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.
2012
Forfattere
Christian Brischke Linda Meyer Gry Alfredsen Miha Humar Lesley Francis Per Otto Flæte Pia Larsson-BrelidSammendrag
The material-inherent resistance of wood is one of the most important qualities influencing the durability of timber. Hence, it has also a major effect on the service life to be expected from a timber construction. In addition, design details and the respective climatic conditions determine durability and make it impossible to treat wood durability as an absolute value. Moreover, the reference magnitude varies between locations because of climatic differences. Durability classification is therefore based on comparing a certain performance indicator between the timber in question and a reference timber. Finally, the relative values (= resistance factors) are grouped and related to durability classes, which can refer to a high range of service lives for a certain location. The insufficient comparability of such durability records turned out to be a key problem for the service life prediction of timber structures, even when the climatic conditions are clearly defined. This study aimed therefore on an inventory of literature data, directly based on service life measures, not masked by a durability classification schedule. It focused on natural durability of timber tested in the field under above-ground conditions. In total 395 durability recordings from 31 different test sites worldwide and based on ten different test methods have been considered for the calculation of resistance factors: 190 for hardwoods and 205 for softwoods. Nevertheless, the considered datasets were heterogeneous in quality and quantity; the resulting resistance factors suffered from high variation. In many cases information was presented too condensed and incompletely, which is inescapable for instance in journal articles. To increase the amount of available, comparable, and directly service-life related data a reliable platform is needed. A proposal for a corresponding data base is provided in part 2 of this paper.
Forfattere
Camilla BaumannSammendrag
De siste fem årene har Genressurssenteret ettersøkt og etterlyst i overkant av 50 navngitte sorter vi gjerne skulle ha inn i samlingene våre. I leteaksjonen har vi funnet 12 av de savnede sortene. Samtidig må vi erkjenne at de øvrige sannsynligvis er borte for alltid.
Sammendrag
K-nearest neighbor (kNN) approaches are popular statistical methods for predicting forest attributes in airborne laser scanning (ALS) based inventories. Their main upsides are the simplicity to predict multivariate response variables and their freeness of distributional assumptions on the conditional response.One of their largest draw-backs is that predictions outside the range of the reference data inherently result in an under- or overestimation. This property of kNN approaches is known as extrapolation bias and aggravates with an increasing number of neighbors (k) used for the prediction.This study presents one possibility to reduce extrapolation biases of predictions based on the area-based approach (ABA) by using individual tree crown (ITC) approaches within those specific areas of a low density ALS acquisition where the point density might be sufficiently high for using ITC methods.In the proposed strategy, additional (or artificial) reference plots augmented field measured plots. Artificial plots were created by applying ITC segmentation to a canopy height model derived from high density ALS data. The response variable biomass per hectare was predicted for every segment following a semi-ITC approach.The segment predictions were aggregated on the artificial plot level. The artificial plots were then treated in the same way as the original reference data to make predictions in areas with low density ALS data based on the ABA. It was hereby assumed that the predicted plot level response on the artificial plots is equivalent with the observed plot level response on the original reference data.The data consisted of 110 reference plots with a smaller data range than the 201 independent validation plots. Considerable extrapolation bias was visible if only the reference plots were used for the prediction. Almost no extrapolation bias was found if the prediction was based on reference plots augmented by artificial plots. The root mean squared error (RMSE) of the biomass predictions based on the reference plots was 39.1%. The RMSE reduced to 29.8% if the reference plots were augmented by artificial plots.
Forfattere
Harald Bratli Håkon Holien Gunhild RønningSammendrag
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Sammendrag
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Forfattere
Tor J. JohansenSammendrag
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Forfattere
Tor J. JohansenSammendrag
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Sammendrag
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Sammendrag
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