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.
2020
Sammendrag
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Sammendrag
Background The age of forest stands is critical information for forest management and conservation, for example for growth modelling, timing of management activities and harvesting, or decisions about protection areas. However, area-wide information about forest stand age often does not exist. In this study, we developed regression models for large-scale area-wide prediction of age in Norwegian forests. For model development we used more than 4800 plots of the Norwegian National Forest Inventory (NFI) distributed over Norway between latitudes 58° and 65° N in an 18.2 Mha study area. Predictor variables were based on airborne laser scanning (ALS), Sentinel-2, and existing public map data. We performed model validation on an independent data set consisting of 63 spruce stands with known age. Results The best modelling strategy was to fit independent linear regression models to each observed site index (SI) level and using a SI prediction map in the application of the models. The most important predictor variable was an upper percentile of the ALS heights, and root mean squared errors (RMSEs) ranged between 3 and 31 years (6% to 26%) for SI-specific models, and 21 years (25%) on average. Mean deviance (MD) ranged between − 1 and 3 years. The models improved with increasing SI and the RMSEs were largest for low SI stands older than 100 years. Using a mapped SI, which is required for practical applications, RMSE and MD on plot level ranged from 19 to 56 years (29% to 53%), and 5 to 37 years (5% to 31%), respectively. For the validation stands, the RMSE and MD were 12 (22%) and 2 years (3%), respectively. Conclusions Tree height estimated from airborne laser scanning and predicted site index were the most important variables in the models describing age. Overall, we obtained good results, especially for stands with high SI. The models could be considered for practical applications, although we see considerable potential for improvements if better SI maps were available.
Forfattere
Elisabeth Pötzelsberger Katharina Lapin Giuseppe Brundu Tim Adriaens Vlatko Andonovski Siniša Andrašev Jean-Charles Bastien Robert Brus Milić Čurović Željka Čurović Branislav Cvjetković Martina Ðodan Juan M. Domingo-Santos Anna Gazda Jean-Marc Henin Cornelia Hernea Bo Karlsson Ljiljana Keča Srđan Keren Zsolt Keserű Thomai Konstantara Johan Kroon Nicola La Porta Vasyl Lavnyy Dagnija Lazdina Aljona Lukjanova Tiit Maaten Palle Madsen Dejan Mandjukovski Francisco J. Marín Pageo Vitas Marozas Antonin Martinik William L. Mason Frits Mohren Maria Cristina Monteverdi Charalambos Neophytou Pat Neville Valeriu-Norocel Nicolescu Per Holm Nygaard Christophe Orazio Taras Parpan Sanja Perić Krasimira Petkova Emil Borissov Popov Mick Power Károly Rédei Matti Rousi Joaquim S. Silva Ahmet Sivacioglu Michalis Socratous Lina Straigyte Josef Urban Kris Vandekerkhove Radosław Wąsik Marjana Westergren Thomas Wohlgemuth Tiina Ylioja Hubert HasenauerSammendrag
Europe has a history rich in examples of successful and problematic introductions of trees with a native origin outside of Europe (non-native trees, NNT). Many international legal frameworks such as treaties and conventions and also the European Union have responded to the global concern about potential negative impacts of NNT that may become invasive in natural ecosystems. It is, however, national and regional legislation in particular that affects current and future management decisions in the forest sector and shapes the landscapes of Europe. We identified all relevant legal instruments regulating NNT, the different legal approaches and the regulatory intensity in 40 European countries (no microstates). Information on hard and effective soft law instruments were collected by means of a targeted questionnaire and consultation of international and national legislation information systems and databases. In total, 335 relevant legal instruments were in place in June/July 2019 to regulate the use of NNT in the investigated 116 geopolitical legal units (countries as well as sub-national regions with their own legislation). Countries and regions were empirically categorized according to ad hoc-defined legislation indicators. These indicators pay respect to the general bans on the introduction of non-native species, the generally allowed and prohibited NNT, approval mechanisms and specific areas or cases where NNT are restricted or prohibited. Our study revealed a very diverse landscape of legal frameworks across Europe, with a large variety of approaches to regulating NNT being pursued and the intensity of restriction ranging from very few restrictions on species choice and plantation surface area to the complete banning of NNT from forests. The main conclusion is that there is a clear need for more co-ordinated, science-based policies both at the local and international levels to enhance the advantages of NNT and mitigate potential negative effects.
Sammendrag
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Sammendrag
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Forfattere
Ulrike BayrSammendrag
Stadig oftere møter vi begreper som stordata, kunstig intelligens, maskinlæring, nevrale nettverk og dyp læring. Slike «smarte teknologier» har for lengst blitt en del av hverdagen vår – iblant også uten at vi vet det. Denne artikkelen gir en kort innføring i hva maskinlæring er og hvordan denne teknologien kan brukes til automatisk bildeanalyse.
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Forfattere
Eva Narten HøbergSammendrag
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Forfattere
Klaus Mittenzwei Eirik RomstadSammendrag
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Forfattere
Eva Narten HøbergSammendrag
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