12.5.2026

Monthly Publications, May 2026

“Monthly Publications” is a monthly summary by the School of Forest Sciences at the University of Eastern Finland, featuring scientific articles published during the month. This summary aims to make the latest research in forest sciences more accessible to practitioners, such as companies, policymakers, and forest owners, to enable more effective practical application of research findings. Researchers from the School of Forest Sciences at the University of Eastern Finland are highlighted in bold

  • Kamula, T., Yrttimaa, T., Cimdins, R., Polvivaara, A., Kukko, A., Holopainen, M. & Vastaranta, M. (2026). Bi-temporal terrestrial laser scanning to measure secondary growth of tree: toward non-destructive assessment of wood properties. Scandinavian Journal of Forest Research. https://doi.org/10.1080/02827581.2026.2646458.

  • Siddharth Sareen, Sirkku Juhola, Adrianna Czarnecka, Aleksandra Kekkonen, Aleksandra Martinovska Stojcheska, Ana Slavec, Anita Uchanska-Bieniusiewicz, Chris Rønningstad, Claudiu Martin, Gintarė Tamašauskaitė-Janickė, Helena Belchior Rocha, Jan Kunnas…(2026). The prefigurative politics of enactable sustainability transformations in the present. NorskGeografisk Tidsskrift – Norwegian Journal of Geography, 1–13. https://doi.org/10.1080/00291951.2026.2648496
  • Liikonen, L., Erkkilä, A., Kamula, T., Honkanen, E., Liimatainen, K., Uhlgren, V., Yrttimaa, T., Nummenmaa, T., Hamari, J. & Vastaranta, M. Crowdsourcing LiDAR Data for Forest Measurements through Gamified Augmented Reality Applications. Urban Forestry and Greening (accepted).
  • Pehkonen, M., Vastaranta, M., Hyyppä, J. & Pyörälä, J. (2026). Segmentation of living and dead tree crowns using terrestrial laser scanning and deep learning. Ecological Informatics, s. 103750. https://doi.org/10.1016/j.ecoinf.2026.103750
  • Puliti, S., Xiang, B., Wielgosz, M., Handegard, E., Cattaneo, N., Vergarechea, M., Gobakken, T., Hyyppä, J., Næsset, E., Vastaranta, M., Yrttimaa, T. & Astrup, R. FOR-age: benchmarking individual tree age estimation using 3D deep learning on dense laser scanning data. Remote Sensing of Environment (accepted).10.1016/j.rse.2026.115462