The process tropical aboveground biomass (AGB) plays in the global carbon cycle is imperative to preserve in the efforts to combat the effects of climate change through climate mitigation strategies. However, there is currently an insufficient understanding of AGB distribution and dynamics in tropical forests, and a lack of time and cost-effective means of estimating AGB. Species identification, location, diameter-at-breast-height (DBH), and AGB were determined at the stem-level in four 0.5 ha plots in a Costa Rican tropical wet forest to assess the distributional patterns of AGB, and its partitioning among various forest stand ages. Remotely-sensed data of the plots was collected utilizing a PrecisionHawk Rev4 unmanned aerial system (UAS) equipped with a dual-return light detection and ranging (LiDAR) sensor to calibrate with field data to determine if it could accurately estimate AGB in a densely forested environment. Species richness varied among forest stand ages, and had a slight negative impact on AGB at a fine spatial scale. Tree stems 5 – 24 cm in DBH represent over 80% of all stems included in the AGB analyses, yet contribute less than half of the total AGB represented among the plots. Vegetation distribution and characteristics of biomass clustering evolved with forest stand age. Height metrics were extracted from a LiDAR-derived digital elevation model (DEM) and digital surface model (DSM), and predictive calibration models were generated to estimate AGB from the remotely-sensed data. However, extracting height metrics from the LiDAR data emphasized the challenges associated with accurate spatial modeling of a dense tropical forest.