Assessment of Oak Roundwood Quality Using Photogrammetry and Acoustic Surveys

Abstract

Hardwood has a variety of applications and can be used for low-value products, such as firewood, or for high-value applications, achieving significantly higher prices. Therefore, assessing the quality of raw material is essential for allocating the wood to the most suitable end use. The aim of this study was to explore the use of the photogrammetry technique to determine dimensional characteristics and perform remote visual grading of round oak timber stored at a log yard. The results of the visual classification were then compared with non-destructive acoustic measurements to assess their level of agreement. Based on the point cloud obtained from photogrammetry, logs were classified into three quality groups according to the European standard for round timber grading. The diameter measurements of the logs obtained through the photogrammetry survey were comparable to those taken manually, with an average difference of 0.46 cm and a mean absolute error of 2.1 cm compared to field measurements. However, the log lengths measured from the 3D survey were, on average, 5 cm shorter than those obtained using a measuring tape. The visual classification performed on the 3D reconstruction was based on the evaluation of log size, knots, buckles, and sweep, resulting in 39%, 27%, and 24% of the pieces being grouped into the high-, medium-, and low-quality classes, respectively. Acoustic measurements, performed using both resonance and time-of-flight (ToF) methods, were highly correlated with each other and successfully distinguished the three quality classes only when sweep was excluded from the classification criteria. When curvature was also considered as a parameter for log grading, acoustic velocity only differentiated the lowest quality class from the other two.

1. Introduction

Hardwood has a wide range of uses and applications, from firewood for energy production to higher-value-added processes such as peeling, slicing, and the production of sawn wood for structural use [1]. Selling wood for an intended use significantly impacts its price. Therefore, for optimal resource utilization, assessing its qualitative characteristics is essential [2,3].

This evaluation is most commonly conducted on sawn wood, following rules that assess either its appearance (for furniture or carpentry) or its mechanical properties (for structural use) [4]. However, the qualitative classification of roundwood itself offers numerous advantages, during buying and selling as well as in the processing. Buyers and sellers benefit from an agreed-upon evaluation; buyers gain confidence in the characteristics of the purchased timber, while sellers receive fair compensation [4]. Additionally, sorting roundwood into quality classes reduces the variability within homogeneous groups [5], facilitating processing and increasing efficiency.

The most common method for qualitative evaluation is visual assessment, based on the surface-visible characteristics of round timber. Three primary factors are considered: species, dimensions (length and diameter, typically measured at the smaller end), and the presence of defects [6]. Quantitative characteristics are particularly important for determining the transformation yield and evaluating recoverable volumes [7]. In contrast, defects are features that may reduce or compromise yield or final product performance. These defects include shape irregularities (sweep, crooks and tapering), characteristics such as knots (size and number), epicormic shoots, mechanical damage, shakes, and biotic degradation caused by fungi or insects [4,8]. Studies focused on oak round timber classification have identified curvatures and knots as the most common negative factors [9,10,11], as well as insect holes in certain provenances [10,11].

Visual assessment is the traditional and most widespread non-destructive method used for log grading since it is cheap and accessible, even for small companies. However, it is subjective and depends on personnel expertise [12]. The use of standardized rules can help mitigate subjectivity. The process primarily involves dividing the timber into quality classes, which may be based on intended use. However, there are also standards independent of wood destination, such as the European standard for round wood qualification [13] and guidelines from the United States [14,15] and Canada [16,17].

More recently, visual evaluation methods have expanded to include image analysis and laser or photogrammetric surveys, which generate 3D reconstructions. These techniques have primarily been applied to standing trees [18] to assess stem shape defects [19,20,21], branchiness [22,23], and, in some cases, surface defects [24].

For logs, laser surveys have long been used in sawmills to assess dimensions [12] and shape defects [8], optimizing the sawing process. Some studies have explored the use of laser scanners in sawmills to detect surface defects on round timber [25,26]. To the best of the authors’ knowledge, photogrammetry is not currently used for round timber evaluation in sawmill production lines. At log yards, laser or image analysis have been tested mostly for the size and volume determination of round wood [27,28,29].

