53 citations as recorded by crossref.

1. A simple oriented search and clustering method for extracting individual forest trees from ALS point clouds W. Ding et al. 10.1016/j.ecoinf.2024.102978
2. Upscaling UAV and Lidar-derived forest gap area and edge length extractions using radar and optical sentinel images M. Naseri et al. 10.1080/01431161.2025.2495996
3. Accuracy comparison of terrestrial and airborne laser scanning and manual measurements for stem curve-based growth measurements of individual trees V. Soininen et al. 10.1016/j.srs.2024.100125
4. Assessing the potential of synthetic and ex situ airborne laser scanning and ground plot data to train forest biomass models J. Schäfer et al. 10.1093/forestry/cpad061
5. CNN-based transfer learning for forest aboveground biomass prediction from ALS point cloud tomography J. Schäfer et al. 10.1080/22797254.2024.2396932
6. Earth-Observation-Based Monitoring of Forests in Germany—Recent Progress and Research Frontiers: A Review S. Holzwarth et al. 10.3390/rs15174234
7. Self-adaptive individual tree modeling based on skeleton graph optimization and fractal self-similarity Z. Hui et al. 10.1016/j.plaphe.2025.100060
8. Three-Dimensional Reconstruction of Forest Scenes with Tree–Shrub–Grass Structure Using Airborne LiDAR Point Cloud D. Xu et al. 10.3390/f15091627
9. Towards Intricate Stand Structure: A Novel Individual Tree Segmentation Method for ALS Point Cloud Based on Extreme Offset Deep Learning Y. Zhang et al. 10.3390/app13116853
10. Comparing airborne laser scanning and UAV photogrammetry for estimating aboveground biomass of individual urban trees in Helsinki O. Shafaat et al. 10.1016/j.ufug.2025.128936
11. A Method for Extracting the Tree Feature Parameters of Populus tomentosa in the Leafy Stage X. Shen et al. 10.3390/f14091757
12. Non-Destructive Estimation of Deciduous Forest Metrics: Comparisons between UAV-LiDAR, UAV-DAP, and Terrestrial LiDAR Leaf-Off Point Clouds Using Two QSMs Y. Gan et al. 10.3390/rs16040697
13. A Method to Order Point Clouds for Visualization on the Ray Tracing Pipeline P. Timokhin & M. Mikhaylyuk 10.1134/S0361768824700075
14. Individual Tree Segmentation Using Deep Learning and Climbing Algorithm: A Method for Achieving High-precision Single-tree Segmentation in High-density Forests under Complex Environments H. Ma et al. 10.14358/PERS.24-00083R2
15. DPFANet: Deep Point Feature Aggregation Network for Classification of Irregular Objects in LIDAR Point Clouds S. Zhang & D. Xu 10.3390/electronics13224355
16. Leveraging Open-Source Tools to Analyse Ground-Based Forest LiDAR Data in South Australian Forests S. O’Keeffe et al. 10.3390/rs17111934
17. A Review of Point Cloud Registration Algorithms for Laser Scanners: Applications in Large-Scale Aircraft Measurement H. Si et al. 10.3390/app122010247
18. Tree Stem Detection and Crown Delineation in a Structurally Diverse Deciduous Forest Combining Leaf-On and Leaf-Off UAV-SfM Data S. Dietenberger et al. 10.3390/rs15184366
19. Benchmarking tree species classification from proximally sensed laser scanning data: Introducing the FOR‐species20K dataset S. Puliti et al. 10.1111/2041-210X.14503
20. The Method to Order Point Clouds for Visualization on the Ray Tracing Pipeline P. Timokhin & M. Mikhailyuk 10.31857/S0132347424030054
21. OpenForest: a data catalog for machine learning in forest monitoring A. Ouaknine et al. 10.1017/eds.2024.53
22. SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data M. Wielgosz et al. 10.1016/j.rse.2024.114367
23. Canopy structure influences arthropod communities within and beyond tree identity effects: Insights from combining LiDAR data, insecticidal fogging and machine learning regression modelling B. Wildermuth et al. 10.1016/j.ecolind.2024.111901
24. Spatial Differentiation of Mangrove Aboveground Biomass and Identification of Its Main Environmental Drivers in Qinglan Harbor Mangrove Nature Reserve K. Wang et al. 10.3390/su16198408
25. Individual-Tree Segmentation from UAV–LiDAR Data Using a Region-Growing Segmentation and Supervoxel-Weighted Fuzzy Clustering Approach Y. Fu et al. 10.3390/rs16040608
26. Validation of the vertical plant area index profile product derived from GEDI over global forest sites Y. Wang et al. 10.1016/j.agrformet.2025.110612
