain conditions (e.g., hilly or flat; Cimoli et al, ; Avanzi et al, ), lighting conditions (i.e., presence of shadows; e.g., zenith angle of the sun and cloud coverage; Goetz et al, ; Nolan et al, ; Cimoli et al, ; Harder et al, ; Bühler, Adams, Stoffel, et al, ; Gindraux et al, ), characteristics of the UAV survey (e.g., flying height, image overlap, and distribution of ground control; Goetz et al, ; James et al, ; Tonkin et al, ; Gindraux et al, ), and the SFM software processing (e.g., settings for sparse and dense point cloud quality; Cimoli et al, ; Hendrickx et al, ; Gindraux et al, ).…”

“…An additional challenge unique to mapping snow depth using elevation differencing is accounting for changes in the surface topography beneath the snow cover that may occur overtime. For example, seasonal erosion of the surface (Avanzi et al, 2018;Bernard et al, 2017), frost heave (Nolan et al, 2015), vegetation compression (Nolan et al, 2015), and permafrost creep (Goetz et al, 2019) can cause errors in the computed snow depths. In the case of monitoring snow accumulation on glaciers, glacier surface lowering due to ice or snow melt and ice flow can also lead to errors (Gindraux et al, 2017).…”