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Optimizing Field Data Collection for Individual Tree Attribute Predictions Using Active Learning Methods., , , , , and . Remote. Sens., 11 (8): 949 (2019)A Model-Dependent Method for Monitoring Subtle Changes in Vegetation Height in the Boreal-Alpine Ecotone Using Bi-Temporal, Three Dimensional Point Data from Airborne Laser Scanning., , and . Remote. Sens., 11 (15): 1804 (2019)Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data., , , , , , , , , and . Remote. Sens., 10 (11): 1832 (2018)Individual tree crown approach for predicting site index in boreal forests using airborne laser scanning and hyperspectral data., , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2017)Detection of biomass change in a Norwegian mountain forest area using small footprint airborne laser scanner data., , , and . Stat. Methods Appl., 22 (1): 113-129 (2013)Unsupervised selection of training plots and trees for tree species classification., , , , and . IGARSS, page 2095-2098. IEEE, (2013)Tree Species Classification in Boreal Forests With Hyperspectral Data., , , , and . IEEE Trans. Geosci. Remote. Sens., 51 (5-1): 2632-2645 (2013)Use of the Weighted Kappa Coefficient in Classification Error Assessment of Thematic Maps.. Int. J. Geogr. Inf. Sci., 10 (5): 591-604 (1996)Unsupervised Selection of Training Samples for Tree Species Classification Using Hyperspectral Data., , , , and . IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 7 (8): 3560-3569 (2014)Constructing Forest Biomass Prediction Maps from Radar Backscatter by Sequential Regression with a Conditional Generative Adversarial Network., , , , and . CoRR, (2021)