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.
2017
Authors
Pia Heltoft ThomsenAbstract
Det er ikke registrert sammendrag
Authors
Eva Narten HøbergAbstract
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Authors
Håvard Steinshamn Mats Höglind Marit Jørgensen Åshild Taksdal Randby Lars Nesheim Anne Kjersti BakkenAbstract
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Authors
Håvard Steinshamn Mats Höglind Marit Jørgensen Åshild Taksdal Randby Lars Nesheim Anne Kjersti BakkenAbstract
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Authors
Signe Kynding Borgen Gry Alfredsen Johannes Breidenbach Lise Dalsgaard Gunnhild Søgaard Aaron SmithAbstract
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Authors
Ola FlatenAbstract
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Authors
Liv Østrem Petter MarumAbstract
Ei meir målretta utvikling av frøblandingar og sikrare utprøving av desse i Norge, kan gje auka engavlingar.
Authors
Gunnhild SøgaardAbstract
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Authors
Eva BrodAbstract
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Authors
Svetlana Saarela Johannes Breidenbach Pasi Raumonen Anton Grafström Göran Ståhl Mark J. Ducey Rasmus AstrupAbstract
This study presents an approach for predicting stand-level forest attributes utilizing mobile laser scanning data collected as a nonprobability sample. Firstly, recordings of stem density were made at point locations every 10th metre along a subjectively chosen mobile laser scanning track in a forest stand. Secondly, kriging was applied to predict stem density values for the centre point of all grid cells ina5m×5m lattice across the stand. Thirdly, due to nondetectability issues, a correction term was computed based on distance sampling theory. Lastly, the mean stem density at stand level was predicted as the mean of the point-level predictions multiplied with the correction factor, and the corresponding variance was estimated. Many factors contribute to the uncertainty of the stand-level prediction; in the variance estimator, we accounted for the uncertainties due to kriging prediction and due to estimating a detectability model from the laser scanning data. The results from our new approach were found to correspond fairly well to estimates obtained using field measurements from an independent set of 54 circular sample plots. The predicted number of stems in the stand based on the proposed methodology was 1366 with a 12.9% relative standard error. The corresponding estimate based on the field plots was 1677 with a 7.5% relative standard error.