Snow Depth Distribution in Canopy Gaps in Central Pyrenees.
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| Title: | Snow Depth Distribution in Canopy Gaps in Central Pyrenees. |
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| Authors: | Rojas‐Heredia, Francisco, Revuelto, Jesús, Deschamps‐Berger, César, Alonso‐González, Esteban, Domínguez‐Aguilar, Pablo, García, Jorge, Pérez‐Cabello, Fernando, López‐Moreno, Juan Ignacio |
| Source: | Hydrological Processes; Nov2024, Vol. 38 Issue 11, p1-17, 17p |
| Subject Terms: | SNOW accumulation, RANK correlation (Statistics), MOUNTAIN forests, DRONE aircraft, RANDOM forest algorithms |
| Geographic Terms: | PYRENEES |
| Abstract: | This research analyses the snow depth distribution in canopy gaps across two plots in Central Pyrenees, to improve understanding of snow–forest and topography interactions. Snow depth maps, forest structure–canopy gap (FSCG) characteristics and topographic variables were generated by applying Structure from Motion algorithms (SfM) to images acquired from Unmanned Aerial Vehicles (UAVs). Six flights were conducted under different snowpack conditions in 2021, 2022 and 2023. Firstly, the snow depth database was analysed in terms of the ratio between the radius of the canopy gap and the maximum height of the surrounding trees (r/h), in order to classify the gaps as small‐size, medium‐size, large‐size, or open areas at both sites independently. Then Kendall's correlation coefficients between the snow depth, FSCG and topographic variables were computed and a Random Forest (RF) model for each survey was implemented, to determine the influence of these variables in explaining snow depth patterns. The results demonstrate the consistency of the UAV SfM photogrammetry approach for measuring snowpack dynamics at fine scale in canopy gaps and open areas. At the northeast exposed Site 1, the larger the r/h observed, the greater was the snow depth obtained. This pattern was not evident at the southwest exposed Site 2, which presented high variability related to the survey dates and categories, highlighting the relevance of topography for determining optimum snow accumulation in forested areas. Slope systematically exhibited a negative and significant correlation with snow depth and was consistently the highest‐ranked variable for explaining snow distribution at both sites according to the RF models. Distance to the Canopy Edge also presented high influence, especially at Site 1. The findings suggest differences in the main drivers throughout each site and surveys of the topographic and FSCG variables are needed to understand snow depth distribution over heterogeneous mountain forest domains. [ABSTRACT FROM AUTHOR] |
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| Database: | Biomedical Index |
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