Detecting snow depth change in avalanche path starting zones using uninhabited aerial systems and structure from motion photogrammetry

Understanding snow depth distribution and change is useful for avalanche forecasting and mitigation, runoff forecasting, and infrastructure planning. Advances in remote sensing are improving the ability to collect snow depth measurements. The development of structure from motion (SfM), a photogrammetry technique, combined with the use of uninhabited aerial systems (UASs) allows for high resolution mapping of snow depth over complex terrain. The primary objective of this study was to determine the feasibility and efficacy of SfM to examine snow depth distribution and variability in complex terrain such as avalanche path starting zones at multiple times during the season. We used a 3DR Solo quadcopter UAS equipped with a Ricoh GR II camera at 90 m above ground level to acquire images of one avalanche starting zone in northwest Montana, USA. We also placed 4 to 13 ground control points (GCPs) around the area of interest to avoid traveling in steep, avalanche terrain. Ground control measurements resulted in 5 to10 cm horizontal accuracy and 5 to 15 cm vertical accuracy for 90 to 95 % of the collected points (a minimum of 100 points collected at each GCP). In-situ measurements of snow depth difference between sampling days ranged from 20 to 60 cm. We processed the images to create point clouds and digital surface models (DSMs). The resolution of the resultant DSMs was approximately 5 cm. Preliminary DSM and point cloud differencing efforts suggest relative change detection of snow depth at 5 to 15 cm resolution. The use of these relatively low cost and easily accessible methods of snow depth data collection will enhance accuracy of snow depth change estimates in starting zones and can be used to inform avalanche forecasting and mitigation efforts.