Effects of a mobile LiDAR-based thinning density assistant (TDA) system on harvester operator performance

Graph SLAM correction for single scanner MLS forest data under boreal forest canopy

Prior tree marking is used to guide loggers or forest machine operators on which trees to cut to achieve the desirable silvicultural quality of a thinning treatment. In the future, this beneficial but expensive human work could be automated with advanced driver assistance systems. This study aimed to investigate the effect of conventional prior tree marking on cutting productivity and harvesting quality of the first and later thinnings. A comparative time study was conducted with four experienced harvester operators. The operators thinned 4825 stems with the cut-to-length (CTL) harvesting method in eight thinning stands. The time consumption of the different time elements of cutting work was measured to model the cutting productivity with average values or regress these values against the stem volume or density of removal. Prior tree marking increased the cutting productivity by an average of 2.8% in the first thinnings and 2.7% in later thinnings by reducing the time consumption of boom-out (positioning the harvester head for cut) and moving. The operator effect was notable, even though only experienced operators participated in the study. For some operators, prior tree marking did not make cutting work more efficient, and sometimes hampered it. Prior tree marking improved the quality of the remaining stands in thinnings by producing a more accurate density of remaining trees after the harvesting operation in relation to thinning guidelines. When the stands were not marked, the operators chose trees of poor quality with almost the same accuracy as the forester. These findings lay the foundation for the next-generation operators' guidance and decision support systems, which could detect trees around the harvester and guide the operator in tree selection and managing better thinning intensity in cutting work. Although prior tree marking increased productivity only marginally, the improvement in the quality of harvesting operations must be acknowledged.

There is an annual increase in the number of modern forestry machines at logging enterprises both in Russia and in foreign countries. At the same time, the fleet of forestry machines is becoming more diverse and multifunctional, and their manufacturers, competing for market outlets, find themselves interested in constant upgrading of existing and development of new design and technological solutions. The research is aimed at finding a new engineering solution that will allow along with harvesting high-quality logs to perform their partial debarking without a significant reduction in the processing speed. The effective result is achieved by the fact that the proposed new method of processing tree trunks provides the possibility of timber barking in strips, i.e. removing the bark from a fallen tree along its entire length, as well as bast removal (partially or completely). The method involves, as in the traditional model of harvesting (processing) head functioning, crosscutting of the trunk by the sawing mechanism and delimbing by delimbing knives. This leads to accelerated natural drying of wood during its storage in the warehouses and significantly increases the floatability of thin coniferous logs during their further timber floating. The implementation of the proposed option of harvesting debarked timber at the logging site and the working body of harvesters (processors) to carry out such harvesting will increase the share of logging waste at the logging site. The design of the new harvesting (processing) head does not require significant financial expenses for its upgrading. The design of the new harvester head allows the logs to be barked in strips along the entire length. Additional drive of working mechanisms is not required. This will enable the implementation of rough debarking with minimal financial costs already at the stage of logging operations and increase the efficiency of logging and timber processing enterprises.

Large-scale inventory in natural forests with mobile LiDAR point clouds

Wildfires burn heterogeneously across the landscape and create complex forest structures. Quantifying the structural changes in post-fire forests is critical to evaluating wildfire impacts and providing insights into burn severities. To advance the understanding of burn severities at a fine scale, forest structural attributes at the individual tree level need to be examined. The advent of drone laser scanning (DLS) and mobile laser scanning (MLS) has enabled the acquisition of high-density point clouds to resolve fine structures of individual trees. Yet, few studies have used DLS and MLS data jointly to examine their combined capability to describe post-fire forest structures. To assess the impacts of the 2017 Elephant Hill wildfire in British Columbia, Canada, we scanned trees that experienced a range of burn severities 2 years post-fire using both DLS and MLS. After fusing the DLS and MLS data, we reconstructed quantitative structure models to compute 14 post-fire biometric, volumetric, and crown attributes. At the individual tree level, our data suggest that smaller pre-fire trees tend to experience higher levels of crown scorch than larger pre-fire trees. Among trees with similar pre-fire sizes, those within mature stands (age class: > 50 years) had lower levels of crown scorch than those within young stands (age class: 15—50 years). Among pre-fire small- and medium-diameter trees, those experiencing high crown scorch had smaller post-fire crowns with unevenly distributed branches compared to unburned trees. In contrast, pre-fire large-diameter trees were more resistant to crown scorch. At the plot level, low-severity fires had minor effects, moderate-severity fires mostly decreased tree height, and high-severity fires significantly reduced diameter at breast height, height, and biomass. Our exploratory factor analyses further revealed that stands dominated by trees with large crown sizes and relatively wide spacing could burn less severely than stands characterized by regenerating trees with high crown fuel density and continuity. Overall, our results demonstrate that fused DLS-MLS point clouds can be effective in quantifying post-fire tree structures, which facilitates foresters to develop site-specific management plans. The findings imply that the management of crown fuel abundance and configuration could be vital to controlling burn severities.

