Abstract
AbstractThe retrieval of detailed, co‐located snow depth and canopy cover information from airborne lidar has advanced our understanding of links between forest snow distribution and canopy structure. In this study, we present two recent high‐resolution (1 m) lidar data sets acquired in (i) a 2017 mission in the Eastern Swiss Alps and (ii) NASA's 2017 SnowEx field campaign at Grand Mesa, Colorado. Validation of derived snow depth maps against extensive manual measurements revealed a RMSE of 6 and 3 cm for plot‐level mean and standard deviation of snow depth, respectively, demonstrating that within‐stand snow distribution patterns were captured reliably. Lidar data were further processed to obtain canopy structure metrics. To this end, we developed a novel approach involving a continuous measure of local distance to canopy edge (DCE), which enabled creating spatially aggregated nondirectional and directional descriptors of the canopy structure. DCE‐based canopy metrics were correlated to mean and standard deviation of snow depth over areas representing grid‐cell sizes typical of watershed and regional model applications (20–200 m). Snow depth increased along the DCE gradient from dense canopy to the center of canopy gaps for all sites and acquisition times, while directional effects particularly evolved during the ablation season. These findings highlight the control of canopy gap distribution on snow distribution in discontinuous forests, with higher snow depths where the open fraction is concentrated in few large gaps rather than many fragmented small gaps. In these environments, dedicated canopy structure metrics such as DCE should advance spatially distributed snow modeling.
Highlights
In forested areas, snow distribution dynamics are shaped by complex interacting processes such as snow interception (Moeser, Stähli, et al, 2016; Storck et al, 2002), shading from solar radiation (Musselman et al, 2015) and enhanced longwave irradiance (Sicart et al, 2006; Webster et al, 2016), which create pronounced variability at small spatial scales
We explore the links between snow depth and canopy structure within forest stands based on recent high‐resolution (1 m) airborne lidar data sets from the Swiss Alps and Grand Mesa in Colorado with the following specific objectives: 1. Use new, comprehensive validation data to demonstrate the capability of airborne lidar to reliably capture within‐forest snow distribution patterns; 2
An initial comparison to a 3‐m resolution product derived by the standard Airborne Snow Observatory (ASO) workflow (Painter et al, 2016) revealed consistently lower standard deviations than those computed from the 1‐m HS map
Summary
In forested areas, snow distribution dynamics are shaped by complex interacting processes such as snow interception (Moeser, Stähli, et al, 2016; Storck et al, 2002), shading from solar radiation (Musselman et al, 2015) and enhanced longwave irradiance (Sicart et al, 2006; Webster et al, 2016), which create pronounced variability at small spatial scales. Understanding and quantifying the control of forest architecture on snow distribution is important, as forests are subject to changes due to clearcutting and timber harvesting (Ellis et al, 2013; Murray & Buttle, 2003; Troendle & King, 1987), wildfires (Burles & Boon, 2011; Harpold, Biederman et al, 2014), and mortality caused by insect infestation (Biederman et al, 2014; Winkler et al, 2014) These shifts affect snow cover dynamics and, given the large spatial overlap of forests and seasonal snow, further impact hydrological regimes and the climate system (Rutter et al, 2009). Existing approaches to represent within‐cell variability such as tiling
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