A recent study, published as a preprint on the ESS Open Archive, introduces a novel approach for estimating Leaf Area Index (LAI) in complex forest scenes using simulated full-waveform Lidar data. LAI is a critical ecological parameter essential for understanding vegetation structure, canopy complexity, and overall ecosystem productivity. The research proposes combining multiple full-waveform-derived metrics, specifically the Gini Coefficient, Centroid (intensity-weighted mean), and Total Return Energy, to characterize forest canopies. Full-waveform Lidar systems are an advanced remote sensing technology that records the complete backscattered energy profile of a laser pulse as it interacts with vegetation, providing more detailed information about the vertical canopy structure than traditional discrete-return Lidar. This capability is particularly advantageous in dense forest environments, where Lidar can effectively penetrate the canopy and mitigate signal saturation issues often encountered by passive optical sensors. The study reports a strong predictive relationship, achieving an Adjusted R² of 69.4% using raw waveform data, which further improved to 72.3% after applying a deconvolution-based preprocessing step to remove system contributions. The 'truth data' for modeling LAI was derived through voxelization of the forest scene, a standard method for obtaining accurate reference values in simulated environments. While the ESS Open Archive is a legitimate platform for disseminating early-stage research outputs and preprints, it's important to note that its content undergoes editorial evaluation but is not formally peer-reviewed. Nevertheless, the scientific concepts and the use of Lidar for LAI estimation are well-established and corroborated by numerous peer-reviewed studies in the fields of remote sensing and forestry. This research contributes valuable methodological advancements for global forest monitoring and ecological analysis.