Silva Fennica vol. 59 no. 2 | 2025

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

This paper examines forest resource mapping from a statistical perspective, highlighting the opportunity to use a design-based approach to ensure inferential congruency with the estimation of averages and totals of forest attributes. Traditionally, in forest surveys estimates of averages and totals are obtained using design-unbiased estimators, with known variance expressions that can be easily estimated using standard sampling methodologies. The paper emphasizes the prominent role of kNN and Random Forest techniques in forest mapping while addressing the methodological limitations identified over more than thirty years of forest literature in efforts to estimate map precision. The critical importance of design-based map consistency, often overlooked in forest literature, is discussed and clarified, demonstrating that it allows for the development of design-based estimators of map precision through bootstrap resampling from the estimated maps.

• Franceschi, Department of Economics and Statistics, University of Siena, Via Banchi di Sotto 55, 53100 Siena, Italy https://orcid.org/0000-0001-6675-4540 E-mail: franceschi2@unisi.it
• Pisani, Department of Economics and Statistics, University of Siena, Via Banchi di Sotto 55, 53100 Siena, Italy E-mail: caterin.pisani@unisi.it
• Fattorini, Department of Economics and Statistics, University of Siena, Via Banchi di Sotto 55, 53100 Siena, Italy E-mail: lorenzo.fattorini@unisi.it
• Corona, CREA, Research Centre for Forestry and Wood, Viale Santa Margherita 80, 52100 Arezzo, Italy E-mail: piermaria.corona@crea.gov.it