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
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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.
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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
RoadSens is a platform designed to expedite the digitalization process of forest roads, a cornerstone of efficient forest operations and management. We incorporate stereo-vision spatial mapping and deep-learning image segmentation to extract, measure, and analyze various geometric features of the roads. The features are precisely georeferenced by fusing post-processing results of an integrated global navigation satellite system (GNSS) module and odometric localization data obtained from the stereo camera. The first version of RoadSens, RSv1, provides measurements of longitudinal slope, horizontal/vertical radius of curvature and various cross-sectional parameters, e.g., visible road width, centerline/midpoint positions, left and right sidefall slopes, and the depth and distance of visible ditches from the road’s edges. The potential of RSv1 is demonstrated and validated through its application to two road segments in southern Norway. The results highlight a promising performance. The trained image segmentation model detects the road surface with the precision and recall values of 96.8 and 81.9 , respectively. The measurements of visible road width indicate sub-decimeter level inter-consistency and 0.38 m median accuracy. The cross-section profiles over the road surface show 0.87 correlation and 9.8 cm root mean squared error (RMSE) against ground truth. The RSv1’s georeferenced road midpoints exhibit an overall accuracy of 21.6 cm in horizontal direction. The GNSS height measurements, which are used to derive longitudinal slope and vertical curvature exhibit an average error of 5.7 cm compared to ground truth. The study also identifies and discusses the limitations and issues of RSv1, which provide useful insights into the challenges in future versions.
Forfattere
Rasmus Bang Stine Samsonstuen Bjørn Gunnar Hansen Mario Guajardo Hanne Møller Jon Kristian Sommerseth Julio C. Goez Ola FlatenSammendrag
CONTEXT Researchers have identified numerous strategies to improve economic performance and reduce greenhouse gas (GHG) emission intensity in combined milk and beef production on dairy farms. However, there remains a need to better understand how the effectiveness of these strategies varies under different operational conditions. OBJECTIVE This study aims to examine how the economic and GHG emission intensity mitigation effectiveness of increased milk yield, extended longevity of dairy cows, reduced age at first calving, and intensified beef production from bulls depend on operational conditions in dual purpose cattle systems. METHOD We present a quantitative framework to (1) economically optimize production at farm level under various constraints and (2) calculate corresponding GHG emissions. The framework is tailored for Norwegian dual-purpose cattle systems and used to assess the economic and GHG emission intensity mitigation effects of incremental adjustments in relevant decisions. RESULTS AND CONCLUSIONS The results show that increased milk yield, extended productive life of dairy cows, reduced age at first calving, and lower slaughter age of bulls can lead to economic and climatic win-wins in terms of higher gross margins and reduced emissions per kg of protein produced. However, they may also result in lose-win and win-lose outcomes depending on the operational conditions. All four measures free up roughage production capacity, which, if used to maintain/increase milk and/or beef production, typically results in economic gains. However, if e.g., the available milk quota or space prevent this, economic losses may occur. The climate impact also depends on how the freed-up capacity is used: if it boosts production, the effects vary based on the scale and type of increase and the farm's initial setup, while unused capacity leads to reduced emission intensity. Conflicts typically arise when: 1) the extra capacity increases less climate-friendly production, raising emission intensity despite economic gains, or 2) extra capacity cannot be used, causing economic losses despite climate benefits. Our results also show that what can be labeled a win in climate terms, and to what extent, depends on the selected target metric(s). SIGNIFICANCE Governments and societies strive to balance food production with environmental goals. In this context, it is essential to identify farm-level economic and climatic win-win and lose-win scenarios, not only for farmers but also for policymakers and the broader society. This study could inform decision-making and policy development, potentially enhancing economic and climatic performance in combined milk and meat production.
Sammendrag
Optimised contributions of green infrastructure (GI) to urban ecosystem services are strongly related to its multifunctionality. The challenge, however, is that the concept of multifunctionality still needs to be transformed into an operationalised assessment to evaluate current performance, which is instrumental in supporting spatial planning and policy strategies. Using the case of Stavanger City (Norway), the study conducted a spatial assessment of the multifunctionality of the urban green infrastructure. The study used a comprehensive set of 27 function indicators estimated for each of the 156 spatial units classified by their type, age, size, and biophysical characteristics. Correlation patterns among indicators and how the average and effective multifunctionality related to unit characteristics were analysed using correlation and multivariate approaches. The study demonstrated weak correlations between function indicators but revealed some potential trade-offs and function bundles. Notably, bundles related to tree cover (e.g. C sequestration, stormwater retention) had negative relationships with facilitation measures. There was a large overlap in functions between GI types associated with public green spaces and parks. Moreover, the characteristics of green infrastructure units, like size and age, primarily affected multifunctionality through effects on function indicators. Regarding the city-wide multifunctionality, we found some turnover and subsetting of functions among units, supporting multifunctionality at larger spatial scales. However, the average contributions from different GI types were similar. The study highlights the need to understand correlation patterns among function indicators and function bundles as critical to benefit from synergies and avoid unintentional trade-offs when designing and managing urban green areas.
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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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