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.
2023
Authors
Karin Juul Hesselsøe Anne Friederike Borchert Trond Olav Pettersen Atle Beisland Bjarni Hannesson Lars H. Nielsen Atle Revheim Hansen Markus Rehnström Janne Lehto Trygve S. AamlidAbstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Tomáš Peterka Petra Hájková Martin Jiroušek Dirk Hinterlang Milan Chytrý Liene Aunina Judit Deme Melinda Lyons Hallie Seiler Harald Zechmeister Iva Apostolova Carl Beierkuhnlein Melanie Bischof Claudia Biţă-Nicolae Lisa Brancaleoni Renata Ćušterevska Jürgen Dengler Yakiv Didukh Daniel Dítě Lyubov Felbaba-Klushyna Emmanuel Garbolino Renato Gerdol Svitlana Iemelianova Florian Jansen Riikka Juutinen Jasmina Kamberović Jutta Kapfer Barbora Klímová Ilona Knollová Tiina H.M. Kolari Predrag Lazarević Ringa Luostarinen Eva Mikulášková Đorđije Milanović Luca Miserere Jesper Erenskjold Moeslund José A. Molina Aaron Pérez-Haase Alessandro Petraglia Marta Puglisi Eszter Ruprecht Eva Šmerdová Daniel Spitale Marcello Tomaselli Kiril Vassilev Michal HájekAbstract
No abstract has been registered
Authors
Laura Elina JaakolaAbstract
Phenolic compounds constitute one of the most important groups of the bioactive molecules in food plants. These compounds have received attention for their beneficial properties for human health and they also are involved in diverse important roles in plants, including signaling and defense against biotic and abiotic stress factors. Vaccinium berries are one of the richest sources of phenolic compounds of which flavonoid classes of anthocyanins, proanthocyanidins, flavonols in addition to hydroxycinnamic acids are the main phenolics in these species. Besides in berries, phenolic compounds are also present in other parts of the plant. Biosynthesis of flavonoids via the phenylpropanoid pathway is well understood and the key enzymes leading to different intermediates or different flavonoid classes have been characterized in many species including wild and cultivated Vaccinium species. At the molecular level, the biosynthesis is regulated via co-ordinated transcriptional control of the enzymes in the pathway by the interaction with transcription factors of the MYB-bHLH-WD40 (MBW) complex. Upstream regulators of the pathway have also been identified. The biosynthesis is controlled both at the level as well as by the surrounding environmental factors. Plant hormones are the key players in the development and the ripening process of the fruits. Especially abscisic acid (ABA) and methyl jasmonate (MeJA) have been shown to have a key role in the flavonoid metabolism of Vaccinium species. Accumulation of transcriptome, genome and metabolome data are currently increasing our understanding on the complicated regulation networks controlling the metabolism of the phenolic compounds in the Vaccinium species. This offers new tools for selection of the species and cultivars with preferred characteristics, for instance berries with higher health benefit potential or plants with better stress resistance.
Authors
Franziska Mohr Vasco Diogo Julian Helfenstein Niels Debonne Thymios Dimopoulos Wenche Dramstad Maria García-Martín Józef Hernik Felix Herzog Thanasis Kizos Angela Lausch Livia Lehmann Christian Levers Robert Pazur Virginia Ruiz-Aragón Rebecca Swart Claudine Thenail Hege Ulfeng Peter H. Verburg Tim Williams Anita Zarina Matthias BürgiAbstract
Farming in Europe has been the scene of several important socio-economic and environmental developments and crises throughout the last century. Therefore, an understanding of the historical driving forces of farm change helps identifying potentials for navigating future pathways of agricultural development. However, long-term driving forces have so far been studied, e.g. in anecdotal local case studies or in systematic literature reviews, which often lack context dependency. In this study, we bridged local and continental scales by conducting 123 oral history interviews (OHIs) with elderly farmers across 13 study sites in 10 European countries. We applied a driving forces framework to systematically analyse the OHIs. We find that the most prevalent driving forces were the introduction of new technologies, developments in agricultural markets that pushed farmers for farm size enlargement and technological optimisation, agricultural policies, but also cultural aspects such as cooperation and intergenerational arrangements. However, we find considerable heterogeneity in the specific influence of individual driving forces across the study sites, implying that generic assumptions about the dynamics and impacts of European agricultural change drivers hold limited explanatory power on the local scale. Our results suggest that site-specific factors and their historical development will need to be considered when addressing the future of agriculture in Europe in a scientific or policy context.
