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
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.
Authors
Belachew Gizachew Zeleke Deo D. Shirima Jonathan Rizzi Collins Byobona Kukunda Eliakimu ZahabuAbstract
Tanzania dedicates a substantial proportion (38%) of its territory to conservation, with a large number of Protected Areas (PAs) managed under various regimes. Nevertheless, the country still experiences high rates of deforestation, which threaten the ecological integrity and socio-economic benefits of its forests. We utilized the Global Forest Change Dataset (2012–2022) and implemented a Propensity Score Matching (PSM) approach followed by a series of binomial logit regression modeling. Our objectives were to evaluate (1) the likelihood of PAs in avoiding deforestation compared with unprotected forest landscapes, (2) the variability in effectiveness among the different PA management regimes in avoiding deforestation, (3) evidence of leakage, defined here as the displacement of deforestation beyond PA boundaries as a result of protection inside PAs. Our findings reveal that, despite ongoing deforestation within and outside of PAs, conservation efforts are, on average, three times more likely to avoid deforestation compared with unprotected landscapes. However, the effectiveness of avoiding deforestation significantly varies among the different management regimes. National Parks and Game Reserves are nearly ten times more successful in avoiding deforestation, likely because of the stringent set of regulations and availability of resources for implementation. Conversely, Nature Forest Reserves, Game Controlled Areas, and Forest Reserves are, on average, only twice as likely to avoid deforestation, indicating substantial room for improvement. We found little evidence of the overall leakage as a consequence of protection. These results highlight the mixed success of Tanzania’s conservation efforts, suggesting opportunities to enhance the effectiveness of many less protected PAs. We conclude by proposing potential strategic pathways to enhance further the climate and ecosystem benefits of conservation in Tanzania.
Abstract
No abstract has been registered
Abstract
No abstract has been registered