Visual grading, however, is limited to externally visible characteristics, such as dimension, shape, and surface-detectable defects. Studies investigating the correlation between visual log grading and the quality of sawn timber obtained from these logs, have often reported disappointing results. Hecker et al. [30] analyzed the relationship between the visual qualification of round softwood timber according to the EN 1927 [31] and the strength grading of sawn timber for structural use, finding only a weak correlation. Similar results have been obtained for oak timber, where the visual grading of round timber had little influence on the yield of boards suitable for construction. Instead, the sawing pattern played a more critical role in improving the yield [32].

Assessing a log’s internal characteristics could provide valuable additional information beyond that detected visually. Internal defects can be predicted using models based on external observations [33]. Alternatively, non-destructive instrumental measurements, extensively and accurately described in [12] for log quality evaluation and in [34] for standing trees and logs, can be used in the field or in the laboratory.

A recently tested and highly accurate technique is computed tomography, but its high cost make it unsuitable for small- to medium-sized enterprises [35,36]. More affordable, rapid non-destructive tools are available for internal quality assessment. Among the most effective are those based on acoustic techniques. These are divided into two methods: time-of-flight (ToF) and acoustic resonance. A comprehensive review and detailed explanation of the methods can be found in [37].

Acoustic measurements have been tested for the pre-classification of round timber intended for sawn structural wood production [38,39,40] and for the direct classification of round timber to be used in construction [41,42]. Other studies have evaluated their effectiveness in distinguishing visually defined quality classes of round timber [43]. However, research particularly in this latter area remains limited, and further studies are needed to assess the effectiveness of these tools as aids in log grading.

This study aimed to test the use of photogrammetric surveys of logs ready for sale at a log yard to determine their dimensional characteristics and enable remote visual grading based on a generated point cloud. Additionally, it aimed to compare visual classification results with non-destructive acoustic measurements to assess their level of agreement.

2. Materials and Methods

2.1. Materials and Field Survey

A total of 160 European oak logs, located in the area of Ticino Park (Vigevano, Italy), that were felled and processed (delimbed and cross-cut), were numbered and surveyed. In the field, the diameters at both ends and the length of each log were measured to the nearest centimeter using a caliper and a metric measuring tape, respectively.

Instrument surveys were conducting using two portable devices, both aimed at determining the wood stiffness. A Microsecond Timer (Fakopp Enterprise Bt, Sopron, Hungary) was used to measure the ToF of the mechanical waves running longitudinally between two transducers. The transducers were inserted into the wood at the cross-cut sections, and three readings were taken per log to derive an average ToF. The acoustic velocity was then calculated as the ratio of the log’s length to its ToF.

Similarly, a resonance-based acoustic method was applied for comparison, as this is a well-established technique for evaluating wood stiffness in logs and sawn timber. An acoustic tool, Hitman HM200 (Fibre-gen, Christchurch, New Zealand), was used to measure and record the velocity of the longitudinal wave generated by a hammer hit on each log.

Following this, a photogrammetry survey was conducted on three batches for a total of 90 logs using an XVS vSLAM 3d Scanner (Stonex, Monza, Italy). The acquired images were processed with XVS Cloud software (version 2.0.1) to generate a 3D point cloud. The point cloud (Figure 1) was then handled and analyzed using open-source software CloudCompare (version 2.12.3) [44].

2.2. Analysis

2.2.1. Instrumental Data

Liu et al. [45] modeled acoustic wave propagation in the context of ToF measurements in standing trees, with a transducer distance of 1.2 m. They described the effects of both tree diameter and slenderness on wave behavior. For small diameters (less than 10 cm) and high slenderness (greater than 12), wave propagation was quasi-plane, making the velocity determined by the ToF comparable to that measured using the resonance method. For large dimeters (>40 cm) and low slenderness (<3), the propagation followed a dilatational wave pattern. In intermediate cases, a transition phase was observed, and mathematical models were proposed to adjust the velocity to the resonance-based velocity.

