Wood quality assessment can occur at various stages within the value chain. Typically, it is evaluated during the semi-finished or finished product stages, where classification into quality classes increases sales efficiency and product value, or when mandatory assessment is required for product marketability, such as for structural applications. For the usability of structural products, strength grading certifies their mechanical performance by assigning them to specific strength classes.
Studies have been conducted to assess material quality already in the post-harvest phase, involving the segregation of logs for different assortments and destinations, or for early evaluation of mechanical quality.
An evaluation further upstream in the supply chain, such as directly in the forest on standing trees, offers several advantages for the many actors involved.
From a forest management perspective, understanding the effects of different factors (climatic, site-specific, and cultivation treatments) not only on growth and volume but also on material quality can assist decision-makers in their intervention choices. Targeting wood quality during silvicultural planning optimises wood resource management and conservation, thereby creating value beyond mere extraction.
Both forest owners and users can benefit from knowing the quality of forest compartments, facilitating more efficient purchasing and selling. Indeed, segregation of material based on quality enables more efficient sales, diversifying destinations to suit the most appropriate markets.
In the same way, timber destinations and uses can be planned based on market prices for the different assortments obtainable. The overall supply chain benefits as manufacturing companies gain better insight into raw material availability, allowing for effective production planning while minimising imports.
Finally, wood quality evaluation is crucial in selecting genetic material for tree breeding programs.
With the recognition of these numerous advantages, research focusing on wood quality in standing trees has significantly increased. A literature search conducted in the Web of Science database using keywords (‘wood quality’ or ‘timber quality’) and ‘standing’ yielded the results. The total number of publications from 1990 to 2023 was 195, steadily rising along with the number of citations.
Despite the extensive research published on the topic, while forestry has numerous verified tools for assessing and projecting growth, yield, and tree size, operational and widely used methods for assessing and predicting wood quality are still lacking. Including wood quality in forest resource management depends primarily on the capacity to clearly define, easily quantify, and collect wood quality attributes.
The primary obstacle lies in defining wood quality itself, as it is a relative concept that cannot overlook the material’s intended use. The measurable attributes of a standing tree may not be sufficiently linked to the qualitative characteristics of the wood product that will result after felling. Moreover, the attributes defining wood quality can be various, and measuring them often demands significant effort in terms of time and necessary equipment, which may not always be cost-effective.
In the past, the technology available to measure wood quality-related characteristics was limited, so assessments focused on only a few attributes and relied on subjective measurements. Manual detection is sometimes impractical, especially for tasks like identifying and measuring branches at significant heights, and it is subject to operator subjectivity and experience. However, advancements in hardware and software technology have opened up new possibilities, enhancing both the speed and accuracy of surveys.
Therefore, the aims of this work are to review the scientific literature to discuss (1) the attributes commonly measured and how they define wood quality already in standing trees; (2) the applicability of the techniques used for their assessment; (3) the effectiveness of these attributes and technologies in predicting quality; and ultimately, (4) areas where research efforts should be concentrated in the immediate future to progress on wood quality evaluation are also identified.
Wood Quality and Attributes
Quality, in general, and wood quality, in particular, cannot be defined in absolute terms but must always be linked to the material’s intended use, i.e., the product to be manufactured, and consequently, to the satisfaction of specific requirements. From a technical perspective, wood quality can be defined by a combination of intrinsic characteristics, key attributes, and factors influencing its suitability for specific applications.
Additionally, considering the economic aspect, market prices and processing yields should not be overlooked. According to Bosela et al., when defining wood quality, all attributes influencing the processing and the value of the product at each stage of the manufacturing chain should be considered.
Thus, it is challenging to parameterise, synthetically represent, and therefore measure or predict it already in the standing tree. “Wood quality” does not refer to a single attribute but can be described by several parameters. These parameters can be divided into three main categories: (1) visual characteristics, which can be seen and measured externally on the tree; (2) intrinsic properties; and (3) internal defects, which are not visible and can be detected, estimated, and modelled only instrumentally.
This contribution focuses on the first one: the visible attributes.
