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.
2025
Authors
Fride Høistad ScheiAbstract
No abstract has been registered
Authors
Fride Høistad ScheiAbstract
No abstract has been registered
Authors
Liping Wei Emiel De Lombaerde Thomas Vanneste Pieter Sanczuk Lander Baeten Jonathan Lenoir Karen De Pauw Kris Verheyen Markus Bernhardt-Römermann Antoine Becker-Scarpitta Jörg Brunet Markéta Chudomelová Guillaume Decocq Martin Diekmann Tomasz Durak Radim Hédl Thilo Heinken Peter J. Horchler Bogdan Jaroszewicz Martin Kopecký Martin Macek František Máliš Tobias Naaf Thomas A. Nagel Anna Orczewska Petr Petřík Kamila Reczyńska Fride Høistad Schei Wolfgang Schmidt Jan Šebesta Alina Stachurska-Swakoń Tibor Standovár Krzysztof Świerkosz Balázs Teleki Hans Van Calster Ondřej Vild Donald M. Waller Monika Wulf Pieter De FrenneAbstract
We face increasing concerns about how the local diversity of native plant communities responds to various drivers of global change, yet often lack comprehensive studies that integrate several components of diversity and the effects of both local and regional drivers of change. We analyzed changes in taxonomic, functional, and phylogenetic diversity across 2681 (semi-)permanent temperate forest understory plots surveyed and resurveyed for all vascular plants over intervals of 15–78 yr, spanning 72 regions distributed across Europe. We quantified temporal changes in these diversity indices and assessed their responses to changes in both local drivers (plot-level overstory cover, indicator values for soil nutrients) and regional shifts in macroclimate and nitrogen deposition. Overall, local changes in taxonomic, functional, and phylogenetic diversity were centered around zero, reflecting – on average – little net change in forest diversity. Observed diversity changes mostly reflected local conditions such as overstory cover change and baseline soil nutrients rather than regional drivers of large-scale change. Changes in phylogenetic diversity correlated positively with changes in taxonomic diversity but negatively with changes in functional diversity. Our findings underscore the importance of local habitat management and multifaceted diversity monitoring for effective biodiversity conservation in temperate forests.
Abstract
Clear-cutting can resemble natural disturbances like forest fire, but key differences exist in biological legacy. One way to enhance similarity is by preserving structural features of old-forests, such as retention trees, within harvested areas. The latest Programme for the Endorsement of Forest Certification (PEFC) standards require not only the preservation of retention trees but also their mapping for centralized reporting. This study evaluates the accuracy of retention tree density and volume predictions using airborne laser scanning (ALS) data with low (2 pulses/m2) and high (~100 pulses/m2) pulse densities, with and without spectral data. We also assess the feasibility of large-area predictions with minimal field data by testing both in-situ and ex-situ sources. The study was conducted in a managed 1300 ha forest in southeast Norway. Three reference datasets were used: (1) 630 in-situ retention trees across 27 stands (for species and DBH predictions), (2) 1604 ex-situ sample trees (for DBH predictions), and (3) 150 ex-situ annotated segments (for species predictions). Retention trees were identified using an individual tree segmentation approach, using adaptive local maxima window size and applying an adaptative height threshold to filter regeneration. ALS at 2 pulses/m2 alone provided reliable total density and volume predictions, while adding spectral data improved species-specific predictions. Species predictions were relatively stable across data source (kappa=0.556 for in-situ, 0.519 for ex-situ), but DBH predictions were notably underpredicted with ex-situ data (RMSE=9.40 cm, MSD=-4.55 cm) compared to in-situ data (RMSE=8.84 cm, MSD=0.20 cm). Using adaptive segmentation methods enhances scalability. We recommend sampling ~40 in-situ retention trees to develop DBH-height models and delineating ex-situ annotated segments for species predictions. This approach balances accuracy and efficiency while enabling retrospective analysis using national ALS datasets and orthophotos.
Authors
Simon BergAbstract
Root rot causes significant losses for Norwegian forestry. Mapping infected stumps and planting rot-resistant species around infected stumps could reduce future impacts. At 20 sites, root rot was mapped by adding specific assortments for rotten logs using a harvester that recorded tree locations with high accuracy. The optimal approach was considered detailed planning of planting a Norway spruce and Scotspine mix, using root rot information at tree positions. The average opportunity cost of business as usual(planting only Norway spruce) for the forest owner was 409 €/ha. Planting only Scots pine and detailed planning with rot information at harvester locations increased opportunity costs to 615–886 €/ha.Considering fertility variations reduced the opportunity cost to 408 €/ha, considering average rot at site level reduced it to 397 €/ha, considering rot information at harvester locations and coarse planning reduced it to 378 €/ha, and considering rot information at tree level and coarse planning reduced it to 268€/ha. The optimal approach is currently impractical, while coarse planning with rot information at tree locations is feasible. Costs for rot registration and multi-species planting, excluded due to high uncertainty, are likely covered by the increase of 141 €/ha in net present value.
Authors
Simon BergAbstract
Root rot (Heterobasidion spp.) causes substantial losses for forest owners due to decreased wood quality in Norway spruce (Picea abies). Containing root rot spread in regeneration can be achieved by planting resistant species around infected stumps. However, detecting rotten trees remains challenging. In this study, ground truth data for root rot was collected by seven contractors by adding assortments for rotten pulpwood and cutoffs, with all energy wood assumed rotten. Root rot occurrence was estimated in two ways: (1) by developing Extreme Gradient Boosting (XGB) models from all data (XGB-only); and (2) trough binary classification for bucking patterns containing only rotten or healthy trees, followed by developing XGB models for remaining trees (combined). XGB models were developed nationwide and for two specific contractors. Classifications showed sensitivity of 83–87% (rot) and specificity of 95–99% (healthy).Whether nationwide, contractor-specific, XGB-only or combined classification was better varied by situation. Compared to prior studies, predictions from harvester data outperformed UAV images in classification but were surpassed by handheld camera images. Despite lower sensitivity compared to previous XGB applications, more rotten trees were detected than when using only energy wood as an indicator. As estimations are almost cost-free, the results may be acceptable.
