Advancements in Wood Quality Assessment: Standing Tree Visual Evaluation—A Review

(1) The early assessment of wood quality, even while trees are standing, provides significant benefits for forest management, sales efficiency, and market diversification. Its definition cannot be in absolute terms but must always be linked to the material’s intended use. (2) In this contribution, a review of the scientific literature is given to discuss the visually evaluable attributes that define wood quality in standing trees, the applicability of the techniques used for their assessment, and the effectiveness of these attributes and technologies in predicting quality, to finally highlight future research needs. (3) The visual characteristics generally used to evaluate wood quality are linked to stem form and dimension, branchiness, and stem damage, but their assessment is challenging due to time and resource constraints. To address these challenges, laser-based and image-based techniques have been applied in field surveys. (4) Laser scanners offer detailed and accurate measurements. Photogrammetry, utilizing images to reconstruct 3D models, provides a cost-effective and user-friendly alternative. Studies have demonstrated the effectiveness of these tools in surveying the visible properties of stems and branches, but further development is necessary for widespread application, particularly in software development, with faster and more effective algorithmic advancements for automatic recognition and subsequent measurement of pertinent characteristics being critical for enhancing tool usability. (5) However, predicting wood quality from these surveys remains challenging, with a limited correlation between the visible tree characteristics assessed and the sawn product quality. Empirical studies evaluating products downstream in the forest-wood supply chain could provide valuable insights. In this sense, the implementation of traceability systems could facilitate the linkage between data on standing trees and the quality of the sawn product. Also, further research is needed to develop models that can accurately predict internal tree characteristics and their impact on product quality.