Following an examination of the European harmonised technical standardisation, the authors were unable to identify a document that defines the criteria for a qualitative classification of standing trees (stems).
However, technical standards such as EN 1927 and EN 1316 specify rules for classifying softwood and hardwood roundwood into four qualitative classes, respectively. While not all characteristics can be assessed in standing trees, some can be directly related (for example, knots and branches).
Among the attributes that are visually detectable even in standing trees, the two standards specify the size (diameter) and straightness of the logs, and the number and size of knots, to group the timber material into quality classes.
Again, from a technical regulatory perspective, National Forest Inventories (NFI) closely resemble technical standards as they necessitate the harmonisation of recorded information and data. Regarding standing trees and visual characteristics, Bosela et al. conducted a survey among 28 European countries to investigate the quality assessment in their NFIs and to discuss potential harmonisation. They referred to the concept of “stem quality” as more suitable for the only visual (external) parameters recorded in the field, distinguishing it from the more complex definition of wood quality.
Factors influencing stem quality were identified, considering the possibility of using them to estimate the assortments obtainable from the harvest. These factors relate to stem form and size, the height of the crown and fork, the presence of abiotic or mechanical damages, and branch characteristics (size, height, number, and artificial pruning).
Bosela et al. observed significant variability among national inventories, with the obvious exception of tree dimensional data (diameter and tree height) recorded by all of them. However, the feasibility of future harmonisation was highlighted, possibly by identifying a smaller number of attributes but highly descriptive ones.
The same was not true for the assortment estimation, which greatly depends on the different definitions of assortment present at the national or even local level. In this sense, the suggestion was to collect the quantitative and qualitative data necessary for the estimate, enabling the assortment generation afterwards.
Recently, Ruano et al. developed a protocol to assess stem quality for inclusion in the NFIs, which could serve as the basis for European-level harmonisation. They similarly proposed the surveying of visual characteristics, considered easier to detect extensively, particularly with the aid of new technologies. The attributes significantly affecting visual stem classification included the height of the first dead or living branch, maximum branch diameter, and crookedness. They also suggested introducing the number of branches, slenderness (ratio of tree height to diameter), and branch insertion angle.
Furthermore, specifically designed for hardwoods are the log grading guidelines developed in the United States. These can and are also applied to standing trees, detailing characteristics for grouping logs into three quality classes. Attributes considered include tree size, defects (knots/branches and fractures), roundwood shape (sweep and crook), and decay presence.
Similar attributes are included in the tree grading rules for Southern pines.
Finally, Canadian rules for hardwood standing trees primarily consider dimensions, stem form, and tree vigour, with some also accounting for visible defects like biological damage.
The attributes mentioned and assessed in the literature above were categorised into four general types and detailed at two levels. This schematic subdivision will be maintained throughout the paper during the review and discussion of the relevant scientific literature.
Regarding size, it is intuitive to assume that larger sizes lead to greater yields and profits. Quality rules indeed prescribe larger dimensions in the highest quality classes.
It has been calculated that larger logs generally entail lower processing costs, potentially resulting in greater earnings per cubic meter. However, larger dimensions do not always guarantee better material quality.
The height from the crown or the first branch provides an indication of the length of the sawlog free from knots, at least externally.
Additionally, slenderness, defined as the ratio of height to diameter, has been found to be positively correlated with the mechanical properties of sawn timber obtained from measured trees.
Regarding size, it is intuitive to assume that larger sizes lead to greater yields and profits. Quality rules indeed prescribe larger dimensions in the highest quality classes. It has been calculated that larger logs generally entail lower processing costs, potentially resulting in greater earnings per cubic meter. However, larger dimensions do not always guarantee better material quality.
The height from the crown or the first branch provides an indication of the length of the sawlog free from knots, at least externally.
Additionally, slenderness, defined as the ratio of height to diameter, has been found to be positively correlated with the mechanical properties of sawn timber obtained from measured trees.
Quality classes based on stem sinuosity and straightness have been shown to differentiate the value of timber or potential sawlog recovery; the straighter the stem, the higher the value.
