Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2024
Abstract
Uneven-aged forests set certain challenges for cut-to-length harvesting work. It is a challenge to cost-effectively remove larger trees while leaving a healthy understory for regrowth. The study’s aim was to evaluate productivity and costs of harvesting two-storied silver birch (Betula pendula Roth) and Norway spruce (Picea abies (L.) H. Karst.) stands by creating time consumption models for cutting, and using existing models for forwarding. Damage to the remaining understory spruce was also examined. Four different harvesting methods were used: 1) all dominant birches were cut; 2) half of them thinned and understory was preserved; compared to 3) normal thinning of birch stand without understory; and 4) clear cutting of two-storied stand. Results showed the time needed for birch cutting as 26–30% lower when the understory was not preserved. Pulpwood harvesting of small sized spruces that prevent birch cutting was expensive, especially because of forwarding of small amounts with low timber density on the strip roads. Generally, when taking the cutting and forwarding into account, the unit cost at clear cuttings was lowest, due to lesser limitations on work. It was noted that with increasing removal from 100 to 300 m3 ha–1, the relative share of initial undamaged spruces after the harvest decreased from 65 to 50% when the aim was to preserve them. During summertime harvesting, the amount of stem damage was bigger than during winter. In conclusion, two-storied stands are possible to transit to spruce stands by accepting some losses in harvesting productivity and damages on remaining trees.
Abstract
Eucalyptus plantations are a notable source of income for smallholders and private landowners in Thailand. The main uses of eucalyptus are for energy purposes and as pulpwood, sawn timber, and veneer. Among private eucalyptus forest owners there is a need for decision support tools that can help in optimizing tree bucking, according to the available properties of the site and bucking patterns. The precise characterization of plantation properties is key to delivering appropriate timber assortment to markets and optimizing timber value. Our study has developed and tested dynamic and linear programming models for optimizing the tree bucking of eucalyptus trees. To achieve this, tree taper curves for use in volumetric models were defined for optimization. Our results indicate that both the tree spacing and the increment of diameter of breast height are significant factors when estimating profitability. The income would be significantly higher if bucking timber in different assortments were used, instead of the current approach of selling as bulk based on mass. For implementation, we created a free mobile application for android phones (EVO—eucalyptus value chain optimization) to utilize the study results at the grass root-level.
Abstract
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Authors
Wim De Schuyter Emiel De Lombaerde Leen Depauw Pallieter De Smedt Alina Stachurska-Swakoń Anna Orczewska Balázs Teleki Bogdan Jaroszewicz Déborah Closset František Máliš Fraser Mitchell Fride Høistad Schei George Peterken Guillaume Decocq Hans Van Calster Jan Šebesta Jonathan Lenoir Jörg Brunet Kamila Reczyńska Krzysztof Świerkosz Martin Diekmann Martin Kopecký Markéta Chudomelová Martin Hermy Martin Macek Miles Newman Monika Wulf Ondřej Vild Ove Eriksson Peter Horchler Petr Petrik Remigiusz Pielech Thilo Heinken Thomas Dirnböck Thomas A. Nagel Tomasz Durak Tibor Standovár Tobias Naaf Wolfgang Schmidt Lander Baeten Pieter De Frenne Markus Bernhardt-Römermann Radim Hédl Don Waller Kris VerheyenAbstract
No abstract has been registered
Authors
Binbin Xiang Maciej Wielgosz Theodora Kontogianni Torben Peters Stefano Puliti Rasmus Astrup Konrad SchindlerAbstract
Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services. Modern airborne laser scanners deliver high-density point clouds with great potential for fine-scale forest inventory and analysis, but automatically partitioning those point clouds into meaningful entities like individual trees or tree components remains a challenge. The present study aims to fill this gap and introduces a deep learning framework, termed ForAINet, that is able to perform such a segmentation across diverse forest types and geographic regions. From the segmented data, we then derive relevant biophysical parameters of individual trees as well as stands. The system has been tested on FOR-Instance, a dataset of point clouds that have been acquired in five different countries using surveying drones. The segmentation back-end achieves over 85% F-score for individual trees, respectively over 73% mean IoU across five semantic categories: ground, low vegetation, stems, live branches and dead branches. Building on the segmentation results our pipeline then densely calculates biophysical features of each individual tree (height, crown diameter, crown volume, DBH, and location) and properties per stand (digital terrain model and stand density). Especially crown-related features are in most cases retrieved with high accuracy, whereas the estimates for DBH and location are less reliable, due to the airborne scanning setup.
