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individual tree segmentation Based on Region-Growing and Density-Guided Canopy 3-D Morphology Detection Using UAV LiDAR Data

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 8897-8909页

作者： Li, Shihua Zhao, Shunda Tian, Zhilin Tang, Hao Su, Zhonghua Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Peoples R China Minist Nat Resources Technol Innovat Ctr Southwest Land Space Ecol Rest Chengdu 610045 Peoples R China Xihua Univ Sch Comp & Software Engn Chengdu 610039 Peoples R China

Forest tree information is crucial for monitoring forest resources and developing forestry management strategies. Airborne laser scanning is an efficient and rapid method for acquiring 3-D point cloud data of forests, making up for the shortcomings of optical remote sensing. Especially for unmanned aircraft vehicle laser scanning, the high-density point cloud provides the possibility of a detailed depiction of forest structures. Extracting individual trees from point cloud data is a prerequisite for fine forestry research. Currently, existing methods fail to fully utilize the height information, density information, and vertical structure details of tree crowns within point cloud data, resulting in complex algorithmic processes and reduced reliability. This study proposes a novel method that combines the canopy height model (CHM) and point cloud data for segmenting individual trees and employs height and density information for morphological detection of tree canopies. First, based on CHM-based segmentation, the point cloud density feature is introduced to identify the wrong segmented trees. Next, the height and density information are combined to guide the selection of vertical profiles. Finally, a 3-D morphological detection is conducted on the profiles to extract the potential trees. We validate the accuracy of the method on German forests. The results indicated that the average F1-scores of six study plots were 0.95, which improved by 5% after the introduction of density information. The primary source of errors was the irregularity of tree morphology. Our method produced reasonable results across different parameter settings, demonstrating its insensitivity to parameter configurations.

关键词： Vegetation Forestry Point cloud compression Accuracy Surveys Three-dimensional displays Morphology Laser radar Autonomous aerial vehicles Clustering algorithms Density individual tree segmentation morphology region growing unmanned aerial vehicle (UAV) light detection and ranging (LiDAR)

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A Dual-Branch Deep Learning Framework at the Grid Scale for individual tree segmentation

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Ding, Ze Zhang, Huaiqing Wang, Ruisheng Zhang, Li Jiang, Hanxiao Yun, Ting Nanjing Forestry Univ Coll Informat Sci & Technol Nanjing 210037 Peoples R China Chinese Acad Forestry Res Inst Forest Resources Informat Tech Beijing 100091 Peoples R China Univ Calgary Dept Geomat Engn Calgary AB T2N 1N4 Canada Georgia Inst Technol Coll Comp Atlanta GA 30332 USA

individual tree segmentation from point clouds is essential for diverse forest applications. A dual-branch segmentation deep learning network operating at the grid scale was proposed, which includes the semantic segmentation branch for partitioning point clouds of tree trunks and the instance segmentation branch for individual trunk extraction. Meanwhile, the network analyzes input forest points at the grid scale instead of pointwise processing to preserve local geometric information of the forest points while reducing computational load. After extraction of each tree trunk in the understory layer using our network, a hierarchical k-nearest neighbors algorithm based on the extracted trunk parts was employed to accomplish individual tree segmentation. For the forest plots, our proposed approach achieves precision, recall, F1 -score, and mean intersection over union (MIoU) of 89.66%, 89.13%, 89.40%, and 90.84%, respectively. These results represent a significant improvement in accuracy and rapid execution capability compared to prior methods.

关键词： Deep learning network forest individual tree segmentation light detection and ranging (LiDAR) Deep learning network forest individual tree segmentation light detection and ranging (LiDAR)

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Potential of hyperspectral LiDAR in individual tree segmentation: A comparative study with multispectral LiDAR

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URBAN FORESTRY & URBAN GREENING 2025年 104卷

作者： Wang, Ao Shi, Shuo Yang, Jian Zhou, Bowei Luo, Yi Tang, Xingtao Du, Jie Bi, Sifu Qu, Fangfang Gong, Chengyu Gong, Wei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan Peoples R China China Univ Geosci Sch Geog & Informat Engn Wuhan Peoples R China Huazhong Agr Univ Coll Resource & Environm Wuhan Peoples R China Wuhan Univ Elect Informat Sch Wuhan Peoples R China Wuhan Inst Quantum Technol Wuhan Peoples R China Minist Educ Engn Res Ctr Percept & Effectiveness Assessment Carbon Neutral Wuhan Peoples R China

