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
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.
Forfattere
Jorunn BørveSammendrag
Det er ikke registrert sammendrag
Sammendrag
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Forfattere
Ryan BrightSammendrag
Populærvitenskapelig Innlegg
Sammendrag
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Forfattere
Annbjørg KristoffersenSammendrag
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Forfattere
Lars Sandved DalenSammendrag
Kursholder er kommunikasjonsrådgiver Lars Sandved Dalen ved NIBIO. Han har siden 2010 hjulpet forskere og fagpersoner med å få over 100 kronikker og debattinnlegg på trykk i norske aviser.
Sammendrag
– I Telemark var fangstene de høyeste siden det store utbruddet av granbarkbiller som startet midt på 1970-tallet, forteller forsker.
Sammendrag
This paper explores the generation of “realistic” 3D representations of individual trees to enhance visualizations of forest simulation tool outcomes. By leveraging remote sensing data, we aim to capture individual tree features and characteristics accurately, linking them to dynamic simulations of forest structures and composition. Employing a deep learning approach, we train models on existing 3D scanned data to produce diverse and realistic visual representations of specific tree species. Our method addresses the limitations of existing synthetic tree generation techniques, which often overlook species-specific characteristics. Our approach emphasizes the generation of diverse tree forms, accounting for differences in trunk shape, canopy size, and branching structures. The resulting 3D data offers potential applications for realistic future forest visualizations and improved data augmentation in tree classification models, ultimately contributing to the creation of virtual forests that represent rich species diversity.