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
Sammendrag
Formålet med denne rapporten var å bruke matematiske modeller for å simulere utvikling i skog med alternative skogbehandlinger og sammenlikne dem med hensyn på omfang av vindskader. Vi spesifiserte fire alternativer som verdiorientert og stabilitetsorientert rotasjonsskogbruk, bledningsskogbruk og skjøtselsbelter langs kraftlinjer. Vi kjørte simuleringen på et 30 km2 område sør for Kongsvinger. Vi brukte modellene Heureka for å simulere bestandsutvikling i 5-årsperioder over 100-år, ForestGales for å beregne kritisk vindstyrke og beregnet volum vindskade ved å kombinere dette med frekvensfordeling for vindstyrke i området. Simuleringene gav en tydelig rangering av skogbehandlingsalternativene. Bledning gav 4,5 ganger mer skadevolum enn verdiorientert skogbehandling som igjen gav tre ganger mer enn stabilitetsorientert. Langs kraftlinjene ble vindskader omtrent eliminert ved å ha skjøtselsbelter med ekstra lav utgangstetthet og sluttavvirkning ved 18 m høyde. Ved å også se på volumproduksjon i sammenlikningene ble rangeringen lite endret. Bledning gav lavest volumproduksjon og verdiorientert skogbehandling gav kun 4% høyere produksjon enn en stabilitetsorientert. Vi konkluderer med at bledning fører til mer vindskader enn rotasjonsskogbruk, og at vi i rotasjonsskogbruk kan redusere skadeomfanget med lav utgangstetthet, ingen tynning og kort omløpstid. Langs kraftledninger kan vindskader nesten elimineres ved å gå enda lenger i samme retning.
2025
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Marie-Claude Jutras-Perreault Fride Høistad Schei Julius Wold Terje Gobakken Hans Ole ØrkaSammendrag
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.
Forfattere
Simon BergSammendrag
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.
Forfattere
Simon BergSammendrag
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.
Sammendrag
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.
Sammendrag
NIBIOs eksperter på skog og kunstig intelligens (KI) trener opp datamodeller til å kjenne igjen enkelttrær i skogen. Utgangspunktet er data fra laserskanning. Jobben er enorm. Målet: Å gå fra bestandsskogbruk til forvaltning av skog på enkelttrenivå.
Sammendrag
Artikkelen tar for seg det økende problemet med rotråte i norsk granskog og spør om det er mulig å avle fram gran med bedre motstandskraft. Gjennom forskningsprosjektet Frisk Skog kombineres genetiske analyser, feltforsøk og data fra hogstmaskiner for å forstå variasjonen i råteresistens og utvikle mer presise verktøy for skogplanteforedling. Samtidig løftes praktiske råd for skogeiere, som bruk av Rotstop, skånsom drift og mer målrettet planting, som viktige tiltak for å redusere tap her og nå.
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Maria Åsnes Moan Stefano Puliti Rasmus Astrup Ole Martin Bollandsås Terje Gobakken Maciej Wielgosz Hans Ole Ørka Lennart NoordermeerSammendrag
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.