individual tree extraction is the basis of obtaining structural parameters of individual tree which will helpful for the inventory, management and protection of urban forest. Single wavelength Light Detection and Ranging (LiDAR) has been widely applied for individual tree segmentation to obtain accurate structural parameters of trees, and it is difficult to segment individual tree of clumped trees. Then, multispectral and hyperspectral LiDAR was developed and used in vegetation remote sensing. Therefore, the main purpose of this study is to discuss the performance of hyperspectral LiDAR on individual tree segmentation with different tree density. Due to the airborne hyperspectral LiDAR data is lack at present, then the simulated airborne hyperspectral LiDAR was used in this study. Random forest algorithm was used to extract tree point clouds. Spectral information and spatial information of simulated hyperspectral LiDAR were applied in individual tree segmentation based on spectral clustering algorithm. Experimental results showed that F1 scores of multispectral and simulated hyperspectral LiDAR in tree point clouds extraction were 89 % and 94 %, respectively. The detection rate of individual tree segmentation by using multispectral and hyperspectral point data in low (medium, high) tree density were 83.53 % (82.46 %, 66.93 %) and 87.05 % (94.74 %, 87.40 %), respectively. It can be found that integrating spectral information can better extract tree point cloud in complex environments and can efficiently improve the accuracy of individual tree segmentation. Therefore, hyperspectral LiDAR possessed the potential for the individual tree segmentation in high tree density region.

关键词： Hyperspectral LiDAR Multispectral LiDAR individual tree segmentation tree extraction

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Research on eucalyptus individual tree segmentation and age estimation utilizing improved Mask R-CNN algorithm based on UAV stereo images

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INDUSTRIAL CROPS AND PRODUCTS 2025年 230卷

作者： Ding, Jirong You, Liyang Liang, Yehua Huang, Juncheng Wu, Zhiyong Huang, Runlian Chen, Jianjun You, Haotian Guilin Univ Technol Coll Geomat & Geoinformat 12 Jiangan Rd Guilin 541006 Peoples R China

Eucalyptus, as one of the world's three major fast-growing tree species, not only provides timber for various industries but also makes significant contributions to carbon sequestration and mitigating climate change. However, due to the rapid growth and diverse variations in eucalyptus, accurate individual tree segmentation poses certain challenges. Additionally, achieving individual tree segmentation while extracting the age of eucalyptus plantations presents greater challenges to segmentation algorithms. Based on this, this study utilizes the Mask R-CNN algorithm framework. Firstly, Transnext is employed to enhance the backbone's performance in extracting image texture and spatial information. Secondly, CARAFEFPN replaces the original FPN structure to increase the receptive field, thereby improving the model's detection performance in dense eucalyptus plantations. Subsequently, Seesawloss is used as the loss function, and SABLHead serves as the detection head to address sample imbalance and enhance model robustness. Finally, the improved Mask R-CNN algorithm is utilized for individual tree segmentation and stand age extraction in eucalyptus plantations. The results indicate that the improved Mask R-CNN model achieves precise individual tree segmentation in different stages of eucalyptus plantations, achieving MAP50 of 80.6 %, MAP75 of 54.2 %, and MAP0.5:0.95 of 48.9 %, all surpassing the Mask R-CNN model. Based on the improved Mask R-CNN model, the SHAI algorithm enables accurate estimation of stand age in eucalyptus plantations. The results of stand uniformity calculated based on individual tree crown width and tree height show relatively significant differences, with stand uniformity calculated based on individual tree height outperforming that calculated based on individual tree crown width. The research findings demonstrate that the improved Mask R-CNN algorithm can achieve both precise individual tree segmentation in different stages of eucalyptus plantations

关键词： Mask R -CNN Eucalyptus individual tree segmentation Stand age estimation

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individual tree segmentation and biomass estimation based on UAV Digital aerial photograph

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Journal of Mountain Science 2023年第3期20卷 724-737页

作者： SUN Zhao WANG Yi-fu DING Zhi-dan LIANG Rui-ting XIE Yun-hong LI Rui LI Hao-wei PAN Lei SUN Yu-jun State Forestry&Grassland Administration Key Laboratory of Forest Resources&Environmental Management Beijing Forestry UniversityBeijing 100083China Northwest Surveying Planning and Designing Institute of National Forestry and Grassland AdministrationXi'an 710048China

