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A method for vertical adjustment of digital aerial photogrammetry data by using a high-quality digital terrain model., , , , и . Int. J. Appl. Earth Obs. Geoinformation, (2020)How much can airborne laser scanning based forest inventory by tree species benefit from auxiliary optical data?, , , , и . Int. J. Appl. Earth Obs. Geoinformation, (2018)Multispectral Airborne LiDAR Data in the Prediction of Boreal Tree Species Composition., , , и . IEEE Trans. Geosci. Remote. Sens., 57 (6): 3462-3471 (2019)Leveraging remotely sensed non-wall-to-wall data for wall-to-wall upscaling in forest inventory., , , , , , , , и . Int. J. Appl. Earth Obs. Geoinformation, (мая 2023)Utility of image point cloud data towards generating enhanced multitemporal multisensor land cover maps., , , и . Int. J. Appl. Earth Obs. Geoinformation, (2020)Estimating Tree Height Distribution Using Low-Density ALS Data With and Without Training Data., , , и . IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 8 (4): 1432-1441 (2015)Combination of Lidar Intensity and Texture Features Enable Accurate Prediction of Common Boreal Tree Species With Single Sensor UAS Data., , и . IEEE Trans. Geosci. Remote. Sens., (2024)Do airborne laser scanning biomass prediction models benefit from Landsat time series, hyperspectral data or forest classification in tropical mosaic landscapes?, , , , и . Int. J. Appl. Earth Obs. Geoinformation, (2019)mgpr: An R package for multivariate Gaussian process regression., , и . SoftwareX, (декабря 2023)Transferability of ALS-based forest attribute models when predicting with drone-based image point cloud data., , , , , и . Int. J. Appl. Earth Obs. Geoinformation, (2021)