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.
2025
Sammendrag
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Forfattere
Heikki Korpunen Yrjö Nuutinen Paula Jylhä Lars Eliasson Aksel Granhus Juha Laitila Stephan Hoffmann Timo MuhonenSammendrag
• Overall forest management objectives and stand properties set the requirements and possibilities for harvesting in continuous cover forestry (CCF). • Harvester and forwarder operators play a key role in successful CCF harvesting, as both productivity and quality of work are essential factors in harvesting operations. • Optimal stand conditions improve work productivity on selection harvesting sites; harvested stem volume correlates well with work productivity in cutting, and density of remaining trees does not significantly reduce work productivity in forwarding. • Carefully executed group cutting and shelterwood harvesting can reduce the number of damaged remaining trees, which is beneficial for future tree generations. • Research-based information is needed about work productivity in harvesting, damage caused by harvesting, and optimisation of strip road and forest road networks for CCF.
Sammendrag
Ole Green, farmer, Benfarm. The farm is regenerative organic, and crops are grown in stripcropping. Robots are used extensively, and in AllEcoSys the effect of diversification of bush-berries (black currant and gooseberries) grown in strips between annual crops is studied together with University of Copenhagen.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
The AllEcoSys project aims to promote sustainable and resilient agricultural systems by implementing a network of living labs across Europe. November 26th is the first AllEcoSys Living Lab seminar. Main speaker was Ole Green from the Danish Living Lab. Ole Green is honorary professor at Aarhus University, founder of Agrointelli, producing innovative farm robotics, and a farmer, developing a novel stripcropping system of berry bushes and annual crops. Project co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible for them.
Forfattere
Mostafa Hoseini Helle Ross Gobakken Stephan Hoffmann Csongor Horvath Johannes Rahlf Jan Bjerketvedt Stefano Puliti Rasmus AstrupSammendrag
RoadSens is a platform designed to expedite the digitalization process of forest roads, a cornerstone of efficient forest operations and management. We incorporate stereo-vision spatial mapping and deep-learning image segmentation to extract, measure, and analyze various geometric features of the roads. The features are precisely georeferenced by fusing post-processing results of an integrated global navigation satellite system (GNSS) module and odometric localization data obtained from the stereo camera. The first version of RoadSens, RSv1, provides measurements of longitudinal slope, horizontal/vertical radius of curvature and various cross-sectional parameters, e.g., visible road width, centerline/midpoint positions, left and right sidefall slopes, and the depth and distance of visible ditches from the road’s edges. The potential of RSv1 is demonstrated and validated through its application to two road segments in southern Norway. The results highlight a promising performance. The trained image segmentation model detects the road surface with the precision and recall values of 96.8 and 81.9 , respectively. The measurements of visible road width indicate sub-decimeter level inter-consistency and 0.38 m median accuracy. The cross-section profiles over the road surface show 0.87 correlation and 9.8 cm root mean squared error (RMSE) against ground truth. The RSv1’s georeferenced road midpoints exhibit an overall accuracy of 21.6 cm in horizontal direction. The GNSS height measurements, which are used to derive longitudinal slope and vertical curvature exhibit an average error of 5.7 cm compared to ground truth. The study also identifies and discusses the limitations and issues of RSv1, which provide useful insights into the challenges in future versions.
