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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.

2026

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Saturated hydraulic conductivity (ks) is a key hydrological property influenced by the size and topology of soil pores, particularly macropores. This study assessed eight (N = 8) published ks models that derive pore size distributions (PSD) from water retention (WR) functions, with either exponential (Brooks-Corey function, BC) or sigmoidal (van Genuchten function, VG) shapes. The models are semi-empirical (N = 3) or based on integration of WR-derived PSD (N = 5). Since measurements of ks are conflated by both natural variability and the method used to determine its values, models were evaluated with hydraulic properties from 378 samples collected using uniform methodology (HYP-UNI), and separately, with data from 1,734 soils measured by multiple institutions using a range of methods (HYPRES). Macroporosity, defined as the fraction of total porosity that is air-filled at −10 kPa (relative air capacity, RAC), was used to detect soil structure signatures on ks predictions. For HYP-UNI, integral-based models, particularly those using the BC function, performed best in soils with RAC ≥ 5%, but tended to underpredict ks when RAC < 5%. Overprediction was less frequent and mainly associated with low total porosity (∼ 40% or lower). Compared to HYP-UNI, model predictions worsened with HYPRES data and semi-empirical models outperformed integral-based approaches. Underprediction in soils with RAC < 5% persisted with HYPRES data but overprediction was not significantly related to porosity. Underprediction in soils with RAC < 5% was pervasive, indicating a limitation in current models that warrants further investigation. Moreover, this study highlights the advantages of using methodologically uniform databases along with high-quality WR-derived PSDs for development/testing of ks models.

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Gjengroing av område som tidlegare vart slått eller nytta som beite er ein av årsakene til at seminaturlege naturtypar er raudlista. Dette er ein stor trugsel for arter som til dømes solblom, dragehode og mogop.

Sammendrag

Webinar for landbruksforvaltningen i Møre og Romsdal. Informasjon om ulike bruksområder for arealressurskartet AR5, ansvar for ajourhold og hvorfor det er viktig at kartet er oppdatert.

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Sammendrag

Excessive tree mortality is a global concern and remains poorly understood as it is a complex phenomenon. We lack global and temporally continuous coverage on tree mortality data. Ground-based observations on tree mortality, e.g., derived from national inventories, are very sparse, and may not be standardized or spatially explicit. Earth observation data, combined with supervised machine learning, offer a promising approach to map overstory tree mortality in a consistent manner over space and time. However, global-scale machine learning requires broad training data covering a wide range of environmental settings and forest types. Low altitude observation platforms (e.g., drones or airplanes) provide a cost-effective source of training data by capturing high-resolution orthophotos of overstory tree mortality events at centimeter-scale resolution. Here, we introduce deadtrees.earth, an open-access platform hosting more than two thousand centimeter-resolution orthophotos, covering more than 1,000,000 ha, of which more than 58,000 ha are manually annotated with live/dead tree classifications. This community-sourced and rigorously curated dataset can serve as a comprehensive reference dataset to uncover tree mortality patterns from local to global scales using space-based Earth observation data and machine learning models. This will provide the basis to attribute tree mortality patterns to environmental changes or project tree mortality dynamics to the future. The open nature of deadtrees.earth, together with its curation of high-quality, spatially representative, and ecologically diverse data will continuously increase our capacity to uncover and understand tree mortality dynamics.