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Optimizing Field Data Collection for Individual Tree Attribute Predictions Using Active Learning Methods.

, , , , , and . Remote. Sens., 11 (8): 949 (2019)

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Automatic Estimation of Tree Position and Stem Diameter Using a Moving Terrestrial Laser Scanner., , , , and . Remote. Sens., 9 (4): 350 (2017)Monitoring tree occupancy and height in the Norwegian alpine treeline using a time series of airborne laser scanner data., , , , , and . Int. J. Appl. Earth Obs. Geoinformation, (March 2023)Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in Tanzania., , , , , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2020)Using airborne & space lidars for large-area inventory., , , , , and . IGARSS, page 2463-2466. IEEE, (2010)Classifications of Forest Change by Using Bitemporal Airborne Laser Scanner Data., , , , , and . Remote. Sens., 11 (18): 2145 (2019)Biomass Change Estimated by TanDEM-X Interferometry and GEDI in a Tanzanian Forest., , , , , and . Remote. Sens., 16 (5): 861 (March 2024)Comparing Three Different Ground Based Laser Scanning Methods for Tree Stem Detection., , , , and . Remote. Sens., 10 (4): 538 (2018)Mapping and Estimating Aboveground Biomass in an Alpine Treeline Ecotone under Model-Based Inference., , , , , , , , , and . Remote. Sens., 15 (14): 3508 (July 2023)Prediction of Forest Attributes with Multispectral Lidar Data., , , , and . IGARSS, page 7528-7531. IEEE, (2018)Generation of Lidar-Predicted Forest Biomass Maps from Radar Backscatter with Conditional Generative Adversarial Networks., , , , and . IGARSS, page 4327-4330. IEEE, (2020)