Current understanding of glacier mass balance changes under changing climate is limited by scarcity of in situ measurements in both time and space, as well as resolution of remote sensing products. Recent innovations in unmanned aerial vehicles (UAVs), as well as structure-from-motion photogrammetry (SfM), have led to increased use of digital imagery to derive topographic data in great detail in many fields, including glaciology. This study tested the capability of UAV surveys to detect surface changes over glacier ice during a three-day period in July 2016. Three UAV imaging missions were conducted during this time over 0.185 km ${}^2$ of the ablation area of Fountain Glacier, NU. These were processed with the SfM algorithms in Agisoft Photoscan Professional and overall accuracies of the resulting point clouds ranged from 0.030 to 0.043 m. The high accuracy of point clouds achieved here is primarily a result of a small ground sampling distance (0.018 m), and is also influenced by GPS precision. Glacier surface change was measured through differencing of point clouds and change was compared to ablation stake measurements. Surface change measured with the UAV-SfM method agreed with the coincident ablation stake measurements in most instances, with RMSE values of 0.033, 0.028, and 0.042 m for one-, two-, and three-day periods, respectively. Total specific melt over the study area measured with the UAV was 0.170 m water equivalent (w.e.), while interpolation of ablation measurements resulted in 0.144 m w.e. Using UAVs to measure small changes in glacier surfaces will allow for new investigations of distribution of mass balance measurements.

Models of glacier surface melt are commonly used in studies of glacier mass balance and runoff; however, with limited data available, most models are validated based on ablation stakes and data from automatic weather stations (AWSs). The technological advances of unmanned aerial vehicles (UAVs) and structure from motion (SfM) have made it possible to measure glacier surface melt in detail over larger portions of a glacier. In this study, we use melt measured using SfM processing of UAV imagery to assess the performance of an energy balance (EB) and enhanced temperature index (ETI) melt model in two dimensions. Imagery collected over a portion of the ablation zone of Fountain Glacier, Nunavut, on 21, 23, and 24 July 2016 was previously used to determine distributed surface melt. An AWS on the glacier provides some measured inputs for both models as well as an additional check on model performance. Modelled incoming solar radiation and albedo derived from UAV imagery are also used as inputs for both models, which were used to estimate melt from 21 to 24 July 2016. Both models estimate total melt at the AWS within 16 % of observations (4 % for ETI). Across the study area the median model error, calculated as the difference between modelled and measured melt (EB = −0.064 m, ETI = −0.050 m), is within the uncertainty of the measurements. The errors in both models were strongly correlated to the density of water flow features on the glacier surface. The relation between water flow and model error suggests that energy from surface water flow contributes significantly to surface melt on Fountain Glacier. Deep surface streams with highly asymmetrical banks are observed on Fountain Glacier, but the processes leading to their formation are missing in the model assessed here. The failure of the model to capture flow-induced melt would lead to significant underestimation of surface melt should the model be used to project future change.

Alberta’s Bow River has its headwaters in the glaciated eastern slopes of the Canadian Rockies and is a major source of water in southern Alberta. Glacial retreat, declining snowpacks and increased water demand are all expected in the coming century, yet there are relatively few studies focusing on quantifying glacial meltwater in the Bow River. We develop a new radiation-temperature melt model for modelling distributed glacier mass balance and runoff in the Bow River basin. The model reflects physical processes through the incorporation of near-surface air temperature and absorbed radiation, while avoiding problems of collinearity through the use of a radiation-decorrelated temperature index. The model is calibrated at Haig Glacier in the southern portion of the basin and validated at Haig and Peyto Glaciers. Application of the model to the entire Bow River basin for 2000-09 shows glacier ice melt is equivalent to 3% of annual discharge in Calgary on average. Modelled ice melt in August is equal to 8-20% of the August Bow River discharge in Calgary. This emphasizes the importance of glacier runoff to late-summer streamflow in the region, particularly in warm, dry years.

