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Predicting Forest Inventory Attributes Using Airborne Laser Scanning, Aerial Imagery, and Harvester Data., , , , , , , and . Remote Sensing, 11 (7): 797 (2019)Laser Scanning in Forests., , and . Remote Sensing, 4 (10): 2919-2922 (2012)Multisource Single-Tree Inventory in the Prediction of Tree Quality Variables and Logging Recoveries., , , , , , and . Remote Sensing, 6 (4): 3475-3491 (2014)Assessing spectral measures of post-harvest forest recovery with field plot data., , , , , , , , and . Int. J. Applied Earth Observation and Geoinformation, (2019)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)Allocating tree crown pruning with ALS-data - A case study in the city of Helsinki., , , , , , and . JURSE, page 1-4. IEEE, (2015)Accuracy of High-Resolution Radar Images in the Estimation of Plot-Level Forest Variables., , , , , and . AGILE Conf., page 67-82. Springer, (2009)Measuring Leaf Water Content with Dual-Wavelength Intensity Data from Terrestrial Laser Scanners., , , , , , , , and . Remote Sensing, 9 (1): 8 (2017)Automation Aspects for the Georeferencing of Photogrammetric Aerial Image Archives in Forested Scenes., , , , , , , and . Remote Sensing, 7 (2): 1565-1593 (2015)Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images., , , , , , , , and . Remote Sensing, 2 (12): 2665-2679 (2010)