Topographic and vegetation controls of the spatial distribution of snow depth in agro-forested environments by UAV lidar
<p>Accurate knowledge of snow depth distributions in forested regions is crucial for applications in hydrology and ecology. In such a context, understanding and assessing the effect of vegetation and topographic conditions on snow depth variability is required. In this study, the spatial distr...
Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2023-03-01
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Series: | The Cryosphere |
Online Access: | https://tc.copernicus.org/articles/17/1225/2023/tc-17-1225-2023.pdf |
Summary: | <p>Accurate knowledge of snow depth distributions in
forested regions is crucial for applications in hydrology and ecology. In
such a context, understanding and assessing the effect of vegetation and
topographic conditions on snow depth variability is required. In this study,
the spatial distribution of snow depth in two agro-forested sites and one
coniferous site in eastern Canada was analyzed for topographic and
vegetation effects on snow accumulation. Spatially distributed snow depths
were derived by unmanned aerial vehicle light detection and ranging
(UAV lidar) surveys conducted in 2019 and 2020. Distinct patterns of snow
accumulation and erosion in open areas (fields) versus adjacent forested
areas were observed in lidar-derived snow depth maps at all sites.
Omnidirectional semi-variogram analysis of snow depths showed the existence
of a scale break distance of less than 10 m in the forested area at all
three sites, whereas open areas showed comparatively larger scale break
distances (i.e., 11–14 m). The effect of vegetation and topographic
variables on the spatial variability in snow depths at each site was
investigated with random forest models. Results show that the underlying
topography and the wind redistribution of snow along forest edges govern the
snow depth variability at agro-forested sites, while forest structure
variability dominates snow depth variability in the coniferous environment.
These results highlight the importance of including and better representing
these processes in physically based models for accurate estimates of
snowpack dynamics.</p> |
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ISSN: | 1994-0416 1994-0424 |