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 advancements to the measurement of snow depth from manned aircraft. Our main airborne hardware consists of a consumer-grade digital camera directly coupled to a dual-frequency GPS; no inertial motion unit (IMU) or on-board computer is required, such that system hardware and software costs less than USD 30 000, exclusive of aircraft. The photogrammetric processing is done using a commercially available implementation of the structure from motion (SfM) algorithm. The system is simple enough that it can be operated by the pilot without additional assistance and the technique creates directly georeferenced maps without ground control, further reducing overall 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 (real-world 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 a similar photogrammetric system. We empirically determined that our DEMs have a geolocation accuracy of ±30 cm and a repeatability of ±8 cm (both 95 % confidence). We then validated our dDEMs against more than 6000 hand-probed snow depth measurements at 3 separate 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 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 limited 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.

Changes in snow distribution and surface topography following a snowstorm on Antarctic sea ice

“…Airborne lidar [e.g., Kurtz et al, 2013], and drone-based photogrammetric methods [e.g., Nolan et al, 2015] can provide the additional spatial coverage required for broader-scale aggregate estimates. Achieving the centimeter-scale vertical accuracies necessary to detect the small changes induced by precipitation and blowing snow processes we show here are a challenge for airborne platforms.…”

Which are the highest peaks in the US Arctic? Fodar settles the debate

“…Fodar specifications also meet or exceed the capabilities of most airborne lidars, the current state-ofthe-art topographic mapping (Deems et al, 2013;Höfle and Rutzinger, 2011), yet fodar hardware costs less than USD 30 000 compared to USD 500 000 to USD 1 million for airborne lidar hardware suitable for mapping mountain ranges. As described in detail in Nolan et al (2015), the primary underlying reason for the difference in price is due to the software utilization of the SfM algorithm that allows for prosumergrade cameras to be used without the need for an IMU, an on-board computer, or a separate equipment operator.…”

“…In previous work, we demonstrated our success by cre- ating maps with 10-20 cm ground sample distances (GSDs) over tens of square kilometers and validating that they had a directly georeferenced accuracy and map precision (repeatability) better than ±30 and ±8 cm, respectively, at 95 % rootmean-square error (RMSE) . The method is distinguished from traditional methods of photogrammetry by its use of the structure-from-motion (SfM) algorithm (Koenderink and Van Doom, 1991;Westoby et al, 2012;Nolan et al, 2015), and it is distinguished from other forms of SfM photogrammetry by the fact that no ground control is required to achieve such accuracy and precision. Ground control is not required because the precise timing between the shutter of the camera and survey-grade GPS yields abso-lute photo locations with less than 10 cm error, and this constrains the SfM bundle adjustment sufficiently to meet these specs.…”

Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from lidar data

“…3) and the ground-return percentage varied across the transition from the tree trunk to the edge of the canopy, interpolation was not applied to data under the canopy. The error rate of the calculated snow depth should be mainly from the instrumental elevation error, which is about 0.10 m (Kirchner et al, 2014;Nolan et al, 2015).…”