Zsofia Koma, Johannes Breidenbach. (2025). Large-scale validation of forest attribute maps across different spatial resolutions. Silva Fennica vol. 59 no. 2 article id 24061. https://doi.org/10.14214/sf.24061

Keywords: airborne laser scanning; National Forest Inventory; forest resource mapping; resolution dependence

Highlights: The study assesses stand-level uncertainty of biomass, volume, basal area, and Lorey’s height estimates resulting from the prediction of maps across varying spatial resolutions (1–30 m); The changes of RMSE and bias across the different spatial resolutions were generally small (< 5%) for additive forest attributes such as biomass, volume, and basal area; The changes of RMSE and bias were also small for Lorey’s height as a non-additive forest attribute if the resolution difference was less than 2 times of the native resolution; The models fitted at the resolution of the NFI plot size can be used to produce forest attribute maps at 10 m resolution without concerning increases in uncertainty at stand-level.

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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

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