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Accounting for permafrost creep in high-resolution snow depth mapping by modelling sub-snow ground deformation

Author:
Goetz, Jason, Fieguth, Paul, Kasiri, Keyvan, Bodin, Xavier, Marcer, Marco, Brenning, Alexander
Source:
Remote sensing of environment 2019 v.231 pp. 111275
ISSN:
0034-4257
Subject:
deformation, digital elevation models, glaciers, monitoring, permafrost, remote sensing, seasonal variation, snow, spring, surveys, topography, winter, Alps region, France
Abstract:
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.By accounting for surface displacements caused by permafrost creep, we found that our method was able to reduce the errors in the estimated snow depths by up to 33% (an interquartile range reduction of 11 cm) compared to using the untransformed snow-free DEM. The accuracy of the snow depths only slightly improved (root-mean-square error decrease of up to 3 cm). Greater reductions in error were observed for the snow depths calculated for the date that was furthest (i.e., the winter survey) in time from the snow-free DEM. Additionally, we found that our approach to scaling the deformation model has promising potential to be adapted for monitoring seasonal variations in permafrost creep by combining in-situ snow depth measurements with high-resolution surface deformation models.
Agid:
6483041