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
Kushan D. Siriwardhana Dimantha I. Jayaneththi Ruchiru D. Herath Randika K. Makumbura Hemantha Jayasinghe Miyuru Gunathilake Hazi Md. Azamathulla Kiran Tota-Maharaj Upaka RathnayakeAbstract
No abstract has been registered
Authors
Milan Mataruga Branislav Cvjetković Bart De Cuyper Ina Aneva Petar Zhelev Pavel Cudlín Marek Metslaid Ville Kankaanhuhta Catherine Collet Peter Annighöfer Thomas Mathes Tsakaldimi Marianthi Paitaridou Despoina Rakel J. Jónsdóttir Maria Cristina Monteverdi Giovanbattista de Dato Barbara Mariotti Dana Dina Kolevska Jelena Lazarević Inger Sundheim Fløistad Marcin Klisz Wojciech Gil Vasco Paiva Teresa Fonseca Valeriu-Norocel Nicolescu Vladan Popović Jovana Devetaković Ivan Repáč Gregor Božič Hojka Kraigher Enrique Andivia Julio J. Diez Henrik Böhlenius Magnus Löf Nebi Bilir Pedro Villar-SalvadorAbstract
The relationship between the quality of forest seedlings and their outplanting survival and growth has long been recognized. Various attributes have been proposed to measure the quality of planted seedlings in forest regeneration projects, ranging from simple morphological traits to more complex physiological and performance attributes, or a combination thereof. However, the utility and meaning of seedling quality attributes can differ significantly among regions, nursery practices, site planting conditions, species and the establishment purpose. Here, forest scientists compiled information using a common agreed questionnaire to provide a review of current practices, experiences, legislation and standards for seedling quality across 23 European countries. Large differences exist in measuring seedling quality across countries. The control of the origin of seed and vegetative material (genetic component of plant quality), and control of pests and diseases are common practices in all countries. Morphological attributes are widely used and mandatory in most cases. However, physiological attributes are hardly used at the operative level and mainly concentrated to Fennoscandia. Quality control legislation and seedling quality standards are less strict in northern European countries where seedling production is high, and quality control relies more on the agreements between producers and local plant material users. In contrast, quality standards are stricter in Southern Europe, especially in the Mediterranean countries. The control of seedling quality based on plantation and reforestation success is uncommon and depends on the conditions of the planting site, the traditional practices and the financial support provided by each country. Overall, European countries do not apply the “target seedling concept” for seedling production except for seed origin. Seedling production in many countries is still driven by traditional “know-how” and much less by scientific knowledge progress, which is not adequately disseminated and transferred to the end-users. Our review highlights the need for greater harmonization of seedling quality practices across Europe and the increased dissemination of scientific knowledge to improve seedling quality in forest regeneration activities.
Abstract
Inthis study, we introduce Point2Tree, a modular and versatile framework that employs a three-tiered methodology, inclusive of semantic segmentation, instance segmentation, and hyperparameter optimization analysis, designed to process laser point clouds in forestry. The semantic segmentation stage is built upon the Pointnet++ architecture and is primarily tasked with categorizing each point in the point cloud into meaningful groups or ’segments’, specifically in this context, differentiating between diverse tree parts, i.e., vegetation, stems, and coarse woody debris. The category for the ground is also provided. Semantic segmentation achieved an F1-score of 0.92, showing a high level of accuracy in classifying forest elements. In the instance segmentation stage, we further refine this process by identifying each tree as a unique entity. This process, which uses a graph-based approach, yielded an F1-score of approximately 0.6, signifying reasonable performance in delineating individual trees. The third stage involves a hyperparameter optimization analysis, conducted through a Bayesian strategy, which led to performance improvement of the overall framework by around four percentage points. Point2Tree was tested on two datasets, one from a managed boreal coniferous forest in Våler, Norway, with 16 plots chosen to cover a range of forest conditions. The modular design of the framework allows it to handle diverse pointcloud densities and types of terrestrial laser scanning data.
Abstract
No abstract has been registered
Authors
Anne Muola Traci Birge Marjo Helander Suni Mathew Vili Harazinova Kari Saikkonen Benjamin FuchsAbstract
No abstract has been registered
Abstract
The durability against decay organisms is an essential material property for wood in outdoor use. A jack of all trades method for above-ground wood durability testing has been sought for decades, but until now no method has found its way into European standardization. The method of choice shall be applicable for untreated and treated wood—ideally also for wood composites. It shall further be reproducible, objective, fast, easy, and inexpensive. Finally, it shall provide high predictive power. This study was aimed at a review of results and practical experience with the Bundle test method which could serve as a standard procedure for above-ground field tests of wood-based materials. The method allows for water-trapping, creates a moderate moisture-induced decay risk typical for UC 3 situations, and was found applicable for a wide range of wood materials. The method allows for rapid infestation and failure of non-durable reference species within five years in Central Europe. Based on results from Bundle tests with different modifications and performed at different locations, a guideline has been developed. The method is recommended as a suitable tool for determining the durability of various wood-based materials including modified and preservative-treated wood and can provide data for durability classification.
Abstract
Active canopy sensors (ACSs) are great tools for diagnosing crop nitrogen (N) status and grain yield prediction to support precision N management strategies. Different commercial ACSs are available and their performances in crop N status diagnosis and recommendation may vary. The objective of this study was to determine the potential to minimize the differences of two commonly used ACSs (GreenSeeker and Crop Circle ACS-430) in maize (Zea mays L.) N status diagnosis and recommendation with multi-source data fusion and machine learning. The regression model was based on simple regression or machine learning regression including ancillary information of soil properties, weather conditions, and crop management information. Results of simple regression models indicated that Crop Circle ACS-430 with red-edge based vegetation indices performed better than GreenSeeker in estimating N nutrition index (NNI) (R2 = 0.63 vs. 0.50–0.51) and predicting grain yield (R2 = 0.56–0.57 vs. 0.49). The random forest regression (RFR) models using vegetation indices and ancillary data greatly improved the prediction of NNI (R2 = 0.81–0.82) and grain yield (R2 = 0.87–0.89), regardless of the sensor type or the vegetation index used. Using RFR models, moderate degree of accuracy in N status diagnosis was achieved based on either GreenSeeker or Crop Circle ACS-430. In comparison, using simple regression models based on spectral data only, the accuracy was significantly lower. When these two ACSs were used independently, they performed similarly in N fertilizer recommendation (R2 = 0.57–0.60). Hybrid RFR models were established using vegetation indices from both ACSs and ancillary data, which could be used to diagnose maize N status (moderate accuracy) and make side-dress N recommendations (R2 = 0.62–0.67) using any of the two ACSs. It is concluded that the use of multi-source data fusion with machine learning model could improve the accuracy of ACS-based N status diagnosis and recommendation and minimize the performance differences of different active sensors. The results of this research indicated the potential to develop machine learning models using multi-sensor and multi-source data fusion for more universal applications.
Abstract
No abstract has been registered
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No abstract has been registered
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No abstract has been registered