LiDAR (Light Detection and Ranging) technology has been increasingly implemented to assess the biophysical attributes of forest canopies. However, LiDAR-based estimation of tree biophysical attributes remains difficult mainly due to the occlusion of vegetative elements in multi-layered tree crowns. In this study, we developed a new algorithm along with a multiple-scan methodology to analyse the impact of occlusion on LiDAR-based estimates of tree leaf area. We reconstructed five virtual tree models using a computer graphic-based approach based on in situ measurements from multiple tree crowns, for which the position, size, orientation and area of all leaves were measured. Multi-platform LiDAR simulations were performed on these 3D tree models through a point-line intersection algorithm. An approach based on the Delaunay triangulation algorithm with automatic adaptive threshold selection was proposed to construct the scanned leaf surface from the simulated discrete LiDAR point clouds. In addition, the leaf area covered by laser beams in each layer was assessed in combination with the ratio and number of the scanned points. Quantitative comparisons of LiDAR scanning for the occlusion effects among various scanning approaches, including fixed-position scanning, multiple terrestrial LiDAR scanning and airborne-terrestrial LiDAR cross-scanning, were assessed on different target trees. The results showed that one simulated terrestrial LiDAR scan alongside the model tree captured only 25-38% of the leaf area of the tree crown. When scanned data were acquired from three simulated terrestrial LiDAR scans around one tree, the accuracy of the leaf area recovery rate reached 60-73% depending on the leaf area index, tree crown volume and leaf area density. When a supplementary airborne LiDAR scanning was included, occlusion was reduced and the leaf area recovery rate increased to 72-90%. Our study provides an approach for the measurement of total leaf area in tree crowns from simulated multi-platform LiDAR data and enables a quantitative assessment of occlusion metrics for various tree crown attributes under different scanning strategies.