Modern forestry is increasingly confronted with challenges that appear with intensive forest management and the progression of the effects of climate change. The forestry sector is able to react to the changing conditions by adapting management plans, forest structure or planting tree species with a higher stress resistance. However, during stand management activities, silvicultural treatments and harvesting operations can have an impact on the further development of the remaining forest ecosystem. In Germany, the most widely used harvesting system for thinning operations is a single-grip harvester used for felling and processing trees followed by a forwarder for timber extraction from the machine operating trails to roadside. In this research project, debarking rollers and other modifications designed for Eucalyptus harvesting heads were tested on conventional harvesting heads for the first time to assess the possibility of adding debarking to mechanized forest operations under Central European conditions. Seven field tests with varying tree species, diameters and age classes, were established within German state forests in Lower Saxony and in Bavaria. These tests were repeated in both summer and winter seasons to evaluate the influence of associated tree sap flows on debarking quality. Three different harvesting heads were modified to assess the altered mechanical characteristics and setups. To assess debarking ability originating from head modifications, a photo-optical measurement system developed within the scope of the project was used. The results demonstrate that especially for summertime operations, simple modifications to currently used harvesting heads are able to provide an average debarking efficiency up to 90% depending on the modifications. Another key finding is that a negatively affected sap flow, experienced during wintertime operations, resulted in 46% lower debarking efficiency, while spruce bark beetle infestations only resulted in a wider spread of the variation. Additionally, the vertical position of the log within the tree proved to have an influence on debarking efficiency, resulting in 15% lower average debarking for butt logs and 9% for top logs as compared to middle logs. Since a debarking process requires the stem to be fed through the harvesting head on multiple occasions to remove bark, average harvesting productivity might be reduced by approx. 10% compared to productivity measured with conventional harvesting heads. Considering the results and the extent of the modifications, the system proved to be a potential addition to existing harvesting methods facing changing challenges in future forestry.

Background and AimsIn addition to terrestrial laser scanning (TLS), mobile laser scanning (MLS) is increasingly arousing interest as a technique which provides valuable 3-D data for various applications in forest research. Using mobile platforms, the 3-D recording of large forest areas is carried out within a short space of time. Vegetation structure is described by millions of 3-D points which show an accuracy in the millimetre range and offer a powerful basis for automated vegetation modelling. The successful extraction of single trees from the point cloud is essential for further evaluations and modelling at the individual-tree level, such as volume determination, quantitative structure modelling or local neighbourhood analyses. However, high-precision automated tree segmentation is challenging, and has so far mostly been performed using elaborate interactive segmentation methods.MethodsHere, we present a novel segmentation algorithm to automatically segment trees in MLS point clouds, applying distance adaptivity as a function of trajectory. In addition, tree parameters are determined simultaneously. In our validation study, we used a total of 825 trees from ten sample plots to compare the data of trees segmented from MLS data with manual inventory parameters and parameters derived from semi-automatic TLS segmentation.Key ResultsThe tree detection rate reached 96 % on average for trees with distances up to 45 m from the trajectory. Trees were almost completely segmented up to a distance of about 30 m from the MLS trajectory. The accuracy of tree parameters was similar for MLS-segmented and TLS-segmented trees.ConclusionsBesides plot characteristics, the detection rate of trees in MLS data strongly depends on the distance to the travelled track. The algorithm presented here facilitates the acquisition of important tree parameters from MLS data, as an area-wide automated derivation can be accomplished in a very short time.

The working environment of timber harvester operators has changed dramatically over the past fifteen years. The operator’s physical workload has decreased while the proportion of mental load has increased, as a consequence of the increased responsibilities involved in the cutting work. The decision making during the work has also increased and speeded up considerably. Therefore, the importance of the operator, with regards to harvester productivity, has been emphasized as a result of the equalization of the different harvester brands. For this reason, more and more attention is paid to the operator with the expectation of reaching certain productivity levels. This also places extra expectations on the operator’s training; especially in demanding cutting conditions, such as in first thinning, where the operator’s abilities are tested the most. The principal objective of this research was to discover and describe a productive working technique for harvester work in first thinning and to improve harvester operator training by highlighting the problems of harvester simulators and determining the important cognitive abilities needed in harvester work. The work of six professional harvester operators was studied using numerous data collection methods: time study, working technique observation, helmet camera video recording, virtual harvester simulator cutting and psychological tests. In addition, 40 harvester operator students participated in the psychological tests. The results indicated that when working productively, in first thinning conditions, the moving distance of the harvester head is minimized. In positioning the harvester head to a removable tree the positioning distance should be short. In felling a removable tree, the tree should be moved only the distance that fluent boom work necessitates. The work should be planned so that reversing is avoided and non-productive time, such as clearing of small trees, is minimal. From the fluent boom working point of view the results showed the operators’ consistent method to locate the harvester optimally according to the edge trees of the strip road. Based on this a productive working technique for harvester work in first thinning was created and described. A productive working technique can increase productivity by 10 to 15%. In addition, the handling of trees located in different places around the harvester was theorized. The results also indicated that the virtual harvester simulators are applicable for harvester training when the trainees are conscious of the limitations of the simulators. From the point of view of harvester operator training and operator selection the psychological tests indicated that productive and skilful harvester operating is not solely explained by one cognitive ability, instead, the mastering of different kinds of abilities appears to be more important. By combining the productive working technique with the operator training and taking into account the cognitive challenges faced in harvester work, for example, work planning and perception, the graduated students are likely to be more productive and ready to meet the challenges of working life.

