Small-scale forestry operations, which are used in non-industrial and site specific forestry, provide alternative logging methods suitable for precision forestry approach. In this study, a small-scale cable yarding system integrated with a portable winch was considered as alternative timber extraction method compared to a standard tower yarder. It was aimed to evaluate productivity of this yarding method, which was implemented during commercial harvesting activities performed in a 10.74 ha black pine stand located in Bursa province of Turkey. The main factors (i.e. slope, yarding distance, lateral pulling distance, log volume, log length), significantly affecting the productivity, were evaluated based on two slope classes (34–50% and 50–70%) and two yarding distances (100 m and 200 m). The results indicated that the average productivity was 0.95 m3/PMH and 0.90 m3/SMH. It was revealed that the productivity of the small-scale cable yarding decreased as the ground slope and the yarding distance increased. Statistical analysis indicated that there was a significant (p<0.001) relationship between productivity and log volume and length in four applications. On the other hand, it was found that there was a significant (p<0.05) relationship between productivity and lateral pulling distance in only the first application (34–50% slope; 100 m yarding distance). Considering the volume classes of the logs, it was determined that the productivity increased from the low volume class to the high volume class.

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.

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.

To further develop forest production, higher automation of forest operations is required. Such endeavour promotes research on unmanned forest machines. Designing unmanned forest machines that exercise forwarding requires an understanding of positioning and angle estimations of logs after cutting and delimbing have been conducted, as support for subsequent crane loading work. This study aims to improve the automation of the forwarding operation and presents a system to realize real-time automatic detection, positioning, and angle estimation of harvested logs implemented on an existing unmanned forest machine experimental platform from the AORO (Arctic Off-Road Robotics) Lab. This system uses ROS as the underlying software architecture and a Zed2 camera and NVIDIA JETSON AGX XAVIER as the imaging sensor and computing platform, respectively, utilizing the YOLOv3 algorithm for real-time object detection. Moreover, the study combines the processing of depth data and depth to spatial transform to realize the calculation of the relative location of the target log related to the camera. On this basis, the angle estimation of the target log is further realized by image processing and color analysis. Finally, the absolute position and log angles are determined by the spatial coordinate transformation of the relative position data. This system was tested and validated using a pre-trained log detector for birch with a mean average precision (mAP) of 80.51%. Log positioning mean error did not exceed 0.27 m and the angle estimation mean error was less than 3 degrees during the tests. This log pose estimation method could encompass one important part of automated forwarding operations.

Uncontrolled spread of eastern red cedar invades the United States Great Plains prairie ecosystems and lowers biodiversity across native grasslands. The eastern red cedar (ERC) infestations cause significant challenges for ranchers and landowners, including the high costs of removing mature red cedars, reduced livestock forage feed, and reduced revenue from hunting leases. Therefore, a fleet of autonomous ground vehicles (AGV) is proposed to address the ERC infestation. However, detecting the target tree or trunk in a rangeland environment is critical in automating an ERC cutting operation. A tree trunk detection method was developed in this study for ERC trees trained in natural rangeland environments using a deep learning-based YOLOv5 model. An action camera acquired RGB images in a natural rangeland environment. A transfer learning method was adopted, and the YOLOv5 was trained to detect the varying size of the ERC tree trunk. A trained model precision, recall, and average precision were 87.8%, 84.3%, and 88.9%. The model accurately predicted the varying tree trunk sizes and differentiated between trunk and branches. This study demonstrated the potential for using pretrained deep learning models for tree trunk detection with RGB images. The developed machine vision system could be effectively integrated with a fleet of AGVs for ERC cutting. The proposed ERC tree trunk detection models would serve as a fundamental element for the AGV fleet, which would assist in effective rangeland management to maintain the ecological balance of grassland systems.