Stem leaning, on the other end, may be associated with the presence of reaction wood and cross-sectional eccentricity. Indeed, local tree slope was positively correlated with tension wood distribution in 10 poplar trees.
Finally, ovality affects both transformation yields (as generally do the diametric irregularities of the cross-section) and may relate to the presence of eccentric pith and reaction wood.
The presence and size of knots in the sawn timber result from the number and size of branches in trees. Knots are major defects in solid wood products, affecting aesthetics and mechanical properties.
Therefore, evaluating branchiness in standing trees is crucial for defining their quality. However, few studies have analysed the correlation between branch attributes in standing trees and the characteristics of sawn timber after processing.
Höwler et al. investigated the relationship between tree characteristics and the properties of the boards produced (performing visual strength grading and mechanical performance after destructive tests). They mainly focused on wood density, which showed no correlation with branch number, but they also noticed only a weak correlation between the number of branches bigger than 4 cm measured in the standing trees and the knot size used as a parameter in the visual strength grading.
In addition to the presence and size of knots in the sawn wood, the characteristics of the branches on the standing tree can also be linked to the development of anomalous internal colouring in the wood.
More specifically, Wernsdörfer et al. linked the characteristics of dead branches and branch scars visible on log surfaces to the presence of red heartwood in beech, an internal defect that decreases the material value, especially aesthetically, because it presents itself as an alteration of the wood colour.
Damage to trees from abiotic degradation or injuries alters woody tissue, reducing its mechanical properties and aesthetic appeal. Such alterations significantly impact product value and are typically excluded from merchantable timber, thereby reducing yields and revenues.
Techniques and Tools
Among the visual characteristics, dimensions (stem diameter, height, and volume) are the basis of forest inventory surveys and will not be addressed in this work. For a comprehensive review of new technologies like LiDAR applications in evaluating above-ground biomass at the tree scale, see Krok et al. and Xu et al. Similar technologies have been utilised to determine the height of the crown or first branch.
Furthermore, the usage of smartphone applications for forest inventory has been described and compared by Sandim et al.
Then, turning to the other visual attributes like stem form, branch characteristics, and the presence of damage, those can be assessed quantitatively or qualitatively.
Quantitative surveys involve measuring each characteristic individually, while qualitative assessments define a class based on rules providing different admissibility criteria for the several attributes to be assigned to the various classes.
Quantitative surveys are more time- and resource-intensive but allow for a more accurate prediction of quality.
On the other hand, qualitative assessment can be quicker but requires greater harmonisation efforts due to historical differences in criteria among countries or local customs.
Several qualitative assessment procedures focus on the first 5–6 m of the tree height due to cost considerations and because the greatest value is often concentrated in this part of the stem, especially for broad-leaved trees.
Visual characteristic measurement can be manual, where an operator detects the parameters of interest, but this is expensive and, especially for some particularly burdensome attributes (for example, branchiness), impractical for large-scale surveys. An alternative involves recording the quality class of each analysed tree, which is faster but leads to the problems mentioned above and can be influenced by operator experience and subjectivity.
Over time, promising technologies have emerged to automate survey work, making it faster but still detailed, enabling large-scale implementation. These can be categorised as laser-based or image-based techniques.
One widely used technology for qualitatively evaluating stem characteristics is the laser scanner. Very briefly, laser scanners measure distances between objects in the environment and the sensor and generate point clouds that reconstruct detected objects in a highly accurate way. These point clouds can be processed using various algorithms to extract the required information.
There are two main types of laser scanners: Airborne Laser Scanners (ALSs), mounted on aerial vehicles, and Terrestrial Laser Scanners (TLSs), ground-based instruments. ALSs are less suitable for evaluating stem attributes beyond dimensions and are primarily used for biomass and forest structure investigations.
TLSs have been tested for evaluating stem form and branchiness, and some studies are available for stem defect detection. Currently, both static (mounted on tripods) and mobile (hand-held or worn in a backpack) TLSs are available.
Further technical and operational details on TLSs are reviewed.
