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Mikko Kukkonen
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2020 – today
- 2024
- [j7]Mikko Kukkonen
, Timo Lähivaara
Combination of Lidar Intensity and Texture Features Enable Accurate Prediction of Common Boreal Tree Species With Single Sensor UAS Data. IEEE Trans. Geosci. Remote. Sens. 62: 1-8 (2024) - 2021
- [j6]Mikko Kukkonen, Matti Maltamo, Lauri Korhonen, Petteri Packalen:
Fusion of crown and trunk detections from airborne UAS based laser scanning for small area forest inventories. Int. J. Appl. Earth Obs. Geoinformation 100: 102327 (2021) - [j5]Janne Toivonen
Transferability of ALS-based forest attribute models when predicting with drone-based image point cloud data. Int. J. Appl. Earth Obs. Geoinformation 103: 102484 (2021) - 2020
- [j4]Daniela Ali-Sisto, Ranjith Gopalakrishnan, Mikko Kukkonen, Pekka Savolainen, Petteri Packalen:
A method for vertical adjustment of digital aerial photogrammetry data by using a high-quality digital terrain model. Int. J. Appl. Earth Obs. Geoinformation 84 (2020) - [j3]Ranjith Gopalakrishnan, Aku Seppänen, Mikko Kukkonen, Petteri Packalen:
Utility of image point cloud data towards generating enhanced multitemporal multisensor land cover maps. Int. J. Appl. Earth Obs. Geoinformation 86 (2020) - [j2]Ranjith Gopalakrishnan
Using ALS Data to Improve Co-Registration of Photogrammetry-Based Point Cloud Data in Urban Areas. Remote. Sens. 12(12): 1943 (2020)
2010 – 2019
- 2019
- [j1]Mikko Kukkonen
Multispectral Airborne LiDAR Data in the Prediction of Boreal Tree Species Composition. IEEE Trans. Geosci. Remote. Sens. 57(6): 3462-3471 (2019)
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