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Classifying Forest Type in the National Forest Inventory Context with Airborne Hyperspectral and Lidar Data.

, , , , , and . Remote. Sens., 13 (10): 1863 (2021)

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Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA., , , , , and . Remote. Sens., 9 (6): 598 (2017)Assessing the influence of return density on estimation of lidar-based aboveground biomass in tropical peat swamp forests of Kalimantan, Indonesia., , , and . Int. J. Appl. Earth Obs. Geoinformation, (2017)High-Resolution Three-Dimensional Remote Sensing for Forest Measurement.. 3D Imaging, Analysis and Applications, Springer, (2012)Evaluating different models to predict biomass increment from multi-temporal lidar sampling and remeasured field inventory data in south-central Alaska., , , and . Math. Comput. For. Nat. Resour. Sci., 7 (2): 66-80 (2015)Impact of Topographic Correction on Estimation of Aboveground Boreal Biomass Using Multi-temporal, L-Band Backscatter., , , and . IEEE J Sel. Topics in Appl. Earth Observ. and Remote Sensing, 7 (8): 3262-3273 (2014)Estimating Net Primary Productivity (NPP) and Debris-Fall in Forests Using Lidar Time Series., , , , , , , and . Remote. Sens., 13 (5): 891 (2021)Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data., , , , , , , , , and . Remote. Sens., 10 (11): 1832 (2018)Classifying Forest Type in the National Forest Inventory Context with Airborne Hyperspectral and Lidar Data., , , , , and . Remote. Sens., 13 (10): 1863 (2021)