Soft Segmentation and Reconstruction of Tree Crown from Laser Scanning Data

Abstract

Point cloud data obtained by laser scanning can be used for object shape modeling and analysis, including forest inventory. One of the inventory tasks is individual tree extraction and measurement. However, individual tree segmentation, especially tree crown segmentation, is challenging. In this paper, we present a novel soft segmentation algorithm to segment tree crowns in point clouds automatically and reconstruct the tree crown surface from the segmented crown point cloud. The soft segmentation algorithm mainly processes the overlapping region of the tree crown. The experimental results showed that the segmented crown was accurate, and the reconstructed crown looked natural. The reconstruction algorithm was highly efficient in calculating the time and memory cost aspects since the number of the extracted boundary points was small. With the reconstructed crown geometry, the crown attributes, including the width, height, superficial area, projecting ground area, and volume, could be estimated. The algorithm presented here is effective for tree crown segmentation.