Canopies are emergent properties of a large number of individual trees and provide the underlying structure which affects many other components of forest ecosystems. Unmanned aerial vehicle (UAV) data fills an important gap in aerial imaging by offering an affordable and repeatable source of data to assess variation between forest stands across a given landscape. Individual tree crowns can be segmented from canopy height models to provide detailed information about forest and canopy structure, and photogrammetric point cloud data can be used to identify key habitat variables which affect bat activity. A greater density of biomass may reflect higher productivity and greater energetic and material exchange between the atmosphere and biosphere, whereas increased animal activity indicates a healthier ecosystem with greater resiliency and stability. In this thesis I use data on forest canopy structure 1) to estimate aboveground biomass (AGB) and 2) to model bat activity. I developed allometric scaling models to relate field observations of diameter-at-breast-height (DBH) and tree height to UAV derived tree height and crown area to estimate AGB of individual crown segments. Using these estimates, I constructed two Bayesian regression models to examine variation of AGB of large stands throughout a forest in response to either a set of stand structural predictors or a set of topographic predictors. With these models, I found that at the stand level, AGB was closely related to several structural variables including canopy-area weighted height and tree density and that AGB showed modest relationships with topographic variables such as topographic position, elevation and soil moisture. These results suggest that UAV-derived data calibrated with field observations can be effective for estimating forest AGB and studying its variation among stands, and that stand structural characteristics are more strongly related to AGB that topographic variables. I also captured acoustic recordings of bat passes from some of these forest stands and assigned automatically classified bat calls into three echolocation guilds (short-, mid-, and long-range echolocators; SRE, MRE, and LRE respectively). Using a Bayesian generalized linear model, I was able to model the activity of these guilds in relation to canopy structure. SRE activity was more frequent in taller stands, in stands with less canopy cover, and in stands that were further away from the canopy edge; MRE activity was slightly greater in stands with shorter canopies; and LRE activity was more likely to be observed in stands with more canopy cover and in stands closer to the forest edge. Greater activity of all three bat guilds was observed in plots closer to open water sources. My results illustrate how photogrammetric point clouds can identify fine-scale features useful for AGB modelling and important to bat habitat use, demonstrating how useful UAV captured data can be for forest researchers and managers.