Analysis and experimentation on ecosystems

Snow depth estimation derived from high-resolution digital elevation models (DEMs) can lead to improved understanding of the spatially highly heterogeneous nature of snow distribution, as well as help us improve our knowledge of how snow patterns influence local geomorphic processes. Slope deformation processes such as permafrost creep can make it challenging to acquire a snow-free DEM that matches the sub-snow topography at the time of the associated snow-covered DEM, which can cause errors in the computed snow depths. In this study, we illustrate how modelling changes in the sub-snow topography can reduce errors in snow depths derived from DEM differencing in an area of permafrost creep. To model the sub-snow topography, a surface deformation model was constructed by performing non-rigid registration based on B-splines of two snow-free DEMs. Seasonal variations in creep were accounted for by using an optimization approach to find a suitable value to scale the deformation model based on in-situ snow depth measurements or the presence of snow-free areas corresponding to the date of the snow-covered DEM. This scaled deformation model was used to transform one of the snow-free DEMs to estimate the sub-snow topography corresponding to the date of the snow-covered DEM. The performance of this method was tested on an active rock glacier in the southern French Alps for two surveys dates, which were conducted in the winter and spring of 2017.