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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)

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Petteri Elsner

Petteri Kaski

Petteri Laihonen

 

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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)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)Transferability of ALS-based forest attribute models when predicting with drone-based image point cloud data., , , , , и . Int. J. Appl. Earth Obs. Geoinformation, (2021)mgpr: An R package for multivariate Gaussian process regression., , и . SoftwareX, (декабря 2023)Fusion of crown and trunk detections from airborne UAS based laser scanning for small area forest inventories., , , и . Int. J. Appl. Earth Obs. Geoinformation, (2021)Forest Change Detection by Using Point Clouds From Dense Image Matching Together With a LiDAR-Derived Terrain Model., и . IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 10 (3): 1197-1206 (2017)Edge-Tree Correction for Predicting Forest Inventory Attributes Using Area-Based Approach With Airborne Laser Scanning., , , , и . IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 8 (3): 1274-1280 (2015)