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
Forfattere
Stephen Amiandamhen Synne Strømmen Ingeborg Olsdatter Ohren Nordraak Andreas Treu Erik LarnøySammendrag
This study investigated the potential of wood particles from Ciol®-treated wood in particleboard production. Ciol® is a renewable formulation from water, citric acid, and sorbitol, which has been commercially developed as a promising alternative for wood modification. Radiata pine wood was impregnated with 60% and 85% concentrations of the Ciol® solution for 150 mins. The impregnated boards were cured and subsequently planned. Particleboards were thereafter produced from the wood shavings using urea formaldehyde (UF) and melamine urea formaldehyde resin (MUF). The boards were produced with or without the use of ammonium nitrate as a hardener. The wood particles and produced boards were characterized via analytical techniques and standard test methods. The effect of Ciol® treatment and its concentration on the properties of the shavings and the particleboards was investigated as well as the effect of the resin type on the panel properties. The use of MUF without the hardener gave the best bending strength of 13 N/mm² and modulus of elasticity of 3187 N/mm². However, there was no significant difference in the results obtained when the hardener was added to MUF resins. Recycling Ciol®-treated wood shavings in particleboard production proved to be a promising approach with MUF resins.
Sammendrag
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.
Forfattere
Binbin Xiang Maciej Wielgosz Theodora Kontogianni Torben Peters Stefano Puliti Rasmus Astrup Konrad SchindlerSammendrag
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.
Sammendrag
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.
Sammendrag
PixSim is a flexible, open-source forest growth simulator designed to operate at the pixel level of high-resolution, wall-to-wall forest resource maps generated through remote sensing approaches. PixSim addresses the need to adapt forest growth simulators to the data produced by modern remote sensing-based forest inventories, rather than relying on stand-level averages from traditional field-based inventories. By operating at the pixel level, PixSim captures intra-stand variability in high-resolution forest resource maps, which is often overlooked by stand-level simulators. This capability aligns with the current focus on precision forestry, aimed at improving management decisions with localized data and small-scale management. Implemented in the R programming language, PixSim features minimal package dependencies, provides flexibility and scalability, and has been optimized for high-resolution, large-scale simulations, ensuring efficient computation. The simulator’s flexibility and open-source nature support the incorporation of management modules and the inclusion of climate change scenarios in simulations.
Forfattere
Marta Vergarechea Clara Antón Fernández Jane Uhd Jepsen Ole Petter Laksforsmo Vindstad Rasmus AstrupSammendrag
No abstract has been registered
Forfattere
Debojyoti Chakraborty Albert Ciceu Dalibor Ballian Marta Benito Garzón Andreas Bolte Gregor Bozic Rafael Buchacher Jaroslav Čepl Eva Cremer Alexis Ducousso Julian Gaviria Jan Peter George André Hardtke Mladen Ivankovic Marcin Klisz Jan Kowalczyk Antoine Kremer Milan Lstibůrek Roman Longauer Georgeta Mihai László Nagy Krasimira Petkova Emil Popov Randolf Schirmer Tore Skrøppa Thomas Solvin Arne Steffenrem Jan Stejskal Srdjan Stojnic Katharina Volmer Silvio SchuelerSammendrag
Climate change threatens the role of European forests as a long-term carbon sink. Assisted migration aims to increase the resilience of forest tree populations to climate change, using species-specific climatic limits and local adaptations through transferring seed provenances. We modelled assisted migration scenarios for seven main European tree species and analysed the effects of species and seed provenance selection, accounting for environmental and genetic variations, on the annual above-ground carbon sink of regrowing juvenile forests. To increase forest resilience, coniferous trees need to be replaced by deciduous species over large parts of their distribution. If local seed provenances are used, this would result in a decrease of the current carbon sink (40 TgC yr−1) by 34–41% by 2061–2080. However, if seed provenances adapted to future climates are used, current sinks could be maintained or even increased to 48–60 TgC yr−1.
Sammendrag
Genetic variation and performance of up to 40-year-old Norway spruce (Picea abies (L.) Karst.) families from half-diallel crosses made in natural populations where studied, and the results were compared with results from nursery test of seedlings (1-2 years from seed) and short-term farm-field tests at 6-10 years from seed. The diallel analyses revealed significant levels of additive genetic variance for growth and phenology traits. The non-additive genetic variance was generally small to moderate. Strong genetic correlations for growth performance were found between the short-term and long-term trials but not between the nursery and long-term trials. Similarly, genetic correlations for phenology traits were strong between short-term trials and long-term trials, whereas the nursery tests generally did not predict phenology at older age well. The findings indicate that early selection for growth and adaptive traits based on short-term trials is effective for improvement of long-term performance in field if the test site is not outside the normal range of deployment of the reproductive material.
Sammendrag
Female flowering and cone production took place in three Norway spruce progeny tests at ages 17 and 20 years, each planted with full-sib families from a half diallel. The number of cones on individual trees were scored in five classes. More than 50 % of the trees produced cones, and a considerable variation was found among families for the ability to produce cones (precocity) and for the number of clones scored in classes (fruitfulness). Both traits were strongly related to tree heights and diameters at the individual and at the family level. In general, tall trees produced the highest number of cones. However, some families produced many cones even if their average heights were low. In two of the half diallels, estimates of GCA variance components for the number of cones produced had twice the value of the SCA component, indicating additive genetic inheritance of cone production. Heritability estimates of cone scores were 0.10, 0.17 and 0.23, and the genetic correlations between cone production and tree heights were 0.40, 0.50 and 0.35 in the three half-diallels, respectively.
Sammendrag
Phenol-formaldehyde (PF) resins can be impregnated and cured in situ to improve the woods dimensional stability and decay resistance. In search of renewable alternatives, the substitution of phenol by lignin cleavage products (LCP) has been discussed. However, the different chemical nature may affect the performance of the resin against fungal decay, formaldehyde emission, and equilibrium moisture content. In this study, 30 % (w/w) of the phenol in PF resins were substituted by LCP obtained from microwave-assisted pyrolysis. Scots pine sapwood was modified with the resin. The decay resistance against Rhodonia placenta, Gloeophyllum trabeum, and Trametes versicolor was determined. Additionally, effects of specimen organisation within the Petri dish, different substrates, length of leaching, and type of inoculum were studied. Further, the materials water vapor sorption properties and formaldehyde emission were determined. All modifications effectively reduced fungal decay. With 10 % weight percent gain (WPG), initial decay was detected, while 20 % WPG and 30 % WPG provided efficient protection. The substitution of phenol increases the formaldehyde emission. While further reduction in formaldehyde in the resin admixture or formaldehyde scavengers may be required, the method described herein can be used to partly replace fossil-based phenol, while maintaining good fungal resistance.