Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2021
Abstract
No abstract has been registered
Authors
Laura Wendling Adina Dumitru K. Arnbjerg-Nielsen C. Baldacchini S. Connop M. Dubovik J. Fermoso K. Hölscher Farrokh Nadim F. Pilla F. Renaud M. L. Rhodes E. San José R. Sánchez J. Skodra J.-M. Tacnet G. Zulian Sebastian Eiter Wendy Fjellstad Kristin Reichborn-KjennerudAbstract
No abstract has been registered
Authors
Kristine Valle Eli Mari Øverdahl Stephanie Degenhardt Kim Weger Wendy Fjellstad Sebastian EiterAbstract
Participating in a neighbourhood and community garden has positive social and emotional impacts, as well as the satisfaction derived from growing food. Adults and teenagers participating in gardening activities at Linderud farm in Oslo report positive experiences most commonly related to social networks, growing food, feelings/emotions and aesthetics.
Authors
Teresa Gómez de la Bárcena Lise Dalsgaard Line Tau Strand Christian Wilhelm Mohr Knut Bjørkelo Rune Eriksen Gunnhild SøgaardAbstract
Denne publikasjonen presenterer en ny metodikk for estimering av endringer i lageret av jordkarbon som følge av arealbruksendringer på mineraljord. Metodikken er utviklet for bruk i den nasjonale rapporteringen av arealbrukssektoren under FNs klimakonvensjon. Metodikken baserer seg på den enkleste tilnærming i følge IPCC sine retningslinjer, en såkaldt Tier 1. Tier 1 metodikken baseres i stor grad på standardverdier fra retningslinjene (IPCC default), men trenger en kopling mot nasjonal arealinformasjon. Denne koplingen beskrives i rapporten. Metodikken tar utgangspunkt i standardverdier for lageret av jordkarbon (SOCREF). Disse er basert på jordtype-grupperinger og klimasone som stammer fra en verdensdekkende jorddatabase. Endringer i jordkarbon etter arealbruksendring estimeres ved hjelp av SOCREF i kombinasjon med et sett faktorer (også standardverdier) som er arealbruksavhengige. Metodikken legger til grunn at endringer i jordkarbon skjer lineært over 20 år (ifølge 2006 IPCC Guidelines). Grunnleggende informasjon for å kunne kople standardverdier mot arealer på en konsistent måte er stort sett manglende for Norge på nasjonal skala. Rapporten gir derfor detaljert informasjon om de datakildene som har vært brukt til å kunne definere hvilke standariserte verdier som tilhører et bestemt areal i overgang....
Authors
Idoia Biurrun Remigiusz Pielech Iwona Dembicz François Gillet Łukasz Kozub Corrado Marcenó Triin Reitalu Koenraad Van Meerbeek Riccardo Guarino Milan Chytrý Robin J Pakeman Zdenka Preislerová Irena Axmanová Sabina Burrascano Sándor Bartha Steffen Boch Hans Henrik Bruun Timo Conradi Pieter De Frenne Franz Essl Goffredo Filibeck Michal Hájek Borja Jiménez-Alfaro Anna Kuzemko Zsolt Molnár Meelis Pärtel Ricarda Pätsch Honor C. Prentice Jan Roleček Laura M. E. Sutcliffe Massimo Terzi Manuela Winkler Jianshuang Wu Svetlana Acíc Alicia T.R. Acosta Elias Afif Munemitsu Akasaka Juha M. Alatalo Michele Aleffi Alla Aleksanyan Arshad Ali Iva Apostolova Parvaneh Ashouri Zoltán Bátori Esther Baumann Thomas Becker Elena Belonovskaya José Luis Benito Alonso Asun Berastegi Ariel Bergamini Kuber Prasad Bhatta Ilaria Bonini Marc-Olivier Büchler Vasyl Budzhak Alvaro Bueno Fabrizio Buldrini Juan Antonio Campos Laura Cancellieri Marta Carboni Tobias Ceulemans Alessandro Chiarucci Cristina Chocarro Luisa Conti Anna Mária Csergő Beata Cykowska-Marzencka Marta Czarniecka-Wiera Marta Czarnocka-Cieciura Patryk Czortek Jiří Danihelka Francesco de Bello Balázs Deák László Demeter Lei Deng Martin Diekmann Jiří Doležal Christian Dolnik Pavel Dřevojan Cecilia Duprè Klaus Ecker Hamid Ejtehadi Brigitta Erschbamer Javier Etayo Jonathan Etzold Tünde Farkas Mohammad Farzam George Fayvush Maria Rosa Fernández Calzado Manfred Finckh Wendy Fjellstad Georgios Fotiadis Daniel García-Magro Itziar García-Mijangos Rosario G. Gavilán Markus Germany Sahar Ghafari Gian Pietro Giusso del Galdo John Arvid Grytnes Behlul Güler Alba Gutiérrez-Girón Aveliina Helm Mercedes Herrera Elisabeth M. Hüllbusch Nele Ingerpuu Annika Jagerbrand Ute Jandt Monika Janišová Philippe Jeanneret Florian Jeltsch Kai Jensen Anke Jentsch Zygmunt Kącki Kaoru Kakinuma Jutta Kapfer Mansoureh Kargar András Kelemen Kathrin Kiehl Philipp Kirschner Asuka Koyama Nancy Langer Lorenzo Lazzaro Jan Lepš Ching-Feng Li Frank Yonghong Li Diego Liendo Regina Lindborg Swantje Löbel Angela Lomba Zdeňka Lososová Pavel Lustyk Arantzazu L. Luzuriaga Wenhong Ma Simona Maccherini Martin Magnes Marek Malicki Michael Manthey Constantin Mardari Felix May Helmut Mayrhofer Eliane S. Meier Farshid Memariani Kristina Merunková Ottar Michelsen Joaquín Molero Mesa Halime Moradi Ivan Moysiyenko Michele Mugnai Alireza Naqinezhad Rayna Natcheva Josep M. Ninot Marcin Nobis Jalil Noroozi Arkadiusz Nowak Vladimir Onipchenko Salza Palpurina Harald Pauli Hristo Pedashenko Christian Pedersen