Lidar and deep learning reveal forest structural controls on snowpack

Forest structure has a strong relationship with abiotic components of the environment. For example, canopy morphology controls snow depth through interception and modifies incoming thermal radiation. In turn, snow water availability affects forest growth, carbon sequestration, and nutrient cycling....

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Bibliographic Details
Published in:Frontiers in ecology and the environment 2023-02, Vol.21 (1), p.49-54
Main Authors: Hojatimalekshah, Ahmad, Gongora, Joel, Enterkine, Josh, Glenn, Nancy F, Caughlin, T Trevor, Marshall, Hans Peter, Hiemstra, Christopher A
Format: Article
Language:English
Subjects:
Artificial neural networks
Canopies
Carbon cycle
Carbon sequestration
Deep learning
Environmental perception
Foliage
Forest ecosystems
Forest growth
Forests
Hydrology
Interception
Lidar
Neural networks
Nutrient cycles
Snow
Snow accumulation
Snow depth
Snowpack
Terrestrial ecosystems
Thermal radiation
Water availability
Water depth
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Summary:Forest structure has a strong relationship with abiotic components of the environment. For example, canopy morphology controls snow depth through interception and modifies incoming thermal radiation. In turn, snow water availability affects forest growth, carbon sequestration, and nutrient cycling. We investigated how structural diversity and topography affect snow depth patterns across scales. The study site, Grand Mesa, Colorado, is representative of many areas worldwide where declining snowpack and its consequences for forest ecosystems are increasingly an environmental concern. On the basis of a convolution neural network model (R2 of 0.64; root mean squared error of 0.13 m), we found that forest structural and topographic metrics from airborne light detection and ranging (lidar) at fine scales significantly influence snow depth during the accumulation season. Moreover, complex vertically arranged foliage intercepts more snow and results in shallower snow depths below the canopy. Assessing forest structural controls on snow distribution and depth will aid efforts to improve understanding of the ecological and hydrological impacts of changing snow patterns.
ISSN:1540-9295
1540-9309
DOI:10.1002/fee.2584