In natural environments,vegetation communities often demonstrate vertical structure due to competition and natural selection among plant groups and between populations and their environment. Different growth stages of the same plant also exhibit various structural and biochemical parameters in the vertical dimension. Detecting these three-dimensional spatial distribution features enables quantitative assessment of ecological environments in three dimensions,crucial for estimating forest carbon reserves and biodiversity conservation. Traditional passive hyperspectral remote sensing and lidar techniques face significant limitations in vertical vegetation profiling. However,the emergence of Hyperspectral LiDAR (HSL),a new type of sensing instrument,offers a fresh approach to studying the vertical distribution of physiological and biochemical parameters in vegetation. Yet,due to hardware constraints,the suitability of HSL under unmanned aerial platforms for complex three-dimensional forest scenes remains insufficiently explored. This paper begins with laboratory experiments using a prototype HSL to conduct small-scale indoor measurements with torch flower plants as the target to verify its integrated capability in extracting spatial and spectral information. Subsequently,the three-dimensional radiative transfer model LESS was used to simulate forest scenes with vertical heterogeneity. The model simulated airborne HSL devices for extracting hyperspectral three-dimensional point clouds of forests. Vegetation indices and a random forest model are utilized to invert chlorophyll and carotenoid concentrations in forest canopy layers. The results show that in indoor experiments,the HSL echo information effectively discriminates the spectral differences at different heights of plant structures. The NDVI values in the upper red leaf area and lower green leaf area of torch flowers are respectively less than and greater than 0.5. The LESS model successfully constructed high-resolution hyperspectral three-dimensional point clouds of forest scenes. Out of 24 groups of vegetation indices,17 groups exhibit good detection accuracy (MAPE<13%). The chlorophyll concentration inversion model demonstrates an R2 of 0.93 with MAE values of 6.26,3.40,and 2.81 for the upper,middle,and lower layers,respectively. The carotenoid concentration inversion model shows an R2 of 0.91 with MAE values of 1.59,2.58,and 0.39 for the upper,middle,and lower layers,respectively. This study indicates that HSL is an effective device for extracting vegetation spectral information in three dimensions and possesses immense potential for investigating the vertical distribution of biochemical components in complex vegetation scenes such as forests when deployed on aerial platforms.