Abstract
This study focuses on advancing individual tree crown (ITC) segmentation in lidar data, developing a sensor- and platform-agnostic deep learning model transferable across a spectrum of dense laser scanning datasets from drone (ULS), to terrestrial (TLS), and mobile (MLS) laser scanning data. In a field where transferability across different data characteristics has been a longstanding challenge, this research marks a step towards versatile, efficient, and comprehensive 3D forest scene analysis. Central to this study is model performance evaluation based on platform type (ULS vs. MLS) and data density. This involved five distinct scenarios, each integrating different combinations of input training data, including ULS, MLS, and their augmented versions through random subsampling, to assess the model's transferability to varying resolutions and efficacy across different canopy layers. The core of the model, inspired by the PointGroup architecture, is a 3D convolutional neural network (CNN) with dedicated prediction heads for semantic and instance segmentation. The model underwent comprehensive validation on publicly available, machine learning-ready point cloud datasets. Additional analyses assessed model adaptability to different resolutions and performance across canopy layers. Our results reveal that point cloud random subsampling is an effective augmentation strategy and improves model performance and transferability. The model trained using the most aggressive augmentation, including point clouds as sparse as 10 points m−2, showed best performance and was found to be transferable to sparse lidar data and boosts detection and segmentation of codominant and dominated trees. Notably, the model showed consistent performance for point clouds with densities >50 points m−2 but exhibited a drop in performance at the sparsest level (10 points m−2), mainly due to increased omission rates. Benchmarking against current state-of-the-art methods revealed boosts of up to 20% in the detection rates, indicating the model's superior performance on multiple open benchmark datasets. Further, our experiments also set new performance baselines for the other public datasets. The comparison highlights the model's superior segmentation skill, mainly due to better detection and segmentation of understory trees below the canopy, with reduced computational demands compared to other recent methods. In conclusion, the present study demonstrates that it is indeed feasible to train a sensor-agnostic model that can handle diverse laser scanning data, going beyond current sensor-specific methodologies. Further, our study sets a new baseline for tree segmentation, especially in complex forest structures. By advancing the state-of-the-art in forest lidar analysis, our work also lays the foundation for future innovations in ecological modeling and forest management.
Abstract
Mapping individual tree quality parameters from high-density LiDAR point clouds is an important step towards improved forest inventories. We present a novel machine learning-based workflow that uses individual tree point clouds from drone laser scanning to predict wood quality indicators in standing trees. Unlike object reconstruction methods, our approach is based on simple metrics computed on vertical slices that summarize information on point distances, angles, and geometric attributes of the space between and around the points. Our models use these slice metrics as predictors and achieve high accuracy for predicting the diameter of the largest branch per log (DLBs) and stem diameter at different heights (DS) from survey-grade drone laser scans. We show that our models are also robust and accurate when tested on suboptimal versions of the data generated by reductions in the number of points or emulations of suboptimal single-tree segmentation scenarios. Our approach provides a simple, clear, and scalable solution that can be adapted to different situations both for research and more operational mapping.
Abstract
There is currently no quality sorting of harvested hardwood timber in Norway on a national scale. Medium- and high-quality logs including those from birch (Betula pubescens Ehrh., B. pendula Roth) are thus not utilized according to their potential monetary value. Increased domestic utilization of quality birch timber requires that the quality of harvested logs be properly assessed for potential end uses. A preferred sorting procedure would use visually detectable external log defects to grade roundwood timber. Knots are an important feature of inner log quality. Thus, the aim of this study was to evaluate whether correlations between branch scar size and knot features could be found in Norwegian birch. Using 168 knots from seven unpruned birch trees, external bark attributes often showed strong correlations with internal wood quality. Both length of the mustache and length of the seal performed well as predictors of stem radius at the time of knot occlusion. The presence of a broken off branch stub as part of an occluded knot significantly increased the knot-effected stem radius, proving that the practice of removing branches and branch stubs along the lower trunk is a crucial measure if quality timber production is the primary management goal.
Abstract
Pollarding in agroforestry systems was traditionally an important practice for fodder acquisition in Western Norway, as well as in many other parts of the world. The practice has long been in decline, but to maintain cultural landscapes and biodiversity enhancement from pollarding, farmers now receive a public grant for each tree they pollard. In this interdisciplinary study we investigate which ecosystem services modern pollarding practices provide, under the influence of the current pollarding policy. We have performed both in-depth interviews and a quantitative survey targeting all pollarding farmers in the county of Vestland in Western Norway. We find that bioresources obtained from the branches from pollarding are to some extent still taken into use, mainly in the form of tree fodder for farm animals and firewood, but a lot of the branches remain unused. Biodiversity benefits are obtained from preserving old trees that often are located on agricultural land as solitary trees, as these trees provide important habitats, particularly for species growing on the bark, such as lichens and mosses, or within the decaying wood, such as, for example, fungi and insects. The modern practice of letting branches rot in the field provide habitats for insects and hence additional benefits to biodiversity. For the farmers, the main motivations to pollard are the cultural, aesthetic and historical values of pollarded trees. They see few disadvantages with pollarding, and most of them plan to continue in the future. The grant provides an incentive for pollarding, but our results indicate that the practice would continue without it, although less than now, especially with the establishment of new pollards.