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.
2019
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Ivar HovlandSammendrag
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Ingrid TengeSammendrag
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Ingrid TengeSammendrag
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Ingrid TengeSammendrag
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Tove Vaaje-KolstadSammendrag
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Rasmus Astrup Johannes Rahlf Knut Bjørkelo Misganu Debella-Gilo Arnt Kristian Gjertsen Johannes BreidenbachSammendrag
This paper describes the development and utility of the Norwegian forest resources map (SR16). SR16 is developed using photogrammetric point cloud data with ground plots from the Norwegian National Forest Inventory (NFI). First, an existing forest mask was updated with object-based image analysis methods. Evaluation against the NFI forest definitions showed Cohen's kappa of 0.80 and accuracy of 0.91 in the lowlands and a kappa of 0.73 and an accuracy of 0.96 in the mountains. Within the updated forest mask, a 16×16 m raster map was developed with Lorey's height, volume, biomass, and tree species as attributes (SR16-raster). All attributes were predicted with generalized linear models that explained about 70% of the observed variation and had relative RMSEs of about 50%. SR16-raster was segmented into stand-like polygons that are relatively homogenous in respect to tree species, volume, site index, and Lorey's height (SR16-vector). When SR16 was utilized in a combination with the NFI plots and a model-assisted estimator, the precision was on average 2–3 times higher than estimates based on field data only. In conclusion, SR16 is useful for improved estimates from the Norwegian NFI at various scales. The mapped products may be useful as additional information in Forest Management Inventories.
Forfattere
Idoia Biurrun Sabina Burrascano Iwona Dembicz Riccardo Guarino Jutta Kapfer Remigiusz Pielech Itziar García-Mijangos Viktoria Wagner Salza Palpurina Anne Mimet Vincent Pellissier Corrado Marcenó Arkadiusz Nowak Ariel Bergamini Steffen Boch Anna Mária Csergő John-Arvid Grytnes Juan Antonio Campos Brigitta Erschbamer Borja Jiménez-Alfaro Zygmunt Kącki Anna Kuzemko Michael Manthey Koenraad Van Meerbeek Grzegorz Swacha Elias Afif Juha M. Alatalo M Aleffi Manuel Babbi Zoltán Bátori Elena Belonovskaya Christian Berg Kuber Prasad Bhatta Laura Cancellieri Tobias Ceulemans Balázs Deák László Demeter Lei Deng Jiří Doležal Christian Dolnik Wenche Dramstad Pavel Dřevojan Klaus Ecker Franz Essl J. Etzold Goffredo Filibeck Wendy Fjellstad Behlul Güler Michal Hájek Daniel Hepenstrick John G. Hodgson João Honrado Annika Jagerbrand Monika Janišová Philippe Jeanneret András Kelemen Philipp Kirschner Ewelina Klichowska Ganna Kolomiiets Łukasz Kozub Jan Lepš Regina Lindborg Swantje Löbel Angela Lomba Martin Magnes Helmut Mayrhofer Marek Malicki Ermin Mašić Eliane S. Meier Denis Mirin Ulf Molau Ivan Y. Moysiyenko Alireza Naqinezhad Josep M. Ninot M Nobis Christian Pedersen Aaron Pérez-Haase Jan Peters Eulàlia Pladevall-Izard Jan Rolecek Vladimir Ronkin Galina Savchenko Dariia Shyriaieva Hanne Sickel Carly Stevens Sebastian Świerszcz Csaba Tölgyesi Nadezda Tsarevskaya Orsolya Valkó Carmen Van Mechelen Iuliia Vashenyak Ole Reidar Vetaas Denys Vynokurov Emelie Waldén Stefan Widmer Sebastian Wolfrum Anna Wróbel Ekaterina Zlotnikova Jürgen DenglerSammendrag
Abstract: GrassPlot is a collaborative vegetation-plot database organised by the Eurasian Dry Grassland Group (EDGG) and listed in the Global Index of Vegetation-Plot Databases (GIVD ID EU-00-003). Following a previous Long Database Report (Dengler et al. 2018, Phyto-coenologia 48, 331–347), we provide here the first update on content and functionality of GrassPlot. The current version (GrassPlot v. 2.00) contains a total of 190,673 plots of different grain sizes across 28,171 independent plots, with 4,654 nested-plot series including at least four grain sizes. The database has improved its content as well as its functionality, including addition and harmonization of header data (land use, information on nestedness, structure and ecology) and preparation of species composition data. Currently, GrassPlot data are intensively used for broad-scale analyses of different aspects of alpha and beta diversity in grassland ecosystems.
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