Although the ToF measurement procedure differed from that in the cited paper—as the measurements were conducted from end to end on logs rather than on one side of a standing tree stem—the correction suggested by Liu et al. [45] was still applied.

ToF- and resonance-based velocities were than compared using linear regression analysis, with resonance-based velocity as the dependent variable and the ToF-derived velocity (calculated as the ratio of the ToF to transducer distance) as the independent variable. The coefficient of determination for the linear regression was then computed.

2.2.2. Point Cloud Processing: Virtual Visual Grading

First, log dimensions (diameter and length) were determined in the point cloud and compared with field measurements. To ensure accuracy and avoid alignment errors when measuring distances between points, the longitudinal axis of each log was aligned with one of the Cartesian axes. Once properly positioned, a slice approximately 1 cm thick was cut perpendicular to the log’s axis to serve as a reference for diameter measurements (Figure 2).

Qualitative classification was performed according to the European technical standard EN 1316-1 [13]. The standard provides four qualitative classes: A, B, C, and D, with class A being the highest quality. Class D was excluded because it allows the presence of all characteristics without limitation and requires logs to be processable as usable sawn timber.

Among the characteristics listed for quality determination, those used are reported in

Table 1. Visual characteristics used for the qualitative classification in the three first classes according to EN 1316-1 [13].

Characteristic		Quality Class
	A	B	C
Size
Minimum length	3 m	3 m	2 m
Minimum average diameter	40 cm	35 cm	30 cm
Sound knots	1 every 3 m (≤2 cm)	1 every 1 m (≤4 cm) or 1 every 3 m (≤6 cm)	Allowed
Unsound knots	Not allowed	1 every 2 m (≤3 cm)	No limit for knots ≤3 cm 1 every 2 m (≤10 cm)
Buckle	Not allowed	1 every 2 m	Allowed
Sweep	≤2 cm/m	≤4 cm/m	≤10 cm/m

for the first three classes. As for the other characteristics mentioned in the standard, epicormic shoot, spiral grain, eccentric pith, mechanical damage, discoloration, and biological damage were never present. Ring shake or other shakes were not observable due to the high moisture content of the logs, and rate of growth was not discernable due to image resolution limitations.

Knot size was measured as the small axis of the ellipses approximating the knot (Figure 3). The sweep was determined by taking a thin longitudinal slice (1–1.5 cm thick) (Figure 4 and Figure 5). Each log was isolated and observed from various angles by rotating it around its longitudinal axis. The orientation with the greatest curvature was then visually identified. In cases of uncertainty, multiple measurements were taken, and the highest curvature was recorded. To obtain the longitudinal slice, the log’s longitudinal axis was aligned with a Cartesian axis, and the section was cut parallel to it. The sides and angles of the tringle formed by the sweep were measured, and the curvature arrow was calculated using trigonometry (Equation (1)):

$$\mathit{curvature\; arrow}={\displaystyle \frac{AC\times \sin \left(\mathit{angle\; A}\right)}{AB}}$$

(1)

Refer to Figure 5 for an explanation of the terms in this equation.

2.2.3. Measurement Comparison and Statistical Analysis

The Bland–Altman plot was used to compare field and cloud-based size measurements. This graphical method helps with assessing the agreement between two measurement techniques [46].

Additionally, mean absolute error (MAE), root mean squared error (RMSE), and normalized RMSE (NRMSE) were calculated. A paired Student’s t-test was also performed, with statistical significance set at p < 0.05.

After visual grading, a qualitative class was assigned to each log. The classes were then compared in terms of the wave velocity measured by the two instruments (ToF- and resonance-based). This comparison was performed using analysis of variance, after verifying the normality of the data distribution and the homogeneity of variance.