Urbanization, shrinking markets, and reduced forestry investment may affect harvesting efficiency in regions of the US South. To monitor these conditions, logging businesses have been tracked by surveys conducted by universities and trade associations. This project used a sampling approach coordinated with FIA utilization studies to sample logging crews based on a harvesting location. The approach was used to develop relationships among firm attributes and site attributes in six southeastern states (AL, GA, FL, NC, SC, and VA) from 2011 to 2018. The data included harvest attributes (location, harvest size and stand type) and logging firm attributes (production, crew labor, crew number, the number of machines by type, and machine age). For crew capital value, an equation was developed for this study using machine number and average machine age. The data from logging crews on 419 harvests were analyzed by region, harvest size, and stand type. Mean values for crew labor ranged from 3.1 to 7.1 workers. The average capital value per crew ranged from $220,000 to $524,000 per crew in the Coastal Plain with a narrower range in the Piedmont. In the Coastal Plain, higher productivity was detected for larger harvests and pine versus hardwood and mixed stands; however, in the Piedmont those trends were less obvious. Ratio of feller-bunchers, skidders and loaders were mostly 1:1:1 or 1:2:1 with 41% and 24% of samples, respectively. There were notable trends among Coastal Plain loggers regarding capital value and productivity with evidence supported by a production function. The differences in Piedmont (e.g., ownership size, market access, terrain, population density, etc.) may combine to limit daily production and labor productivity.

The influence of elevation on the forest development and also on more structural elements is evident. The aim of this paper is to research the impact of elevation on the relationship between diameter at breast height (DBH), tree height (H), crown length (L) and stem volume (V). In the area of the Pelister National Park in North Macedonia, 22 experimental plots (EPs) were established in the even-age Pinus peuce Gris. stands with an average age of 90 years. The EPs were of a circular form and covered an area of 500 m2 each; 6 of them were established at 1150 meter above sea level (m asl), 7 at 1350 m asl and 9 at 1550 m asl. DBH, tree height, and crown length of 481 trees were measured in all EPs. The DBH – H model was prepared in accordance with Prodan, as well as nonlinear (polynomial) regression for the relation between DBH and L and nonlinear (power) regression for the relation between DBH and V. The stem volume was calculated with a formula by Parishko for Pinus peuce Gris. The quadratic mean of DBH, average Loray height, average crown ratio, and the density of the stands were also calculated. The relationship between DBH and H, L, and V was examined with Pearson correlation and root mean square deviation (RMSE). The differences between averages of H, L, and V from the EPs were tested with analysis of variance (ANOVA) with an elevation class (1150, 1350 and 1550 m asl) as single factor. The density of stands was 490, 429 and 409 trees per ha on 1150 m, 1350 m and 1550 m asl, respectively. The average DBH was 39.8 cm, 46.5 cm and 45.5 cm, and Loray height was 23.9 m, 24.1 m and 22.6 m at 1150 m, 1350 m and 1550 m asl, respectively. Crown ratio (CR) pointed out different results on the different elevations, with the average value of 40.5%, 43.7%, and 39.3% at 1150 m, 1350 m, and 1550 m asl, respectively. Differences between average structural elements at different elevations can be confirmed with ANOVA with a significance of p<0.05 and F of 3.4 for H, the significance of p<0.05 and F 3.2 for L data and p<0.05 and F of 9.7 for the value of V. In that way, the regression model for H is higher at a lower elevation, the tree has a longer crown length at lower elevation and also has a bigger volume at lower elevation. From the results, it can be concluded that the elevation has an influence on the relationship between DBH on the one hand and H, L and V as structural elements on the other hand. It can be said that at higher elevation trees have a smaller average height, DBH, and volume and have longer crown length than trees at lower elevation.

Salvage harvesting is common in the US South following natural disasters such as tornadoes and hurricanes; nevertheless, few studies have evaluated the productivity and costs of these harvests because of their geographic dispersion and the short interval between natural disasters and salvage harvesting. An Enhanced Fujita Scale 3 (EF3) tornado with winds in excess of 250 km per hour struck Aiken County, South Carolina in April of 2020, uprooting trees and severing other stems above breast height. The goal of this study was to estimate the productivity and cost of salvage harvesting in loblolly pine (Pinus taeda L.) stands following severe tornado damage. Salvage harvests were conducted with a rubber-tired drive-to-tree feller-buncher, grapple skidder, tracked loader, and chipper. All stems were chipped and used to produce energy; no roundwood was produced from the harvests. Elemental time-and-motion studies were conducted in three pulpwood-sized stands (<30 cm large-end diameter) and three sawtimber-sized stands (≥30 cm large-end diameter). Hourly harvesting costs were estimated using the machine rate method and per-ton costs were estimated using a modified version of the Auburn Harvesting Analyzer. Skidding productivity was low in each harvest unit, but especially so in the three pulpwood-sized stands because of stem breakage and low weight per stem. Harvesting costs averaged $29.78 and $19.97 (USD) per tonne (onboard truck) in the pulpwood- and sawtimber-sized stands, respectively. High salvage harvesting costs mean that landowners can expect significantly reduced stumpage prices from these harvests; nonetheless, landowners do benefit from reduced reforestation costs. Harvesting promptly after a tornado can reduce harvesting costs and increase timber value recovery.

