Identifying Canopy Snow in Subalpine Forests: A Comparative Study of Methods
Abstract 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‐la...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2023WR036996 |
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| author | Natasha Harvey Sean P. Burns Keith N. Musselman Holly Barnard Peter D. Blanken |
| author_facet | Natasha Harvey Sean P. Burns Keith N. Musselman Holly Barnard Peter D. Blanken |
| author_sort | Natasha Harvey |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-9b0a24b60c8e440b8e20e0df082fbd16 |
| institution | Kabale University |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-9b0a24b60c8e440b8e20e0df082fbd162025-08-20T03:31:01ZengWileyWater Resources Research0043-13971944-79732025-01-01611n/an/a10.1029/2023WR036996Identifying Canopy Snow in Subalpine Forests: A Comparative Study of MethodsNatasha Harvey0Sean P. Burns1Keith N. Musselman2Holly Barnard3Peter D. Blanken4Department of Geography University of Colorado Boulder CO USADepartment of Geography University of Colorado Boulder CO USADepartment of Geography University of Colorado Boulder CO USADepartment of Geography University of Colorado Boulder CO USADepartment of Geography University of Colorado Boulder CO USAAbstract 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.https://doi.org/10.1029/2023WR036996snow interceptioneddy covarianceimage analysisconvolutional neural network (CNN)modeling |
| spellingShingle | Natasha Harvey Sean P. Burns Keith N. Musselman Holly Barnard Peter D. Blanken Identifying Canopy Snow in Subalpine Forests: A Comparative Study of Methods Water Resources Research snow interception eddy covariance image analysis convolutional neural network (CNN) modeling |
| title | Identifying Canopy Snow in Subalpine Forests: A Comparative Study of Methods |
| title_full | Identifying Canopy Snow in Subalpine Forests: A Comparative Study of Methods |
| title_fullStr | Identifying Canopy Snow in Subalpine Forests: A Comparative Study of Methods |
| title_full_unstemmed | Identifying Canopy Snow in Subalpine Forests: A Comparative Study of Methods |
| title_short | Identifying Canopy Snow in Subalpine Forests: A Comparative Study of Methods |
| title_sort | identifying canopy snow in subalpine forests a comparative study of methods |
| topic | snow interception eddy covariance image analysis convolutional neural network (CNN) modeling |
| url | https://doi.org/10.1029/2023WR036996 |
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