3. Results

3.1. Acoustic Measurements

Figure 6 presents a comparison of the wave velocity measurements obtained using the ToF- and resonance-based methods, illustrated through scatterplots along with the corresponding linear regression analysis. The results indicate a moderately strong correlation between the two measurements. However, this relationship was significantly strengthened after applying the correction for the dilatation wave mode, as reflected by an increase in the coefficient of determination from 0.61 to 0.74. Consequently, the adjusted values were used for all subsequent analyses.

The velocity measured using the ToF technique was generally higher than that obtained with the resonance-based method. On average, the ratio between the two was 1.19 for uncorrected ToF values and 1.16 for corrected values.

3.2. Log Size Determination

The logs had an average diameter of 53.3 ± 10.5 cm, a length of 5.4 ± 1.6 m, and a mean slenderness (length-to-diameter ratio) of 10.8. The average volume was 1.2 ± 0.6 m³.

Figure 7 compares the size measurements obtained in the field using traditional methods (caliper and measuring tape) with those derived from the point cloud. On average, a difference of 0.46 cm (0.9%) was observed for log diameter and 5 cm (1.0%) for log length. Diameter measurements from the point cloud were generally consistent with field assessments, with 93% of measurements falling within ±5 cm (±9%) and 65% within ±2 cm (±4%) of the field measurements. The MAE was 2.1 cm, the RMSE was 2.8 cm, and the NRMSE was 0.27. The mean difference was well centered within the agreement interval (red dotted lines), indicating that the point cloud measurements did not exhibit any systematic bias (Figure 7, left). This was further confirmed by a paired Student’s t-test comparing field and photogrammetry-based diameter measurements, which showed no significant difference (p-value = 0.12).

In contrast, the log length measured in the field was, on average, 5 cm longer than the corresponding values obtained from the point cloud. In the Bland–Altman plot, the zero line was close to the lower limit of the agreement interval, though still within it (Figure 7, right). In this case, Student’s t-test revealed a highly significant difference (p-value < 0.001), indicating a measurement bias. The MAE was 5.5 cm, the RMSE was 6.6 cm, and the NRMSE was 0.05.

3.3. Visual Classification

The visual qualification of the logs resulted in the majority being high-quality logs, with 43% classified as class A. Similar quantities were found in the second and third classes, with 30% in class B and 27% in class C (Figure 8, left). The most relevant characteristic that downgraded the log was the presence and dimension of sound knots (24%). An important role was also played by the overall curvature, which downgraded 15% of the logs from class A to B (Figure 8).

The log dimensions and velocity measured in the logs of the three quality classes are reported in

Table 2. Mean and standard deviation of the dimensional characteristics and wave velocity calculated for each quality class (A, B, and C). Different lowercase letters (a, b) indicate significant differences in means (p < 0.05).

Class	Diameter (mm)	Length (mm)	Velocity HM (km/s)	Velocity Fakopp Adj (km/s)
A	53.4 ± 8.3 a	5163 ± 1333 b	2.88 ± 0.23 a	3.34 ± 0.24 a
B	56.0 ± 13.2 a	5176 ± 883 b	2.89 ± 0.23 a	3.36 ± 0.27 a
C	50.7 ± 10.3 a	6289 ± 1800 a	2.66 ± 0.15 b	3.07 ± 0.21 b

.

Notably, when all the characteristics were considered in the grading, classes A and B did not differ in velocity, with only class C showing significantly lower values (Table 2 and Figure 9, left). However, when log curvature was not taken into account during grading, velocity was effective in differentiating all three classes (Figure 9, right).

Similar results were observed for the ToF measurements, which are not shown here.

4. Discussion

4.1. Using Photogrammetry for the Visual Qualification of Logs

The use of photogrammetry in a log yard was tested for visually assessing round timber, measuring its dimensions, and classifying each log into one of three quality classes.

