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Moan, M.Å., Noordermeer, L., White, J.C.,  Coops, N.C., Bollandsås, O.M. (2023). Detecting and excluding disturbed forest areas improves site index determination using bitemporal airborne laser scanner data. Forestry: An International Journal of Forest Research, 2023; cpad025, https://doi.org/10.1093/forestry/cpad025
Noordermeer, L., Næsset, E., Gobakken, T. (2022). Effects of harvester positioning errors on merchantable timber volume predicted and estimated from airborne laser scanner data in mature Norway spruce forests. Silva Fennica, 56(1). https://doi.org/10.14214/sf.10608
Puliti, S., McLean, J.P., Cattaneo, N., Fischer, C., Astrup, R. (2022). Tree height-growth trajectory estimation using uni-temporal UAV laser scanning data and deep learning. Forestry: An International Journal of Forest Research, https://doi.org/10.1093/forestry/cpac026
Puliti, S., Astrup, R. (2022). Automatic detection of snow breakage at single tree level using YOLOv5 applied to UAV imagery. International Journal of Applied Earth Observation and Geoinformation. 112, 102946. https://doi.org/10.1016/j.jag.2022.102946
Lingren, N., Nyström, K., Saarela, S., Olsson H., Ståhl, G. (2022). Importance of calibration for improving the efficiency of data assimilation for predicting forest characteristics. Remote Sensing, 14(18), 4627. https://doi.org/10.3390/rs14184627
Noordermeer, L., Sørngård, E., Astrup, R., Næsset, E., Gobakken, T. (2021). Coupling a differential global navigation satellite system to a cut-to-length harvester operating system enables precise positioning of harvested trees. International Journal of Forest Engineering, 32(2), 119-127. https://doi.org/10.1080/14942119.2021.1899686
Noordermeer, L., Næsset, E., & Gobakken, T. (2021). Estimating Timber Volume using Harvester Data and Airborne Laser Scanner Data from Multiple Acquisitions. Proceedings of the SilviLaser Conference 2021 (pp. 31-34). https://doi.org/10.34726/wim.1906