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.
2025
Abstract
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.
Abstract
No abstract has been registered
Authors
Anne B. NilsenAbstract
NIBIO produces Green Structure Maps (GSM) for Norway that cover built-up areas, including cabin areas. GSM is a hybrid product based on information from remote sensing data and detailed national vector datasets such as roads, water, buildings, and land use. GSM contains 8 classes: Ground, Shrub, Tree, Grey, Road, Water, Building, and Agriculture. QGIS is excellently suited for visual control of GSM. Based on the size of the dataset (number of polygons), a significant random sample of each class is selected to check whether it is correctly classified. You can organize the map layers into different themes, set up QGIS with multiple map windows showing different themes and zoom levels, and use existing plugins to jump from polygon to polygon and compare with aerial images and code whether the classification is correct or not - quickly and efficiently. More comprehensive statistics can then be calculated, and the results can be compared against the requirements to determine if the GSM meets the standards.
Abstract
No abstract has been registered
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No abstract has been registered
2024
Authors
Jonathan Rizzi Nicolai Munsterhjelm Robert Barneveld Arnt Kristian Gjertsen Shivesh Karan Thi Phuong Huyen Vu Bjørn Tobias BorchseniusAbstract
No abstract has been registered
Abstract
To shift towards low-fossil carbon economies, making more out of residual biomass is increasingly promoted. Yet, it remains unclear if implementing advanced technologies to reuse these streams really achieves net environmental benefits compared to current management practices. By integrating spatially-explicit resource flow analysis, consequential life cycle assessment (LCA), and uncertainty analysis, we propose a single framework to quantify the residual biomass environmental baseline of a territory, and apply it to the case of France. The output is the environmental threshold that a future large-scale territorial bioeconomy strategy should overpass. For France, we estimate the residual biomass baseline to generate 18.4 ± 2.7 MtCO2-eq·y−1 (climate change), 255 ± 35 ktN-eq·y−1 (marine eutrophication), and 12,300 ± 800 disease incidences per year (particulate matter formation). The current use of crop residues and livestock effluents, being essentially a return to arable lands, was found to represent more than 90 % of total environmental impacts and uncertainties, uncovering a need for more certain data. At present, utilizing residual streams as organic fertilizers fulfills over half of France's total phosphorus (P) and potassium (K) demands. However, it only meets 6 % of the nitrogen demand, primarily because nitrogen is lost through air and water. This, coupled with the overall territorial diagnosis, led us to revisit the idea of using the current situation (based on 2018 data) as a baseline for future bioeconomy trajectories. We suggest that these should rather be compared to a projected baseline accounting for ongoing basic mitigation efforts, estimated for France at 8.5 MtCO2-eq·y−1.
Authors
Heine Nygard Riise Erlend Hustad Honningdalsnes Dagmar Hagen Markus A. K. Sydenham Anne Catriona Melhoop Trond Aalvik Simensen Jonathan Rizzi Torunn KjeldstadAbstract
No abstract has been registered
Authors
Jonathan RizziAbstract
No abstract has been registered
Authors
Geir-Harald Strand Eva Solbjørg Flo Heggem Linda Aune-Lundberg Agata Hościło Adam WaśniewskiAbstract
Land cover maps are frequently produced via the classification of satellite imagery. There is a need for a practicable and automated approach for the generalization of these land cover classification results into scalable, digital maps while minimizing information loss. We demonstrate a method where a land cover raster map produced using the classification of Sentinel 2 imagery was generalized to obtain a simpler, more readable land cover map. A replicable procedure following a formal generalization framework was applied. The result of the initial land cover classification was separated into binary layers representing each land cover class. Each binary layer was simplified via structural generalization. The resulting images were merged to create a new, simplified land cover map. This map was enriched by adding statistical information from the original land cover classification result, describing the internal land cover distribution inside each polygon. This enrichment preserved the original statistical information from the classified image and provided an environment for more complex cartography and analysis. The overall accuracy of the generalized map was compared to the accuracy of the original, classified land cover. The accuracy of the land cover classification in the two products was not significantly different, showing that the accuracy did not deteriorate because of the generalization.