Articles by Johannes Breidenbach

Accurate field plot data on forest attributes are crucial in area-based forest inventories assisted by airborne laser scanning, providing an essential reference for calibrating predictive models. This study assessed how sample tree selection methods and plot data calculation methods affect the accuracy of field plot values of timber volume, Lorey’s mean height, and dominant height. We used data obtained from 12 420 circular sample plots of 250 m², measured as part of the Norwegian national forest inventory and 45 local forest management inventories. We applied Monte Carlo simulations by which we tested various numbers of sample trees, methods to select sample trees, and methods to calculate plot-level values from tree-level measurements. Accuracies of plot values were statistically significantly affected by the number of sample trees, sample tree selection method, and calculation method. Obtained values of root mean square error ranged from 5% to 16% relative to the mean observed values, across the factors studied. Accuracy improved with increasing numbers of sample trees for all forest attributes. We obtained greatest accuracies by selecting sample trees with a probability proportional to basal area, and by retaining field-measured heights for sample trees and using heights predicted with a height-diameter model for non-sample trees. This study highlights the importance of appropriate sample tree selection methods and calculation methods in obtaining accurate field plot data in area-based forest inventories.

• Noordermeer, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, NO-1432 Ås, Norway https://orcid.org/0000-0002-8840-0345 E-mail: lennart.noordermeer@nmbu.no
• Gobakken, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, NO-1432 Ås, Norway https://orcid.org/0000-0001-5534-049X E-mail: terje.gobakken@nmbu.no
• Breidenbach, Division of Forest and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, NO-1431 Ås, Norway E-mail: johannes.breidenbach@nibio.no
• Eriksen, Division of Forest and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, NO-1431 Ås, Norway E-mail: rune.eriksen@nibio.no
• Næsset, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, NO-1432 Ås, Norway E-mail: erik.naesset@nmbu.no
• Ørka, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, NO-1432 Ås, Norway E-mail: hans-ole.orka@nmbu.no
• Bollandsås, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, P.O. Box 5003, NO-1432 Ås, Norway https://orcid.org/0000-0002-1231-7692 E-mail: ole.martin.bollandsas@nmbu.no

Fine-scale, spatially explicit forest attribute maps are essential for guiding forest management and policy decisions. Such maps, based on the combination of National Forest Inventory (NFI) and remote sensing datasets, have a long tradition in the Nordic countries. Harmonizing the pixel size among national forest attribute maps would considerably improve the utility of the maps for users. However, the maps are often aligned with the NFI plot size, and the influence of creating these maps at different spatial resolutions (i.e. pixel sizes) is little studied. We assess the stand-level uncertainty (RMSE) of biomass, volume, basal area, and Lorey’s height estimates resulting from the aggregation of maps across varying spatial resolutions. Models fit at 16 m native resolution using more than 14 000 NFI plots were applied for predictions at pixels sizes (side lengths) of 1, 5, 10, 16, and 30 m. For independent validation, we used more than 600 field plots – that cover a total area of 24 ha and were clustered within 65 stands across Norway. For all attributes, the lowest RMSEs, ranging from 6.86% for Lorey’s height to 13.86% for volume, were observed for predictions at pixel sizes of 5 m to 16 m. The RMSE changes across resolutions were generally small (< 5%) for biomass, volume, and basal area. For Lorey’s height, changing the spatial resolution resulted in large RMSEs of up to 25%. Overall, our findings suggest that the main forest attributes can be mapped at a finer resolutions without complex adjustments.

• Koma, Norwegian Institute for Bioeconomy Research (NIBIO), Division of Forest and Forest Resources, Department National Forest Inventory, Høgskoleveien 7, 1433 Ås, Norway https://orcid.org/0000-0002-0003-8258 E-mail: zsofia.koma@nibio.no
• Breidenbach, Norwegian Institute for Bioeconomy Research (NIBIO), Division of Forest and Forest Resources, Department National Forest Inventory, Høgskoleveien 7, 1433 Ås, Norway https://orcid.org/0000-0002-3137-7236 E-mail: johannes.breidenbach@nibio.no