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Key message <p>Using laser scanning and industrial data, we found that over 70% of wood quality variability occurred within Norway spruce (<i>Picea abies</i> H. Karst) trees. The most important wood quality predictors were stem size, crown vigor, and growth rate inferred from laser scans. Random Forest models based on the laser-scanned features captured 25% of the industrially measured wood quality variability with 39.9% RMSE on average. The low crown plasticity of Norway spruce introduced biological constraints to laser scanning-based wood quality modeling.</p> Context <p>Wood quality models that also predict wood and timber properties in addition to size and growth variables are essential for increasing the precision of forest management and forest use, yet they remain notoriously untransferable. Laser scanning offers a powerful tool for their parameterization, but its ability to capture the within-tree variability of wood quality is still poorly understood in many species.</p> Aims <p>Our aim was to test whether multi-viewpoint laser scanning can capture within-tree gradients of wood quality in Norway spruce trees (<i>Picea abies</i> H. Karst.), thereby enabling more robust and transferable models.</p> Methods <p>We analyzed 479 mature Norway spruce trees, combining handheld and airborne laser scanning with industrial wood quality data. We modeled 18 industrially relevant variables related to log geometry, heartwood, knottiness, and timber strength IP value against laser-scanned features at stand, tree, and log levels.</p> Results <p>Most wood quality variability (73%) occurred within trees. Log-level laser features explained 25% of the variation across stands and log types in the test data, with average RMSEs of 39.9%. The most stable predictions were obtained for heartwood ring width, heartwood density, and knot percentage.</p> Conclusion <p>Overall, external crown and stem attributes captured key growth responses but failed to robustly represent most wood quality factors in Norway spruce. These results underscore biological constraints in laser scanning-based wood quality modeling depending on the species-specific adaptiveness of the crown structure to the environment.</p>

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From between-stand to within-tree variation: wood and timber quality of Norway spruce (Picea abies H. Karst) analyzed at scale using laser scanning and industrial data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月14日

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  • Jiri Pyörälä,
  • Mika Pehkonen,
  • Otto Saikkonen,
  • Olli Winberg,
  • Xiaowei Yu,
  • Johan Holmgren,
  • Markus Holopainen,
  • Juha Hyyppä,
  • Harri Kaartinen,
  • Antero Kukko

摘要

Key message

Using laser scanning and industrial data, we found that over 70% of wood quality variability occurred within Norway spruce (Picea abies H. Karst) trees. The most important wood quality predictors were stem size, crown vigor, and growth rate inferred from laser scans. Random Forest models based on the laser-scanned features captured 25% of the industrially measured wood quality variability with 39.9% RMSE on average. The low crown plasticity of Norway spruce introduced biological constraints to laser scanning-based wood quality modeling.

Context

Wood quality models that also predict wood and timber properties in addition to size and growth variables are essential for increasing the precision of forest management and forest use, yet they remain notoriously untransferable. Laser scanning offers a powerful tool for their parameterization, but its ability to capture the within-tree variability of wood quality is still poorly understood in many species.

Aims

Our aim was to test whether multi-viewpoint laser scanning can capture within-tree gradients of wood quality in Norway spruce trees (Picea abies H. Karst.), thereby enabling more robust and transferable models.

Methods

We analyzed 479 mature Norway spruce trees, combining handheld and airborne laser scanning with industrial wood quality data. We modeled 18 industrially relevant variables related to log geometry, heartwood, knottiness, and timber strength IP value against laser-scanned features at stand, tree, and log levels.

Results

Most wood quality variability (73%) occurred within trees. Log-level laser features explained 25% of the variation across stands and log types in the test data, with average RMSEs of 39.9%. The most stable predictions were obtained for heartwood ring width, heartwood density, and knot percentage.

Conclusion

Overall, external crown and stem attributes captured key growth responses but failed to robustly represent most wood quality factors in Norway spruce. These results underscore biological constraints in laser scanning-based wood quality modeling depending on the species-specific adaptiveness of the crown structure to the environment.