Resolving the Influence of Forest-Canopy Structure on Snow Depth Distributions with Terrestrial Laser Scanning

Predicting changes in forested seasonal snowpacks under altered climate scenarios is one of the most pressing hydrologic challenges facing today's society. Approximately 2 billion people worldwide, as well as numerous ecosystems and ecosystem services depend on water released from snowmelt. Airborne- and satellite-based remote sensing methods hold the potential to transform measurements of terrestrial water stores in snowpack, improve process representations of snowpack accumulation and ablation, and generate high quality predictions that inform potential strategies to better manage water resources. While the effects of forest on snowpack are well documented, many of the fine-scale processes influenced by the forest-canopy are not directly accounted for because most snow models don't explicitly represent canopy structure and canopy heterogeneity. The goal of this project is improving snow remote-sensing methods in forested ecosystems using fine scale lidar measurements to identify capabilities and limitations of coarser scale remote sensing. We use terrestrial laser scanning (TLS) data collected during NASA's 2017 SnowEX campaign to resolve canopy and sub-canopy snow distributions at high resolution, and to understand the relationships between canopy and snow distributions across scales. Our sample scales range from individual trees to patches of trees across the Grand Mesa, Colorado, USA, NASA SnowEx site.