Unmanned Aerial Systems (UAS) are a cost-effective means of collecting forest data conventionally used above the forest canopy. Where forest canopies are dense, limited information about stem structures can be extracted directly due to obscuration by foliage. In these circumstances, complementary ground-based methods including manual measurement and terrestrial laser scanning are deployed, but these techniques are often limited in terms of the scope and scale of data collected by factors including time, field cost and site accessibility. This paper describes the application of a UAS flown below the forest canopy as an efficient and effective approach for stem measurement in areas where the canopy is difficult to penetrate, and as a potential solution to measuring trees in areas of dense undergrowth. The study sites were scanned with a helicopter-mounted VUX-1LR LiDAR sensor and the resulting point clouds were used as a comparison dataset. The measurements extracted from these point-clouds were compared with ground-based measurements of diameter at breast height and relative positions. The below-canopy UAS and the VUX-1LR at 30m had the lowest root-mean-squarederror (RMSE) of 4.1cm, followed by the VUX-1LR at 90m with a RMSE of 4.4cm. The VUX-1LR 60m flight was the most consistent with the highest coefficient of determination, however due to a positive bias, there was an RMSE of 4.5cm. The photogrammetry-based, below-canopy UAS was found to be an efficient and accurate method of extracting DBH and relative position of stems in forests.