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.
2021
Forfattere
Henrik Forsberg MathiesenSammendrag
Det er ikke registrert sammendrag
Forfattere
Henrik Forsberg MathiesenSammendrag
Det er ikke registrert sammendrag
Forfattere
Henrik Forsberg MathiesenSammendrag
Det er ikke registrert sammendrag
Forfattere
Henrik Forsberg MathiesenSammendrag
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Sammendrag
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Forfattere
Bikal Ghimire Marcia Saraiva Christian B. Andersen Anupam Gogoi Mona Saleh Nicola Zic Pieter van West May Bente BrurbergSammendrag
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Forfattere
Lars Högbom Dalia Abbas Kestutis Armolaitis Endijs Baders Martyn Futter Aris Jansons Kalev Jõgiste Andis Lazdins Diana Lukmine Mika Mustonen Knut Øistad Anneli Poska Pasi Rautio Johan Svensson Floor Vodde Iveta Varnagiryte-Kabasinskiene Jan Weslien Lars Wilhelmsson Daiga ZuteSammendrag
Forests are the dominant land cover in Nordic–Baltic countries, and forestry, the management of forests for improved ecosystem-service (ES) delivery, is an important contributor to sustainability. Forests and forestry support multiple United Nations Sustainability Goals (UN SDGs) and a number of EU policies, and can address conflicting environmental goals. Forests provide multiple ecosystem services and natural solutions, including wood and fibre production, food, clear and clean water and air, animal and plant habitats, soil formation, aesthetics, and cultural and social services. Carbon sequestered by growing trees is a key factor in the envisaged transition from a fossil-based to a biobased economy. Here, we highlight the possibilities of forest-based solutions to mitigate current and emerging societal challenges. We discuss forestry effects on forest ecosystems, focusing on the optimisation of ES delivery and the fulfilment of UN SDGs while counteracting unwanted effects. In particular, we highlight the trilemma of (i) increasing wood production to substitute raw fossil materials, (ii) increasing forest carbon storage capacity, and (iii) improving forest biodiversity and other ES delivery.
Forfattere
Alexandre Tisserant Marjorie Morales Otávio Cavalett Adam O'Toole Simon Weldon Daniel Rasse Francesco CherubiniSammendrag
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Sammendrag
There are neither volume nor velocity thresholds that define big data. Any data ranging from just beyond the capacity of a single personal computer to tera- and petabytes of data can be considered big data. Although it is common to use High Performance Computers (HPCs) and cloud facilities to compute big data, migrating to such facilities is not always practical due to various reasons, especially for medium/small analysis. Personal computers at public institutions and business companies are often idle during parts of the day and the entire night. Exploiting such computational resources can partly alleviate the need for HPC and cloud services for analysis of big data where HPC and cloud facilities are not immediate options. This is particularly relevant also during testing and pilot application before implementation on HPC or cloud computing. In this paper, we show a real case of using a local network of personal computers using open-source software packages configured for distributed processing to process remotely sensed big data. Sentinel-2 image time series are used for the testing of the distributed system. The normalized difference vegetation index (NDVI) and the monthly median band values are the variables computed to test and evaluate the practicality and efficiency of the distributed cluster. Computational efficiencies of the cluster in relation to different cluster setup, different data sources and different data distribution are tested and evaluated. The results demonstrate that the proposed cluster of local computers is efficient and practical to process remotely sensed data where single personal computers cannot perform the computation. Careful configurations of the computers, the distributed framework and the data are important aspects to be considered in optimizing the efficiency of such a system. If correctly implemented, the solution leads to an efficient use of the computer facilities and allows the processing of big, remote, sensing data without the need to migrate it to larger facilities such as HPC and cloud computing systems, except when going to production and large applications.
Sammendrag
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