RADIOMETRIC AND GEOMETRIC APPROACH FOR MAJOR WOODY PARTS SEGMENTATION IN FOREST LIDAR POINT CLOUDS

Segmenting major woody parts is a critical prerequisite to derive structural and biophysical attributes of trees. Static Terrestrial laser scanning (TLS) has been widely used due to its accurate and non-destructive scanning capability; wood parts segmentation has been experimented using the raw radiometric feature. However, due to the challenges of fixed scanning positions and occlusion, using TLS to capture an entire tree is time-consuming. Additionally, the raw intensity of TLS data cannot accurately represent objects' physical characteristics. Here, using LiDAR data acquired by an in-house developed backpack Mobile Mapping System (MMS), we introduce a fast and fully unsupervised method that combines automatic thresholding of normalized radiometric and geometric features to extract major woody parts in the point clouds. We show that using MMS LiDAR data, our method can achieve higher performance than existing methods for major woody parts segmentation on 14 trees with different sizes and species in both leaf-on and leaf-off seasons.