As a result of the point cloud analysis, the log diameter measurements showed no bias compared to the field survey. The mean absolute error was 2.1 cm, which also accounts for the error caused by the imperfect alignment of the measuring vectors with those used in the field. This mismatch could lead to greater errors, particularly when the cross-sections are not perfectly circular. A previous study using the LiDAR of a mobile phone [27] reported a low MAE of 0.68 cm between the diameters measured with LiDAR and those obtained through manual surveys, but the cited study was conducted on Norway spruce, which has a more circular cross-section than oak logs, and the average diameter was smaller (22 cm versus 53 cm).

In contrast, length determination showed a negative bias, with values obtained from the point cloud analysis being, on average, 5 cm lower than those measured in the field. The measurement of the length of a log is particularly prone to errors when traditional methods are used. A likely explanation for this systematic difference may be the difficulty of keeping the measuring tape straight during field measurements. These differences in length values led to an average overestimation of 3.4% in log volume when based on field measurements.

Visual qualification and log size determination were effectively carried out on the point cloud. Obviously, using photogrammetry, the quality of the images is of major importance, and proper lighting during the survey is crucial: pictures should be taken during daylight but avoiding overexposure or backlighting. However, considering other 3D survey techniques like LiDAR, despite the need for these precautions during image capture, the presence of photos allows for the creation of an RGB point cloud, which makes the visual qualification easier or possible [20,47].

In our sample, the knots were clearly visible because they were usually large, but smaller defects may have been more difficult to spot or may have been misinterpreted. Surface dirt or foreign objects on the logs could also be mistaken for growths. Again, image quality is key.

To give an idea of the time needed for the survey, 80 logs were surveyed in 15 min (about 19 s/log), excluding the time required for the point cloud to be reconstructed by the software. To improve the quality of the point cloud (more details) and achieve higher resolution and data density, it may be necessary to extend these times. A similar study examining the time required to survey round timber using LiDAR (smartphone and a mobile laser scanner (MLS)) reported the following: with the smartphone, an average time of 1.5 min per log was recorded for individual surveys. This decreased when scanning a small group of 4–5 logs (67 s/log) or a medium group of 7–14 logs (42 s/log). The MLS provided the best results in terms of time, averaging 25 s/log for small groups and 19 s/log for larger groups (27–35 logs). Qualitative assessments of the scans were not reported in that study [48].

Obviously, the photogrammetry survey revealed approximately three-fourths of the log surface, but this aligns with the standard practice in industry. In a recent interview with several sawmills, it was revealed that 90% of companies do not typically roll the logs during their evaluation [6].

4.2. Acoustic Measurement and Visual Classification

The acoustic velocity of the waves running longitudinally through the logs, determined using the ToF technique, was in good agreement with the resonance-based measurements. The resonance-based method is usually taken as a reference for the quality estimation of wood, and the correlation between the two was high, with the coefficient of determination being 0.74 after the adjustment prosed by Liu et al. [45]. As expected, the velocity based on ToF measurements was higher than that recorded using the resonance method, and the ratio between the two aligned with previous findings reported in the literature: on average, ranging from 1.15 to 1.31 [37].

Even though the ToF was measured on logs after harvesting (rather than on standing trees), the good agreement between the two techniques suggests the possibility of the early evaluation of standing trees. The main drawback in this case is the short length typically involved in the measurements, which usually does not exceed the first 1.5 m of the stem. Previous studies reported how involving greater lengths in the measurement showed better correlations with the characteristics of the boards produced from the log [49].

Velocity effectively separated the visual quality classes but only for the first two classes when graded without considering log curvature. The presence of knots (large branches) influences the longitudinal propagation of mechanical waves, as they disturb the direction of the wood grain. The larger and more numerous the knots, the lower the velocity of the waves passing through the wood [37]. For this very reason, acoustic velocity is often used for the classification of wood products intended for construction, where the primary focus is on mechanical performance, where knots are a significant downgrading defect [50]. Consequently, lower quality classes with more and larger knots have a lower acoustic velocity.

