Advancing Measurement and Modelling of Glacier Change Using Unmanned Aerial Vehicles and Structure-From-Motion

• Main Author: Bash, Eleanor A.
• Other Authors: Moorman, Brian J., McDermid, Gregory J., Marshall, Shawn, Lichti, Derek D., Mueller, Derek R.
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: Arts 2019
• Subjects: Unmanned Aerial Vehicles; Structure-from-Motion; Arctic Canada; Glacier; Canadian Rockies; Point Cloud; Snow; Physical Geography; Remote Sensing; Arctic; Canada; Canada Glacier; Climate change; glacier*
• Online Access: http://hdl.handle.net/1880/110909; https://doi.org/10.11575/PRISM/36981

Glaciers throughout Canada are responding to climate change with rapid changes in mass balance. There are limitations in current methods of measuring and predicting these changes in mass balance, including accessibility, spatial and temporal resolution of remotely sensed data, and cost of data acquisition. Technological developments in unmanned aerial vehicles (UAVs) and structure-from-motion (SfM) have created new opportunities to overcome these limitations. This dissertation investigated uncertainties in UAV-SfM data and used that data to understand spatial patterns and drivers of summer glacier melt. A study of glacier snow surface reconstruction in the Canadian Rockies used lidar data acquired simultaneously with UAV imagery to assess the spatial distribution of errors in the UAV-SfM data. The study revealed patterns in the errors related to snow surface illumination which must be considered when using UAVs over snow covered glaciers. Short term summer melt in the ablation zone of a glacier in the Canadian Arctic was investigated using UAV surveys. The study showed that UAV-SfM melt measurements agreed with ablation stake measurements and was a reliable method of measuring distributed melt patterns. The study found the lower limits on change detection were related to flying height and dGPS precision. A melt model was used to estimate surface melt for the three-day window where UAV-SfM measurements were collected and model results were validated against spatially distributed measurements. This study revealed patterns in model error which show that simplified melt models fail to capture important melt drivers on the glacier surface. The model errors would have cumulative effects in long term projections, which would lead to significant misrepresentation of total surface melt. UAV and SfM technologies were shown to be an effective method for gathering highly detailed information on glacier surface characteristics and change. However, the technology is not the answer to every problem and limitations still exist ...