The results indicated that visual classification was ineffective for distinguishing stem form and lean qualitatively, as no significant differences were detected among the classes. TLSs provided more precise results for use in stem quality grading.
Similar comparisons were conducted by Mengesha et al. for the taper, sweep, and lean of ash stems calculated from TLS data and manually in the field. They observed superior results when the laser scanner survey was performed during the leaf-off season.
In a study on Scots pine, Pyörälä et al. compared geometric data (diameter, volume, taper, and sweep) obtained from point clouds on standing trees with the same measurements taken at the sawmill at the log level using an operational laser scanner. They found that dimensional data from stem and log assessments were closely correlated, but the same was not true for sweep, which was difficult to predict from standing trees.
Numerous studies have examined the viability of assessing branch characteristics using laser scanner technology.
For instance, in Scots pine, the attributes of the whorls (number, position, and max diameter) were extracted from a TLS survey and compared with the same variables obtained by X-ray analysis on cut logs.
Branch attributes were not directly comparable with knot features derived from the logs, partly due to measurement inaccuracy and partly due to the differences between internal knot and external branch characteristics. However, some quality indexes, such as the maximum knot diameter, showed no statistically significant difference from the maximum branch diameter at tree level.
Similarly, the number, diameter, and insertion angle of branches were detected both automatically and by an operator from the TLS point cloud. The results indicated good agreement between the two methods for branch angle; however, automatic calculation tended to underestimate diameter, and precision in branch detection decreased with tree height due to the presence of the leaving crown.
Noteworthy is the work of Winberg et al., who utilised a mobile handheld laser scanner (HHLS) to assess branching properties in standing Norway spruce trees.
Two different algorithms for data extraction from point clouds were applied and compared in terms of branch detection, determination of branch diameter, and insertion angle. The collected data were also compared with sawmill data (log and knot volume, and whorl characteristics) obtained with X-rays on the same material (at sawlog level).
Overall, the laser scanner successfully predicted branch structures in standing trees, although these attributes showed only a moderate correlation with sawlog features.
TLSs have also been effectively applied to detect and measure stem damage from various sources, including mechanical actions, insects, animals, weathering, and physical defects such as fork or butt swell. Damage was visually detected and measured both in the field and on the point clouds by an operator; the comparison revealed no significant difference in the size of damage between the field and desk surveys. However, the defect count was underestimated when assessed using point clouds, likely due to the limited tree height captured instrumentally.
Kretschmer et al. developed two approaches to detect and measure surface defects, such as bark scars, in TLS point clouds: one based on data intensity and the other using bark surface models. These automatic techniques were compared to manual measurements. Difficulties arose when the scars were not evident, resulting in errors greater than 5 cm. In all other cases, the error was less than 1 cm.
Finally, machine learning techniques were effectively applied for the classification of stem surface defects, including branch scars, epicormic shoots, burls, and small defects identified in laser point clouds.
A step further was performed to quantify the dimensions of these singularities with promising results. However, difficulties for some flat defects were observed in accurately defining their borders when compared to assessments made by human operators.
Photogrammetry uses images (photographs) captured from different angles of an object to construct a 3D model and determine its dimensions. Either a multiple-image or single-image approach can be utilised in the analysis.
A recent advancement in photogrammetric applications, known as Structure from Motion (SfM), has significantly contributed to the advancement and widespread adoption of this technique. SfM utilises overlapping images captured from moving cameras to reconstruct a 3D model through computer vision techniques.
In a more recent study, Kędra et al. compared single-image photogrammetry with TLS results to characterise the tree architecture of sessile oak. While the primary focus was on crown characteristics, branch length, thickness, and angle were also obtained through image analysis.
Again, the already-cited Morgan et al. utilised both HHLS and photogrammetry techniques to count and measure the length of stem damages. Their results showed that the extent of damage was similar among assessment methods.
Computer vision and deep learning techniques were also employed by Niknejad et al. on stereo images to determine stem diameter as well as branch diameter and angle in loblolly pine trees.
