Canopy height model (CHM) and leave area index (LAI) are essential forest structure attributes that are estimated to understand the ecological states and processes occurring in forest ecosystems. Airborne light detection and ranging (LiDAR) systems have proven efficient in producing both CHM and LAI maps for heterogeneous forests at the regional scale. The unique advantage of airborne LiDAR over optical and radar sensors is its vegetation penetration capability. Although the LiDAR penetration capability decreases in dense, complex forests, full-waveform LiDAR systems are currently available to provide critical point observations under the forest canopy. This research developed and tested methods to map CHM and LAI in heterogeneous forests using airborne waveform LiDAR datasets acquired using two different LiDAR systems and flight altitudes. Since using waveform data significantly increases the laser penetration rate, the test results strongly recommend using waveform data for the estimation of both CHM and LAI. These experiments also revealed that the flight data collection altitude will not affect LAI estimation. Through the analysis of CHMs and LAI data variations derived from 4 different datasets, CHM estimation may be good to 0.8 m and LAI estimation may be as precise as 0.5.