Abstract. Road markings as critical feature in high-defination maps, which are Advanced Driver Assistance System (ADAS) and self-driving technology required, have important functions in providing guidance and information to moving cars. Mobile laser scanning (MLS) system is an effective way to obtain the 3D information of the road surface, including road markings, at highway speeds and at less than traditional survey costs. This paper presents a novel method to automatically extract road markings from MLS point clouds. Ground points are first filtered from raw input point clouds using neighborhood elevation consistency method. The basic assumption of the method is that the road surface is smooth. Points with small elevation-difference between neighborhood are considered to be ground points. Then ground points are partitioned into a set of profiles according to trajectory data. The intensity histogram of points in each profile is generated to find intensity jumps in certain threshold which inversely to laser distance. The separated points are used as seed points to region grow based on intensity so as to obtain road mark of integrity. We use the point cloud template-matching method to refine the road marking candidates via removing the noise clusters with low correlation coefficient. During experiment with a MLS point set of about 2 kilometres in a city center, our method provides a promising solution to the road markings extraction from MLS data.

In this work, we compared six emerging mobile laser scanning (MLS) technologies for field reference data collection at the individual tree level in boreal forest conditions. The systems under study were an in-house developed AKHKA-R3 backpack laser scanner, a handheld Zeb-Horizon laser scanner, an under-canopy UAV (Unmanned Aircraft Vehicle) laser scanning system, and three above-canopy UAV laser scanning systems providing point clouds with varying point densities. To assess the performance of the methods for automated measurements of diameter at breast height (DBH), stem curve, tree height and stem volume, we utilized all of the six systems to collect point cloud data on two 32 m-by-32 m test sites classified as sparse (n = 42 trees) and obstructed (n = 43 trees). To analyze the data collected with the two ground-based MLS systems and the under-canopy UAV system, we used a workflow based on our recent work featuring simultaneous localization and mapping (SLAM) technology, a stem arc detection algorithm, and an iterative arc matching algorithm. This workflow enabled us to obtain accurate stem diameter estimates from the point cloud data despite a small but relevant time-dependent drift in the SLAM-corrected trajectory of the scanner. We found out that the ground-based MLS systems and the under-canopy UAV system could be used to measure the stem diameter (DBH) with a root mean square error (RMSE) of 2–8%, whereas the stem curve measurements had an RMSE of 2–15% that depended on the system and the measurement height. Furthermore, the backpack and handheld scanners could be employed for sufficiently accurate tree height measurements (RMSE = 2–10%) in order to estimate the stem volumes of individual trees with an RMSE of approximately 10%. A similar accuracy was obtained when combining stem curves estimated with the under-canopy UAV system and tree heights extracted with an above-canopy flying laser scanning unit. Importantly, the volume estimation error of these three MLS systems was found to be of the same level as the error corresponding to manual field measurements on the two test sites. To analyze point cloud data collected with the three above-canopy flying UAV systems, we used a random forest model trained on field reference data collected from nearby plots. Using the random forest model, we were able to estimate the DBH of individual trees with an RMSE of 10–20%, the tree height with an RMSE of 2–8%, and the stem volume with an RMSE of 20–50%. Our results indicate that ground-based and under-canopy MLS systems provide a promising approach for field reference data collection at the individual tree level, whereas the accuracy of above-canopy UAV laser scanning systems is not yet sufficient for predicting stem attributes of individual trees for field reference data with a high accuracy.

