Multiple studies have analyzed the error budget of biomass predictions from airborne laser scanning (ALS) data utilising the area-based approach (ABA). While some error sources, such as high-incidence scan angles, can be excluded from analyses, the main error sources of (a) geolocation errors, (b) quality of the reference data, and (c) residual modelling errors remain. In this work, we aim to focus on the contribution of inherent uncertainty of the trees themselves, e.g., caused by wind during the ALS data acquisition. At Petawawa Research Forest in Ontario, Canada, we analysed overlapping pairs of ALS flight lines and trained a Random Forest to estimate biomass from 223 forest inventory plots, both on the merged dataset and separated by flight strips using a set of state-of-the-art LiDAR metrics as independent variables. Quantifying inference performance on withheld validation data, we compared single-strip predictions with estimates from the merged dataset to examine how much of model error is due to the model’s capacity and reference data quality, and how much is caused by different representations from the separated strips. Our results show that after excluding problematic scan angles, the RMSE differences between merged and separated strips amount to approximately 50-80% of the predictor RMSE. These errors tend to be larger for taller trees, especially when close to clearings, where effects of wind on the point cloud metrics are larger. Furthermore, we analysed multiple data acquisitions acquired from several sensors. Overall, increased footprint size and increased point density corresponded to a decrease in biomass RMSE. Considering current developments towards small-footprint (UAV-based) laser scanning, uncertainty caused by movement of tree crowns may be an increasingly limiting factor for accurate biomass- and other forest-related metric extraction from laser scanning. We therefore strongly suggest that this uncertainty should be quantified when analysing laser scanning data of forests.