Comparison with manual measurements indicated sufficient accuracy in stem diameter measurement, while branch assessment was affected by the small size of objects to be detected, resulting in values only suitable for determining size classes (small, medium, and large) due to insufficient accuracy.
Future Directions
Initially focused on measuring inventory variables like tree dimensions and biomass, lasers or image-based techniques have expanded to include parameters linked to wood quality, such as stem form and branch characteristics.
Studies have shown these methods to be capable of providing versatile, quantitative measurements. However, despite promising results, widespread adoption is hindered by the need to balance the advantages and disadvantages of each technique.
Currently, static TLSs serve as a reference for point cloud-based inventory measurements. One advantage is its capability to quickly and automatically detect tree attributes with millimetre-level detail.
However, most sensor devices are very expensive, and the technique demands substantial computing power for data processing along with expert personnel to extract numerical variables.
To mitigate some of these drawbacks, low-cost TLSs have entered the market, alongside Mobile Laser Scanners (MLSs) and Personal Laser Scanners (PLSs), thanks to a reduction in sensor size.
MLSs do not require stationary positioning during data acquisition, enabling integration with aerial vehicles or processor harvesters, while PLSs can be handheld or worn in a backpack. Both allow faster data acquisition over larger areas within the same amount of time compared to static TLSs but have lower accuracy (centimetre-level) and increased acquisition noise. Nonetheless, comparisons between mobile and static instruments have reported similar results.
More recently, image-based solutions have gained more and more attention due to their affordability, portability, and easy-to-use devices for data acquisition (typically a camera), coupled with the availability of automated, user-friendly software for data processing and rapid algorithm advancements for data computing.
Another advantage of photogrammetry is the capture of realistic images, which facilitate rapid, easy, and intuitive validation of the collected information.
However, being a passive sensing technique, photogrammetry is highly influenced by variations in light and atmospheric conditions, which impact image quality. Additionally, the success of 3D reconstruction relies on factors like picture angle and overlap. Therefore, for all these reasons, the development of an acquisition protocol could help the user optimise the results.
While data acquisition in photogrammetry is relatively fast thanks also to easy mobility in field measurements (like for PLSs), its accuracy is lower than laser-based techniques but still adequate for operational requirements. Though direct comparisons between photogrammetry and TLSs remain limited, initial results suggest good agreement between the two techniques.
Finally, it is worth mentioning the research aimed at combining the two technologies. Since both have strengths and weaknesses, their combination can lead to more effective results compared to using them individually.
The fusion of laser scanning with optical imaging has been applied in various fields, as reviewed by Zhang and Lin, including forest inventory. This approach could also be a promising area of research in the evaluation of wood quality.
Overall, further developments are needed before achieving their widespread application, not only for assessing tree size but also for evaluating qualitative attributes. One aspect that certainly holds great potential for progress is software development.
Faster and more effective algorithmic advancements for automatic recognition and subsequent measurement of pertinent characteristics are critical for enhancing tool usability.
For instance, computer vision and deep learning methods appear very promising, so they enable real-time evaluations.
Apart from further enhancements to improve the usability of survey techniques, as discussed previously, it is undeniable that they hold great potential for evaluating the visible characteristics of standing trees.
The next question, therefore, concerns the effectiveness of the metrics detected in predicting the quality of the product obtained from cutting the tree and if there are better evaluation criteria for wood quality that could be included in the survey.
Addressing this involves investigating how current metrics correlate with end-product quality and identifying additional parameters that provide a more accurate assessment.
Research in this area is crucial to develop more reliable and predictive models for wood quality, ultimately enhancing the precision and utility of forestry assessments.
Better estimations were reported when linking branch scars on the stem surface with the knots and the clear wood amount measured with X-ray computer tomography on logs.
Thus, further studies are certainly necessary to explore the most effective metrics for describing product quality.
Additionally, the development of models estimating internal tree characteristics from externally visible ones appears very promising.
A detailed review of studies dedicated to modelling wood quality is provided by Drew et al. In this work, the models were divided into two main types: empirical and process-based models. The former established relationships between empirically measured variables to create the most effective prediction model possible.
