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

Abstract

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 km 2 , 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.

Spatial Heterogeneity of the Temporal Dynamics of Arctic Tundra Vegetation

“…The temporal dynamics of vegetation across Arctic latitudes and continents could provide essential information for understanding the processes driving current tundra greening/browning patterns, as well as being able to predict future trends. Vegetation changes affect land‐surface albedo, permafrost thaw, animal migration patterns, carbon sequestration, human activities, and more (e.g., Fauchald et al, ; Frost et al, ; Horstkotte et al, ; Kępski et al, ). This study has identified (1) the dynamic patterns of arctic tundra vegetation and (2) the degree to which summer warmth drives tundra vegetation change, and results will provide inputs to analyses that evaluate vegetation change effects on other tundra properties.…”

Using stable isotopes to estimate travel times in a data‐sparse Arctic catchment: Challenges and possible solutions

“…In the absense of measured field data, recent developments in remote sensing may provide new methods of data assimilation. Such methods have already been used in modelling snowmelt-dominated alpine catchments (Bach, Braun, Lampart, & Mauser, 2003) and estimating snowmelt in Arctic tundra (Kepski et al, 2017).…”

Automated Classification of Terrestrial Images: The Contribution to the Remote Sensing of Snow Cover

“…From this perspective, the availability of webcam networks is an important data source for calibration and validation processes. The attention of the scientific community of this proxy is increasing, and the literature about this topic is growing [9,[14][15][16][17]. Furthermore, several tools (for example, FMIPROT and PRACTISE) can be considered for research purposes [18][19][20].…”