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
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.
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This article presents SkyPlanter, the first drone-based system for aerial reforestation with tree seedlings. Traditional tree planting is labor-intensive, physically demanding, and expensive—making it ideal for automation. Current mechanized solutions depend on large, heavy, ground-based excavator-based solutions best suited for extensive clear-cuts, but which struggle on steep or uneven terrain, and carry prohibitive relocation costs for smaller operations. SkyPlanter is a drone-mounted seedling-planting system that enables it to easily traverse rugged or steep terrain while remaining inexpensive, easily transported, and highly scalable. It uses an ultra-lightweight compressed air planting mechanism that inserts seedlings and compacts the surrounding soil. Its innovative double-telescoping design reduces vehicle weight to 15.2 kg (without batteries) or 16.4–20.8 kg (with batteries, depending on flight duration). This article details the system’s novel planting and ground compression mechanisms, its unique high-pressure pneumatic power systems, and its custom quadrotor carrier drone. We demonstrate its feasibility in the first-ever aerial seedling-planting tests in a forest environment. The system is proposed as a cost-effective, scalable reforestation solution with high automation potential.
Authors
Simon BergAbstract
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Accurately predicting whether pedestrians will cross in front of an autonomous vehicle is essential for ensuring safe and comfortable maneuvers. However, developing models for this task remains challenging due to the limited availability of diverse datasets containing both crossing (C) and non-crossing (NC) scenarios. Therefore, we propose a procedure that leverages synthetic videos with C/NC labels and an untrained model whose architecture is designed for C/NC prediction to automatically produce C/NC labels for a set of real-world videos. Thus, this procedure performs a synth-to-real unsupervised domain adaptation for C/NC prediction, so we term it S2R-UDA-CP. To assess the effectiveness of S2R-UDA-CP in self-labeling, we utilize two state-of-the-art models, PedGNN and ST-CrossingPose, and we rely on the publicly-available PedSynth dataset, which consists of synthetic videos with C/NC labels. Notably, once the real-world videos are self-labeled, they can be used to train models different from those used in S2R-UDA-CP. These models are designed to operate onboard a vehicle, whereas S2R-UDA-CP is an offline procedure. To evaluate the quality of the C/NC labels generated by S2R-UDA-CP, we also employ PedGraph+ (another literature referent) as it is not used in S2R-UDA-CP. Overall, the results show that training models to predict C/NC using videos labeled by S2R-UDA-CP achieves performance even better than models trained on human-labeled data. Our study also highlights different discrepancies between automatic and human labeling. To the best of our knowledge, this is the first study to evaluate synth-to-real self-labeling for C/NC prediction.
Authors
Matti Koivula Adam Felton Mari Jönsson Therese Löfroth Fride Høistad Schei Juha Siitonen Jörgen SjögrenAbstract
• This chapter summarises biodiversity responses to continuous cover forestry (CCF). The comparator throughout this chapter is rotation forestry (RF) and its main harvesting method—clearcutting—unless otherwise stated. • Research on the biodiversity effects of logging methods applied in CCF (mostly selection or gap cutting) mainly concerns the short-term effects of measures taken in mature, originally fairly even-aged forests, at best 10–15 years after cutting. Thus far, no surveys or chronosequences cover the whole rotation period (60–100 years). • Continuous cover forestry is likely to beneft species that suffer when the tree cover is removed, such as bilberry and its associated species. Species requiring spatial continuity in host trees or canopy cover may also benefit. • Selection cutting may preserve the majority of species in the mature forest, but the most sensitive species may decline or even disappear. Gap cutting (diameter 20–50 m) affects forest-interior species relatively little, but species’ abundances in gaps change with increasing gap size. Shelterwood cutting seems to closely resemble selection cutting in terms of species responses. In the long term, however, shelterwood cutting results in an even-aged and sparse overstorey, which does not produce the biodiversity benefits of CCF. • Species that have declined due to forestry mostly require large living and dead trees. The preservation of these species is not ensured by CCF alone, but requires deliberately maintaining these structural features. • A mosaic of different forest-management practices within landscapes may provide complementary ways to maintain rich biodiversity.
Abstract
This article presents a novel, ultralight tree planting mechanism for use on an aerial vehicle. Current tree planting operations are typically performed manually, and existing automated solutions use large land-based vehicles or excavators which cause significant site damage and are limited to open, clear-cut plots. Our device uses a high-pressure compressed air power system and a novel double-telescoping design to achieve a weight of only 8 kg: well within the payload capacity of medium to large drones. This article describes the functionality and key components of the device and validates its feasibility through experimental testing. We propose this mechanism as a cost-effective, highly scalable solution that avoids ground damage, produces minimal emissions, and can operate equally well on open clear-cut sites as in denser, selectively-harvested forests.