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Intelligent Open Data 3D Maps in a Collaborative Virtual World., , , , , and . ISPRS Int. J. Geo-Information, 4 (2): 837-857 (2015)Deriving canopy metrics of urban trees from airborne laser scanning data., , , , , and . JURSE, page 1-4. IEEE, (2015)Retrieval of Forest Aboveground Biomass and Stem Volume with Airborne Scanning LiDAR., , , , , , , , and . Remote Sensing, 5 (5): 2257-2274 (2013)Classification of Spruce and Pine Trees Using Active Hyperspectral LiDAR., , , , , , , and . IEEE Geosci. Remote. Sens. Lett., 10 (5): 1138-1141 (2013)Multisource Single-Tree Inventory in the Prediction of Tree Quality Variables and Logging Recoveries., , , , , , and . Remote Sensing, 6 (4): 3475-3491 (2014)Predicting Forest Inventory Attributes Using Airborne Laser Scanning, Aerial Imagery, and Harvester Data., , , , , , , and . Remote Sensing, 11 (7): 797 (2019)Assessing spectral measures of post-harvest forest recovery with field plot data., , , , , , , , and . Int. J. Applied Earth Observation and Geoinformation, (2019)Allocating tree crown pruning with ALS-data - A case study in the city of Helsinki., , , , , , and . JURSE, page 1-4. IEEE, (2015)Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes., , , , , , , , , and 6 other author(s). Remote Sensing, 7 (12): 15933-15954 (2015)Accuracy of High-Resolution Radar Images in the Estimation of Plot-Level Forest Variables., , , , , and . AGILE Conf., page 67-82. Springer, (2009)