27. Individual tree segmentation from UAS Lidar data based on hierarchical filtering and clustering C. Zhang et al. 10.1080/17538947.2024.2356124
28. Benchmarking airborne laser scanning tree segmentation algorithms in broadleaf forests shows high accuracy only for canopy trees Y. Cao et al. 10.1016/j.jag.2023.103490
29. Evaluating forest aboveground biomass estimation model using simulated ALS point cloud from an individual-based forest model and 3D radiative transfer model across continents Z. Yu et al. 10.1016/j.jenvman.2024.123287
30. A new method for individual treetop detection with low-resolution aerial laser scanned data G. Diószegi et al. 10.1007/s40808-024-02060-w
31. Species-specific responses of tree productivity to diversity effects in young planted forests: Insights from terrestrial laser scanning M. Wang et al. 10.1016/j.foreco.2025.122962
32. Individual Tree Segmentation Based on Region-Growing and Density-Guided Canopy 3-D Morphology Detection Using UAV LiDAR Data S. Li et al. 10.1109/JSTARS.2025.3546651
33. LiPheStream - A 18-month high spatiotemporal resolution point cloud time series of Boreal trees from Finland S. Wittke et al. 10.1038/s41597-024-04143-w
34. Deep point cloud regression for above-ground forest biomass estimation from airborne LiDAR S. Oehmcke et al. 10.1016/j.rse.2023.113968
35. Regional‐Scale Landscape Response to an Extreme Precipitation Event From Repeat Lidar and Object‐Based Image Analysis S. DeLong et al. 10.1029/2022EA002420
36. Tree parameter extraction method based on new remote sensing technology and terrestrial laser scanning technology A. Wang et al. 10.1016/j.bdr.2024.100460
37. Exploring unmanned aerial systems operations in wildfire management: data types, processing algorithms and navigation P. Keerthinathan et al. 10.1080/01431161.2023.2249604
38. Tree Branch Characterisation from Point Clouds: a Comprehensive Review R. Hartley et al. 10.1007/s40725-024-00225-5
39. MMTSCNet: Multimodal Tree Species Classification Network for Classification of Multi-Source, Single-Tree LiDAR Point Clouds J. Vahrenhold et al. 10.3390/rs17071304
40. Graph-Based Leaf–Wood Separation Method for Individual Trees Using Terrestrial Lidar Point Clouds Z. Tian & S. Li 10.1109/TGRS.2022.3218603
41. TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds J. Henrich et al. 10.1016/j.ecoinf.2024.102888
42. The Role of RPAS in Vegetation Height Estimation: Challenges and Future Perspectives in the Forestry Context F. Moreira et al. 10.1007/s40725-025-00248-6
43. A Review of Software Solutions to Process Ground-based Point Clouds in Forest Applications A. Murtiyoso et al. 10.1007/s40725-024-00228-2
44. A Reliable DBH Estimation Method Using Terrestrial LiDAR Points through Polar Coordinate Transformation and Progressive Outlier Removal Z. Hui et al. 10.3390/f15061031
45. Efficient Registration of Airborne LiDAR and Terrestrial LiDAR Point Clouds in Forest Scenes Based on Single-Tree Position Consistency X. Cheng et al. 10.3390/f15122185
46. ALS Point Cloud Semantic Segmentation Based on Graph Convolution and Transformer With Elevation Attention S. Huang et al. 10.1109/JSTARS.2023.3347224
47. Deep learning with simulated laser scanning data for 3D point cloud classification A. Esmorís et al. 10.1016/j.isprsjprs.2024.06.018
48. Robust Multisource Forest Point Cloud Registration With Distribution Similarity Analysis X. Liu et al. 10.1109/TGRS.2025.3545448
49. Fine-Scale Quantification of Absorbed Photosynthetically Active Radiation (APAR) in Plantation Forests with 3D Radiative Transfer Modeling and LiDAR Data X. Zhao et al. 10.34133/plantphenomics.0166
50. Testing treecbh in Central European forests: an R package for crown base height detection using high-resolution aerial laser-scanned data G. Diószegi et al. 10.1093/forestry/cpae044
51. Pointwise deep learning for leaf-wood segmentation of tropical tree point clouds from terrestrial laser scanning W. Van den Broeck et al. 10.1016/j.isprsjprs.2025.06.023
52. Tree Species Classification Using Optimized Features Derived from Light Detection and Ranging Point Clouds Based on Fractal Geometry and Quantitative Structure Model Z. Hui et al. 10.3390/f14061265
53. An Automatic Individual Tree 3D Change Detection Method for Allometric Parameters Estimation in Mixed Uneven-Aged Forest Stands from ALS Data C. Spadavecchia et al. 10.3390/rs14184666