Snow depth mapping over large, high-alpine regions by airplane photogrammetry

Abstract.

Snow depths are traditionally determined by point measurements at automatic weather stations or field observations, which cannot capture the complexity of snow depth distribution in alpine terrain. Therefore, remote sensing techniques have become key tools for spatially continuous snow depth mapping. Only satellites, airborne laser scanners (ALS) or photogrammetry from piloted aircrafts are capable of covering large regions of more than 100 km². However, the accuracy of satellite data does not match/achieve the requirements for exact snow depth mapping yet. In comparison to ALS, photogrammetric methods are considerably more economic, but have the disadvantages of light and weather dependence as well as the lacking ability to penetrate high vegetation. Nevertheless, previous studies of photogrammetric snow depth mapping on a large scale have already proven the accurate implementation, but those studies were either limited to only one recording or characterized by a spatial resolution of around 2 m. These properties limit the comparison of snow depth distribution and the analysis of small-scale features.

In our study we apply airborne imagery from the current state-of-the-art survey camera Vexcel Ultracam acquired during the annual peak of winter for the five years from 2017 to 2021 in an area of approximately 300 km² around Davos, Switzerland. This enables the calculation of outstandingly improved annual snow depth maps. The high spatial resolution of the snow depth maps (0.5 m) in combination with the high-resolution orthophoto (0.25 m) enables the identification of small-scale snow depth features. Additionally, the development of masks for high-vegetated and settled areas in combination with the high accuracy of the unmasked snow depth values (root mean square error of around 0.15 m) represents a significant step forward for reliable snow depth mapping of large alpine regions with photogrammetric methods. Our study focuses on the consistent workflow used for processing the snow depth maps, demonstrates the special characteristics of the snow depth distribution and presents the potential for investigations and applications based on this unique snow depth time-series.