Affiliation:
1. College of Forestry, Beijing Forestry University, Beijing 100083, China
2. Beijing Key Laboratory of Precision Forestry, Beijing Forestry University, Beijing 100083, China
3. Beijing Ocean Forestry Technology Co., Ltd., Beijing 100083, China
Abstract
The dynamic monitoring of forest resources is an integral component of forest resource management and forest eco-system stability maintenance. In recent years, LiDAR (Light Detection and Ranging) has been increasingly utilized in precision forest surveys due to its great penetrating ability and capacity to detect forest vertical structure information. However, the present airborne LiDAR data individual tree segmentation algorithms are not highly adaptable to forest types, particularly in mixed coniferous and broad-leaved forest zones, where the accuracy of individual tree extraction is low, and trees are incorrectly recognized and missed. In order to address these issues, in this study, spectral images and LiDAR data of a red pine conifer–broadleaf mixed forest in the Changbai Mountain Nature Reserve in Jilin Province were chosen, and the normalized point cloud was segmented iteratively using the distance-threshold-based individual tree segmentation method to obtain the initial segmented individual tree vertices. For individual trees with deviations in the initial vertex identification position, and unidentified individual trees, identification anchor points of real tree vertices are added within the canopy of the trees. These identification anchor points have strong position directivity in LiDAR data, which can mark the individual trees whose vertices were misidentified or missed during the initial individual tree segmentation process and identify these two tuples. The tree vertices may be inserted precisely based on the 3D shape of the individual tree point cloud, and the seed-point-based individual tree segmentation method is used to segment the normalized point cloud and finish the extraction of individual trees in red pine mixed conifer forests. The results indicate that, compared to the previous individual tree segmentation approach based on the relative spacing between individual trees, this study enhances the accuracy of individual tree segmentation from 83% to 96%. The extremely high segmentation accuracy indicates that the proposed method can accurately identify individual trees based on remote sensing techniques to segment forest individual trees, can provide a data basis for subsequent individual tree information extraction, and has great potential in practical applications.
Funder
National Natural Science Foundation of China, ‘Biomass Precision Estimation Model Research for Large-Scale Regions Based on Multi-View Heterogeneous Stereographic Image Pairs of Forests’
Reference35 articles.
1. Applications in Remote Sensing to Forest Ecology and Management;Lechner;One Earth,2020
2. Changes of forest stand dynamics in Europe. Facts from long-term observational plots and their relevance for forest ecology and management;Pretzsch;For. Ecol. Manag.,2014
3. Application of airborne LiDAR data in dynamic monitoring of forest resources;Zhu;East China Forest Management,2018
4. Meissner, B., Wyss, D., Hoffmann, H., and Teusan, S. (, January Ulaanbaatar,). Application of Remote Sensing and Gis for Sustainable Forest Management and Capacity Building in Mongolia. Proceedings of the 27th Asian Conference on Remote Sensing (ACRS2006), 9–13 October 2006.
5. Models for supporting forest management in a changing environment;Luis;For. Syst.,2011
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献