The vertical distribution of plant constituents is a key parameter to describe vegetation structure and influences several processes, such as radiation interception, growth and habitat. Terrestrial laser scanning (TLS), also referred to as terrestrial LiDAR, has the potential to measure the canopy structure with high spatial detail and accuracy. Vertical plant profiles, which describe the plant area per unit volume (PAVD) as a function of height, are often used to quantify the vertical structure. However, most studies do not account for topography, use registered multiple TLS scans or use a detailed airborne LiDAR digital terrain model to account for this variation in ground height. Airborne LiDAR is often not available or expensive to acquire. Here, we present an approach that facilitates rapid, robust and automated assessment of the vertical structure of vegetation. We use single scans and local plane fitting to correct for topographic effects in vertical plant profiles and test our approach in five different Australian forest types with different topography and understorey. We validate our approach with topography-corrected vertical plant profiles with digital terrain models derived from airborne LiDAR. Our results demonstrate that not correcting for topography can lead to significant errors in the vertical distribution of plant constituents (CV(RMSE) up to 66.2%, typically ranging from 4.2% to 13.8%). This error decreases significantly when topography is accounted for with TLS plane fitting (CV(RMSE) up to 20.6%, typically ranging from 1.5% to 12.6%). We demonstrate that height metrics from vertical plant profiles that are not corrected for topography depart significantly from those that are inferred from the reference profile. The effect is most noticeable for canopy top height and the peak PAVD height. Correcting topography with a TLS plane fitting approach reduces the error in canopy top height by at least 77% and up to 100%, and reduces the error in peak PAVD height by 83.3% and up to 100%. We also show the advantage of a multiple return over a first return TLS instrument. The definition of the ground returns with a first return instrument might be problematic in environments with dense herbaceous understorey and there is an overall trend of lower height metrics compared to multiple return instruments. We present a data-driven approach that is based on single scan TLS data. The latter is of importance for large area sampling as it allows more sites to be sampled from existing resources and facilitate consistent processing of archived TLS data, which is often single scan data with no survey control.