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.
2021
Forfattere
Arnt Kristian GjertsenSammendrag
The national forest authority monitors forest regeneration on clear-cut areas annually and needs a more objective and unbiased sample. This can be solved with satellite images, and a method to detect new clear-cuts with time series of Sentinel-2 satellite images has been developed and tested. The 25 % percentile of the Normalized Burn Ratio (NBR) index, based on near-infrared and short wave infrared bands, is calculated and the differential (dNBR) between two years is used to detect new forest clearings. The method has been tested against a management plan with new clear-cuts in 2017. A total of 162 points, 81 in clear-cut and 81 in other stands, was used to test the accuracy. Based on the confusion matrix, the F1 score was 0.97 and the more balanced Matthews correlation coefficient 0.95.
Forfattere
Erling MeisingsetSammendrag
Det er ikke registrert sammendrag
Sammendrag
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Forfattere
Mekjell Meland Milica Fotiric-Aksic Frank A. MaasSammendrag
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Forfattere
Mekjell Meland Milica Fotiric-Aksic Frank A. MaasSammendrag
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Forfattere
Eva Narten HøbergSammendrag
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Sammendrag
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Forfattere
Arne SteffenremSammendrag
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Forfattere
Sophie Mentzel Merete Grung Knut-Erik Tollefsen Marianne Stenrød Karina Petersen S. Jannicke MoeSammendrag
Conventional environmental risk assessment of chemicals is based on a calculated risk quotient, representing the ratio of exposure to effects of the chemical, in combination with assessment factors to account for uncertainty. Probabilistic risk assessment approaches can offer more transparency by using probability distributions for exposure and/or effects to account for variability and uncertainty. In this study, a probabilistic approach using Bayesian network modeling is explored as an alternative to traditional risk calculation. Bayesian networks can serve as meta-models that link information from several sources and offer a transparent way of incorporating the required characterization of uncertainty for environmental risk assessment. To this end, a Bayesian network has been developed and parameterized for the pesticides azoxystrobin, metribuzin, and imidacloprid. We illustrate the development from deterministic (traditional) risk calculation, via intermediate versions, to fully probabilistic risk characterization using azoxystrobin as an example. We also demonstrate the seasonal risk calculation for the three pesticides.
Sammendrag
Det er ikke registrert sammendrag