基于区域生长和冠层形态特征的机载激光扫描数据对落叶松人工林个体树冠分割

The detection of individual trees in a larch plantation could improve the management efficiency and production prediction. This study introduced a two-stage individual tree crown (ITC) segmentation method for airborne light detection and ranging (LiDAR) point clouds, focusing on larch plantation forests with different stem densities. The two-stage segmentation method consists of the region growing and morphology segmentation, which combines advantages of the region growing characteristics and the detailed morphology structures of tree crowns. Results showed that the proposed method significantly increased ITC detections compared with that of using only the region growing algorithm, where the correct matching rate increased from 73.5% to 86.1%, and the recall value increased from 0.78 to 0.89.落叶松人工林的单木检测可有效提升经营管理效率与产量预测精度。本研究针对不同林分密度的落叶松人工林，提出了一种面向机载激光雷达（Light Detection and Ranging，简称LiDAR）点云的两阶段单木树冠（Individual Tree Crown，简称ITC）分割方法。该两阶段分割方法由区域生长与形态学分割组成，融合了区域生长的特征优势与树冠的精细形态结构特性。研究结果显示，相较于仅使用区域生长算法的方案，本研究提出的方法显著提升了单木树冠检测性能，其中正确匹配率从73.5%提升至86.1%，召回率从0.78提升至0.89。

搜集汇总

背景与挑战

背景概述

该数据集提出了一种结合区域生长与冠层形态特征的两阶段方法，用于机载激光扫描数据对落叶松人工林的个体树冠分割。该方法显著提升了树冠检测的正确匹配率和召回率，旨在优化人工林的管理效率与产量预测。