Terrestrial Remote Sensing of Snowmelt in a Diverse High-Arctic Tundra Environment Using Time-Lapse Imagery

Snow cover is one of the crucial factors influencing the plant distribution in harsh Arctic regions. In tundra environments, wind redistribution of snow leads to a very heterogeneous spatial distribution which influences growth conditions for plants. Therefore, relationships between snow cover and v...

Full description

Bibliographic Details
Published in:Remote Sensing
Main Authors: Daniel Kępski, Bartłomiej Luks, Krzysztof Migała, Tomasz Wawrzyniak, Sebastian Westermann, Bronisław Wojtuń
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2017
Subjects:
Online Access:https://doi.org/10.3390/rs9070733
Description
Summary:Snow cover is one of the crucial factors influencing the plant distribution in harsh Arctic regions. In tundra environments, wind redistribution of snow leads to a very heterogeneous spatial distribution which influences growth conditions for plants. Therefore, relationships between snow cover and vegetation should be analyzed spatially. In this study, we correlate spatial data sets on tundra vegetation types with snow cover information obtained from orthorectification and classification of images collected from a time-lapse camera installed on a mountain summit. The spatial analysis was performed over an area of 0.72 km2, representing a coastal tundra environment in southern Svalbard. The three-year monitoring is supplemented by manual measurements of snow depth, which show a statistically significant relationship between snow abundance and the occurrence of some of the analyzed land cover types. The longest snow cover duration was found on “rock debris” type and the shortest on “lichen-herb-heath tundra”, resulting in melt-out time-lag of almost two weeks between this two land cover types. The snow distribution proved to be consistent over the different years with a similar melt-out pattern occurring in every analyzed season, despite changing melt-out dates related to different weather conditions. The data set of 203 high resolution processed images used in this work is available for download in the supplementary materials.