Mapping snow depth and spatial variability using SFM photogrammetry of UAV images over rugged mountainous regions of the Western Himalaya

Abstract

Accurate snow depth observations are one of the most important inputs in the process of avalanche hazard estimation, forecasting, and mitigation. This study presents the high-resolution snow depth mapping of avalanche-prone regions of the Manali–Dhundi area, Himachal Pradesh (HP), India, using, repeated UAV Photogrammetric surveys. The images captured by an RGB camera mounted on a fixed-wing UAV (e-Bee X) were used to generate the high-resolution Digital Surface Model (DSM) and Orthophotos of the study area. Mapping of snow depth of the study area is crucial for avalanche hazard assessment and safe movement of the personnel and vehicles passing through the highways in this area during winters. A total of 12 km² area covering six major avalanche sites was surveyed. A snow depth map was generated by differencing co-registered snow-covered DSM and bare surface DSM. UAV-derived snow depth values have been validated with the point snow depth measurements of Wireless Sensor Networks (WSN), manual observation station, and snow depth around a structure of known height in the survey area. The UAV-retrieved snow depth values were found in good correlation with field-measured snow depth (R = 0.95). The maximum and minimum observed snow depth values at different locations on open slopes and flat areas were 1.62 m and 0.79 m. The estimated Mean Error (ME) and RMSE based on these measured values were found 0.15 and 0.21 m, respectively. The study reveals that the snow depth map generated from the UAV survey provides reliable estimates of snow depth values for flat areas and open slopes but the method is not suitable for forested areas. The study also infers that the statistical measures (i.e., µ, σ, RMSE, etc.) derived from the complete survey dataset without considering topographic factors and features of the region will not provide realistic estimates of snow depth and its accuracy. The study suggests that regular monitoring (before and after every snow storm) of the snow depth using a UAV-based method is quick, cost-effective, and provides accurate snow conditions for operational use and planning in snow-bound regions.

Keywords:

• Photogrammetry
• UAV
• snow depth
• SFM
• spatial variability
• wireless sensor network
• digital surface model

Acknowledgments

The authors are thankful to Director, Defence Geoinformatics Research Establishment (DGRE), Chandigarh, for providing facilities to carry out this work and constant motivation during the investigation. The authors sincerely thank the reviewers of the paper for critical reviews and constructive suggestions for improving the manuscript. The authors would like to acknowledge the DGRE staff for collecting ground data. Thanks are due to R. K. Das for providing WSN data, Dr. Chander Shekhar for the DGPS support during the survey, and Kirpal Singh for providing the technical support during the survey. We are also thankful to Google earth for providing high-resolution images of the the study area.

Disclosure statement

No potential conflict of interest was reported by the author(s)