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Modelling Site Index in Forest Stands Using Airborne Hyperspectral Imagery and Bi-Temporal Laser Scanner Data., , , , and . Remote Sensing, 11 (9): 1020 (2019)Automatic Estimation of Tree Position and Stem Diameter Using a Moving Terrestrial Laser Scanner., , , , and . Remote Sensing, 9 (4): 350 (2017)Relative Efficiency of ALS and InSAR for Biomass Estimation in a Tanzanian Rainforest., , , , , , and . Remote Sensing, 7 (8): 9865-9885 (2015)Effects of UAV Image Resolution, Camera Type, and Image Overlap on Accuracy of Biomass Predictions in a Tropical Woodland., , , , and . Remote Sensing, 11 (8): 948 (2019)Prediction of Forest Attributes with Multispectral Lidar Data., , , , and . IGARSS, page 7528-7531. IEEE, (2018)Prediction of Species-Specific Volume Using Different Inventory Approaches by Fusing Airborne Laser Scanning and Hyperspectral Data., , , , and . Remote Sensing, 9 (5): 400 (2017)Accuracy and Precision for Remote Sensing Applications of Nonlinear Model-Based Inference., , and . IEEE J Sel. Topics in Appl. Earth Observ. and Remote Sensing, 6 (1): 27-34 (2013)Cost-Sensitive Active Learning With Lookahead: Optimizing Field Surveys for Remote Sensing Data Classification., , , , , and . IEEE Trans. Geoscience and Remote Sensing, 52 (10): 6652-6664 (2014)Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data., , , , , , , , , and . Remote Sensing, 10 (11): 1832 (2018)Optimizing Field Data Collection for Individual Tree Attribute Predictions Using Active Learning Methods., , , , , and . Remote Sensing, 11 (8): 949 (2019)