UAV-Based Estimate of Snow Cover Dynamics: Optimizing Semi-Arid Forest Structure for Snow Persistence

Adam Belmonte,Temuulen Sankey,John Bradford,Joel Biederman,Thomas Kolb,Scott Goetz

doi:10.3390/rs13051036

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 rapidly melting snow cover dynamics and examine the effects of forest structure shading on persistent snow cover in a recently thinned ponderosa pine forest. Using repeat UAV multispectral imagery (n = 11 dates) across the 76 ha forest, we first developed a rapid and effective method for identifying persistent snow cover with 90.2% overall accuracy. The SfM model correctly identified 98% (n = 1280) of the trees, when compared with terrestrial laser scanner validation data. Using the SfM-derived forest structure variables, we then found that canopy shading associated with the vertical and horizontal metrics was a significant driver of persistent snow cover patches (R2 = 0.70). The results indicate that UAV image-derived forest structure metrics can 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. 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.

Highlights

We demonstrate an accurate method to provide near real-time estimates of mid-scale snow covered area (SCA) and assess how forest structure influences persistent snow patch size in a thinned ponderosa pine forest on the southern edge of the North American continental snow distribution
This study considered the inclusion of detailed forest structure metrics in quantifying persistent snow cover in a dry southwestern U.S forest
We found the size (m2 ) of persistent snow patches can be effectively predicted using targeted forest structure metrics

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Runoff from seasonal snowpack in semi-arid regions provides drinking water and agricultural irrigation for at least one-sixth of the world’s population [1]. Snowpack provides important ecosystem services, including water for vegetation, aquatic ecosystems, and shallow groundwater recharge [2,3,4,5,6]. Throughout the western United States, higher annual mean temperatures and changes to winter air humidity are contributing to an earlier and faster spring snowmelt [7]. Regional climate projections indicate that these effects will become more pronounced [8,9,10] and inconsistent snowpack will contribute to higher rates of drought-induced tree stress and mortality [11]

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