Robert K. Peet Aaron Pérez-Haase Jan Peters Nataša Pipenbaher Chrisoula Pirini Eulàlia Pladevall-Izard Zuzana Plesková Giovanna Potenza Soroor Rahmanian Maria Pilar Rodríguez-Rojo Vladimir Ronkin Leonardo Rosati Eszter Ruprecht Solvita Rusina Marko Sabovljević Anvar Sanaei Ana M. Sánchez Francesco Santi Galina Savchenko Maria Teresa Sebastia Dariia Shyriaieva Vasco Silva Sonja Skornik Eva Šmerdová Judit Sonkoly Marta Gaia Sperandii Monika Staniaszek-Kik Carly Stevens Simon Stifter Sigrid Suchrow Grzegorz Swacha Sebastian Świerszcz Amir Talebi Balázs Teleki Lubomír Tichý Csaba Tölgyesi Marta Torca Péter Török Nadezda Tsarevskaya Ioannis Tsiripidis Ingrid Turisová Atushi Ushimaru Orsolya Valkó Carmen Van Mechelen Thomas Vanneste Iuliia Vasheniak Kiril Vassilev Daniele Viciani Luis Villar Risto Virtanen Ivana Vitasović-Kosić András Vojtkó Denys Vynokurov Emelie Waldén Yun Wang Frank Weiser Lu Wen Karsten Wesche Hannah White Stefan Widmer Sebastian Wolfrum Anna Wróbel Zuoqiang Yuan David Zelený Liqing Zhao Jürgen DenglerAbstract
Aims Understanding fine-grain diversity patterns across large spatial extents is fundamental for macroecological research and biodiversity conservation. Using the GrassPlot database, we provide benchmarks of fine-grain richness values of Palaearctic open habitats for vascular plants, bryophytes, lichens and complete vegetation (i.e., the sum of the former three groups). Location Palaearctic biogeographic realm. Methods We used 126,524 plots of eight standard grain sizes from the GrassPlot database: 0.0001, 0.001, 0.01, 0.1, 1, 10, 100 and 1,000 m2 and calculated the mean richness and standard deviations, as well as maximum, minimum, median, and first and third quartiles for each combination of grain size, taxonomic group, biome, region, vegetation type and phytosociological class. Results Patterns of plant diversity in vegetation types and biomes differ across grain sizes and taxonomic groups. Overall, secondary (mostly semi-natural) grasslands and natural grasslands are the richest vegetation type. The open-access file ”GrassPlot Diversity Benchmarks” and the web tool “GrassPlot Diversity Explorer” are now available online (https://edgg.org/databases/GrasslandDiversityExplorer) and provide more insights into species richness patterns in the Palaearctic open habitats. Conclusions The GrassPlot Diversity Benchmarks provide high-quality data on species richness in open habitat types across the Palaearctic. These benchmark data can be used in vegetation ecology, macroecology, biodiversity conservation and data quality checking. While the amount of data in the underlying GrassPlot database and their spatial coverage are smaller than in other extensive vegetation-plot databases, species recordings in GrassPlot are on average more complete, making it a valuable complementary data source in macroecology.
Authors
Anna Palmé Birgitte Lund Elina Kiviharju Heli Fitzgerald Hjörtur Thorbjörnsson Jenny Hagenblad Jens Weibull Kjersti Bakkebø Fjellstad Kristina Bjureke Linn Borgen Nilsen Magnus Göransson Maija Häggblom Marko Hyvärinen Mora Aronsson Virva LyytikäinenAbstract
No abstract has been registered
Abstract
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.
Abstract
Rapid technological advances in airborne hyperspectral and lidar systems paved the way for using machine learning algorithms to map urban environments. Both hyperspectral and lidar systems can discriminate among many significant urban structures and materials properties, which are not recognizable by applying conventional RGB cameras. In most recent years, the fusion of hyperspectral and lidar sensors has overcome challenges related to the limits of active and passive remote sensing systems, providing promising results in urban land cover classification. This paper presents principles and key features for airborne hyperspectral imaging, lidar, and the fusion of those, as well as applications of these for urban land cover classification. In addition, machine learning and deep learning classification algorithms suitable for classifying individual urban classes such as buildings, vegetation, and roads have been reviewed, focusing on extracted features critical for classification of urban surfaces, transferability, dimensionality, and computational expense.
Abstract
No abstract has been registered
Abstract
Rapporten utforsker og diskuterer potensialet for økt bruk av Stordata (engelsk: big data) teknologi og metode innenfor instituttets arbeidsområder. I dag benyttes Stordata-tilnærminger til å løse forvaltningsstøtteoppgaver, samt til forskningsformål, særlig i sentrene for presisjonslandbruk og presisjonsjordbruk. Potensialet for økt bruk av Stordata innenfor instituttet er stort. For å realisere potensialet er det behov for god samordning mellom organisasjonsenhetene og utvikling av strategisk kompetanse på fagområdet.