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
Sammendrag
Forest stands are important units of management. A stand-by-stand estimation of the mean and variance of an attribute of interest (Y) remains a priority in forest enterprise inventories. The advent of powerful and cost effective remotely sensed auxiliary variables (X) correlated with Y means that a census of X in the forest enterprise is increasingly available. In combination with a probability sample of Y, the census affords a modeldependent stand-level inference. It is important, however, that the sampling design affords an estimation of possible stand-effects in the model linking X to Y.We demonstrate, with simulated data, that failing to quantify non-zero stand-effects in the intercept of a linear population-level model can lead to a serious underestimation of the uncertainty in a model-dependent estimate of a stand mean, and by extension a confidence interval with poor coverage.We also provide an approximation to the variance of stand-effects in an intercept for the case when a sampling design does not afford estimation. Furthermore, we propose a method to correct a potential negative bias in an estimate of the variance of stand-effects when a sampling design prescribes few stands with small within-stand sample sizes.
Forfattere
Svetlana Saarela Johannes Breidenbach Pasi Raumonen Anton Grafström Göran Ståhl Mark J. Ducey Rasmus AstrupSammendrag
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.
Forfattere
Anna Sandak Jakub Sandak Athanasios Dimitriou Ingunn Burud Thomas Kringlebotn Thiis Lone Ross Graham Alan Ormondroyd Dimitrios KraniotisSammendrag
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Schistidium relictum is described as a new northwest North American and Siberian species of moss. Important distinguishing characters include dull, nearly black plants, with stems densely and evenly foliated, weakly spreading leaves that usually lack awns, and the mostly 1-stratose distal leaf laminae with 2(–3) -stratose margins. The species has a remarkable disjunct distribution pattern with most of the sites where it has been found having been unglaciated during the Pleistocene glaciations. It is restricted to areas with occurrence of calcareous bedrock, especially limestones. It appears to be rather isolated genetically based on molecular studies of total ITS. It is sister to the large clade, ‘Apocarpum’, which consists of species which probably embody its closest known extant relatives.
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«This is a perspective for the article 2016 Environ. Res. Lett. 11 095010”