Yarding whole trees is the most efficient way of extracting timber in steep terrain and allows reaping the combined benefits of mechanization and biomass recovery. In downhill yarding, however, whole-tree extraction is associated with a greater risk of loosening rocks or debris by the incoming loads as they bounce around along the extraction corridor. That may also cause damage to the cables and anchors by corresponding shock loads, ultimately endangering the yarder and its crew. To avoid these risks, »double-hitch carriages« can be employed. They combine a conventional motorized dropline carriage with a secondary carriage (»trailer«), equipped with a further, independent dropline winch. Thus, loads can be attached at two points and transported fully suspended above the ground in a horizontal position.Operation of these carriages may not be limited to the »horizontal« mode: the main carriage could also be operated without trailer (»single« mode), or separate loads may be attached to the two droplines (»double« mode), but their impact on the efficiency and economy of yarding operations is yet unknown. Therefore, the present study investigated how these modes affect the productivity and cost of downhill whole tree yarding. To this end, a classic time and motion study was conducted during a salvage logging operation in Northern Italy under a strictly controlled experimental design.Average productivity (18.2±7.2 to 24.5±15.4 m³ PSH0-1 merchantable volume per productive system hour excluding delays) and extraction cost (18 to 20 Euro m-³) did not differ significantly between treatments, while load composition and time consumption by task did. More (2.2±0.5) pieces per load were yarded under the »double«, than under the »single« (1.4±0.5) and »horizontal« (1.1±0.3) treatments. Inhaul speed (3.1±0.6 m s-1) was significantly higher under the »horizontal« treatment, which compensated for increased loading time derived from attaching the load at least at one point outside the corridor. Unloading took significantly longer under the »double« treatment, as loads had to be dropped successively due to the confined conditions on the landing. Though slowest (2.5±0.9 m s-1) during inhaul, the »single« treatment exhibited none of the other treatments disadvantages and larger loads could be accumulated due to partial suspension. From an economic viewpoint, the »horizontal« mode may only be warranted over yarding distances substantially beyond average. On shorter ones, it must be justified by other reasons, such as minimizing product contamination, soil disturbance or excessive strain to the skyline when the terrain profile impedes sufficient ground clearance.

Drying forest biomass at roadside can reduce transport costs and greenhouse gas emissions by reducing its weight and increasing its net calorific value. Drying models are required for forest supply chain analysis to determine optimum storage times considering storage costs and returns. The study purpose was to evaluate the impact of the source of meteorological data on the goodness of fit and practical application of Eucalyptus nitens log pile drying models. The study was conducted in Long Reach, NE Tasmania, Australia from the 6th of February to 6th of August 2020. Four data sources were compared: the nearest meteorological station, interpolated meteorological data, a portable weather station, and digital temperature/RH sensors. Predicted moisture content (MC) values from the only previously published E. nitens log pile drying model were also evaluated using the current study data sources as inputs.Log pile MC changes were determined from weight changes measured by placing the study logs on a steel frame bolted to load cells at each corner. As the study was based on debarked logs, dry matter losses were assumed to be negligible. Initial MC of the logs was determined by extracting samples using an electric drill and drying them until constant weight was achieved.Initial log pile drying rates were high with several daily MC losses >2%. Portable weather station data produced the best goodness of fit drying model. The second-best goodness of fit model was based on meteorological station data. From a user acceptability perspective (highest proportion of results within ±5% of measured values), the best model was based on temperature/RH sensor data. Goodness of fit measures for the temperature/RH sensor data model were poorer than for the other data sources, but still acceptable. The published E. nitens log drying model had the poorest results for goodness of fit and user acceptability.In conclusion, portable weather stations are best suited to research trials due to the expense of placing a weather station at each log pile. Drying models based on data from the nearest meteorological station or temperature/RH sensors are best suited for practical applications, such as forest supply chain analysis. Additional benefits could accrue from a forest estate-wide network of low cost temperature/RH sensors potentially supplying data to forest supply chain analysis as well as fire prediction and tree growth models.