Classification of pine and hardwood by the density and intensity of lidar canopy returns

The primary objectives of this study were to: (1) determine if pines can be distinguished from hardwoods based on the density of LIDAR returns, (2) determine if pines can be distinguished from hardwoods based on the intensity of LIDAR returns, (3) determine if pines can be distinguished from hardwoods based on both the density and intensity of LIDAR returns, and (4) develop a landscape scale classification procedure.; Discriminant analysis was used to classify immature pine, mature pine, and mature hardwood on the John W. Starr Memorial Forest and the Louisiana State University (LSU), Lee Experimental Forest. The number of LIDAR hits/m 3 and the variance of the intensity values were used as the variables for the discriminant analysis. There were 64 validation plots on the Lee Forest. Out of the 64 plots, 49 (76.6%) were classified correctly. The results suggest that the classification program can be used to map the forest types/conditions that were sampled.