At the Priest River Experimental Forest in northern Idaho, USA, snow water equivalent (SWE) was recorded over a period of six years on random, equally-spaced plots in ~4.5 ha small watersheds (n=10). Two watersheds were selected as controls and eight as treatments, with two watersheds randomly assigned per treatment as follows: harvest (2007) followed by mastication (2008), harvest (2007) followed by prescribed fire (2008), prescribed fire alone (2006), and prescribed fire (2006) followed by salvage (2007). Canopy closure was recorded using a digital hemispherical camera, and snow water equivalent (SWE) was measured with a Polyvinyl chloride (PVC) snow tube before and after treatments. In addition, canopy densities derived from two LiDAR datasets taken at the beginning and the end of the study were compared to the hemispherical photos. The objectives of the study were to 1) assess changes in snow accumulation and melt as a result of changes in canopy cover, and 2) compare the digital photograph method and LiDAR for quantifying canopy cover changes. A Before-After/Control-Impact (BACI) analysis was conducted to examine how the reduction in canopy cover due to treatments influenced the snow accumulation and melt. The BACI assesses an experimental design in which measurements are made on both control and impacted experimental units before and after prescribed treatments. Such a design is often used to account for natural temporal variations that occur regardless of the treatment applied. Within the BACI framework, mixed-effects models were tested where Before/After and Control/Impact were considered fixed effects while the variable year was kept as a random effect. The results showed that there was an effect of the treatments on the SWE for the watersheds that received a thinning operation while no effect on the snowmelt was detectible. Field canopy estimates were significant in the models applied to the thinned watersheds while LiDAR estimates, although correlated (p = 0.66, 0.58, 0.77, and 0.62, for Treatments 1, 2, 3, and 4, respectively) to the field estimates, had no significance in the statistical models. Some of the statistical models mildly violated assumptions of normality and equal variance, so further work is needed in order to correct for these violations.