Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos

Abstract

Differencing of digital surface models derived from structure from motion (SfM) processing of airborne imagery has been used to produce snow depth (SD) maps with between ∼ 2 and ∼ 15 cm horizontal resolution and accuracies of ±10 cm over relatively flat surfaces with little or no vegetation and over alpine regions. This study builds on these findings by testing two hypotheses across a broader range of conditions: (i) that the vertical accuracy of SfM processing of imagery acquired by commercial low-cost unmanned aerial vehicle (UAV) systems can be adequately modelled using conventional photogrammetric theory and (ii) that SD change can be more accurately estimated by differencing snow-covered elevation surfaces rather than differencing a snow-covered and snow-free surface. A total of 71 UAV missions were flown over five sites, ranging from short grass to a regenerating forest, with ephemeral snowpacks. Point cloud geolocation performance agreed with photogrammetric theory that predicts uncertainty is proportional to UAV altitude and linearly related to horizontal uncertainty. The root-mean-square difference (RMSD) over the observation period, in comparison to the average of in situ measurements along ∼ 50 m transects, ranged from 1.58 to 10.56 cm for weekly SD and from 2.54 to 8.68 cm for weekly SD change. RMSD was not related to microtopography as quantified by the snow-free surface roughness. SD change uncertainty was unrelated to vegetation cover but was dominated by outliers corresponding to rapid in situ melt or onset; the median absolute difference of SD change ranged from 0.65 to 2.71 cm. These results indicate that the accuracy of UAV-based estimates of weekly snow depth change was, excepting condi-tions with deep fresh snow, substantially better than for snow depth and was comparable to in situ methods.

Applications of Unmanned Aerial Vehicles in Cryosphere: Latest Advances and Prospects

“…Snow is challenging for building products from Structure from Motion (SfM) algorithms because of the high reflectance of homogenous surfaces and lack of contrast. True to photogrammetric theory, limited variation in surface features could hinder SfM software in identifying tie points to match between images, leading to increased inaccuracy or failure to utilize images within an orthomosaic [94]. Because of this, Bühler et al [34] suggested fresh snow to be less suitable than older, weathered snow for reliable photogrammetry.…”

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

“…In general, the precision of the SFM DEMs is related to flying height, distance to GCP, and image overlap (Goetz et al, 2018;James, Robson, Smith, et al, 2017), as well as field site conditions (Bühler, Adams, Bösch, et al, 2016;Nolan et al, 2015). That is, errors in the computed snow depths can also vary temporally due to different snow-cover conditions (Adams et al, 2018;Bühler, Adams, Bösch, et al, 2016;Fernandes et al, 2018;Harder et al, 2016). The precision for most of the study area was less than 4 cm (at 1σ).…”

“…Adding oblique imagery instead of using only nadir viewing angles or including more images (e.g., higher image overlap) can improve the quality of SFM reconstruction without having to rely solely on GCPs for mitigating SFM reconstruction errors (James & Robson, 2014;James, Robson, d'Oleire-Oltmanns, et al, 2017). Additionally, the density of the GCP network can be further reduced with the use of RTK or post-processing kinematic solutions for correction of the UAVs onboard GNSS for higher-quality camera location estimates (Fernandes et al, 2018;James, Robson, d'Oleire-Oltmanns, et al, 2017). Therefore, by surveying with a strong image network, using high-quality geotagging and ground control (e.g., RTK/PKK GNSS measurements), large-scale SFM snow depth mapping is likely more feasible.…”

“…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, ).…”

Shallow snow depth mapping with unmanned aerial systems lidar observations: A case study in Durham, New Hampshire, United States

“…The primary drawbacks of UAS SfM as compared to lidar for mapping snow depth are that the DSM needs to be georeferenced using ground control points (GCPs) with known coordinates and may require significant manual steps (Tonkin et al, 2016;Meyer and Skiles, 2019), although new 85 techniques are emerging that may reduce field data collection time (Gabrlik et al, 2019;Meyer and Skiles, 2019). Dense canopy or vegetation can reduce performance when snow compresses the vegetation relative to the snow-off imagery or when above-canopy vegetation is falsely interpreted to be the snow surface (Bühler et al, 2017;Cimoli et al, 2017;De Michele et al, 2016;Fernandes et al, 2018;Harder et al, 2016;Nolan et al, 2015). Canopy effects impact SfM snow mapping capability in regions where shallow snowpacks are masked by dense forest canopies like the northeastern United 90…”