RPAS-SfM snow depth and snow density mapping in disturbed vegetated mountainous environments of Coastal British Columbia

Abstract

Concurrent advancements in Remotely Piloted Aircraft Systems (RPAS) and Structure from Motion (SfM) processing technologies have added powerful new methods for remotely sensing the cryosphere. Highly accurate snow depth (SD) estimates derived from RPAS-SfM workflows have been attained, however, most studies have examined open, relatively simple terrain. Results from the few RPAS-SfM SD studies that have examined complex vegetated terrain are of insufficient accuracy for meaningful use in water-resource research and management, prompting further development of RPAS-SfM SD and snow water equivalent (SWE) survey methods to better represent such areas. This study researched the use of RPAS-SfM methods to map SD and SWE across a 52 hectare study plot located on Vancouver Island, British Columbia during two snow seasons. This mid-elevation plot contains steep and complex terrain, including roads, ground covering perennial shrubs, regenerating forest, and old-growth forest. Optical imagery was captured using an off-the-shelf RPAS, and processed into digital elevation models (DEMs) using SfM software. Bare earth DEMs were then subtracted from snow surface DEMs to derive SD estimates. Manual SD measurements were used to validate RPAS-SfM SD estimates, and manual SWE measurements were used to estimate SWE across the study area. Additionally, the efficiency and accuracy of a novel, permanent above snow Ground Control Point (GCP) network was assessed. Root mean square error (RMSE) as low as 0.08 m was found in open terrain, which is consistent with previous research. In off-road areas, RMSE initially ranged from 0.36 m to 0.59 m, however, a bias correction based on ground cover classifications was found to be effective for dealing with underestimations of SD values caused by thick perennial vegetation; with vegetation caused bias ranging from -0.25 m to -0.46 m the application of a positive offsets reduced RMSE by up to 0.27 m in off-road sections, resulting in best case RMSE of 0.18 m in such areas. Multi-temporal SD and SWE outputs captured peak and melt period snowpack conditions, presenting highly detailed information on snow distribution, melt dynamics, and total stored water across the study plot. The elevated permanent GCP network was found to greatly improve the efficiency of both field surveys and data processing, while providing sub-centimeter accuracy levels similar to traditional ground level GCPs. Methods developed through this research show that RPAS-SfM techniques can be successfully applied to previously logged areas containing ground covering vegetation, and offer promise for application in water management of such areas.