INDIVIDUAL TREE DETECTION FROM UAV LIDAR DATA IN A MIXED SPECIES WOODLAND

30 citations as recorded by crossref.

1. Unmanned Aerial Vehicle–Light Detection and Ranging-Based Individual Tree Segmentation in Eucalyptus spp. Forests: Performance and Sensitivity Y. Yan et al. https://doi.org/10.3390/f15010209
2. A Field-Calibrated UAV LiDAR Workflow-Level Case Study for Individual-Tree Inventory in Jilin Larch Plantations Using PCS, MCRG, and RHCSA C. Du et al. https://doi.org/10.3390/su18147321
3. Individual tree segmentation from UAS Lidar data based on hierarchical filtering and clustering C. Zhang et al. https://doi.org/10.1080/17538947.2024.2356124
4. Spatial pattern analysis of forest trees based on the vectorial mark H. Xin et al. https://doi.org/10.1007/s11676-021-01417-6
5. Semi-Automatic Stand Delineation Based on Very-High-Resolution Orthophotographs and Topographic Features: A Case Study from a Structurally Complex Natural Forest in the Southern USA C. Vatandaslar et al. https://doi.org/10.3390/f16040666
6. Estimation of Northern Hardwood Forest Inventory Attributes Using UAV Laser Scanning (ULS): Transferability of Laser Scanning Methods and Comparison of Automated Approaches at the Tree- and Stand-Level B. Vandendaele et al. https://doi.org/10.3390/rs13142796
7. Using Unoccupied Aerial Systems (UAS) and Structure-from-Motion (SfM) to Measure Forest Canopy Cover and Individual Tree Height Metrics in Northern California Forests A. Kelly et al. https://doi.org/10.3390/f16040564
8. Individual Tree Identification and Segmentation in Pinus spp. Stands through Portable LiDAR A. Solares-Canal et al. https://doi.org/10.3390/f15071133
9. UAV-g 2019: Unmanned Aerial Vehicles in Geomatics F. Nex https://doi.org/10.3390/drones3030074
10. Evaluating the reliability of the 1-meter resolution meta-estimated global canopy height map for estimating forest structural metrics in Alpine environment M. Torresani et al. https://doi.org/10.1016/j.ecoinf.2026.103950
11. Developing landscape-scale forest restoration targets that embrace spatial pattern M. Rudge et al. https://doi.org/10.1007/s10980-022-01461-5
12. Effective UAV Photogrammetry for Forest Management: New Insights on Side Overlap and Flight Parameters A. Dhruva et al. https://doi.org/10.3390/f15122135
13. An Individual Tree Segmentation Method Based on Watershed Algorithm and Three-Dimensional Spatial Distribution Analysis From Airborne LiDAR Point Clouds J. Yang et al. https://doi.org/10.1109/JSTARS.2020.2979369
14. Developing a more accurate method for individual plant segmentation of urban tree and shrub communities using LiDAR technology Y. Liu et al. https://doi.org/10.1080/01426397.2022.2144813
15. Individual tree detection and crown delineation from Unmanned Aircraft System (UAS) LiDAR in structurally complex mixed species eucalypt forests D. Jaskierniak et al. https://doi.org/10.1016/j.isprsjprs.2020.10.016
16. Comparing Mobile Laser Scanner and manual measurements for dendrometric variables estimation in a black pine (Pinus nigra Arn.) plantation S. Chiappini et al. https://doi.org/10.1016/j.compag.2022.107069
17. Super-resolution supporting individual tree detection and canopy stratification using half-meter aerial data Z. Mao et al. https://doi.org/10.1016/j.isprsjprs.2025.04.005
18. Evaluation of UAS LiDAR data for tree segmentation and diameter estimation in boreal forests using trunk- and crown-based methods M. Kukkonen et al. https://doi.org/10.1139/cjfr-2021-0217
19. The Use of an Airborne Laser Scanner for Rapid Identification of Invasive Tree Species Acer negundo in Riparian Forests D. Mielczarek et al. https://doi.org/10.3390/rs15010212
20. UAV-Based LiDAR Scanning for Individual Tree Detection and Height Measurement in Young Forest Permanent Trials F. Rodríguez-Puerta et al. https://doi.org/10.3390/rs14010170
21. A hybrid method for tree-level forest planning Y. Sun et al. https://doi.org/10.1007/s11676-025-01856-5
22. Canopy Top, Height and Photosynthetic Pigment Estimation Using Parrot Sequoia Multispectral Imagery and the Unmanned Aerial Vehicle (UAV) V. Kopačková-Strnadová et al. https://doi.org/10.3390/rs13040705
23. Quantifying the Accuracy of UAS-Lidar Individual Tree Detection Methods Across Height and Diameter at Breast Height Sizes in Complex Temperate Forests B. Fraser et al. https://doi.org/10.3390/rs17061010
24. Integrating Drone-Based LiDAR and Multispectral Data for Tree Monitoring B. Savinelli et al. https://doi.org/10.3390/drones8120744
25. Individual Tree Crown Segmentation in Two-Layered Dense Mixed Forests from UAV LiDAR Data C. Torresan et al. https://doi.org/10.3390/drones4020010
26. Comparison of Individual Tree Segmentation Algorithms and DBH Retrieval for Pinus massoniana Based on Multi-Source LiDAR Data H. Wang et al. https://doi.org/10.3390/f17091092
27. Mapping Tree Canopy in Urban Environments Using Point Clouds from Airborne Laser Scanning and Street Level Imagery F. Rodríguez-Puerta et al. https://doi.org/10.3390/s22093269
28. TreeSeg—A Toolbox for Fully Automated Tree Crown Segmentation Based on High-Resolution Multispectral UAV Data S. Speckenwirth et al. https://doi.org/10.3390/rs16193660
29. Benchmarking of Individual Tree Segmentation Methods in Mediterranean Forest Based on Point Clouds from Unmanned Aerial Vehicle Imagery and Low-Density Airborne Laser Scanning A. Nemmaoui et al. https://doi.org/10.3390/rs16213974
30. Assessing lean and positional error of individual mature Douglas-fir (Pseudotsuga menziesii) trees using active and passive sensors C. Garms et al. https://doi.org/10.1139/cjfr-2020-0041