Airborne, remotely controlled drones facilitate high-resolution geospatial data collection on a user-defined temporal scale. A challenging research gap is understanding snow accumulation and interception dynamics in forest regions, as remote sensing approaches still struggle to resolve sub-canopy snow distributions. The goal of this research is to assess the utility of high-resolution, drone-based lidar for the study of snow accumulation and redistribution in a forested subalpine region. The study examined how various parameters such as canopy coverage, snow-on-trees, and subsampling resolution influenced the agreement between lidar and in-situ observed snow depth. In situ and drone measurements of snow depth were captured approximately every two weeks over two winters on a subalpine ridgetop. In situ snow depth measurements were taken at fixed locations between-trees and within tree-wells across the study site. High-resolution drone-based lidar (~1000 pts m-2) was collected and processed into snow depth rasters at a range of subsample resolutions. Lidar snow depths between trees were found to have a consistent negative bias compared to in situ snow depths (mean bias 2 cm – 32 cm at 5 cm resolution). The strongest agreement between lidar and in situ snow depths was for 5 cm raster resolution at sites between trees (average agreement of 9.8 cm), and 50 cm resolution within tree-wells (average agreement of 5 cm). The results suggest differing optimal data collection and processing practices for lidar snow measurements between trees and under canopies, and provide an estimate of the errors associated with various data collection practices and environmental influences.