Snow process monitoring using time-lapse structure-from-motion photogrammetry with a single camera

Abstract

Abstract Snow-surface processes in high-mountain snow-covered regions can be both complicated and highly variable. It remains a challenge to monitor snow-surface processes due to its tough environment. In this study, a novel One-camera Time-lapse Structure-from-Motion photogrammetry system (O-T-SfM), which is achieved by mounted a camera on a slider to take seven images from different viewing angles, was build-up to monitor snow surface automatically. The novel O-T-SfM was installed next to the August-one ice cap in the Qilian mountains, northwestern China to monitor snow-surface processes by taking oblique digital photographs every three hours from 8:00 to 17:00 from August 24, 2019, to September 7, 2020. Agisoft Photoscan was used to process the images and to derive point clouds and plot 1.5\xa0×\xa01.5\xa0m scale digital elevation models (DEMs). The O-T-SfM measurements is highly consistent with three different methods such as manual measurement, checkpoints, and manual photogrammetry, which indicates that O-T-SfM photogrammetry can achieve centimeter-scale precision. We found that O-T-SfM photogrammetry was generally successful at monitoring snow surfaces and the performance varies with the variation of surface condition in different season. Best performance was reached with snow-free conditions and O-T-SfM has a good performance when snow bedforms were abundant from October to March. It is hard for O-T-SfM to capture the snow melting processes July to September and the smooth fresh snowfalls from April to June. Alignment achievement was greatest in the morning and declined throughout the day. We found that our digital DEMs could also be used to assess the settling characteristics of the snowpack (snow accumulation and ablation, snow-surface roughness). Our results suggest that remote O-T-SfM photogrammetry can be successfully used to monitor the snow processes.