Seeing through the forest: New forest structure metrics improve process understanding of snow-forest dynamics

To date, snow models have incorporated simplistic forest structure representations which omit fundamental aspects of canopy structure such as canopy radius and geometry, branch geometry, and gap size distribution. Sub-canopy processes are dramatically affected by air temperature across the snow-zone elevation gradient and canopy structure. Current remote sensing methods lack the ability to measure sub-canopy snow instead relying on imperfect assumptions to estimate snow in forested environments. Here we present new metrics of forest structure as it pertains to canopy interception across elevational gradients in the forests of Oregon and Colorado from lidar data derived from the Airborne Snow Observatory (ASO) and the NASA SnowEx field campaign. Canopy gap size is typically considered as a single spatial value that is uniform vertically. Although we know this is not the case in nature, there is no canopy metric that yet defines gap size change with height. The change of gap size with height has significant effects of how a forest interacts with snow through variations of the wind field to canopy surface area available for interception. Vertical gap size change and volume based gap metrics are analyzed through discrete vertical `slices' of an ASO lidar derived return data cube. The effects of scale, both vertically and horizontally, on canopy metric identification are presented. Distribution analysis of these slices yield valuable rate of change canopy metrics that have largely been unstudied within the snow science community and are important to canopy interception process relationships.