Digital aerial photograph(DAP)data is processed based on Structure from Motion(Sf M)algorithm and regional net adjustment method to generate digital surface discrete point clouds similar to Light Detection and Ranging(LiDAR)and digital orthophoto mosaic(DOM)similar to optical remote sensing *** this study,we obtained highresolution images of mature forests of Chinese fir by unmanned aerial vehicle(UAV)flying through crossroute flight,and then reconstructed the threedimensional point clouds in the UAV aerial area by SfM *** point cloud segmentation(PCS)algorithm was used for the individual tree segmentation,and the F-score of the three sample plots were 0.91,0.94,and 0.94,*** tree biomass modeling was conducted using 155 mature Chinese fir forests which were correctly *** relative root mean squared error(rRMSE)values of random forest(RF),bagged tree(BT)and support vector regression(SVR)were 34.48%,35.74%and 40.93%,*** study demonstrated that DAP point clouds had great potential to extract forest vertical parameters and could be applied successfully in individual tree segmentation and individual tree biomass modeling.

关键词： UAV images Structure from motion DAP point clouds individual tree segmentation individual tree biomass models

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individual tree segmentation in occluded complex forest stands through ellipsoid directional searching and point compensation

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Forest Ecosystems 2024年第6期11卷 832-847页

作者： Qingjun Zhang Shangshu Cai Xinlian Liang State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhan430079China

Terrestrial laser scanning(TLS)accurately captures tree structural information and provides prerequisites for treescale estimations of forest biophysical *** tree-scale attributes from TLS point clouds requires segmentation,yet the occlusion effects severely affect the accuracy of automated individual tree *** this study,we proposed a novel method using ellipsoid directional searching and point compensation algorithms to alleviate occlusion ***,region growing and point compensation algorithms are used to determine the location of tree ***,the neighbor points are extracted within an ellipsoid neighborhood to mitigate occlusion effects compared with k-nearest neighbor(KNN).Thirdly,neighbor points are uniformly subsampled by the directional searching algorithm based on the Fibonacci principle in multiple spatial directions to reduce memory ***,a graph describing connectivity between a point and its neighbors is constructed,and it is utilized to complete individual tree segmentation based on the shortest path *** proposed method was evaluated on a public TLS dataset comprising six forest plots with three complexity categories in Evo,Finland,and it reached the highest mean accuracy of 77.5%,higher than previous studies on tree *** also extracted and validated the tree structure attributes using manual segmentation reference *** RMSE,RMSE%,bias,and bias%of tree height,crown base height,crown projection area,crown surface area,and crown volume were used to evaluate the segmentation accuracy,***,the proposed method avoids many inherent limitations of current methods and can accurately map canopy structures in occluded complex forest stands.

关键词： Terrestrial laser scanning individual tree segmentation Graph The shortest path Ellipsoid directional searching Point compensation

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individual tree segmentation FROMBLS DATA BASED ON GRAPH AUTOENCODER 5

INDIVIDUAL TREE SEGMENTATION FROMBLS DATA BASED ON GRAPH AUT...

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5th International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS) Geospatial Week (GSW)

作者： Fekry, Reda Yao, Wei Sani-Mohammed, Abubakar Amr, Doha Benha Univ Dept Geomat Fac Engn Shoubra Cairo Egypt Hong Kong Polytechn Univ Dept Land Surveying & Geo Informat Hong Kong Peoples R China

In the last two decades, Light detection and ranging (LiDAR) has been widely employed in forestry applications. individual tree segmentation is essential to forest management because it is a prerequisite to tree reconstruction and biomass estimation. This paper introduces a general framework to extract individual trees from the LiDAR point cloud based on a graph link prediction problem. First, an undirected graph is generated from the point cloud based on K-nearest neighbors (KNN). Then, this graph is used to train a convolutional autoencoder that extracts the node embeddings to reconstruct the graph. Finally, the individual trees are defined by the separate sets of connected nodes of the reconstructed graph. A key advantage of the proposed method is that no further knowledge about tree or forest structure is required. Seven sample plots from a plantation forest with poplar and dawn redwood species have been employed in the experiments. Though the precision of the experimental results is up to 95 % for poplar species and 92 % for dawn redwood trees, the method still requires more investigations on natural forest types with mixed tree species.

关键词： LiDAR individual tree segmentation Backpack Laser Scanning Graph Neural Network Graph Autoencoder

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individual tree segmentation OVER LARGE AREAS USING AIRBORNE LIDAR POINT CLOUD AND VERY HIGH RESOLUTION OPTICAL IMAGERY

INDIVIDUAL TREE SEGMENTATION OVER LARGE AREAS USING AIRBORNE...

