Classification of Snow Depth Measurements for tracking plant phenological shifts in Alpine regions (Papers Track)
Jan Svoboda (WSL Institute for Snow and Avalanche Research SLF); Michael Zehnder (WSL Institute for Snow and Avalanche Research SLF); Marc Ruesch (WSL Institute for Snow and Avalanche Research SLF); David Liechti (WSL Institute for Snow and Avalanche Research SLF); Corinne Jones (Swiss Data Science Center); Michele Volpi (Swiss Data Science Center, ETH Zurich); Christian Rixen (WSL Institute for Snow and Avalanche Research SLF); Jürg Schweizer (WSL Institute for Snow and Avalanche Research SLF)
Abstract
Ground-based snow depth measurements are often realized using ultrasonic or laser technologies, which by their nature measure the height of any underlying object, whether it is snow or vegetation in snow-free periods. We propose a machine learning approach to the automated classification of snow depth measurements into a snow cover class and a class corresponding to everything else, which takes into account both the temporal context and the dependencies between snow depth and other sensor measurements. Through a series of experiments we demonstrate that our approach simplifies the detection of seasonal snowmelt and corresponding onset of plant growth, which we used to assess climate-change related phenological shifts in otherwise rather poorly monitored high alpine regions.