Accounting for permafrost creep in high-resolution snow depth mapping by modelling sub-snow ground deformation
International audience 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 pro...
Published in: | Remote Sensing of Environment |
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Online Access: | https://doi.org/10.1016/j.rse.2019.111275 https://hal.archives-ouvertes.fr/hal-02183113 |
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fttriple:oai:gotriple.eu:10670/1.dntagj 2023-05-15T17:57:13+02:00 Accounting for permafrost creep in high-resolution snow depth mapping by modelling sub-snow ground deformation Goetz, Jason Fieguth, Paul Kasiri, Keyvan Bodin, Xavier Marcer, Marco Brenning, Alexander Friedrich-Schiller-Universität = Friedrich Schiller University Jena Jena, Germany System Design Engineering (SYDE) University of Waterloo Waterloo Environnements, Dynamiques et Territoires de la Montagne (EDYTEM) Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry ) Pacte, Laboratoire de sciences sociales (PACTE) Sciences Po Grenoble - Institut d'études politiques de Grenoble (IEPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes 2016-2019 (UGA 2016-2019 ) 2019-09-01 https://doi.org/10.1016/j.rse.2019.111275 https://hal.archives-ouvertes.fr/hal-02183113 en eng HAL CCSD Elsevier hal-02183113 doi:10.1016/j.rse.2019.111275 10670/1.dntagj https://hal.archives-ouvertes.fr/hal-02183113 undefined Hyper Article en Ligne - Sciences de l'Homme et de la Société ISSN: 0034-4257 EISSN: 1879-0704 Remote Sensing of Environment Remote Sensing of Environment, Elsevier, 2019, 231, pp.111275. ⟨10.1016/j.rse.2019.111275⟩ geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2019 fttriple https://doi.org/10.1016/j.rse.2019.111275 2023-01-22T16:49:40Z International audience 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 ... Article in Journal/Newspaper permafrost Unknown Remote Sensing of Environment 231 111275 |
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English |
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geo envir Goetz, Jason Fieguth, Paul Kasiri, Keyvan Bodin, Xavier Marcer, Marco Brenning, Alexander Accounting for permafrost creep in high-resolution snow depth mapping by modelling sub-snow ground deformation |
topic_facet |
geo envir |
description |
International audience 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 ... |
author2 |
Friedrich-Schiller-Universität = Friedrich Schiller University Jena Jena, Germany System Design Engineering (SYDE) University of Waterloo Waterloo Environnements, Dynamiques et Territoires de la Montagne (EDYTEM) Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry ) Pacte, Laboratoire de sciences sociales (PACTE) Sciences Po Grenoble - Institut d'études politiques de Grenoble (IEPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes 2016-2019 (UGA 2016-2019 ) |
format |
Article in Journal/Newspaper |
author |
Goetz, Jason Fieguth, Paul Kasiri, Keyvan Bodin, Xavier Marcer, Marco Brenning, Alexander |
author_facet |
Goetz, Jason Fieguth, Paul Kasiri, Keyvan Bodin, Xavier Marcer, Marco Brenning, Alexander |
author_sort |
Goetz, Jason |
title |
Accounting for permafrost creep in high-resolution snow depth mapping by modelling sub-snow ground deformation |
title_short |
Accounting for permafrost creep in high-resolution snow depth mapping by modelling sub-snow ground deformation |
title_full |
Accounting for permafrost creep in high-resolution snow depth mapping by modelling sub-snow ground deformation |
title_fullStr |
Accounting for permafrost creep in high-resolution snow depth mapping by modelling sub-snow ground deformation |
title_full_unstemmed |
Accounting for permafrost creep in high-resolution snow depth mapping by modelling sub-snow ground deformation |
title_sort |
accounting for permafrost creep in high-resolution snow depth mapping by modelling sub-snow ground deformation |
publisher |
HAL CCSD |
publishDate |
2019 |
url |
https://doi.org/10.1016/j.rse.2019.111275 https://hal.archives-ouvertes.fr/hal-02183113 |
genre |
permafrost |
genre_facet |
permafrost |
op_source |
Hyper Article en Ligne - Sciences de l'Homme et de la Société ISSN: 0034-4257 EISSN: 1879-0704 Remote Sensing of Environment Remote Sensing of Environment, Elsevier, 2019, 231, pp.111275. ⟨10.1016/j.rse.2019.111275⟩ |
op_relation |
hal-02183113 doi:10.1016/j.rse.2019.111275 10670/1.dntagj https://hal.archives-ouvertes.fr/hal-02183113 |
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op_doi |
https://doi.org/10.1016/j.rse.2019.111275 |
container_title |
Remote Sensing of Environment |
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231 |
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111275 |
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