Remote Sensing of Snow Dynamics over the Vissátvuopmi Palsa Mire, Northern Sweden
Abstract
Palsas are peat hills made of a permafrost core with ice lenses and are very important areas for
biodiversity. Today, palsas are also climate indicators for global warming. Due to their
sensitive location at the edge of the permafrost zone, they are very vulnerable to a changed
climate. Palsas are disappearing faster and faster, as the climate has become increasingly
warmer for the past decades. In order to observe these periglacial landforms before they
disappear, it is of importance to investigate how a warmer, wetter and more snow-covered
climate affects the palsa mires. Above all, snow dynamics is a climate variable that affects the
entire permafrost zone. With the help of technological developments, new remote sensing
methods have been used in recent decades to map snow dynamics such as snow depth and
snow cover, and these can also be applied over palsa mires.
There are yet few studies that use several different types of remote sensing methods to map
snow dynamics over the same palsa. Even fewer have combined various remote sensing
methods with measurements taken in the field over a palsa mire. The purpose of this study
was therefore to investigate how the different remote sensing methods LiDAR (light detection
and ranging) and optical satellite data can be used to measure snow dynamics and how their
measurements agree to those taken in situ. Weather data was also processed in order to relate
climate and topographic parameters with snow distribution over the palsa mire.
Measurements of snow depth and snow cover were performed on two palsa types, a domeshaped
and a ridge-shaped palsa in Vissátvuopmi, Sweden's largest continuous palsa mire.
Both UAV (unmanned aerial vehicle) LiDAR and in situ measurements were performed in the
field, where Sentinel-2 optical satellite data were collected over the palsas during the time
period 2017-2022. Weather data over the study area was also collected and processed.
The results of the snow depth from in situ and LiDAR measurements showed that some
measuring points had relatively the same snow depth between the methods, while other points
showed greater differences in snow depth between them. A correlation analysis between the
different snow depth measurements resulted only in an intermediate correlation of 0.41 for the
ridge-shaped palsa and 0.28 for the dome-shaped palsa. The P-values for each correlation was
found to be lower than 0.05, indicating that the correlations were statistically significant. The
study also shows how much impact meteorological variables such as air temperature and wind
have on how snow is distributed over the ridge-shaped and dome-shaped palsas, especially in
relation to the palsa's topography. The analysis of Sentinel-2 optical satellite data showed that
the ridge-shaped and dome-shaped palsas received an earlier first snowmelt date during the
year and a later first snow-free date, which in 2022 resulted in an increase in the snowmelt
period by 15 days for the ridge-shaped palsa and 14 days for the dome-shaped palsa since
2019.
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Date
2023-08-18Author
Samie, Simon
Series/Report no.
B1266
Language
eng