Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2026
Forfattere
Katrin Zimmer Ulf Lemke Irena Fundova Henrik Heräjärvi Mateusz Liziniewicz Marlene Cramer Stefanie Wieland Lukas Emmerich Thomas Uibel Anti Rohumaa Heikki Korpunen Grace Jones Ekaterina Nikolskaya Tom Jansen Anneli Adler Tom Mortelmans Ian Short Vincent Defays Karol TomczakSammendrag
Birch is the third most abundant tree species in northern Europe and the Baltic region, but remains underutilized in several countries despite wood properties that support a broad range of applications, including pulp, veneer, plywood, furniture, flooring, joinery, and potentially structural products. Constraints on higher-value utilization include insufficient logistics for sorting and transport, the lack of standardized grading methods, and limited data infrastructure for systematic quality assessment across supply chains. The Nordic Forest Research (SNS) network VALUE:BIRCH was established to examine these constraints through transnational collaboration among researchers, industry actors, and students across northern Europe. In 2025 and 2026, the network held workshops in Borås, Sweden, and North Rhine-Westphalia, Germany, with emphasis on strength properties, grading, and quality assessment of birch wood. The activities integrated technical presentations, laboratory and field visits, and student contributions, enabling comparison of birch value chains across countries with differing levels of industrial development. The network identified shared technical and organizational bottlenecks related to birch silviculture and management, grading, mobilization, and market formation, while also strengthening inter-institutional co-creation and collaboration. The results indicate that coordinated work on grading systems, quality data, logistics, and market development is essential to support more efficient and value-adapted utilization of birch.
Sammendrag
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Sammendrag
Broadleaved tree species from Norwegian forests are a valuable raw material if this resource can be utilized effectively. Broadleaved tree species make for approximately one fourth of the total standing volume in Norway today. This paper provides detailed data on standing volume and annual volume increment of ash (Fraxinus excelsior), oak Quercus robur og Q. petrea), silver birch (Betula pendula), downy birch (Betula pubescens), aspen (Populus tremula), grey alder (Alnus incana), and black alder (Alnus glutinosa) in Norway, and give their distribution across the regions of Eastern Norway, Southern Norway, Western Norway, Trøndelag, and Northern Norway. Information on stand age, site quality, and diameter distribution will be provided.
Sammendrag
Birch has regained interest in Norwegian forestry within the last few years, partly to increase the share of broadleaves and tree-species diversity under climate change. However, timber yield and quality assortments from Norwegian birch stands remain largely unknown. We established temporary sample plots in planted and naturally regenerated birch stands on high-quality sites in Eastern Norway and carried out tree measurements to quantify stand-level production. In addition, the first 6 m of each standing tree were subjected to a detailed timber‑quality assessment and subsequently graded accordingly. Volume production in the investigated stands was frequently higher than values reported in birch yield tables from the 1970s and did not differ between planted and naturally regenerated stands. Volume production in the studied birch stands was similar to or higher than for Norway spruce on comparable high-quality sites up to a stand age of about 40 years. Despite the lack of quality-oriented management in most stands, nearly all produced sawlogs and many contained higher-quality assortments. Stand-level sawlog proportion increased with mean tree diameter. While planting and thinning showed no clear relationship with stand-level sawlog proportion, artificial pruning increased the share of higher-quality sawlogs. Overall, productive birch stands in Norway can deliver high yields and meaningful volumes of quality sawlogs.
Forfattere
Stephan Hoffmann Mostafa Hoseini Moritz Wingartz Mahmoud Rajabi Helle Ross Gobakken Rasmus AstrupSammendrag
A functional and low-impact forest road network is essential for sustainable forest management, yet maintaining such infrastructure is costly and requires monitoring tools that are reliable and simple enough for operational use. We present an automated approach to detect, map, and evaluate forest road surface deterioration, designed to support end-users, including those with limited road expertise, to indicate required maintenance actions. The system relies on data collected by the vehicle-mounted near-field sensor platform RoadSens, which integrates stereo camera imagery with GNSS-based geo-referencing to capture detailed road surface information. Collected data are processed within a monitoring and scheduling environment using a YOLOv8 object detection model trained on nearly 14,000 annotated images. The model identifies six key deterioration features: potholes, wheel ruts, gullies, washboards, stones, and vegetation. These detections are used to locate maintenance-relevant features and classify road segments into three deterioration levels based on coverage thresholds, which are then visualized through a traffic-light system. A case study on a forest road in southern Norway demonstrated the system’s ability to detect and classify maintenance needs. While performance was strong for more uniform features such as vegetation, irregular structures like wheel ruts proved more challenging, occasionally leading to misclassification of actual maintenance requirements. Nevertheless, the findings confirm the technical feasibility of integrating object detection models into data-driven forest road maintenance scheduling. Future improvements will require larger and more diverse training datasets, as well as classification frameworks tailored to local conditions and specific road-user needs.309671
Sammendrag
Time and motion studies in forest operations benefit from video-based analysis, but manual annotation is time consuming. This pilot study aims to reduce analysis time by developing a deep-learning framework that classifies dashcam video into four work elements: crane out, cutting and processing, driving, and processing. Using a 3D ResNet-50 (PyTorchVideo) trained on manually annotated clips, the model achieved validation F1 = 0.88 and precision = 0.90, showing that spatiotemporal CNNs can capture rele-vant motion and appearance cues in forest environments. Overfitting indicates that more diverse data and better class balance are needed, but the approach shows clear potential to scale automated work-element monitoring and efficiency analysis.