On the other hand, process-based models reconstruct the tree’s architecture. Obviously, the development of wood quality modelling relies on data collected in the field, making them vital for improving model validity and their applicability on a large scale.
Finally, particular attention should be directed towards studies that reconstruct the development of branches internally to the stem (branch geometry) starting from externally detectable parameters, such as branch diameter, insertion angle, position in the stem, tree height, and stem diameter.
Accurate modelling in this regard could significantly improve the qualitative evaluation by simulating knottiness in the sawn product obtained from log processing.
Returning to the definition reported in the first paragraphs, wood quality is inextricably linked to the requirements of the final product. To better analyse which attributes of the standing tree best allow it to satisfy the quality requirements of the sawn product, it is necessary to connect the information collected in the forest with the data collected in the sawmill.
To achieve this, the traceability of tree-log-sawn timber information is a key aspect of linking the data to be used in the learning of prediction algorithms.
Implementing traceability systems would track the resource through all the processing phases, optimising both management and cultivation decisions, as well as harvesting and processing, with the conscious aim of achieving the highest possible product quality.
Several traceability solutions have been developed and tested. Some are further behind, mainly due to high application costs, while others are already ready for practical use.
For instance, Radio Frequency Technology (RFID) had been proven suitable for marking standing trees with relatively inexpensive passive tags, which remained operative over two years once inserted in the trunk and even after the hauling operations of the roundwood.
Pichler et al. presented a demonstration case where different technologies were integrated along the forest-timber supply chain. The data flow was guaranteed by RFID tags applied to standing trees and logs; all information was stored on an accessible server.
Data collected by remote sensing (laser-based) technologies were linked to trees tagged in the forests. The authors also identified potential bottlenecks at each processing step and conducted a risk analysis. In conclusion, they highlighted that the technology is ready, but there is still reticence in its operational adoption, especially due to the higher (but still accessible) costs and the difficulties in coordinating and aligning the different actors in the supply chain.
Conclusions of the Study
Assessing the wood quality already on the standing trees can bring numerous benefits for forest management, harvesting, and wood product manufacturing.
This work focuses on attributes that are visually detectable on the stem. The overview of technical standards and traits assessed as wood quality attributes in forest inventories revealed the absence of a generalised rule. However, it was possible to group the main characteristics into categories: dimension, stem form, branchiness, and damage.
Scientific studies that applied laser- or image-based survey techniques for automatic definition of these attributes were also reviewed. Initially, laser or image-based techniques were primarily focused on measuring common inventory variables such as dimensions (tree diameter, height) and biomass.
However, due to promising results, interest has expanded to include the survey of other parameters closely linked to wood quality, such as stem form, branch characteristics, and detectable defects on the stem.
Studies have demonstrated the effectiveness of these tools in surveying the visible properties of stems and branches, often performing better than visual quality classification conducted by operators.
Moreover, the ability to obtain quantitative measurements of various attributes makes these techniques versatile and adaptable to different information needs (for example, different qualitative classes can be reconstructed based on quantitative data).
Despite these positive outcomes, widespread application has not yet been achieved. The aspects that deserve attention for progress towards their more common use are the reduction in hardware costs, but above all, the development of user-friendly software and algorithms that speed up the processing of data collected in the field.
An interesting research field could also be the fusion of the two technologies to mitigate their respective limitations and utilise the benefits of both.
At the same time, our review highlighted the need for further research into how measurable attributes on standing trees are truly descriptive of the quality of the sawn product. In this regard, empirical studies could be valuable, which also assess the products produced downstream of the supply chain.
Wood quality modelling, then, is a rapidly developing field, as is similar modelling for forest growth. However, this requires data that connect the several phases of the production process, from the forest, to logging operations up to sawmill processing. Data could be stored effectively with the use of traceability systems so that information is not lost from one phase to the next.
Finally, increasing the collection of forest and wood quality data in accessible databases would allow the application of innovative information management and processing techniques. The recent growth of deep learning and other artificial intelligence techniques is extremely promising, but these require large amounts of data to implement efficiently.
