Vis enkel innførsel

dc.contributor.authorRouyet, Line
dc.contributor.authorKarjalainen, Olli
dc.contributor.authorNiittynen, P.
dc.contributor.authorAalto, Juha
dc.contributor.authorLuoto, Miska
dc.contributor.authorLauknes, Tom Rune
dc.contributor.authorLarsen, Yngvar
dc.contributor.authorHjort, J.
dc.date.accessioned2021-08-25T08:03:41Z
dc.date.available2021-08-25T08:03:41Z
dc.date.created2021-08-11T11:28:56Z
dc.date.issued2021
dc.identifier.citationJournal of Geophysical Research (JGR): Earth Surface. 2021, 126 (7), .en_US
dc.identifier.issn2169-9003
dc.identifier.urihttps://hdl.handle.net/11250/2771096
dc.description.abstractPeriglacial environments are characterized by highly dynamic landscapes. Freezing and thawing lead to ground movement, associated with cryoturbation and solifluction. These processes are sensitive to climate change and variably distributed depending on multiple environmental factors. In this study, we used multi-geometry Sentinel-1 Synthetic Aperture Radar Interferometry (InSAR) to investigate the spatial distribution of the mean annual ground velocity in a mountainous landscape in Northern Norway. Statistical modeling was employed to examine how periglacial ground velocity is related to environmental variables characterizing the diverse climatic, geomorphic, hydrological and biological conditions within a 148 km2 study area. Two-dimensional (2D) InSAR results document mean annual ground velocity up to 15 mm/yr. Vertical and horizontal velocity components in the East–West plane show variable spatial distribution, which can be explained by the characteristics of cryoturbation and solifluction operating differently over flat and sloping terrain. Statistical modeling shows that slope angle and mean annual air temperature variables are the most important environmental factors explaining the distribution of the horizontal and vertical components, respectively. Vegetation and snow cover also have a local influence, interpreted as indicators of the ground material and moisture conditions. The results show contrasted model performance depending on the velocity component used as a response variable. In general, our study highlights the potential of integrating radar remote sensing and statistical modeling to investigate mountainous regions and better understand the relations between environmental factors, periglacial processes and ground dynamics
dc.language.isoengen_US
dc.rightsCC BY-NC-ND 4.0*
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleEnvironmental Controls of InSAR-Based Periglacial Ground Dynamics in a Sub-Arctic Landscapeen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.rights.holder© 2021, Author(s)
dc.description.versionpublishedVersion
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2
dc.identifier.doi10.1029/2021JF006175
dc.identifier.cristin1925282
dc.source.journalJournal of Geophysical Research (JGR): Earth Surfaceen_US
dc.source.volume126en_US
dc.source.issue7en_US
dc.source.pagenumber24en_US
dc.relation.projectNorges forskningsråd: 263005


Tilhørende fil(er)

Thumbnail

Denne innførselen finnes i følgende samling(er)

Vis enkel innførsel

CC BY-NC-ND 4.0
Med mindre annet er angitt, så er denne innførselen lisensiert som CC BY-NC-ND 4.0