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IEEE Joint International Geoscience and Remote Sensing Symposium (IGARSS) / 35th Canadian Symposium on Remote Sensing

作者： Qin, Yuchu Ferraz, Antonio Mallet, Clement Iovan, Corina Univ Paris Est Lab MATIS IGN Paris France

ISBN: (纸本)9781479957750

Timely and accurate measurements of forest parameters are critical for ecosystem studies, sustainable forest resources management, monitoring and planning. This paper presents a processing chain for individual tree segmentation over large areas with airborne LiDAR 3D point cloud and very high resolution (VHR) optical imagery. The proposed processing chain consists of forest stand level delineation with optical imagery, individual tree segmentation with Canopy Height Model (CHM) derived from LiDAR point cloud, rough characterization of trees at forest stand level, and point clustering of individual tree with an Adaptive Mean Shift 3D (AMS3D) algorithm. The processing chain is developed with the expectation of supporting operational forest inventory at individual tree level. Experiment is conducted using LiDAR data acquired in Ventoux region, France. Results suggest that the proposed processing chain can be successfully adopted for individual tree characterization over large areas with different forest stands.

关键词： Point cloud VHR imagery individual tree segmentation Forest parameter estimate tree level Large areas

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Research on Deep Learning individual tree segmentation Method Coupling RetinaNet and Point Cloud Clustering

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IEEE ACCESS 2021年 9卷 126635-126645页

作者： Zhu, Weigang Zhu, Chao Zhang, Yaqiu Changchun Inst Technol Sch Prospecting & Surveying Changchun 130022 Peoples R China

Increasing human activities have caused serious disturbance to global forest resources, so how to accurately identify individual trees has become an important task of forest resources investigation. In order to get the accurate number of individual trees, this paper took coniferous forest and mixed coniferous and broad-leaved forest as experimental samples, as well as Digital Orthophoto Map and airborne LiDAR Point Cloud as research data. We propose a deep Learning individual tree segmentation method based on RetinaNet model and PCS algorithm by doing comparative analysis (classical Watershed Algorithm and Layer Stacking Algorithm) at the plots (with high, medium, and low densities). The experimental results show that the method proposed in this paper can solve the problem of individual tree segmentation in high density forest and improve its degree of automation. Compared with Watershed algorithm and Layer stacking algorithm, F-Measure is improved by 6%-29% and 7%-20%, respectively. In other words, the results of individual tree segmentation presented in this paper can not only improve the precision of individual tree segmentation, but also maintain a high detection rate, which can meet the accuracy and high efficiency of individual tree extraction, so as to meet the needs for modern forestry investigation.

关键词： Vegetation Forestry Three-dimensional displays Deep learning Laser radar Clustering algorithms Feature extraction Deep Learning individual tree segmentation airborne LiDAR Digital Orthophoto Map computer graphics

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An individual tree segmentation Method Based on Watershed Algorithm and Three-Dimensional Spatial Distribution Analysis From Airborne LiDAR Point Clouds

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2020年 13卷 1055-1067页

作者： Yang, Juntao Kang, Zhizhong Cheng, Sai Yang, Zhou Akwensi, Perpetual Hope China Univ Geosci Sch Land Sci & Technol Beijing 100083 Peoples R China Minist Educ Peoples Republ China Subctr Int Cooperat & Res Lunar & Planetary Explo Ctr Space Explorat Beijing 100083 Peoples R China Shanxi Key Lab Resources Environm & Disaster Moni Jinzhong 030600 Peoples R China

Accurate individual tree segmentation is an important basis for the subsequent calculation and analysis of forestry parameters. However, rasterized canopy height model based methods often suffer from 3-D information loss due to the interpolation operation. Therefore, this article proposes an individual tree segmentation method based on the marker-controlled watershed algorithm and 3-D spatial distribution analysis from airborne LiDAR point clouds. First, based on the potential tree apices derived from the local maxima filtering, the marker-controlled watershed segmentation algorithm is conducted to obtain the coarse point clusters. Then, within the principal component analysis defined local coordinate reference framework, a multidirectional 3-D spatial profile analysis is performed on each point cluster to refine the potential tree apex positions. Finally, the refined potential tree apex positions are used as a prior of K-means clustering to achieve the coarse-to-fine individual tree segmentation. Comparative experiments were conducted on the public NEWFOR dataset to evaluate the proposed method. Results indicate that the proposed method is efficient and robust for segmenting individual trees.

关键词： Vegetation Three-dimensional displays Forestry Laser radar Clustering algorithms Remote sensing Image segmentation Airborne LiDAR canopy height model (CHM) individual tree segmentation profile analysis watershed algorithm

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