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Prediction of Individual Tree Diameter Using a Nonlinear Mixed-Effects Modeling Approach and Airborne LiDAR Data., , , , , , , , и . Remote Sensing, 12 (7): 1066 (2020)Improving Estimation of Forest Canopy Cover by Introducing Loss Ratio of Laser Pulses Using Airborne LiDAR., , , , , , , и . IEEE Trans. Geosci. Remote. Sens., 58 (1): 567-585 (2020)Building height extraction from overlapping airborne images in urban environment using computer vision approach., , , , и . IGARSS, стр. 5767-5769. IEEE, (2017)Forest canopy cover analysis using UAS lidar., , , , и . IGARSS, стр. 2863-2866. IEEE, (2017)Compatible Biomass Model with Measurement Error Using Airborne LiDAR Data., , , , , , и . Remote. Sens., 15 (14): 3546 (июля 2023)Airborne LIDAR-Derived Aboveground Biomass Estimates Using a Hierarchical Bayesian Approach., , , , и . Remote. Sens., 11 (9): 1050 (2019)Forest Canopy Gap Dynamics based on Time-Series of Airborne Lidar Data., , , , , и . IGARSS, стр. 6119-6121. IEEE, (2022)Forest canopy height estimation at footprint scale based on airborne lidar metric in the heterogeneous landscape., и . IGARSS, стр. 2855-2858. IEEE, (2017)Comparison of Coniferous Plantation Heights Using Unmanned Aerial Vehicle (UAV) Laser Scanning and Stereo Photogrammetry., , , и . Remote. Sens., 13 (15): 2885 (2021)Learning Robust Discriminant Subspace Based on Joint L₂, ₚ- and L₂, ₛ-Norm Distance Metrics., , , , , , , , и . IEEE Trans. Neural Networks Learn. Syst., 33 (1): 130-144 (2022)