Skeletonization and segmentation for single corn using terrestrial LiDAR data

Ground-based leaf area and leaf direction measurements are crucial for remote sensing validation of Leaf Area Index (LAI) and Leaf Angle Distribution (LAD) products. The acquisition of field data is a time-consuming and labor-intensive manual operation. Terrestrial LiDAR (light detection and ranging) has potential to characterize and rebuild the three dimensional structure of vegetation. A method was developed to acquire the skeleton of selected individual corn using terrestrial LiDAR data. Individual leaf was segmented according to classified skeleton. Then we extracted the structure parameters including the leaf length and width, leaf area, leaf inclination angle for each segmented single leaf. Although the terrestrial LiDAR data which came from an individual corn are unable separated from other corn automatically, it could estimate structure parameters such as location, height, and leaf inclination angle of corn and replace part of manual measurement automatically.