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.
2025
Forfattere
Margit Oami Kollstrøm Ulrike Böcker Anne Kjersti Uhlen Annbjørg Kristoffersen Jon Arne Dieseth Erik Tengstrand Shiori KogaSammendrag
Det er ikke registrert sammendrag
Forfattere
Alexander N. Hristov André Bannink M Battelli Alejandro Belanche M.C. Cajarville Sanz G Fernandez-Turren F Garcia Arjan Jonker D.A. Kenny Vibeke Lind S.J. Meale D Meo Zilio Camila Muñoz David Pacheco Nico Peiren Mohammad Ramin L Rapetti Angela Schwarm Sokratis Stergiadis Katerina Theodoridou E.M. Ungerfeld S van Gastelen D.R. Yanez-Ruiz S.M. Waters Peter LundSammendrag
Det er ikke registrert sammendrag
Sammendrag
Urban green structures (UGS) play important roles in enhancing urban ecosystems by providing benefits such as mitigating the urban heat island effect, improving air quality, supporting biodiversity, and aiding in stormwater management. Accurately mapping UGS is important for sustainable urban planning and management. Traditional methods of mapping such as manual mapping, aerial photography interpretation and pixel-based classification have limitations in terms of coverage, accuracy, and efficiency. Object-based image analysis (OBIA) has gained prominence due to its ability to incorporate both spectral and spatial information making it particularly effective for classification of high-resolution satellite data. This paper reviews the application of OBIA on satellite images for UGS mapping, focusing on various data sources, popular segmentation methods, and classification techniques, highlighting their respective advantages and limitations. Key segmentation methodologies discussed include multi-resolution segmentation and watershed segmentation. For classification, the review covers machine learning techniques such as random forests, support vector machines, and convolutional neural networks, among others. Several case studies highlight the successful implementation of OBIA in diverse urban environments by demonstrating improvements in classification accuracy and detail. The review also addresses the challenges associated with OBIA, such as dealing with heterogenous urban landscapes, data sources and with OBIA methods itself. Future directions for UGS mapping include the integration of deep learning algorithms, advancements in satellite data technologies, and the development of standardized classification frameworks. By providing a detailed analysis of the current state-of-the-art in object-based UGS mapping, this review aims to guide future research and practical applications in UGS management.
Forfattere
Mostafa Hoseini Helle Ross Gobakken Stephan Hoffmann Csongor Horvath Johannes Rahlf Jan Bjerketvedt Stefano Puliti Rasmus AstrupSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Jian Liu Faruk Djodjic Barbro Ulén Helena Aronsson Marianne Bechmann Lars Bergström Tore Krogstad Katarina KyllmarSammendrag
Det er ikke registrert sammendrag
2024
Sammendrag
Forests play a major role in the mitigation of avalanche risk in Norway, but the regulations surrounding the management of “protection forests” are still being worked out. To promote protection forest management, avalanche hazard indication maps for Norway have been produced with the automated mapping tool NAKSIN in a way that makes it possible to quantity the effects of the current forests in a spatially explicit way. NAKSIN makes use of published relations for forest effects on snow properties and uses national models of forest characteristics to estimate the effects on release probability and runout given local climate and topography. The forest properties contain parameters that are directly measured (canopy cover), and properties that are predicted (tree diameter, number of trees) with approximately 70% precision according to ground truth data. NAKSIN uses these forest properties in long chains of models, comprising of both mechanistic and empirical elements, some of which are iterated over timesteps during avalanche flow. This means that errors could be propagated throughout those model chains in unexpected ways. The aim of this study was to conduct a sensitivity analysis to examine the effects of errors in the forest data for hazard mapping in a relevant case study region in fjordic western Norway. We examined hazard maps produced using 95% prediction errors for tree diameter and the number of trees per hectare to determine if these would dramatically affect the hazard zones. These hazard maps focused on runout properties as common release areas were implied for avalanches through a common forest canopy cover percentage applied across the two extreme scenarios. Across the entire region, the hazard zones were generally stable with respect to potential errors in the forest data, suggesting the approach is robust and the braking effect of forest is not overstated. There was one exception, where the prediction errors could reduce the forest braking function to negligible. This exception was easy to identify from the difference in hazard zones and the process allows us to consider where more precise measurements of forests could be required in areas with high consequences. The implications of various approaches to estimate forest leaf area index, and how this might impact on release probability are illustrated to further consider this in the next steps of this research.