Forfattere
Stefano Puliti Binbin Xiang Maciej Wielgosz Eivind Handegard Nicolas Cattaneo Marta Vergarechea Terje Gobakken Juha Hyyppä Erik Næsset Mikko Vastaranta Tuomas Yrttimaa Rasmus AstrupSammendrag
Accurately determining the age of individual trees is important for understanding forest dynamics, tree growth, site productivity and describing ecological processes. Traditional methods, such as dendrochronological coring, are invasive, labor-intensive, and costly. This study investigates the use of deep learning (DL) to predict tree age from high-density laser scanning data as a scalable, non-invasive alternative. The dataset includes approximately 1700 tree point clouds from approx. 1 K trees across Norway, Sweden, and Finland, encompassing Norway spruce (Picea abies) and Scots pine (Pinus sylvestris) and a broad range of tree age and developmental stages, from young seedlings (1 year) to old trees (∼350 years). Data were collected using terrestrial, mobile, and high-density airborne laser scanning platforms, enabling the development of sensor-agnostic models. We evaluated multiple modelling approaches, from linear regression to transformer architectures, using both training-from-scratch and fine-tuning strategies. Models fine-tuned starting from pre-trained weights from ForestFormer3D's U-Net as well as the transformer architecture (PointTransformerV3) trained from scratch, proved effective for age regression (RMSE ≤23 years). Although our analysis was limited to two tree species, we demonstrated that a single joint age-estimation model can be successfully trained for both species. We demonstrate that models trained on high-resolution data can generalize to lower-resolution, less costly inputs, provided that data augmentations that mimic reduced resolutions are included during training. This study presents a data-driven framework for estimating tree age without destructive sampling. The findings support the potential for AI-based methods to complement or replace traditional age estimation techniques in forest inventory and monitoring.
Forfattere
Marta Vergarechea C Antón-Fernández J.U Jepsen Ole Petter Laksforsmo Vindstad Nicolas Cattaneo J.J Camarero Rasmus AstrupSammendrag
resilience. In Norway, birch species (Betula pendula and Betula pubescens) dominate large areas of boreal forest, yet large-scale patterns of their age distribution and growth dynamics remain poorly quantified. Using increment core data from 2818 trees sampled across the Norwegian National Forest Inventory, spanning five vegetation zones (58–71◦N) and a broad productivity gradient, we analyzed the drivers of birch age structure and growth variation across age classes and historical cohorts. Intermediate-aged trees (35–80 years) dominated most regions, whereas older individuals were scarce, particularly on productive sites, reflecting the combined effects of forest management and the life-history strategy of fast-growing pioneer species. When compared at equivalent biological ages, younger trees consistently showed higher basal area increment (BAI) than older trees, with differences strongest during early development and on productive sites. Cohort analyses showed a pronounced long-term increase in juvenile growth: mean BAI during the first ten years after reaching breast height increased steadily across successive cohorts over the past 150 years. This increase became more pronounced after ~1960 and was consistent across vegetation zones and site productivity classes. Although sampling and survivor bias cannot be fully excluded, the consistency across environmental gradients points to broad-scale changes in early growth dynamics of birch forests in Norway. These results underscore the importance of considering both age structure and cohort-related variation when interpreting forest dynamics and planning future management.
Sammendrag
Collection, processing and provision of comprehensive geometric information of forest roads is decisive for its technical classification to facilitate sustainable timber supply chains. An automized classification system based on the mobile proximal sensor platform RoadSens was developed, applied and validated through a case study approach in Eastern Norway. Six sample roads of various vegetation stages were surveyed through RoadSens and complemented through sampled total station measurements for validation purposes. The determined geometric parameters road slope, curvature and width were used for technical classification following the national forest road standard. Road width was identified as the main constraint in meeting the standard, resulting in a general downgrading of the sampled roads according to its technical class. The results showed a root mean square error (RMSE) ranging from ±0.53 to 1.50 m (12–33%) depending on the road and vegetation stage compared to the validation data. Despite these accuracy constraints, the application case study already indicates a general need for improvement of road data acquisition and updating of associated databases. The study underscores that, despite the challenges and limitations, there is a clear need for an automated sensing and classification system, which offers a cost-effective alternative to manual surveying and requires less specialized expertise.
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