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
David Chludil Curt Almqvist Mats Berlin Arne Steffenrem Steven E. McKeand Jiří Korecký Jan Stejskal Jaroslav Čepl Fikret Isik Debojyoti Chakraborty Silvio Schueler Torsten Pook Christi Sagariya Milan LstibůrekSammendrag
Det er ikke registrert sammendrag
Forfattere
Sean P Healey Zhiqiang Yang Angela M Erb Ryan Bright Grant M Domke Tracey S Frescino Crystal B SchaafSammendrag
Rapidly maturing frameworks for investing in and committing to mitigation of climate change through forest management have focused almost exclusively on the benefits of carbon sequestration, without accounting for collateral changes in geophysical factors such as surface albedo. Newly available 30 m albedo retrievals derived from imagery acquired by the Landsat 8 satellite, analyzed at 273 652 field plots monitored by the United States Forest Service, suggest that large areas of the US Inter-Mountain West’s forests have a net warming impact on the planet’s surface energy balance. For the conterminous US, the impacts of albedo offset approximately half of the recognized non-soil forest carbon storage benefit. The emerging capacity to resolve albedo evolution at the scale of a large number of inventory plots also provides unprecedented empirical evidence that albedo impacts vary strongly as a function of both tree age and species group. This research highlights a correctable source of uncertainty in operational monitoring of forest-climate interactions, and it may temper expectations for forest establishment as a means of mitigating global climate change.
Sammendrag
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.
Forfattere
Daniel Moreno-Fernández Johannes Breidenbach Isabel Cañellas Gherardo Chirici Giovanni D’amico Marco Ferretti Francesca Giannetti Stefano Puliti Sebastian Schnell Ross Shackleton Mitja Skudnik Iciar AlberdiSammendrag
Forest biodiversity is a multifaceted term encompassing tree and shrub diversity and the diversity of other life forms such as animals or fungi. Extensive forest monitoring networks such as National Forest Inventories or the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forest plots have implemented biodiversity-monitoring protocols to satisfy increasing information demands. However, these protocols often evaluate biodiversity through potential biodiversity indicators (e.g., stand structure and deadwood), which may not provide sufficient information on other aspects of the current forest biodiversity status. In this study, we present the forest biodiversity monitoring results and lessons from a cross-country study to support large-scale monitoring systems. We developed, evaluated, and discussed harmonized protocols, mainly focused on birds and mammals, which extend beyond the traditional features captured in large-scale forest inventories. We leverage information from 30 intensively monitored plots established in six European countries to achieve these goals. The protocols were helpful in recording data that could be used to reproduce biodiversity-related attributes such as measures of forest structure, regeneration, deadwood features, and bird and mammal diversity. Specifically, field data on trees was used to describe structural features of forests such as stand composition and forest complexity. In contrast, composition and regeneration data provided helpful information for other biodiversity indicators. Data gathering to monitor bird and mammal diversity requires revisiting the plots, which involves greater economic investment and human effort. Once the bird and mammal data have been collected, advanced algorithms could facilitate and enhance the efficiency of the analyses. To optimize the monitoring efficiency, we recommend including these two new biodiversity assessments in a subset of extensive survey plots. Furthermore, using standard guidelines for these new assessments across all countries would facilitate the comparison and reporting of statistical data.
Forfattere
Shaohui Zhang Poul Erik Lærke Mathias Neumann Andersen Junxiang Peng Esben Øster Mortensen Johannes Wilhelmus Maria Pullens Sheng Wang Klaus Steenberg Larsen Davide Cammarano Uffe Jørgensen Kiril ManevskiSammendrag
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Sammendrag
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Forfattere
Siri Svendgård-Stokke Eva Solbjørg Flo Heggem Anne B. Nilsen Svein Olav Krøgli Sebastian Eiter Henrik Forsberg Mathiesen Jonathan Rizzi Torgeir Tajet Ole Einar Ellingbø TveitoSammendrag
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Sammendrag
Dokumentet presenterer resultatene fra sortstesting av jordbær utført i 2024 ved NIBIOs forskningsanlegg i Tromsø og Apelsvoll. Hensikten med testen var å identifisere jordbærsorter som kombinerer høy avling med god smak og kvalitet, spesielt under de varierende vekstforholdene i Norge. Den største avlingen på engangsbærene sorter ble registrert hos sorten ‘Parlando’, etterfulgt av ‘Sonsation’ og ‘Falco’. Generelt hadde Tromsø høyere andel store bær sammenlignet med Apelsvoll, og det ble registrert lav utsortering av bær. Avlingen for remonterende sorter varierte fra 900 til 1150 g per plante. ‘Aurora Karima’ hadde høyest avling, mens ‘Florice’ og ‘Favori’ også hadde høye avlinger. Smaken av jordbær ble vurdert basert på sukker- og syreinnhold. ‘Jenkka’ og ‘Magnum’ hadde høyest sukkerinnhold, mens ‘Dahli’ og ‘Parlando’ hadde lavere innhold. Bær dyrket i Tromsø hadde generelt bedre smak enn de fra Apelsvoll.
Sammendrag
Det er ikke registrert sammendrag