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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.

2023

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Sammendrag

Up-to-date and reliable information on land cover and land use status is important in many aspects of human activities. Knowledge about the reference dataset, its coverage, nomenclature, thematic and geometric accuracy, spatial resolution is crucial for appropriate selection of reference samples used in the classification process. In this study, we examined the impact of the selection and pre-processing of reference samples for the classification accuracy. The classification based on Random Forest algorithm was performed using firstly the automatically selected reference samples derived directly from the national databases, and secondly using the pre-processed and verified reference samples. The verification procedures involved the iterative analysis of histogram of spectral features derived from the Sentinel-2 data for individual land cover classes. The verification of the reference samples improved the accuracy of delineation of all land cover classes. The highest improvement was achieved for the woodland broadleaved and non- and sparce vegetation classes, with the overall accuracy increasing from 51% to 73%, and from 33% to 74%, respectively. The second objective of this study was to derive the best possible land cover classification over the mountain area in Norway, therefore we examined whether the use of the Digital Elevation Model (DEM) can improve the classification results. Classifications were carried out based on Sentinel-2 data and a combination of Sentinel-2 and DEM. Using the DEM the accuracy for nine out of ten land cover classes was improved. The highest improvement was achieved for classes located at higher altitudes: low vegetation and non- and sparse vegetation.

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Sammendrag

Several actors have an impact on the quality of drinking water, but ultimately drinking water treatment plants (DWTPs) play a decisive role in ensuring that water quality complies with public regulations. Several developing technologies are combined in water treatment processes. In this paper, we are analysing the technological development of DWTPs in the South Bohemian region of the Czech Republic. The empirical basis is five DWTPs of varying size, and data are gathered through semi-structured interviews with relevant staff inside and outside of the five DWTPs. This study identifies the interplay of factors driving technological development: public regulations, the economic capacity of local DWTP owners together with subsidies from the European Union and national authorities, political priorities by local authorities, and the knowledge network. The paper addressess learning–knowledge–change processes of DWTPs, thereby contributing to our understanding of developing competence in producing drinking water. Generally, large DWTPs are front-runners in introducing new technologies while the smaller ones are lagging. Still, private companies operating small plants on behalf of municipal owners ensure that those DWTPs are part of a wider knowledge network, aiding to introduce a necessary and cost-effective upgrade to treatment steps.