UAV-based estimate of snow cover dynamics: Optimizing semi-arid forest structure for snow persistence

Authors: BELMONTE, A. - Northern Arizona University; SANKEY, T. - Northern Arizona University; Biederman, Joel; BRADFORD, J. - Us Geological Survey (USGS); GOETZ, S. - Northern Arizona University; KOLB, T. - Northern Arizona University

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/4/2021
Publication Date: 3/9/2021
Citation: Belmonte, A., Sankey, T., Biederman, J.A., Bradford, J., Goetz, S., Kolb, T. 2021. UAV-based estimate of snow cover dynamics: Optimizing semi-arid forest structure for snow persistence. Remote Sensing. 13(5):1036. https://doi.org/10.3390/rs13051036.
DOI: https://doi.org/10.3390/rs13051036

Interpretive Summary: After 120 years of fire suppression, many forests on public lands of the American West have become excessively dense, increasing the risk of catastrophic wildfire. Therefore, the US Forest Service and other stakeholders have planned forest thinning on millions of acres of public lands. It is unknown how the resulting arrangement of trees across the landscape may impact the amount and melt timing of snowpack. Here we used emerging technology based on low-cost images from unmanned aerial vehicles (UAV) to map forest structure and snow cover. Then we determined how the arrangement of trees remaining after forest thinning relates to the area and persistence of snow cover following several large snowstorms. These results show the importance of remaining trees for sheltering snowpack from solar radiation. This work contributes to improved forest management strategies to conserve or enhance water resources while reducing wildfire risk.

Technical Abstract: Seasonal snow cover in the dry forests of the American West provides essential water resources to both human and natural systems. The structure of trees and their arrangement across the landscape are important drivers of snow cover distribution across these forests, varying widely in both space and time. We used unmanned aerial vehicle (UAV) multispectral imagery and Structure-from-Motion (SfM) models to quantify snow cover dynamics and examine the effects of forest structure shading on persistent snow cover in a recently thinned ponderosa pine forest. First, we developed a rapid and effective method for accurately identifying persistent snow cover using UAV multispectral imagery. We then found that canopy shading associated with the forest structure, namely vertical and horizontal metrics, were significant drivers of persistent snow cover patches. The UAV image-derived forest structure metrics can, therefore, be used to accurately predict snow patch size and persistence. Our results provide insight into the importance of forest structure, specifically canopy shading, in the amount and distribution of persistent seasonal snow cover in a typical dry forest environment. As more of this forest type is slated for thinning-based restoration, an operational understanding of forest structure effects on snow cover will help drive forest management that can target snow cover dynamics in addition to forest health.