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
Ulrike BayrSammendrag
Det er ikke registrert sammendrag
Sammendrag
The Norwegian food industry has over the past centuries constituted a growing share of a declining industrial sector in Norway. Seafood processing is a main driver of growth in the sector, although a dominating share of seafood is exported unprocessed. Operating results in food processing is growing at a nominal rate of seven percent annually. When looking at the food value chain as a whole, primary agriculture provides a significantly higher share of gross investments than its share of operating results. The Norwegian food industry provides employment in all counties, With particular high growth rates in northern regions.
Sammendrag
Utredningen av utviklingsmuligheter og samarbeidsløsninger for norske planteskoler viser at planteskolene utgjøre en liten hagebruksnæring med stadig færre produsenter. Vi ser også at kundemarkedet til planteskolene i stor grad er i vekst og viser attraktive utviklingsmuligheter. Dersom planteskoleprodusentene skal dra nytte av dette bør det utvikles gode fellesløsninger. For mer informasjon, se eget sammendrag.
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.
Sammendrag
The size and location of agricultural fields that are in active use and the type of use during the growing season are among the vital information that is needed for the careful planning and forecasting of agricultural production at national and regional scales. In areas where such data are not readily available, an independent seasonal monitoring method is needed. Remote sensing is a widely used tool to map land use types, although there are some limitations that can partly be circumvented by using, among others, multiple observations, careful feature selection and appropriate analysis methods. Here, we used Sentinel-2 satellite image time series (SITS) over the land area of Norway to map three agricultural land use classes: cereal crops, fodder crops (grass) and unused areas. The Multilayer Perceptron (MLP) and two variants of the Convolutional Neural Network (CNN), are implemented on SITS data of four different temporal resolutions. These enabled us to compare twelve model-dataset combinations to identify the model-dataset combination that results in the most accurate predictions. The CNN is implemented in the spectral and temporal dimensions instead of the conventional spatial dimension. Rather than using existing deep learning architectures, an autotuning procedure is implemented so that the model hyperparameters are empirically optimized during the training. The results obtained on held-out test data show that up to 94% overall accuracy and 90% Cohen’s Kappa can be obtained when the 2D CNN is applied on the SITS data with a temporal resolution of 7 days. This is closely followed by the 1D CNN on the same dataset. However, the latter performs better than the former in predicting data outside the training set. It is further observed that cereal is predicted with the highest accuracy, followed by grass. Predicting the unused areas has been found to be difficult as there is no distinct surface condition that is common for all unused areas.
Forfattere
Arnt Kristian GjertsenSammendrag
Det er ikke registrert sammendrag
Forfattere
Henrik Forsberg MathiesenSammendrag
Det er ikke registrert sammendrag
Forfattere
Henrik Forsberg MathiesenSammendrag
Det er ikke registrert sammendrag
Forfattere
Henrik Forsberg MathiesenSammendrag
Det er ikke registrert sammendrag
Forfattere
Henrik Forsberg MathiesenSammendrag
Det er ikke registrert sammendrag