On the other hand, shape defects, such as sweep, have less or no impact on these instrumental surveys, so a generalized sweep may not significantly reduce the velocity of the stress waves traveling through the timber. However, curvature can be important depending on the final use of the round wood, especially for the processing yield [17,51]. Logs with a pronounced curvature or local crooks are also more likely to contain reaction wood [52], which can result in sawing or drying defects during the subsequent processing phases.

Despite this limitation, acoustic tools are quick as well as easy to use and can provide useful information about wood quality. Similar conclusions were reached by Straže and Novak [43], who conducted acoustic measurements on oak logs prepared for auction, which had previously undergone visual grading.

4.3. Perspectives and Practical Implications

Photogrammetry clouds can effectively replace a field visit by a qualified grader, enabling remote assessments. Photogrammetry surveys require logs to be properly positioned (not stacked) and spaced to view three-fourths of the log surface, which also aligns with the requirements for visual assessments on site. A next implementation should consider the automation of point cloud analysis. Size measurements are already widely practiced in point cloud surveys conducted on standing trees for forest inventory purposes, mainly diameter and stem volume [47], and these techniques could be effectively adopted for logs lying on the ground after harvesting [27,28,29].

The measurement of curvature may prove more challenging. This evaluation has been conducted using LiDAR data in sawmills to properly position logs during sawing to obtain the highest possible yield or to classify curvature as well as sweep types and thus locate internal defects [8,52,53]. In this case, however, the lasers are fixed on the production line, and the logs move through them, so that the log orientation with respect to the reference axes in the three-dimensional cloud is always the same. Here, a greater computational effort appears necessary to first segment the individual logs, orient them properly, and then process them.

Also, the detection and measurement of surface defects could be challenging. Previous research on standing trees [18] may be applied and verified on logs [25,26]. Automating point cloud processing, however, is a key aspect to make the process exploitable at a large scale.

Overall, the application of “virtual” visual grading in timber sales has great potential. In some countries, particularly for species with higher added value, timber auctions are a well-established tradition. Buyers evaluate each piece of round timber individually and offer a price based on its characteristics and the products that can be derived from it. Even outside formal auctions, sales often rely on a buyer’s assessment to determine an appropriate price, aiming to reach an agreement with the seller. A possible approach could be providing a 3D survey to multiple buyers, enabling them to assess the timber remotely. This could expand market opportunities and increase accessibility.

A future challenge, also, is integrating visual assessments, potentially performed on point cloud surveys, with outputs from acoustic or other non-destructive instruments [54], since visual and instrumental assessments appear complementary in some aspects. Visual grading is crucial when the final product prioritizes esthetic qualities, while acoustic grading is more important when mechanical properties are critical.

No significant obstacles are expected in applying both techniques to other wood species. However, visual classification should follow rules specifically established for the species being assessed, while acoustic measurements should be verified and tailored, if necessary, to account for the velocity values specific to each species. Different species may exhibit different characteristics that define their quality. Nonetheless, the instrument survey remains entirely feasible.

5. Conclusions

Using a 3D reconstruction from a photogrammetry survey of logs, both remote dimensional measurements and visual classification into three quality groups proved feasible by analyzing a point cloud.

The quality groups obtained through visual grading were effectively distinguished using acoustic measurements taken on the same logs, using either the ToF- or resonance-based technique. However, complete agreement was only achieved when the classification was based on structural defects, particularly knots. When curvature was included as a parameter in grading, the agreement between the visual and instrumental qualification diminished. Sweep, indeed, is a defect with a low impact on the stress wave velocity but may have an important effect on the further processing of round timber.

The two evaluation techniques (visual and acoustic) can provide complementary information and could be used in combination or weighted depending on the timber’s intended use.