林业科学研究2025，Vol.38Issue(6)：33-47,15.DOI:10.12403/j.1001-1498.20240416
基于UAV-LiDAR的落叶松子代测定林单木分割方法研究
Individual Tree Segmentation Methods for Larch Progeny Test Forest Based on UAV-LiDAR
摘要
Abstract
[Objective]To identify optimal laser pulse repetition frequencies and individual tree segmenta-tion algorithms for progeny test forests,providing technical references for long-term phenotypic monitoring of breeding trial forests characterized by high canopy density and complex terrain based on UAV LiDAR.[Method]Based on a 37-year-old Japanese larch progeny test forest in Dagujia Forest Farm,Liaoning,three sets of fused point cloud data were obtained through 380 kHz forward and reverse fusion(380),550 kHz forward and reverse fusion(550),and the fusion of two laser pulse repetition frequencies(380550).By combining seed points derived from real locations with the Point Cloud-based Cluster Segmentation(PCS),and seed points from three Canopy Height Models(CHMs)with the Marker-Controlled Watershed Segmentation(MWS),the Seeded Region Growing(SRG),and PCS,a total of 30 single tree segmenta-tion combinations were generated.The appropriate laser pulse repetition frequency and individual tree segmentation algorithm were selected based on the accuracy of individual tree segmentation and tree height extraction,and the characteristics of unmatched individual trees were analyzed in conjunction with slope information.[Results]The PCS algorithm using ground-truth seeds achieved superior performance across all datasets(F-scores:380-PRF=0.96,380550-PRF=0.96,550-PRF=0.93),outperforming CHM-based approaches(mean F-scores:0.80,0.79,0.74 respectively);380-FR was identified as the optimal PRF configuration,yielding 0.96 segmentation F-score and 0.84 R² height accuracy with PCS;MWS and PCS using Kriging/IDW CHM seeds maintained robust performance(F=0.82-0.83,R²=0.83-0.84)without ground truth.Slope analysis revealed 67%of mismatches occurred on slopes>25°,predominantly sup-pressed trees within 4m radius zones.[Conclusion]This study identifies five UAV-LiDAR individual tree segmentation methods that are effective for high canopy density progeny test forests.The most effective segmentation method utilizes the PCS algorithm with real location points,enhancing the efficiency of phen-otypic trait surveys and supporting the long-term monitoring needs of tree breeding.关键词
无人机/激光雷达/单木分割/育种/子代测定林/日本落叶松/脉冲重复频率Key words
UAV/LiDAR/individual tree segmentation/breeding/progeny test forest/Larix kaempferi(Lamb.)Carrière/pulse repetition frequency分类
农业科技引用本文复制引用
蔡天润,孙晓梅,陈东升,谢允慧..基于UAV-LiDAR的落叶松子代测定林单木分割方法研究[J].林业科学研究,2025,38(6):33-47,15.基金项目
中央级公益性科研院所基本科研业务费专项资金(LYSZX202002,CAFYBB2022ZC001) （LYSZX202002,CAFYBB2022ZC001）
"十四五"国家重点研发计划课题"林木优异种质维持的遗传基础"(2022YFD2200103) （2022YFD2200103）