Sammendrag
Forests play a major role in the mitigation of avalanche risk in Norway, but the regulations surrounding the management of “protection forests” are still being worked out. To promote protection forest management, avalanche hazard indication maps for Norway have been produced with the automated mapping tool NAKSIN in a way that makes it possible to quantity the effects of the current forests in a spatially explicit way. NAKSIN makes use of published relations for forest effects on snow properties and uses national models of forest characteristics to estimate the effects on release probability and runout given local climate and topography. The forest properties contain parameters that are directly measured (canopy cover), and properties that are predicted (tree diameter, number of trees) with approximately 70% precision according to ground truth data. NAKSIN uses these forest properties in long chains of models, comprising of both mechanistic and empirical elements, some of which are iterated over timesteps during avalanche flow. This means that errors could be propagated throughout those model chains in unexpected ways. The aim of this study was to conduct a sensitivity analysis to examine the effects of errors in the forest data for hazard mapping in a relevant case study region in fjordic western Norway. We examined hazard maps produced using 95% prediction errors for tree diameter and the number of trees per hectare to determine if these would dramatically affect the hazard zones. These hazard maps focused on runout properties as common release areas were implied for avalanches through a common forest canopy cover percentage applied across the two extreme scenarios. Across the entire region, the hazard zones were generally stable with respect to potential errors in the forest data, suggesting the approach is robust and the braking effect of forest is not overstated. There was one exception, where the prediction errors could reduce the forest braking function to negligible. This exception was easy to identify from the difference in hazard zones and the process allows us to consider where more precise measurements of forests could be required in areas with high consequences. The implications of various approaches to estimate forest leaf area index, and how this might impact on release probability are illustrated to further consider this in the next steps of this research.
Sammendrag
Black scurf and stem canker on potatoes, caused by the destructive soil-borne pathogen Rhizoctonia solani Kühn are a major problem for potato growers worldwide. Biological control agents such as plant leaf extracts can influence the severity of R. solani infection and help to reduce the risks to human health and the environment associated with the use of hemical fungicides. In this study, the inhibitory effect of the secondary plant metabolites aucubin, catalpol (iridoid glucosides) and acteoside (phenylethanoid glycoside) from methanolic extracts of Plantago lanceolata (Ribwort plantain (en), Smalkjempe (no)), a native plant in the Nordic countries, on the growth of R. solani mycelium on potato dextrose agar growth medium will be tested for the first time. Plant extracts will be obtained from plants of different age classes and metabolic profiling will be performed with LC-(HR)MS analyses and the concentrations of identified metabolites will be determined. To analyse whether the inhibitory interactions on fungal growth originate from the known secondary metabolites or are caused by the bulk plant extract, we will first expose the fungus to different concentrations of extracts, redissolved in aqueous solution and added to the growth medium, and in a further step we will carry out the same approach with the isolated secondary metabolites as pure substances. The fugus will be incubated for 5 days and the mycelium growth radius will be measured every 24 hours during incubation. Thereafter a suppression index will be calculated and compared to the untreated control. The results are pending at the time of submission of the abstract but will provide a good initial understanding to determine whether extracts of P. lanceolata can be used as a natural biological control agent as an additional component of a more sustainable strategy to manage the risk of infection of potato with R.solani and to reduce the severity of the disease caused by this pathogen.
Sammendrag
RIBI Bioenergi har hatt gårdsbiogassanlegg siden 2019. I 2022/2023 har RIBI med støtte fra Innovasjon Norge bygget en egen reaktor for å kunne drive med testing/utprøving for biogass. Dette prosjektet har sett på utråtning av en blanding av husdyrgjødsel fra storfe og gris, og fiskeslam fra Salmon Evolution. Prosjektet har fulgt opp anlegget med en kombinasjon av onsite analyser og analyser ved biogasslaben på Ås. Annet analyseutstyr for fremtidig testvirksomhet har blitt vurdert. Produksjonen med fiskeslam er fulgt i ca. 1 års tid. Egenskapene til gjødsel og fiskeslam er analysert, og påvirkning på gassproduksjon og prosesstabilitet studert. Innhold av nitrogen i fiskeslam er avhengig av avvanningsgrad. Innholdet av nitrogen påvirker igjen grad av inhibering av biogassprosessen og innhold av næringsstoffer i bioresten. Resultatene fra prosjektet har blitt brukt til informasjonsutveksling mellom blå og grønn næring, og nye biogassaktører.