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.
2019
Authors
Xiao Huang Höglind Mats Knut Bjørkelo Torben Christensen Kjetil Fadnes Teresa Gómez de la Bárcena Åsa Kasimir Leif Klemedtsson Bjørn Kløve Anders Lyngstad Mikhail Mastepanov Hannu Marttila Marcel van Oijen Peter Petros Ina Pohle Jagadeesh Yeluripati Hanna Marika SilvennoinenAbstract
Cultivated organic soils account for ∼7% of Norway’s agricultural land area, and they are estimated to be a significant source of greenhouse gas (GHG) emissions. The project ‘Climate smart management practices on Norwegian organic soils’ (MYR), commissioned by the Research Council of Norway (decision no. 281109), aims to evaluate GHG (e.g. carbon dioxide, methane and nitrous oxide) emissions and impacts on biomass productivity from three land use types (cultivated, abandoned and restored) on organic soils. At the cultivated sites, impacts of drainage depth and management intensity will be measured. We established experimental sites in Norway covering a broad range of climate and management regimes, which will produce observational data in high spatiotemporal resolution during 2019-2021. Using state-of-the-art modelling techniques, MYR aims to predict the potential GHG mitigation under different scenarios. Four models (BASGRA, DNDC, Coup and ECOSSE) will be further developed according to the soil properties, and then used independently in simulating biogeochemical processes and biomass dynamics in the different land uses. Robust parameterization schemes for each model will be based in the observational data from the project for both soil and crop combinations. Eventually, a multi-model ensemble prediction will be carried out to provide scenario analyses by 2030 and 2050. By integrating experimental results and modelling, the project aims at generating useful information for recommendations on environment-friendly use of Norwegian peatlands.
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Authors
Jana Spulerova Alexandra Kruse Paola Branduini Csaba Centeri Sebastian Eiter Viviana Ferrario Bénédicte Gaillard Fausto Gusmeroli Suzan Jurgens Drago Kladnik Hans Renes Michael Roth Giovanni Sala Hanne Sickel Maurizia Sigura Dagmar Štefunková Kari Stensgaard Peter Strasser Cosmin Marius Ivascu Kinga ÖllererAbstract
Hay-making structures are part of the agricultural landscape of meadows and pastures. Hay meadows are still used and found all over Europe, but their distribution patterns as well as their characteristics and regional features depend on geographical area, climate, culture, and intensity of agriculture. Intensively used hay meadows are the most dominant, using heavy machinery to store hay mostly as rounded or square bales. Traditional hay-making structures represent structures or constructions, used to quickly dry freshly cut fodder and to protect it from humidity. The ‘ancient’ forms of traditional hay-making structures are becoming a relic, due to mechanisation and the use of new technologies. Both the need for drying hay and the traditional methods for doing so were similar across Europe. Our study of hay-making structures focuses on their current state, their development and history, current use and cultural values in various European countries. Regarding the construction and use of hay-making structures, we have distinguished three different types, which correlate to natural and regional conditions: (1) temporary hay racks of various shapes; (2) hay barracks, a special type of shelters for storing hay and (3) different types of permanent construction and buildings for drying and storing hay. Hay-making structures have been mostly preserved in connection with traditional agricultural landscapes, and particularly in the more remote regions or where associated with strong cultural identity.
Abstract
Repeat photography is an efficient method for documenting long-term landscape changes. So far, the usage of repeat photographs for quantitative analyses is limited to approaches based on manual classification. In this paper, we demonstrate the application of a convolutional neural network (CNN) for the automatic detection and classification of woody regrowth vegetation in repeat landscape photographs. We also tested if the classification results based on the automatic approach can be used for quantifying changes in woody vegetation cover between image pairs. The CNN was trained with 50 × 50 pixel tiles of woody vegetation and non-woody vegetation. We then tested the classifier on 17 pairs of repeat photographs to assess the model performance on unseen data. Results show that the CNN performed well in differentiating woody vegetation from non-woody vegetation (accuracy = 87.7%), but accuracy varied strongly between individual images. The very similar appearance of woody vegetation and herbaceous species in photographs made this a much more challenging task compared to the classification of vegetation as a single class (accuracy = 95.2%). In this regard, image quality was identified as one important factor influencing classification accuracy. Although the automatic classification provided good individual results on most of the 34 test photographs, change statistics based on the automatic approach deviated from actual changes. Nevertheless, the automatic approach was capable of identifying clear trends in increasing or decreasing woody vegetation in repeat photographs. Generally, the use of repeat photography in landscape monitoring represents a significant added value to other quantitative data retrieved from remote sensing and field measurements. Moreover, these photographs are able to raise awareness on landscape change among policy makers and public as well as they provide clear feedback on the effects of land management.
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