Machine Learning and LiDAR Snowheight Maps from UAVs Reveal Clusters of Snow Variability in a Sub-Alpine Forest.
- 1Faculty of Environment and Natural Sciences, Albert-Ludwigs University Freiburg, Friedrichstr.39, 79098 Freiburg, Germany
- 2Department of Sustainable Systems Engineering - INATECH, Albert-Ludwigs-University Freiburg, Emmy- Noether-Str. 2, 79098 Freiburg, Germany
Snow plays a crucial role in the hydrological cycle as it serves as an intermediate storage of winter precipitation and renews groundwater reserves. It is therefore of central importance for, among others, our drinking water supply and agriculture. Snow interacts with its environment in many ways, is constantly changing with time, and thus has a highly heterogeneous spatial and temporal distribution. Therefore, modelling snow variability is difficult, especially when additional components such as forests add complexity. To increase our understanding of the spatiotemporal variability of snow as well as to validate snow models, we need reliable validation data. For these purposes, airborne LiDAR surveys or time series derived from snow sensors on the point scale are commonly used. However, these are disadvantageously limited to one point either in space or in time. In this study, we profited from current advances in LiDAR and drone technology, as well as machine learning, to bridge this gap. We present a new dataset on snow variability in forests for the Alptal, a sub-alpine, forested valley in the pre-alps, Switzerland. The core dataset consists of a dense sensor network, repeated UAV-based LiDAR flights and manual snow height and density measurements. Using modern machine learning algorithms, we determine four clusters of similar spatiotemporal behaviour regarding their snowheight. These clusters are characterized and further used to derive daily snow depth and snow water equivalent maps. By using the latter, we obtain spatially continuous key hydrological variables. The results suggest that snow occurs in clusters that reoccur in space. These clusters underline the relation between canopy cover and spatial snow accumulation patterns and (the much more complex) spatial ablation patterns. The presented dataset and derived products are the first to our knowledge that provide daily, high-resolution snow height and hydrologic variables based on field data. The results of this study can therefore improve our understanding of the spatiotemporal variability of snow in forested environments. Moreover, they are ideally suited for the validation of modern snow models.
How to cite: Geissler, J., Rathmann, L., and Weiler, M.: Machine Learning and LiDAR Snowheight Maps from UAVs Reveal Clusters of Snow Variability in a Sub-Alpine Forest., EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-32, https://doi.org/10.5194/egusphere-egu23-32, 2023.