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
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
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
No abstract has been registered
Authors
Ishita Ahuja Inger Sundheim Fløistad Per Grande Øyvind Meland Edvardsen Adam O’Toole Arne SteffenremAbstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
No abstract has been registered
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.
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
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
Purpose of Review Mechanized logging operations with ground-based equipment commonly represent European production forestry but are well-known to potentially cause soil impacts through various forms of soil disturbances, especially on wet soils with low bearing capacity. In times of changing climate, with shorter periods of frozen soils, heavy rain fall events in spring and autumn and frequent needs for salvage logging, forestry stakeholders face increasingly unfavourable conditions to conduct low-impact operations. Thus, more than ever, planning tools such as trafficability maps are required to ensure efficient forest operations at reduced environmental impact. This paper aims to describe the status quo of existence and implementation of such tools applied in forest operations across Europe. In addition, focus is given to the availability and accessibility of data relevant for such predictions. Recent Findings A commonly identified method to support the planning and execution of machine-based operations is given by the prediction of areas with low bearing capacity due to wet soil conditions. Both the topographic wetness index (TWI) and the depth-to-water algorithm (DTW) are used to identify wet areas and to produce trafficability maps, based on spatial information. Summary The required input data is commonly available among governmental institutions and in some countries already further processed to have topography-derived trafficability maps and respective enabling technologies at hand. Particularly the Nordic countries are ahead within this process and currently pave the way to further transfer static trafficability maps into dynamic ones, including additional site-specific information received from detailed forest inventories. Yet, it is hoped that a broader adoption of these information by forest managers throughout Europe will take place to enhance sustainable forest operations.