The combined use of unmanned aerial vehicles (UAVs) and structure-from-motion (SfM) is rapidly growing as a cost-effective alternative to airborne laser scanning (lidar) for reconstructing glacier surfaces. Here we present a thorough analysis of the precision and accuracy of a photogrammetric point cloud (PPC) constructed through SfM from UAV-acquired imagery over the spring snow surface at Haig Glacier, Alberta, Canada, the first of its kind in a glaciological setting. An aerial lidar survey conducted concurrently with UAV surveys was used to examine spatial patterns in the PPC accuracy. We found a median error in the PPC of −0.046 ± 0.067 m, with a 95% quantile of 0.218 m. Mean precision of the PPC was 0.199 m, with large spatially clustered outliers. We found an association between high-error, low-precision, and high-surface roughness in the PPC, likely due to illumination characteristics of the snow surface. Glacier surface reconstructions are important for geodetic mass balance measurements, giving key insights into changing climate where in situ measurements are difficult to obtain. The PPC errors are small enough that they would have minimal effects on total mass balance, should the technique be applied across the glacier.

Terrestrial photographic imagery combined with structure-from-motion (SfM) provides a relatively easy-to-implement method for monitoring environmental systems, even in remote and rough terrain. However, the collection of in-situ positioning data and the identification of control points required for georeferencing in SfM processing is the primary roadblock to using SfM in difficult-to-access locations; it is also the primary bottleneck for using SfM in a time series. We describe a novel, computationally efficient, and semi-automated approach for georeferencing unreferenced point clouds (UPC) derived from terrestrial overlapping photos to a reference dataset (e.g., DEM or aerial point cloud; hereafter RPC) in order to address this problem. The approach utilizes a Discrete Global Grid System (DGGS), which allows us to capitalize on easily collected rough information about camera deployment to coarsely register the UPC using the RPC. The DGGS also provides a hierarchical set of grids which supports a hierarchical modified iterative closest point algorithm with natural correspondence between the UPC and RPC. The approach requires minimal interaction in a user-friendly interface, while allowing for user adjustment of parameters and inspection of results. We illustrate the approach with two case studies: a close-range (3 km) scene of relatively flat glacier ice reconstructed from four cameras overlooking Nàłùdäy (Lowell Glacier), Yukon, Canada. We assessed the accuracy of the georeferencing by comparing the UPC to the RPC, as well as surveyed control points; the consistency of the registration was assessed using the difference between successive registered surfaces in the time series. The accuracy of the registration is roughly equal to the ground sampling distance and is consistent across time steps. These results demonstrate the promise of the approach for easy-to-implement georeferencing of point clouds from terrestrial imagery with acceptable accuracy, opening the door for new possibilities in remote monitoring for change-detection, such as monitoring calving rates, glacier surges, or other seasonal changes at remote field locations.

Supraglacial drainage networks play an integral role in both glacier dynamics and run-off timing, and mapping them provides insight into their role in glacial systems. Here we present a reproducible approach for semi-automated mapping of supraglacial hydrologic features, which complements existing work in automated and manual mapping by providing clear definitions for identification of features. This framework uses a digital terrain model (DTM) to identify potential flow routes on the glacier surface, which are then classified using a set of standardized rules based on the DTM and an orthomosaic. We found that the normalized difference water index calculated from digital imagery was influenced by image brightness and introduce a new approach using average RGB values to correct for this. Using this framework we mapped supraglacial drainage networks at Nàłùdäy and Thores Glacier, Canada. The framework was easier to implement with high-resolution (0.5 m) imagery and DTMs, compared to data with lower resolution (10 m), due to the increased detail in topography and feature boundaries at high-resolution. Lower-resolution data captured larger streams (>2 pixels wide), however, indicating that the framework can still be used at this resolution. Mapping supraglacial hydrology using standardized methods opens possibilities for investigating many questions relating to changes in supraglacial hydrology over time.