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
Authors
Lone RossAbstract
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Authors
Martha Irene Grøseth Linda Karlsson Håvard Steinshamn Marianne Johansen Alemayehu Kidane Sagaye Egil PrestløkkenAbstract
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Authors
Alexander N. Hristov Andre Bannink Marco Battelli Alejandro Belanche Cecilia Cajarville G Fernandez-Turren Florencia Garcia Arjan Jonker David A Kenny Vibeke Lind Sarah Meale David Meo Zilio Camilla Muñoz David Pacheco Nico Peiren Mohammad Ramin Luca Rapetti Angela Schwarm Sokratis Stergiadis Katerina Theodoridou Emilio M. Ungerfeld Sanne van Gastelen David R. Yáñez-Ruíz Sinead M. Waters Peter LundAbstract
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Authors
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 LundAbstract
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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
Urban agriculture is often considered a tool to increase the economic, social and environmental sustainability of cities and city food systems. However, sustainability is difficult to measure, resulting in debate about how sustainable urban agriculture truly is. There is therefore a lack of incentive to promote urban agriculture or protect existing initiatives that are threatened by development pressure on urban land. Monitoring the sustainability impact of urban agriculture could provide evidence and enable politicians and decision makers to make informed decisions about whether and where to prioritise different forms of urban agriculture above competing interests. We used case examples from five European cities to identify the challenges involved in monitoring urban agriculture, from selecting indicators and gathering data, to using the results. We found large differences in approach in terms of what topics to monitor and who was responsible, who gathered the data and when, what data was recorded and how they were stored, and how findings were disseminated or published. Based on these experiences, we recommend stronger involvement of existing interest groups and educational institutions in monitoring urban agriculture, and promotion of convenient tools for data collection by citizen science and for long-term data storage.