Speaker
Description
Accurate timber measurement is essential for forestry and the wood industry. Lower-value assortments such as pulpwood or energy wood are commonly assessed by manually measuring the gross stack volume and converting it to net volume using conversion factors. These traditional methods are labor-intensive and prone to human error, highlighting the need for fast and objective digital alternatives.
This study evaluates three LiDAR systems (a terrestrial laser scanner (TLS), a personal laser scanner (PLS), and a LiDAR-equipped tablet) and four commercial photo-optical applications (iFovea, LogStackLIDAR, PolterMAX, and Timbeter) for measuring gross volume, net volume, and conversion factors of wood stacks under operational conditions representative of Austrian forestry practice. In total, 22 wood stacks were measured at a timber yard in Austria. Reference net volumes were obtained from electronic 3D log measurements at a sawmill, while reference gross volumes were derived by simulating measurements according to the German roundwood trading framework on high-precision TLS point clouds.
For LiDAR-based measurements, log ends were extracted from the 3D point clouds, rasterized, and analyzed separately for both stack faces. Gross volume was estimated by constructing an alpha-hull around the rasterized log ends to determine the face area and multiplying it by the assortment length. Net volume was calculated from the summed raster area representing solid wood, and also multiplied by log length. The conversion factor was derived as the ratio of net to gross volume.
The LiDAR-equipped tablet showed the highest combined accuracy (RMSE_gross = 2.60%; RMSE_net = 3.58%; RMSE_conversion = 3.33%), outperforming both the photo-optical applications and even the TLS. This result demonstrates that low-cost, consumer-grade LiDAR sensors can provide high measurement precision under practical field conditions. The TLS also produced reliable results, though minor occlusion effects likely reduced its performance in net volume estimation. The PLS delivered accurate gross volume estimates but showed substantial variability in net volume and conversion factor results, mainly due to fluctuations in point cloud density linked to scan duration.
Photo-optical applications achieved high accuracy for gross volume (RMSE_gross = 4.73% – 8.78%). However, net volume accuracy varied more (RMSE_net = 5.28% – 10.74%). The widely used quadrant method, simulated in this study, showed accuracy comparable to photo-optical approaches but lacked their efficiency and automation advantages.
Regarding field efficiency, photo-optical methods were fastest on average per stack, with Timbeter (0:53 min) and PolterMAX (1:14 min) requiring the least time. The PLS (1:42 min) and LiDAR-equipped tablet (2:39 min) required comparable field times, whereas TLS measurements were substantially slower (6:54 min). Field time variability was particularly high for iFovea and LogStackLIDAR due to occasionally required manual corrections caused by wood stacks in the background.
Overall, the results confirm the strong potential of LiDAR-based approaches for accurate, efficient, and automated wood stack measurement. By enabling reliable estimation of gross volume, net volume, and conversion factors, LiDAR technology can enhance transparency, traceability, and data continuity across the forestry supply chain. The demonstrated performance of the consumer-grade tablet suggests promising opportunities for cost-effective implementation in forestry operations.
| Keywords | photo-optical; LiDAR; forestry digitalization |
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