Abstract
The European Union Deforestation Regulation (EUDR) mandates traceability of timber that makes up wood products from its harvest site to the end product to ensure sustainable wood sourcing. This study proposes a cost-effective, image-based method for tracing logs using alphabetic codes printed onto logs at the harvest site. These codes are detected and interpreted through a two-stage system leveraging deep learning models. The detection stage employs YOLOv8 to locate tracking codes in images of log piles. It is trained and evaluated on a dataset of 125 images, achieving an F1-score of 0.811 on unseen images. The recognition stage, trained on 1,020 images, uses YOLOv8 models to detect individual characters and their positions within each code. On a set of unseen images, the interpretation stage is able to identify 92.8% of the individual logs despite the limited quality of the printer and degradation of the codes due to stem wetness. Analysis indicates that errors predominantly arise in the character detection step. Compared to existing traceability approaches, this method is more cost-effective than RFID tags and attains higher accuracy than image-based biomarker tracking methods.
Abstract
Soil disturbance following forestry operations is influenced by multiple factors. Reducing disturbance requires placing strip and base roads in locations with minimal risk of disturbance. However, identifying these areas is a complex task. To address this, we have begun developing a forwarding risk index ranging from 1 to 100 that integrates several geographical information sources in the area around Oslo. This forwarding index seems to provide good estimates of areas with a higher risk of ground disturbance during forwarding operations at the sites used for development. With further development of geographical inputs, their combination into a risk index, and later on nationwide validation, the forwarding risk raster combined with a terrain map could improve the identification of suitable areas for forwarding trails. The risk raster was tested for path planning and performed well in areas with a low to moderate frequency of high-risk pixels but was less effective in areas with a high concentration of high-risk pixels. In these areas, an assessment of the potential ecological impact (erosion, sedimentation of streams, mobilisation of mercury, soil carbon impact, changes in hydrology, soil compaction) of ground disturbance is needed alongside the risk index to determine the least unsuitable trail locations.
Authors
Maria Åsnes Moan Stefano Puliti Rasmus Astrup Ole Martin Bollandsås Terje Gobakken Maciej Wielgosz Hans Ole Ørka Lennart NoordermeerAbstract
The site index (SI) describes a site’s potential to produce wood volume. Accurate information on SI in young forests is essential for planning thinning operations and projecting future growth and yield. For tree species that form annual branch whorls, information on interwhorl distances along the stem may be used to determine the SI in young forests. Branch whorls, and consequently tree height growth trajectories, can be detected automatically using deep learning on very dense laser scanning data. In the current study, we demonstrate this approach in a case study in a young Norway spruce forest. We trained a pose estimation Convolutional Neural Network and detected branch whorls of 97 dominant trees in 54 plots scanned with mobile laser scanning data. We predicted SI determined from detected branch whorls in three different sections of each tree, selected in the stem height range between 2.5 and 8 m: all whorls, the lowest six whorls, and whorls selected with an automatic selection procedure. We compared the obtained SI to the SI determined from field-measured branch whorls. Obtained values of precision, recall, and F1 score for the branch whorl detection were 0.66, 0.58, and 0.62, respectively. Values of root mean square error and mean differences between reference and predicted SI ranged between 19.8%–20.9% and −3.6%–4.0%, respectively. Although the tested approach showed potential for SI determination in young forests, the obtained errors were large. This was due to detection errors and high sensitivity to small changes in height increment. These issues highlight the need for further research to improve branch whorl detection accuracy and address challenges associated with determining the SI in young forests.
Authors
Ignacio Sevillano Aksel Granhus Clara Antón Fernandéz Heleen de Wit Fride Høistad Schei Rannveig Margrete Jacobsen Ulrika Jansson Asplund Heikki Korpunen Christian Wilhelm Mohr Jenni Nordén Jørund Rolstad Svein Solberg Ken Olaf Storaunet Marta VergarecheaAbstract
There is an increasing interest in continuous cover forestry (CCF) as an alternative to clearcutting to promote multi-objective forests and preserve continuous maintenance of forest cover. Here, we assessed the effect that an increased use of CCF harvesting methods (shelterwood and selection cutting) in Norwegian forests can have on carbon sequestration. Thus, we simulated CO2 uptake in Norwegian forest stands throughout the 21st century under three scenarios that represent different levels of clearcutting and CCF harvesting methods, keeping the annual harvest volumes constant across all scenarios. The three scenarios are: 1) Business-as-usual (reference scenario where 3.5% of the harvested volume is obtained using CCF harvesting methods); 2) Harvested volume using CCF harvesting methods is increased to 15%; 3) Harvested volume using CCF harvesting methods is increased to 25%. Increasing the proportion of CCF would increase CO2 removals in the long-term (2100), resulting in an additional uptake of nearly 32 and 24 Tg CO2 when increasing CCF up to 25% and 15%, respectively. However, the simulations also showed that to be able to harvest the same timber volume as in the reference scenario that reflects current practice, an increased proportion of CCF would also require logging on a larger proportion of the forest area. CCF could have also positive implications for certain aspects of biodiversity, such as species that require shaded conditions, but harvesting across a larger total area could negatively impact other animals, plants and fungi.