Gollob Christoph, MSc

Cross-cutting of a tree into a set of assortments (»bucking pattern«) presents a large potential for optimizing the volume and value recovery; therefore, bucking pattern optimization has been studied extensively in the past. However, it has not seen widespread adoption in chainsaw bucking, where time consuming and costly manual measurement of input parameters is required for taper curve estimation. The present study investigated an alternative approach, where taper curves are fit based on terrestrial laser scanning data (TLS), and how deviations from observed taper curves (REF) affect the result of bucking pattern optimization. In addition, performance of TLS was compared to a traditional, segmental taper curve estimation approach (APP) and an experienced chainsaw operator’s solution (CHA).

A mature Norway Spruce stand was surveyed by stationary terrestrial laser scanning. In TLS, taper curves were fit by a mixed-effects B-spline regression approach to stem diameters extracted from 3D point cloud data. A network analysis technique algorithm was used for bucking pattern optimization during harvesting. Stem diameter profiles and the chainsaw operator’s bucking pattern were obtained by manual measurement. The former was used for post-operation fit of REF taper curves by the same approach as in TLS. APP taper curves were fit based on part of the data. For 35 trees, TLS and APP taper curves were compared to REF on  tree, trunk and crown section level. REF and APP bucking patterns were optimized with the same algorithm as in TLS. For 30 trees, TLS, APP and CHA bucking patterns were compared to REF on operation and tree level.

Taper curves were estimated with high accuracy and precision (underestimated by 0.2 cm on average (SD=1.5 cm); RMSE=1.5 cm) in TLS and the fit outperformed APP. Volume and value recovery were marginally higher in TLS (0.6%; 0.9%) than in REF on operation level, while substantial differences were observed for APP (–6.1%; –4.1%). Except for cumulated nominal length, no significant differences were observed between TLS and REF on tree level, while APP result was inferior throughout. Volume and value recovery in CHA was significantly higher (2.1%; 2.4%), but mainly due to a small disadvantage of the optimization algorithm.

The investigated approach based on terrestrial laser scanning data proved to provide highly accurate and precise estimations of the taper curves. Therefore, it can be considered a further step towards increased accuracy, precision and efficiency of bucking pattern optimization in chainsaw bucking.

The demand for increased efficiency in timber harvesting has traditionally been met by continuous technical improvements in machines and an increase in mechanisation. The use of active and passive sensors on machines enables improvements in aspects such as operational efficiency, fuel consumption and worker safety. Timber harvesting machine manufacturers have used these technologies to improve the maintenance and control of their machines, to select and optimise harvesting techniques and fuel consumption. To a more limited extent, it has also been used to evaluate the time taken to complete tasks. The systematic use of machine sensor data, in a central database or cloud solution is a more recent trend.

Machine data is recorded over long periods of time and at high resolution. This data therefore has considerable potential for scientific investigations. For mechanised timber harvesting operations, this could include a better understanding of the interaction between productivity and operational parameters, which first of all requires an efficient determination of cycle time.

This study was the first to automatically delimitate tower yarder cycle times from machine sensor data. In addition to machine sensor data, cycle times were collected through a traditional manual time and motion study, and cycle times from both studies were compared to a reference cycle time determined from video footage of the yarder in operation.

Based on three days of detailed time study, the total cycle time in the classic manual time (–1.3%) and in the machine sensor data (–1.2%) was only slightly shorter than in the reference study, and the average cycle time did not differ significantly (classic manual time study: –0.08±0.94 min, p=0.997; machine sensor data study: –0.08±0.26 min, p=0.997). However, the accuracy of the machine sensor approach (RMSE=0.92) was more than three times higher than that of the classic manual time study (RMSE=0.27).

With the integration of sensors on forestry machines now being commonplace, this study shows that machine sensor data can be reliably interpreted for time study purposes such as machine or system optimisation. This eliminates the need for manual time study, which can be both cumbersome and dependent on the experience of the observer, and allows long term data sets to be obtained and analysed with comparatively little effort. However, a truly automated time study needs to be supplemented with automated determination of and linkage to other operational parameters, such as yarding and lateral yarding distance or load volume.

Introduction: Cable yarding is a technology that enables efficient and sustainable use of timber resources in mountainous areas. Carriages as an integral component of cable yarding systems have undergone significant development in recent decades. In addition to mechanical and functional developments, carriages are increasingly used as carrier platforms for various sensors. The goal of this study was to assess the accuracy of individual standing tree and stand variable estimates obtained by a mobile laser scanning system mounted on a cable yarder carriage.

Methods: Eight cable corridors were scanned across two forest stands. Four different scan variants were conducted, differing in the movement speed of the carriage and the direction of movement during scanning. An algorithm for tree detection, diameter and height estimation was applied to the 3D datasets and evaluated against manual tree measurements.

Results: The analysis of the 3D scans showed that the individual tree parameters strongly depend on the scan variant and the distance of each individual tree to the skyline. This was due to changing 3D point densities and occlusion effects. It turned out that scan variant 1, in which the scan was performed during slow carriage movement downwards and back upwards again, was advantageous. At a distance of 10 m, which is half of the recommended corridor spacing of 20 m for whole tree cable yarding, 95.44% of the trees in stand 1 and 92.16% of the trees in stand 2 could be detected automatically. The corresponding root mean sqare errors of the diameter at breast height estimatimations were 1.59 cm and 2.23 cm, respectively. The root mean square errors of the height measurements were 2.94 m and 4.63 m.

Conclusions: The results of this study can help to further advance the digitization of cable yarding and timber flow from the standing tree to the sawmill. However, this requires further development steps in cable yarder, carriage, and laserscanner technology. Furthermore, there is also a need for more efficient software routines to take the next steps towards precision forestry.