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Publications

NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.

2024

Abstract

Climate change is already reducing carbon sequestration in Central European forests dramatically through extensive droughts and bark beetle outbreaks. Further warming may threaten the enormous carbon reservoirs in the boreal forests in northern Europe unless disturbance risks can be reduced by adaptive forest management. The European spruce bark beetle (Ips typographus) is a major natural disturbance agent in spruce-dominated forests and can overwhelm the defences of healthy trees through pheromone-coordinated mass-attacks. We used an extensive dataset of bark beetle trap counts to quantify how climatic and management-related factors influence bark beetle population sizes in boreal forests. Trap data were collected during a period without outbreaks and can thus identify mechanisms that drive populations towards outbreak thresholds. The most significant predictors of bark beetle population size were the volume of mature spruce, the extent of newly exposed clearcut edges, temperature and soil moisture. For clearcut edge, temperature and soil moisture, a 3-year time lag produced the best model fit. We demonstrate how a model incorporating the most significant predictors, with a time lag, can be a useful management tool by allowing spatial prediction of future beetle population sizes. Synthesis and Applications: Some of the population drivers identified here, i,e., spruce volume and clearcut edges, can be targeted by adaptive management measures to reduce the risk of future bark beetle outbreaks. Implementing such measures may help preserve future carbon sequestration of European boreal forests.

2023

To document

Abstract

Global warming necessitates urgent action to reduce carbon dioxide (CO2) emissions and remove CO2 from the atmosphere. Biochar, a type of carbonized biomass which can be produced from crop residues (CRs), offers a promising solution for carbon dioxide removal (CDR) when it is used to sequester photosynthetically fixed carbon that would otherwise have been returned to atmospheric CO2 through respiration or combustion. However, high-resolution spatially explicit maps of CR resources and their capacity for climate change mitigation through biochar production are currently lacking, with previous global studies relying on coarse (mostly country scale) aggregated statistics. By developing a comprehensive high spatial resolution global dataset of CR production, we show that, globally, CRs generate around 2.4 Pg C annually. If 100% of these residues were utilized, the maximum theoretical technical potential for biochar production from CRs amounts to 1.0 Pg C year−1 (3.7 Pg CO2e year−1). The permanence of biochar differs across regions, with the fraction of initial carbon that remains after 100 years ranging from 60% in warm climates to nearly 100% in cryosols. Assuming that biochar is sequestered in soils close to point of production, approximately 0.72 Pg C year−1 (2.6 Pg CO2e year−1) of the technical potential would remain sequestered after 100 years. However, when considering limitations on sustainable residue harvesting and competing livestock usage, the global biochar production potential decreases to 0.51 Pg C year−1 (1.9 Pg CO2e year−1), with 0.36 Pg C year−1 (1.3 Pg CO2e year−1) remaining sequestered after a century. Twelve countries have the technical potential to sequester over one fifth of their current emissions as biochar from CRs, with Bhutan (68%) and India (53%) having the largest ratios. The high-resolution maps of CR production and biochar sequestration potential provided here will provide valuable insights and support decision-making related to biochar production and investment in biochar production capacity.

Abstract

In Norway we now get more up-to-date maps for land resource map (AR5), because the domain experts on agriculture in the municipalities in Norway have got access to a easy to use client. This system includes a simple web browser client and a database built on Postgis Topology. In this talk we will focus on, what is it with Postgis Topology that makes it easier to build user friendly and secure tools for updating of land resource maps like AR5. We will also say a couple of words about advantages related to traceability and data security, when using Postgis Topology. In another project, where we do a lot ST_Intersection and ST_Diff on many big Simple Feature layers that covers all of Norway, we have been struggling with Topology exceptions, wrong results and performance for years. Last two years we also tested JTS OverlayNG, but we still had problems. This year we are switching to Postgis Topology and tests so far are very promising. We also take a glance on this project here in this talk. A Postgis Topology database modell has normalised the data related to borders and surfaces as opposed to Simple Feature where this is not the case. Simple Feature database modell may be compared to not using foreign keys between students and classes in a database model, but just using a standard spreadsheet model where each student name are duplicated in each class they attend. URL’s that relate this talk https://gitlab.com/nibioopensource/pgtopo_update_gui https://gitlab.com/nibioopensource/pgtopo_update_rest https://gitlab.com/nibioopensource/pgtopo_update_sql https://gitlab.com/nibioopensource/resolve-overlap-and-gap

To document

Abstract

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.

2022