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.
2023
Forfattere
Helmer BelboSammendrag
Det er ikke registrert sammendrag
Forfattere
Helmer BelboSammendrag
Det er ikke registrert sammendrag
Forfattere
Helmer BelboSammendrag
Gjennom prosjektet «Variabelt dekktrykk for tømmerbiler» er et system som gir tømmerbilføreren anledning til løpende regulering av dekktrykket etter hjulbelastning og kjøreforhold testet ut. Videre er det gjort analyser av vegslitasje og fremkommelighet, samt økonomiske effekter systemet kan få for både virkestransportør, skogsveg-eier og skogindustri. Denne fagrapporten summerer opp kunnskapsstatus for konseptet, presenterer resultatene fra prosjektet, og gir forslag til videre arbeid på tematikken. Bruk av dekktrykkregulering kan tillate større virkesbil-lass på offentlige veger med aksellastbegrensninger, gi jevnere virkestransport i våte perioder, og redusere vegvedlikeholdskostnadene på skogsveg. Bedre friksjon på vinterføre gir også bedre fremkommelighet, og kan muliggjøre virkestransport på en del av veiene som per i dag kun kan benyttes i barmarksesongen. Samlet kan aktiv bruk av dekktrykkregulering på tømmerbiler redusere kostnadene til transport og vegvedlikehold med anslagsvis 5 – 7 kr per m3. Virkestransportøren har likevel små økonomiske incentiver til investering i dekktrykkregulering. Derfor er det også lite sannsynlig at virkestransportørene vil investere i slike systemer om ikke en tilstrekkelig andel av de økonomiske fordelene ved systemet kanaliseres til virkestransportøren.
Forfattere
Helmer BelboSammendrag
Det er ikke registrert sammendrag
Forfattere
Alexander Tischer Julian Klaus Christian Kuehne Bernd Hoffmann Martin Hartig Robert SchmidtSammendrag
Det er ikke registrert sammendrag
Sammendrag
The materials used in construction have a significant environmental impact and this is becoming more important as operational energy requirements continue to fall. It is therefore becoming increasingly important to take into account the environmental burdens associated with materials used in construction. Life cycle assessment (LCA) and Environmental Product Declarations (EPD) are useful tools for this purpose. When comparing the results of numerous LCA studies of different construction materials, the main question is often ‘Which material is better for the environment?’. The answer, however, is usually not as simple – but why is it so difficult to decide which material has the lowest environmental impact? To answer this question, we have to consider what life cycle assessment is and how an LCA is undertaken. The report covers the stages of an LCA, from defining the goal and scope of the respective study to the creation of the life cycle inventory (LCI), the life cycle impact assessment (LCIA) to the reporting and interpretation of the results. Additionally, the report goes in detail into how to approach published LCA studies, how to work with EPDs and the much-discussed issue of Carbon storage in buildings. In the final chapter, the report assesses the comparability of published studies evaluating the environmental impact of different building materials.
Sammendrag
Extractives from silver birch (Betula pendula) can play an important role in the future bioeconomy by delivering the feedstock, for instance, for antioxidative applications. It is, therefore, inevitable to gain knowledge of the distribution of extractive content and composition in the different tissues of the tree for estimating the potential volumes of valuable extractable compounds. This study examines the extractable compound distribution of different tree tissues such as outer and inner bark and wood, respectively, considering the original height of the stem and comparing the yields after Soxhlet and accelerated solvent extraction (ASE). Eleven parts of the model tree (seven stem discs and four branches) were separated into primary tissues and extracted with a ternary solvent system. The investigated extraction methods resulted in a comparable performance regarding yields and the composition of the extractives. The extractives were divided into single compounds such as betulin, lupeol, γ-sitosterol, and lupeone and substance groups such as carbohydrates, terpenes, aromatics, and other groups. The distribution of single substances and substance groups depends on the location and function of the examined tissues. Furthermore, the evidence for the correlation of a single substance’s location and original tree height is stronger for lupeol than for betulin. Primary betulin sources of the calculated betulin output are the outer bark of the stem and the branches. By using small branches, further potential for the extraction of betulin can be utilized. A model calculation of the betulin content in the current birch tree revealed a significant potential of 23 kg of betulin available as a valuable chemical resource after by-product utilization.
Forfattere
Alexander Becker Stefania Russo Stefano Puliti Nico Lang Konrad Schindler Jan Dirk WegnerSammendrag
Monitoring and managing Earth’s forests in an informed manner is an important requirement for addressing challenges like biodiversity loss and climate change. While traditional in situ or aerial campaigns for forest assessments provide accurate data for analysis at regional level, scaling them to entire countries and beyond with high temporal resolution is hardly possible. In this work, we propose a method based on deep ensembles that densely estimates forest structure variables at country-scale with 10-m resolution, using freely available satellite imagery as input. Our method jointly transforms Sentinel-2 optical images and Sentinel-1 syntheticaperture radar images into maps of five different forest structure variables: 95th height percentile, mean height, density, Gini coefficient, and fractional cover. We train and test our model on reference data from 41 airborne laser scanning missions across Norway and demonstrate that it is able to generalize to unseen test regions, achieving normalized mean absolute errors between 11% and 15%, depending on the variable. Our work is also the first to propose a variant of so-called Bayesian deep learning to densely predict multiple forest structure variables with well-calibrated uncertainty estimates from satellite imagery. The uncertainty information increases the trustworthiness of the model and its suitability for downstream tasks that require reliable confidence estimates as a basis for decision making. We present an extensive set of experiments to validate the accuracy of the predicted maps as well as the quality of the predicted uncertainties. To demonstrate scalability, we provide Norway-wide maps for the five forest structure variables.
Forfattere
Stefano PulitiSammendrag
Det er ikke registrert sammendrag
Forfattere
Stefano PulitiSammendrag
Det er ikke registrert sammendrag