Mapping snow-depth from manned-aircraft on landscape scales at centimeter resolution using Structure-from-Motion photogrammetry

Abstract

Abstract. Airborne photogrammetry is undergoing a renaissance: lower-cost equipment, more powerful software, and simplified methods have significantly lowered the barriers-to-entry and now allow repeat-mapping of cryospheric dynamics at spatial resolutions and temporal frequencies that were previously too expensive to consider. Here we apply these techniques to the measurement of snow depth from manned aircraft. The main airborne hardware consists of a consumer-grade digital camera coupled to a dual-frequency GPS. The photogrammetric processing is done using a commercially-available implementation of the Structure from Motion (SfM) algorithm. The system hardware and software, exclusive of aircraft, costs less than USD 30 000. The technique creates directly-georeferenced maps without ground control, further reducing costs. To map snow depth, we made digital elevation models (DEMs) during snow-free and snow-covered conditions, then subtracted these to create difference DEMs (dDEMs). We assessed the accuracy (geolocation) and precision (repeatability) of our DEMs through comparisons to ground control points and to time-series of our own DEMs. We validated these assessments through comparisons to DEMs made by airborne lidar and by another photogrammetric system. We empirically determined an accuracy of ± 30 cm and a precision of ± 8 cm (both 95% confidence) for our methods. We then validated our dDEMs against more than 6000 hand-probed snow depth measurements at 3 test areas in Alaska covering a wide-variety of terrain and snow types. These areas ranged from 5 to 40 km2 and had ground sample distances of 6 to 20 cm. We found that depths produced from the dDEMs matched probe depths with a 10 cm standard deviation, and these depth distributions were statistically identical at 95% confidence. Due to the precision of this technique, other real changes on the ground such as frost heave, vegetative compaction by snow, and even footprints become sources of error in the measurement of thin snow packs (< 20 cm). The ability to directly measure such small changes over entire landscapes eliminates the need to extrapolate isolated field measurements. The fact that this mapping can be done at substantially lower costs than current methods may transform the way we approach studying change in the cryosphere.

Tracking Forest and Open Area Effects on Snow Accumulation by Unmanned Aerial Vehicle Photogrammetry

“…However, the validated snow depth statistics are coherent with previous studies of Jagt et al (2015) and Bühler et al (2016), giving RMSEs of 9.6 cm to 18.4 cm for snow depth distributions in Tasmania (study area of ~7,000 m 2 ) and RMSEs of 7 cm to 30 cm for snow depth distributions in Switzerland (study areas of 363,000 m 2 and 57,000 m 2 ), respectively. Similar achievements have been found using digital photogrammetry for snow depth distributions by UAV (Nolan et al, 2015;Bühler et al, 2016). Recent investigations by De Michele et al (2016), using the same approach, provided UAV-based snow depth values with a precision of ~10 cm.…”

“…Summary of accuracy of snow depth generated UAV DEMs. Usually, minimum snow depth values appear negative in both maps, which can be attributed to the effect of compressible vegetation (Nolan et al, 2015), but these values have been replaced by the lowest snow depth values, measured on the ground. (Table 3, 4).…”

“…The utilization of SfM for snow surfaces is considered problematic due to the homogenous surface and high reflectance (Nolan et al, 2015), but recent attempts combining it with UAV-based photogrammetry show promising results (e.g., Bühler et al, 2015;Bühler et al, 2016;Harder et al, 2016;De Michele et al, 2016). Nolan et al (2015) compared airborne measurements with 6000 ground measurements of snow depth in Alaska revealing a difference of 10 cm (1) between these two data sets. By applying a similar method in Switzerland, Bühler et al (2016) determined snow depth values with a root of square error (RMSE) ranging from 7 cm to 15 cm on meadows and rocks, and a RMSE of 30 cm on areas covered by bushes or tall grass.…”

“…This algorithm enables to reconstruct georeferenced point clouds, high precision digital elevation models (DEMs), and orthomosaics by automatic matching of common features from a set of overlapping images (Westoboy et al, 2012;Cimoli and Marcer, 2014;Harder et al, 2016). The utilization of SfM for snow surfaces is considered problematic due to the homogenous surface and high reflectance (Nolan et al, 2015), but recent attempts combining it with UAV-based photogrammetry show promising results (e.g., Bühler et al, 2015;Bühler et al, 2016;Harder et al, 2016;De Michele et al, 2016). Nolan et al (2015) compared airborne measurements with 6000 ground measurements of snow depth in Alaska revealing a difference of 10 cm (1) between these two data sets.…”

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

“…Remote sensing captures the spatial and temporal patterns of snow and thus overcomes the potential undersampling of point measurements and the long surveys needed for snow courses [16,17]. 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].…”