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Tuomas Yrttimaa Aapo Erkkilä Teemu Kamula Carolin Fischer Nicolas Cattaneo Mostafa Hoseini Juha Hyyppä Mikko VastarantaSammendrag
Efficient use of forest resources requires identifying the origin of wood to determine its intended purpose before harvesting. This purpose depends on the wood’s quality, which is influenced by the tree’s growth process and only fully revealed during processing at the sawmill. Identifying which attributes of a standing tree align with the quality requirements of sawn timber necessitates linking forest-collected data to information obtained at the sawmill. However, a nondestructive approach for establishing this connection without artificial marking of logs is currently unavailable. We propose a potential solution employing “tree fingerprints”—biometric patterns that capture trees’ unique branching arrangements along the stem. The tree architecture reflects a hierarchical growth pattern shaped by the interplay between genetics and the environment. Environmental variation leads to unique resource availability between individuals, and thus we assume that each tree develops distinct architectural characteristics, akin to the uniqueness of a human fingerprint. To investigate whether this uniqueness can be captured using terrestrial laser scanning (TLS), we conducted an experiment with 65 Scots pine (Pinus sylvestris L.) trees in a managed boreal forest stand. We derived tree fingerprints from two independent TLS data acquisitions (September 2021, November 2022) and matched corresponding fingerprints. In total, 52 trees (80.0%) were identified based on their architectural characteristics. The results showed that identifying ≥10 branch origins from independent reconstructions was sufficient to establish architectural uniqueness, resulting in 100% identification accuracy (n = 20 trees). These findings suggest that tree fingerprints can be used to condense the complex three-dimensional tree architecture into a two-dimensional pattern of points representing unique branch arrangement. Further, we demonstrate how this tree fingerprinting concept could be expanded across laser scanning methods to enable operational-scale wood traceability if point cloud data of standing trees is collected during forest operations and corresponding sawlogs are scanned at sawmills using X-ray computed tomography. Existing incentives support this kind of development: laser scanners on harvesters can assist operators, and sawlog scanning is essential for optimising timber yield. Seamlessly integrating wood traceability into industry practices would enable automated recording of data that can be further used for linking architectural characteristics of standing trees, grown within specific site conditions, to sawlog properties. This integration would enhance understanding of how tree architecture, environmental factors, and forest management influence desired properties of processed wood, enabling more informed decision-making for the wood procurement process.
Forfattere
Henrik Persson Stefano Puliti Tuomas Yrttimaa Vikash Ghildyal Rasmus Astrup Johan Holmgren Carolin Fischer Nicolas Cattaneo Mikko VastarantaSammendrag
Tracing wood along the value chain is needed to preserve information about wood origin and understand associations between standing tree characteristics and the resulting wood quality. Linking timber products with standing trees without artificial marking remains a formidable challenge where detailed reconstruction of trees’ architectural characteristics could provide a solution. The objective is to develop an automated method for single-tree tracing using dense laser scanning from standing trees, leveraging branch patterns as unique fingerprints. A secondary objective is to explore how these fingerprints can be derived from computer tomography (CT) scans at sawmills, enabling the reconstruction of individual branch patterns. We use the AI algorithm BranchPoseNet to detect tree whorls and individual branch vectors from a terrestrial laser scanner-derived point cloud. A tree's unique fingerprint is derived by presenting the branch origins as a function of height and azimuth around the stem. This fingerprint is then reconstructed from a mobile laser scanner-derived point cloud collected from the same trees as well as from CT scans of knots and their positions in processed logs. By minimizing residuals between corresponding branch locations between the initial and reconstructed fingerprints, individual trees can be accurately linked, enabling full traceability from living trees to sawlogs. Preliminary results indicate that this approach is feasible for pine trees and that a limited number of unique branch connections may be sufficient for tree traceability in managed forest stands. More testing is needed to assess the performance of other species. We conclude that this method can be integrated into industry practices, being viable for automatically tracing trees from the harvested forest stands to the sawmill, thereby closing critical gaps in the value chain and enabling the attribution of additional information (e.g., origin, carbon sequestration potential) to wood products and other forest-based applications without artificial marking of logs.
Sammendrag
Efficient and objective measures of tree and stand structural complexity are essential to understanding the relationship between forest management, biodiversity, and ecosystem functioning, with laser scan-based structural complexity metrics playing an innovative role in monitoring and analysis.The objective is to develop an individual-tree crown complexity metric capable of distinguishing between structurally more or less complex trees while being scale-invariant. The second objective is to efficiently scale this metric to the stand level, enabling differentiation between forests of varying complexity and guiding precision silviculture.We developed a method to quantify crown complexity using individual-tree dense airborne LiDAR point clouds. Through optimization, we generate a 3D alpha shape crown model and calculate its volume and exposed surface area. This surface area is compared to that of a reference sphere with the same volume, as the sphere is the solid with the lowest surface-to-volume ratio, serving as a baseline for minimal complexity. This provides a scale-invariant measure of crown complexity. Summing this measure across all trees in a stand and applying a penalty for low vertical distribution yields a stand-level complexity metric that reflects structural heterogeneity.Applying our methodology to the FOR-instance dataset showed that the calculation of the 3D alpha shape crown model through optimization was successful, although sparse point clouds can present challenges. The crown complexity measure behaved as expected, ranking crowns according to their complexity, primarily determined by the roughness of the tree crowns, which increases the exposed surface area. When scaling the metric to the sample plot level, the measure effectively distinguishes between forests with structurally complex trees but low vertical stratification and those with less complex trees but high vertical stratification, identifying the latter as the more structurally complex forests.