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
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.
Sammendrag
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Forfattere
Anders Lorentzen Kolstad Joachim Paul Töpper Marianne Evju Line Johansen Magni Olsen Kyrkjeeide Anders Lyngstad Jan Ketil Rød Lise Tingstad Liv Guri VelleSammendrag
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Forfattere
Mostafa Hoseini Helle Ross Gobakken Stephan Hoffmann Csongor Horvath Johannes Rahlf Jan Bjerketvedt Stefano Puliti Rasmus AstrupSammendrag
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Forfattere
Jian Liu Faruk Djodjic Barbro Ulén Helena Aronsson Marianne Bechmann Lars Bergström Tore Krogstad Katarina KyllmarSammendrag
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Sammendrag
Informasjon om frøavl av engknoppurt, svartknoppurt og fagerknoppurt
2024
Forfattere
Mojtaba Shafiei Csilla Farkas Eva Skarbøvik Nikolai Friberg Moritz Shore Dominika Krzeminska Anne-Grete Buseth Blankenberg Katrin BiegerSammendrag
Nature-based solutions (NBS) are an increasingly popular strategy to water resources management, with a growing number of research projects and policy interventions. Development, implementation, and decision on NBS for retention of water, sediments, and nutrients in the landscape often require substantial investment in data acquisition and modeling efforts. SWAT+ provides several new approaches compared to SWAT in terms of i) enhanced catchment configuration with more spatial objects, ii) improved representation of the connectivity between man-made hydrologic systems and the catchment stream network, and iii) new algorithms to implement complex rule-based management actions. These advantages make SWAT+ very suitable for simulating catchment processes in relation to NBS and for developing catchment-based tools for optimizing the type, location, and design of NBS. To further propel the simulation and optimization of NBS at the catchment scale with SWAT+, we propose establishing a community to harmonize and synergize the efforts of SWAT+ users worldwide in the field of NBS implementation and management. The envisioned SWAT+ NBS community would comprise researchers and scientists sharing a common vision, engaged in co-developing frameworks, addressing policy imperatives, co-creating knowledge, and advocating for best practices in applying the SWAT+ model for advanced NBS optimization and management. The objectives of this presentation are 1) to discuss the phases of NBS development and management at catchment scale, 2) to identify the research gaps in using SWAT+ in NBS studies, and 3) to propose a structure and a coordination framework to shape the SWAT+ NBS community.
Sammendrag
The use of SWAT+ model is increasingly prevalent in the simulation and evaluation of Nature-Based Solutions (NBS) at the catchment scale. Compared to the SWAT model, the SWAT+ model offers several advancements, including enhanced catchment configuration, improved representation of connectivity between man-made hydrologic systems and the catchment stream network, and new algorithms for implementing rule sets for management actions. In this presentation, we outline our methodology and present some preliminary findings from implementing SWAT+ in a South-Eastern Norwegian catchment. The catchment has a relatively large proportion of agricultural land based on Norwegian standards, with several natural lakes. Our modelling results underscore the importance of the spatial configuration phase, particularly in detailed spatial settings and HRU characterization, for simulating catchment-NBS interactions. We found that integrating reanalysis of spatial meteorological data in 1x1 km resolution could significantly improve streamflow simulation. In our case study, by using Met Nordic Reanalysis Dataset from Norwegian Meteorological Institute, the NS efficiency increased from -0.05 to 0.4 prior to any calibrations. Furthermore, we discuss challenges in simulating catchment-NBS interactions with SWAT+, particularly concerning “prospective impact evaluation” in the planning phase of constructed wetlands (design, placement, and optimization).