Multispectral Airborne LiDAR Data in the Prediction of Boreal Tree Species Composition. - Mikko Kukkonen; Matti Maltamo; Lauri Korhonen; Petteri Packalen (IEEE Trans. Geosci. Remote. Sens., 2019)

A method for vertical adjustment of digital aerial photogrammetry data by using a high-quality digital terrain model. - Daniela Ali-Sisto; Ranjith Gopalakrishnan; Mikko Kukkonen; Pekka Savolainen; Petteri Packalen (Int. J. Appl. Earth Obs. Geoinformation, 2020)

Utility of image point cloud data towards generating enhanced multitemporal multisensor land cover maps. - Ranjith Gopalakrishnan; Aku Seppänen; Mikko Kukkonen; Petteri Packalen (Int. J. Appl. Earth Obs. Geoinformation, 2020)

Using ALS Data to Improve Co-Registration of Photogrammetry-Based Point Cloud Data in Urban Areas. - Ranjith Gopalakrishnan; Daniela Ali-Sisto; Mikko Kukkonen; Pekka Savolainen; Petteri Packalen (Remote. Sens., 2020)

Fusion of crown and trunk detections from airborne UAS based laser scanning for small area forest inventories. - Mikko Kukkonen; Matti Maltamo; Lauri Korhonen; Petteri Packalen (Int. J. Appl. Earth Obs. Geoinformation, 2021)

Transferability of ALS-based forest attribute models when predicting with drone-based image point cloud data. - Janne Toivonen; Lauri Korhonen; Mikko Kukkonen; Eetu Kotivuori; Matti Maltamo; Petteri Packalen (Int. J. Appl. Earth Obs. Geoinformation, 2021)

Combination of Lidar Intensity and Texture Features Enable Accurate Prediction of Common Boreal Tree Species With Single Sensor UAS Data. - Mikko Kukkonen; Timo Lähivaara; Petteri Packalen (IEEE Trans. Geosci. Remote. Sens., 2024)