Journal Article

Identifying Canopy Snow in Subalpine Forests: A Comparative Study of Methods

Barnard, Holly, Burns, Sean P., Blanken, Peter D., Harvey, Natasha, Musselman, Keith N.

2025

Overview

The interception of snow by the canopy is an important process in the water and energy balance in cold‐region coniferous forests. Direct measurements of canopy snow interception are difficult at scales larger than individual trees, requiring indirect methods such as eddy covariance, time‐lapse photography, or modeling. At the Niwot Ridge Subalpine Forest AmeriFlux site in the Colorado Front Range, USA, we compared methods that estimate or simulate the presence of snow interception. Timelapse photography images were analyzed using thresholding analysis and used to train a Convolutional Neural Network (CNN) model to estimate canopy snow presence. Interception was also estimated from eddy covariance measurements above and below the canopy, as well as from model simulations. These methods were applied over January 2019, with binarized results compared to a “ground truth” of human labeled images to calculate the Balanced Accuracy Score. The highest accuracy was achieved by the CNN predictions. Based on the Balanced Accuracy Scores, select methods were extended to estimate the presence of canopy snow for the 2018/2019 winter. All methods provided insight into the process of interception in a subalpine forest but presented challenges, including differing flux footprints of the above‐ and below‐canopy eddy covariance measurements and the inability of red‐green‐blue imagery to monitor snow interception at night, during sunrise, and during sunset. Key Points Snow interception in subalpine forests, identified via flux measurements, imagery and models, indicates its significance Convolutional Neural Network models trained with Phenocam imagery offer insights into snow interception beyond model development period Consider data availability and research goals when choosing a method to estimate the presence of snow interception

• Publisher: John Wiley & Sons, Inc,Wiley
• Subject: Accuracy / Artificial neural networks / Canopies / Canopy / Cold / Colorado / Comparative analysis / Comparative studies / comparative study / Coniferous forests / convolutional neural network (CNN) / Covariance / Eddy covariance / Energy balance / Forests / Ground truth / humans / image analysis / Interception / modeling / Neural networks / Photography / Plant cover / Precipitation / Precipitation monitoring / Snow / snow interception / Subalpine environments / Sunset / Vortices / water / winter