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
Abstract
This study focuses on advancing individual tree crown (ITC) segmentation in lidar data, developing a sensor- and platform-agnostic deep learning model transferable across a spectrum of dense laser scanning datasets from drone (ULS), to terrestrial (TLS), and mobile (MLS) laser scanning data. In a field where transferability across different data characteristics has been a longstanding challenge, this research marks a step towards versatile, efficient, and comprehensive 3D forest scene analysis. Central to this study is model performance evaluation based on platform type (ULS vs. MLS) and data density. This involved five distinct scenarios, each integrating different combinations of input training data, including ULS, MLS, and their augmented versions through random subsampling, to assess the model's transferability to varying resolutions and efficacy across different canopy layers. The core of the model, inspired by the PointGroup architecture, is a 3D convolutional neural network (CNN) with dedicated prediction heads for semantic and instance segmentation. The model underwent comprehensive validation on publicly available, machine learning-ready point cloud datasets. Additional analyses assessed model adaptability to different resolutions and performance across canopy layers. Our results reveal that point cloud random subsampling is an effective augmentation strategy and improves model performance and transferability. The model trained using the most aggressive augmentation, including point clouds as sparse as 10 points m−2, showed best performance and was found to be transferable to sparse lidar data and boosts detection and segmentation of codominant and dominated trees. Notably, the model showed consistent performance for point clouds with densities >50 points m−2 but exhibited a drop in performance at the sparsest level (10 points m−2), mainly due to increased omission rates. Benchmarking against current state-of-the-art methods revealed boosts of up to 20% in the detection rates, indicating the model's superior performance on multiple open benchmark datasets. Further, our experiments also set new performance baselines for the other public datasets. The comparison highlights the model's superior segmentation skill, mainly due to better detection and segmentation of understory trees below the canopy, with reduced computational demands compared to other recent methods. In conclusion, the present study demonstrates that it is indeed feasible to train a sensor-agnostic model that can handle diverse laser scanning data, going beyond current sensor-specific methodologies. Further, our study sets a new baseline for tree segmentation, especially in complex forest structures. By advancing the state-of-the-art in forest lidar analysis, our work also lays the foundation for future innovations in ecological modeling and forest management.
Authors
Nina SvartedalAbstract
No abstract has been registered
Abstract
Cultivation of microalgae has gained significant interest as an alternative protein source, potentially becoming a target commodity recovered from microalgae-based wastewater treatment. This study examined a semi-continuous cultivation strategy to optimize protein accumulation of the indigenous freshwater chlorophytes, Lobochlamys segnis and Klebsormidium flaccidum, and simultaneously remove nutrients from wastewater efficiently. A strain-specific regime was made based on a fixed biomass concentration at the start of 24-h cultivation cycle, i.e., a constant initial cell density, which regulated harvesting and fresh medium supply volume according to the dilution rate. Six cultivation cycles were conducted in lab-scale 1L reactors with a synthetic municipal wastewater. Lobochlamys segnis and K. flaccidum grew exponentially in all cycles. The biomass productivity was 573 and 580 mg L–1 day–1, in which the total protein consisted of 62 and 45% of dry cell weight (dw), respectively. When a culture medium deficient in nitrogen and phosphorus was used, protein level was significantly reduced. L. segnis consumed all NH4+ and PO43– supplied by the medium replacement, giving the removal rate of 9.2 and 5.2 mg L–1 day–1. Whereas K. flaccidum removed 13.8 mg L–1 day–1 NH4+ without completing PO43– removal. The amino acid profile of both strains was characterized by glutamic acids content (4–5% dw). We concluded that the designed cultivation regime would support a constant biomass production with stable and high protein content, along with an efficient removal of nutrient from the wastewater.
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No abstract has been registered
Abstract
Semi-natural hay meadows are among the most species-rich habitats in Norway as well as in Europe. To maintain the biodiversity of hay meadows, it is important to understand local management regimes and the land use history that has shaped them and their biodiversity. There is however a general erosion of Traditional Ecological Knowledge (TEK), related to hay meadows and other semi-natural habitats. This review aims to examine historical and written sources of land use practices related to hay meadows and to discuss the implications of a re-introduction of TEK in present and future management practices. Traditional land use practices and TEK obtained from written sources from four Norwegian regions and for the country as a whole are compared with present management practices. Written sources show that hay meadows have been managed in a complex but flexible way. Today's management regimes of hay meadows in Norway are streamlined and strongly simplified, most often involving only one late mowing and in some cases grazing. This simplification may result in loss of biodiversity. The potential to include more variety of management practices in hay meadows, by utilizing knowledge from written sources more systematically in combination with farmers’ experienced knowledge (TEK) should be better utilized. Such an approach may secure both the biodiversity in hay meadows and TEK for the future. Former and present landscape ecological contexts in the infield-outlying land system show that management should be done for larger landscapes rather than small, isolated hay meadows, to optimize biodiversity conservation. For this study, we conducted a Norwegian literature review, based on ethnographical and ethnobotanical sources, as well as historical and present agricultural statistics, historical maps, results from research projects, and other sources. Our findings are discussed with similar European studies focusing on the historical management of hay meadows.
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Authors
Erik J. JonerAbstract
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No abstract has been registered