Method of individual tree crown segmentation from airborne lidar data using novel gaussian filter and energy function minimization

Abstract

Provided are a method of individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization. First, a dual Gaussian filter was designed with automated adaptive parameter assignment and a screening strategy for false treetops. This preserved the geometric characteristics of sub-canopy trees while eliminating false treetops. Second, anisotropic water expansion controlled by the energy function was applied to accurate crown segmentation. This utilized gradient information from the digital surface model and explored the morphological structures of tree crown boundaries as analogous to the maximal valley height difference from surrounding treetops. We demonstrate the generality of our approach using seven diverse plots in the subtropical Gaofeng Forest, China, coupled with ground verification. Our approach enhanced the detection rate of treetops and ITC segmentation relative to the marked-control watershed method, especially in complicated intersections of multiple crowns.