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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

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Sammendrag

Rapid technological advances in airborne hyperspectral and lidar systems paved the way for using machine learning algorithms to map urban environments. Both hyperspectral and lidar systems can discriminate among many significant urban structures and materials properties, which are not recognizable by applying conventional RGB cameras. In most recent years, the fusion of hyperspectral and lidar sensors has overcome challenges related to the limits of active and passive remote sensing systems, providing promising results in urban land cover classification. This paper presents principles and key features for airborne hyperspectral imaging, lidar, and the fusion of those, as well as applications of these for urban land cover classification. In addition, machine learning and deep learning classification algorithms suitable for classifying individual urban classes such as buildings, vegetation, and roads have been reviewed, focusing on extracted features critical for classification of urban surfaces, transferability, dimensionality, and computational expense.

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The purpose of this research is to develop a method for estimating the spatially and temporally resolved moisture content of thermally modified Scots pine (Pinus sylvestris) using remote sensing. Hyperspectral time series imaging in the NIR wavelength region (953–2516 nm) was used to gather information about the absorbance of eight thermally modified pine samples each minute as they dried during a period of approximately 20 h. After preprocessing the collected spectral data and identifying an appropriate wavelength selection, partial least squares regression (PLS) was used to map the absorbance data of each pine sample to a distribution of moisture contents within the samples at different time steps during the drying process. To enable separate studying and comparison of the drying dynamics taking place within the early- and latewood regions of the pine samples, the collected images were spatially segmented to separate between early- and latewood pixels. The results of the study indicate that the 1966–2244 nm region of a NIR spectrum, when preprocessed with extended multiplicative scatter correction and first order derivation, can be used to model the average moisture content of thermally modified pine using PLS. The methods presented in this paper allows for estimation and visualization of the intrasample spatial distribution of moisture in thermally modified pine wood.

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Sammendrag

The effects of climate change-induced ice melting on the microbial communities in different glacial-fed aquatic systems have been reported, but seasonal dynamics remain poorly investigated. In this study, the structural and functional traits of the aquatic microbial community were assessed along with the hydrological and biogeochemical variation patterns of the Arctic Pasvik River under riverine and brackish conditions at the beginning (May = Ice-melt (−)) and during the ice-melting season (July = Ice-melt (+)). The microbial abundance and morphometric analysis showed a spatial diversification between the riverine and brackish stations. Results highlighted different levels of microbial respiration and activities with different carbon and phosphorous utilization pathways, thus suggesting an active biogeochemical cycling along the river especially at the beginning of the ice-melting period. At Ice-melt (−), Gammaproteobacteria and Alphaproteobacteria were dominant in riverine and brackish stations, respectively. Conversely, at Ice-melt (+), the microbial community composition was more homogeneously distributed along the river (Gammaproteobacteria > Alphaproteobacteria > Bacteroidetes). Our findings provide evidence on how riverine microbial communities adapt and respond to seasonal ice melting in glacial-fed aquatic ecosystems.