Estimation of PAI_e using airborne LiDAR data in South Korea

In this study, the Effective Plant Area Indices (PAI_e) for areas of Korean Pine (Pinus koraiensis) and Oaks (Quercus spp.) were estimated by calculating the ratio of intercepted and penetrated LIDAR laser pulses for the canopies of the three forest types. Initially, the canopy gap fraction (G _LiDAR) was generated by extracting the LiDAR data reflected from the canopy surface, or inner canopy area. The LiDAR-derived PAI_e was then estimated by using G_LIDAR with the Beer-Lambert law. A comparison of the LiDAR-derived and field-derived PAI_e revealed the coefficients of determination for Korean Pine and Oak to be 0.82 and 0.59, respectively. These differences between field-based and LIDAR-based PAI_e for the different forest types were attributed to the amount of leaves and branches in the forest stands. The probability that the LiDAR pulses are reflected from bare branches is low as compared to the reflection from branches with a high leaf density.