The rapid proliferation of unmanned aerial vehicle (UAV) mounted Light Detection and Ranging (LiDAR) technology provides new opportunities to capture three-dimensional structure of forest ecosystems at high spatial resolution. This study examined the use of high-density UAV LiDAR surveys (ULS) for extracting biometric information from two different-age temperate Eastern White Pine (Pinus strobus) forests in the Great Lakes region. These forest sites, known as CA-TP4 (85-yrs old) and CA-TP3 (50-yrs old) (CanFlux and Ameriflux notation) are located north of Lake Erie in Southern Ontario, Canada. Estimates of overstory biomass and leaf area index (LAI) were derived for both forest sites. For this purpose, point cloud rasterization, a local maximum function, and a marker-controlled watershed algorithm were combined to delineate individual trees and derive height, crown width, and diameter at base height. Tree-level biomass was then estimated using species-specific allometric relationships. LAI was derived by applying the Beer–Lambert law to ULS-based gap fraction estimates and compared with ground-based photosynthetically active radiation measurements. Continued monitoring of these different-age forests using UAV LiDAR, in combination with field biometric data, will allow future assessments of growth and structural changes in these conservation forests in response to forest management, disturbances, climate change and extreme weather events. Methodologies developed in this study can be utilized to monitor other forest ecosystems across Canada, which are undergoing rapid and profound changes due to climate change.