Analyzing vegetation effects on snow depth variability in Alaska's boreal forests with airborne lidar

Abstract

Abstract. Lidar-derived snow depth and canopy height maps were used to analyze snow depth spatial variability at a boreal forest site in Alaska. High-resolution (0.5 m) airborne lidar data were acquired during NASA's SnowEx Alaska field campaigns during peak snow-on accumulation (March 2022) and snow-off (May 2022). The impact of canopy height on snow distribution was studied at the Caribou Poker Creeks Research Watershed, located northeast of Fairbanks, Alaska, US. Ground-based snow depth measurements were collected concurrently with the March snow-on lidar survey and were compared to collocated lidar-derived snow depths. The comparison between ground-based and lidar-derived snow depths produced a bias of 2.0 cm and a root mean square error (RMSE) of 12.0 cm. The lidar snow depth map showed a mean snow depth of HS‾=98 cm and a standard deviation of SD=15 cm for the study site. The influence of vegetation on end-of-winter snow depth distribution was analyzed using three canopy height classes: (1) forest, (2) shrub and short stature trees (SSS), and (3) treeless. Results showed a statistically significant difference in median snow depths across canopy height classes, with the largest significant differences between forest and treeless (12–14 cm) and between forest and SSS (8–14 cm). These differences in snow depth correspond to a snow water equivalent range of approximately 20–30 mm. This study provides insights into the spatial variability of snow depths in Alaska's boreal forests by using ground-based measurements to evaluate the accuracy of airborne lidar to estimate snow depths in a boreal forest ecosystem. The results of this research can be used to assist water and resource managers in determining best practices for estimating snow depth and its spatial variability in the boreal forest of Alaska.