资源环境科技发展态势分析平台

The 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.