Authors
Adrian Straker Stefano Puliti Johannes Breidenbach Christopher Kleinn Grant Pearse Rasmus Astrup Paul MagdonAbstract
Fine-grained information on the level of individual trees constitute key components for forest observation enabling forest management practices tackling the effects of climate change and the loss of biodiversity in forest ecosystems. Such information on individual tree crowns (ITC's) can be derived from the application of ITC segmentation approaches, which utilize remotely sensed data. However, many ITC segmentation approaches require prior knowledge about forest characteristics, which is difficult to obtain for parameterization. This can be avoided by the adoption of data-driven, automated workflows based on convolutional neural networks (CNN). To contribute to the advancements of efficient ITC segmentation approaches, we present a novel ITC segmentation approach based on the YOLOv5 CNN. We analyzed the performance of this approach on a comprehensive international unmanned aerial laser scanning (UAV-LS) dataset (ForInstance), which covers a wide range of forest types. The ForInstance dataset consists of 4192 individually annotated trees in high-density point clouds with point densities ranging from 498 to 9529 points m-2 collected across 80 sites. The original dataset was split into 70% for training and validation and 30% for model performance assessment (test data). For the best performing model, we observed a F1-score of 0.74 for ITC segmentation and a tree detection rate (DET %) of 64% in the test data. This model outperformed an ITC segmentation approach, which requires prior knowledge about forest characteristics, by 41% and 33% for F1-score and DET %, respectively. Furthermore, we tested the effects of reduced point densities (498, 50 and 10 points per m-2) on ITC segmentation performance. The YOLO model exhibited promising F1-scores of 0.69 and 0.62 even at point densities of 50 and 10 points m-2, respectively, which were between 27% and 34% better than the ITC approach that requires prior knowledge. Furthermore, the areas of ITC segments resulting from the application of the best performing YOLO model were close to the reference areas (RMSE = 3.19 m-2), suggesting that the YOLO-derived ITC segments can be used to derive information on ITC level.
Authors
Binbin Xiang Torben Peters Theodora Kontogianni Frawa Vetterli Stefano Puliti Rasmus Astrup Konrad SchindlerAbstract
Panoptic segmentation is the combination of semantic and instance segmentation: assign the points in a 3D point cloud to semantic categories and partition them into distinct object instances. It has many obvious applications for outdoor scene understanding, from city mapping to forest management. Existing methods struggle to segment nearby instances of the same semantic category, like adjacent pieces of street furniture or neighbouring trees, which limits their usability for inventory- or management-type applications that rely on object instances. This study explores the steps of the panoptic segmentation pipeline concerned with clustering points into object instances, with the goal to alleviate that bottleneck. We find that a carefully designed clustering strategy, which leverages multiple types of learned point embeddings, significantly improves instance segmentation. Experiments on the NPM3D urban mobile mapping dataset and the FOR-instance forest dataset demonstrate the effectiveness and versatility of the proposed strategy.
Abstract
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.
Authors
Stefano Puliti Grant Pearse Peter Surovy Luke Wallace Markus Hollaus Maciej Wielgosz Rasmus AstrupAbstract
The FOR-instance dataset (available at this https URL) addresses the challenge of accurate individual tree segmentation from laser scanning data, crucial for understanding forest ecosystems and sustainable management. Despite the growing need for detailed tree data, automating segmentation and tracking scientific progress remains difficult. Existing methodologies often overfit small datasets and lack comparability, limiting their applicability. Amid the progress triggered by the emergence of deep learning methodologies, standardized benchmarking assumes paramount importance in these research domains. This data paper introduces a benchmarking dataset for dense airborne laser scanning data, aimed at advancing instance and semantic segmentation techniques and promoting progress in 3D forest scene segmentation. The FOR-instance dataset comprises five curated and ML-ready UAV-based laser scanning data collections from diverse global locations, representing various forest types. The laser scanning data were manually annotated into individual trees (instances) and different semantic classes (e.g. stem, woody branches, live branches, terrain, low vegetation). The dataset is divided into development and test subsets, enabling method advancement and evaluation, with specific guidelines for utilization. It supports instance and semantic segmentation, offering adaptability to deep learning frameworks and diverse segmentation strategies, while the inclusion of diameter at breast height data expands its utility to the measurement of a classic tree variable. In conclusion, the FOR-instance dataset contributes to filling a gap in the 3D forest research, enhancing the development and benchmarking of segmentation algorithms for dense airborne laser scanning data.
Authors
Stefano Puliti Grant Pearse Peter Surovy Luke Wallace Markus Hollaus Maciej Wielgosz Rasmus AstrupAbstract
The challenge of accurately segmenting individual trees from laser scanning data hinders the assessment of crucial tree parameters necessary for effective forest management, impacting many downstream applications. While dense laser scanning offers detailed 3D representations, automating the segmentation of trees and their structures from point clouds remains difficult. The lack of suitable benchmark datasets and reliance on small datasets have limited method development. The emergence of deep learning models exacerbates the need for standardized benchmarks. Addressing these gaps, the FOR-instance data represent a novel benchmarking dataset to enhance forest measurement using dense airborne laser scanning data, aiding researchers in advancing segmentation methods for forested 3D scenes. In this repository, users will find forest laser scanning point clouds from unamnned aerial vehicle (using Riegl sensors) that are manually segmented according to the individual trees (1130 trees) and semantic classes. The point clouds are subdivided into five data collections representing different forests in Norway, the Czech Republic, Austria, New Zealand, and Australia. These data are meant to be used either for developement of new methods (using the dev data) or for testing of exisitng methods (test data). The data splits are provided in the data_split_metadata.csv file. A full description of the FOR-instance data can be found at http://arxiv.org/abs/2309.01279