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.
2024
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Svein Solberg Ole Martin Bollandsås Terje Gobakken Erik Næsset Paromita Basak Laura Innice DuncansonAbstract
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Tore SkrøppaAbstract
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Sunil Mundra Dinesh Sanka Loganathachetti Håvard Kauserud Anna Maria Fiore-Donno Tonje Økland Jørn-Frode Nordbakken O. Janne KjønaasAbstract
Large-scale replacements of native birch with spruce have been carried out in Western Norway for economic reasons. This tree species shift potentially affects biotic components such as the eucaryome, consisting of microscopic animals (Metazoa), protists and fungi, which are key players in the functioning of forest ecosystem. The impact on the belowground eukaryome and its interactions with vegetation and soil properties is not well assessed. We examined the impact of replacing native birch with Norway spruce plantations on the eukaryome of the boreal forest floor in Western Norway using 18S rDNA metabarcoding. The tree species shift from birch to spruce had significant impacts on the eukaryome at both taxonomic (Metazoa) and functional categories (phagotrophs, phototrophs, parasites and osmotrophs). The distinct differences in eukaryome communities were related to changes in understorey vegetation biomass and soil chemistry following the tree species shift. This had a negative effect on eukaryome richness, particularly affecting phagotrophs and parasites, while the opposite was observed for osmotroph richness. Our results indicated that the spruce plantations altered the eukaryome communities and their food-web patterns compared to what was found in the native birch forest soil. This information should be taken into consideration in forest management planning.
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Katherine Ann Gredvig Nielsen Magne Nordang Skårn Venche Talgø Martin Pettersson Inger Sundheim Fløistad Gunn Strømeng May Bente Brurberg Arne StensvandAbstract
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2023
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Information on tree height-growth dynamics is essential for optimizing forest management and wood procurement. Although methods to derive information on height-growth information from multi-temporal laser scanning data already exist, there is no method to derive such information from data acquired at a single point in time. Drone laser scanning data (unmanned aerial vehicles, UAV-LS) allows for the efficient collection of very dense point clouds, creating new opportunities to measure tree and branch architecture. In this study, we examine if it is possible to measure the vertical positions of branch whorls, which correspond to nodes, and thus can in turn be used to trace the height growth of individual trees. We propose a method to measure the vertical positions of whorls based on a single-acquisition of UAV-LS data coupled with deep-learning techniques. First, single-tree point clouds were converted into 2D image projections, and a YOLOv5 (you-only-look-once) convolutional neural network was trained to detect whorls based on a sample of manually annotated images. Second, the trained whorl detector was applied to a set of 39 trees that were destructively sampled after the UAV-LS data acquisition. The detected whorls were then used to estimate tree-, plot- and stand-level height-growth trajectories. The results indicated that 70 per cent (i.e. precision) of the measured whorls were correctly detected and that 63 per cent (i.e. recall) of the detected whorls were true whorls. These results translated into an overall root-mean-squared error and Bias of 8 and −5 cm for the estimated mean annual height increment. The method’s performance was consistent throughout the height of the trees and independent of tree size. As a use case, we demonstrate the possibility of developing a height-age curve, such as those that could be used for forecasting site productivity. Overall, this study provides proof of concept for new methods to analyse dense aerial point clouds based on image-based deep-learning techniques and demonstrates the potential for deriving useful analytics for forest management purposes at operationally-relevant spatial-scales.
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