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.
2022
Authors
Johannes Schumacher Håvard Toft Larsen Paul McLean Marius Hauglin Rasmus Astrup Johannes BreidenbachAbstract
The number of people affected by snow avalanches during recreational activities has increased over the recent years. An instrument to reduce these numbers are improved terrain classification systems. One such system is the Avalanche Terrain Exposure Scale (ATES). Forests can provide some protection from avalanches, and information on forest attributes can be incorporated into avalanche hazard models such as the automated ATES model (AutoATES). The objectives of this study were to (i) map forest stem density and canopy-cover based on National Forest Inventory and remote sensing data and, (ii) use these forest attributes as input to the AutoATES model. We predicted stem density and directly calculated canopy-cover in a 20 Mha study area in Norway. The forest attributes were mapped for 16 m × 16 m pixels, which were used as input for the AutoATES model. The uncertainties of the stem number and canopy-cover maps were 30% and 31%, respectively. The overall classification accuracy of 52 ski-touring routes in Western Norway with a total length of 282 km increased from 55% in the model without forest information to 67% when utilizing canopy cover. The F1 score for the three predicted ATES classes improved by 31%, 9%, and 6%.
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Learn about the challenges and the beauty of farming on islands far off into the Norwegian sea. The material was prepared for the project EDU-ARCTIC 2: from polar research to scientific passion – innovative nature education in Poland and Norway, which receives a grant of ca. 240 000 EUR received from Iceland, Liechtenstein and Norway under EEA funds. View with VR goggles or look around by moving your smartphone or by dragging the image left and right with the mouse.
Authors
Marta Vergarechea Rasmus Astrup Clemens Blattert Astor Toraño Caicoya Daniel Burgas Mikko Monkkonen Kyle Eyvindson Fulvio Di Fulvio Knut Øistad Jani Lukkarinen Antón-Fernández ClaraAbstract
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Authors
Saheba Bhatnagar Stefano Puliti Bruce Talbot Joachim Bernd Heppelmann Johannes Breidenbach Rasmus AstrupAbstract
Wheel ruts, i.e. soil deformations caused by harvesting machines, are considered a negative environmental impact of forest operations and should be avoided or ameliorated. However, the mapping of wheel ruts that would be required to monitor harvesting operations and to plan amelioration measures is a tedious and time-consuming task. Here, we examined whether a combination of drone imagery and algorithms from the field of artificial intelligence can automate the mapping of wheel ruts. We used a deep-learning image-segmentation method (ResNet50 + UNet architecture) that was trained on drone imagery acquired shortly after harvests in Norway, where more than 160 km of wheel ruts were manually digitized. The cross-validation of the model based on 20 harvested sites resulted in F1 scores of 0.69–0.84 with an average of 0.77, and in total, 79 per cent of wheel ruts were correctly detected. The highest accuracy was obtained for severe wheel ruts (average user’s accuracy (UA) = 76 per cent), and the lowest accuracy was obtained for light wheel ruts (average UA = 67 per cent). Considering the nowadays ubiquitous availability of drones, the approach presented in our study has the potential to greatly increase the ability to effectively map and monitor the environmental impact of final felling operations with respect to wheel ruts. The automated mapping of wheel ruts may serve as an important input to soil impact analyses and thereby support measures to restore soil damages.
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Authors
Stephan Hoffmann Marian Schönauer Joachim Heppelmann Antti Asikainen Emmanuel Cacot Benno Eberhard Hubert Hasenauer Janis Ivanovs Dirk Jaeger Andis Lazdins Sima Mohtashami Tadeusz Moskalik Tomas Nordfjell Krzysztof Stereńczak Bruce Talbot Jori Uusitalo Morgan Vuillermoz Rasmus AstrupAbstract
No abstract has been registered