Genetic implications of silvicultural regimes

The acquisition of high-quality reference data is essential for effectively modelling forest attributes. Incorporating close-range Light Detection and Ranging (LiDAR) systems into the reference data collection stage of remote sensing-based forest inventories can not only increase data collection efficiency but also increase the number of attributes measured with high quality. Therefore, we propose a model-based forest inventory method that uses reference data collected by a car-mounted mobile laser scanning (MLS) system along boreal forest roads. This approach is used for the estimation of diameter at breast height (DBH) and stem volume at the individual tree-level from airborne laser scanning (ALS) data. In addition, we compare the estimates obtained using the proposed method with the ones derived from reference data collected by traditional field inventory of 265 field plots systematically distributed over the study area. The accuracy of the estimates remained comparable regardless of the reference dataset used for estimation of DBH and stem volume. When using the field inventory dataset for model training, the root mean square error (RMSE) of DBH estimates were 4.06 cm (18.8 %) for Norway spruce trees, 6.3 cm (29.6 %) for Scots pine and 8.61 cm (55.9 %) for deciduous trees. Similarly, when evaluating predictions based on the MLS dataset as reference, RMSEs were equal to 3.97 cm (18.4 %) for Norway spruce, 6.12 cm (28.8 %) for Scots pine, and 8.98 cm (58.3 %) for deciduous trees. In general, biases were below 1 cm for most species classes, with the exception of deciduous trees. The accuracy of stem volume also had RMSEs varying across different tree species. For the estimates based on traditional field inventory, the RMSEs were 0.176 m3 (38.8 %) for Norway spruce, 0.228 m3 (52.4 %) for Scots pine and 0.246 m3 (158 %) for deciduous trees. When using the MLS dataset as a reference, the RMSEs were equal to 0.176 m3 (38.8 %), 0.228 m3 (52.4 %), and 0.246 m3 (158 %) for Norway spruce, Scots pine, and deciduous trees, respectively. Car-mounted MLS demonstrated its potential as an efficient alternative for collecting reference data in remote sensing-based forest inventories, which could complement traditional methods.

The point clouds acquired by a vehicle-borne mobile laser scanning (MLS) system have shown great potential for many applications such as intelligent transportation systems, road infrastructure inventories, and high-definition (HD) maps to support the advanced driver-assistance systems (ADAS) and autonomous vehicles (AVs). This paper presents a novel two-step approach to automated detection and reconstruction of three-dimensional (3D) highway curves from MLS point clouds. However, when dealing with noisy, unstructured, dense point clouds, we often face some challenges, most notably in handling of the outliers introduced during road marking detection and in recognition of curve types during 3D curve reconstruction. Our approach is formed by two main algorithms: a detector based on intensity variance and a robust model fitting estimator. The experimental results obtained using both a virtual scan dataset and a real MLS dataset demonstrated that our approach is very promising in handling of the outliers and reconstruction of 3D road curves. Specifically, a relative accuracy of 0.6% has been achieved in estimation of circle radii based on the virtual scan dataset. A comparative study also showed that our road marking detection approach is more effective and more stable than state-of-the-art approaches.

Loader/mixers/applicators and workers engaged in thinning and harvesting grapes in the central valley of California, were monitored for exposure to captan. Urinalyses for the captan metabolite, tetrahydrophthalimide (THPI), was intended to be the biological, index for exposure to captan. Dislodgeable residues of captan and THPI on foliage and grapes were measured as well as levels found in high volume air samplers placed in the field at the time of foliage sampling. Loader/mixer/ applicators were monitored following an application of a 10% dust and a wettable powder (W.P.) formulation just prior to thinning operations, and again at harvest with a W.P. formulation. Loader/ mixer/applicators were also monitored with and without respiratory protection with each treatment. The half-life of captan for the dust application was 12.8 days, and 19.6 days for W.P. Tetrahydrophthalimide levels were very low in all the field and clothing samples and were not a significant factor in worker exposure. Levels of captan in the air during the loading/mixing operation were higher. Although there were exceptions, gloves contained the highest levels of residues in the loader/mixer/applicators and the field workers. The relative levels found on the patches were reflective of the work habits of the workers. The applicators of the 10% dust had the highest captan levels in the personnel air samplers and on their mask or respirator pads. The results from the urine analysis, although THPI was found, showed no significant differences in trends between the pre- and post-exposure samples except possibly the individual who wore the respirator during the dusting operation. Pre- and post-exposure urine samples of field workers engaged in the thinning and harvesting operation showed no significant differences except workers engaged in the dusted plot. Dislodgeable foliage levels and glove residues from workers are used to measure potential dermal exposure, and since these two parameters were lowest in the dusted plots coupled with the same or slightly higher levels found in the high volume air samples in the dusted plot, they support the importance of investigating inhalation as a primary source of exposure to captan. Estimated dermal exposure in mg/person in relation to each pound of captan applied by loader/mixer/applicators was highest with those engaged in the dusting operation; the applicators during the harvest operation had much lower levels compared to the loader/ mixer/applicators during the thinning operation.

Confidence-guided roadside individual tree extraction for ecological benefit estimation