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.
2026
Abstract
Errors in thematically detailed land-cover maps have large consequences for downstream applications. Moreover, simulation-based studies suggest that land-cover classifiers are sensitive to errors in reference data. We (1) quantified the expected error from field interpretation of land-cover types; (2) the sensitivity of classifiers to reference data errors; and (3) the error transferred from reference data to classifiers. Lastly, we (4) recommended strategies to reduce errors. The study area was mapped by 12 field interpreters divided into three equal-sized experience-level groups. The field-based land-cover maps were aggregated to three thematic resolutions and used to train 6804 land-cover classifiers by varying inputs, algorithm, and hyperparameter values. Separately from the first field campaign, four field interpreters classified validation data points, which were used to quantify error for each field interpreter and land-cover classifier, as the proportion of incorrectly classified validation points. We observed (1) generally high and varying levels of interpreter error; (2) a strong relationship between interpreter and classifier error; and (3) a net positive transfer of errors from reference data to classifiers. Because classifier error seems largely driven by interpreter error at the levels commonly observed in thematically detailed land-cover mapping, we (4) recommend strategies to reduce interpreter error before modelling.
Abstract
The continued use of the red seaweed name Eucheuma cottonii in applied research reflects a persistent gap between taxonomic revisions and their adoption in the scientific literature. Although widely reported in food and industrial studies, E. cottonii is an obsolete name now reclassified as Kappaphycopsis cottonii, a species not currently known to be cultivated commercially. Most studies are therefore referring to commonly cultivated carrageenophytes Kappaphycus alvarezii or K. striatus, which may result in misidentification of the biological material. This issue is evident across diverse applications, including food fermentation, bioethanol production, animal nutrition, and biomaterials development, and is particularly apparent in publications originating from Southeast Asia, particularly Indonesia. This suggests that taxonomic inaccuracies may not be consistently recognized during peer review and editorial processes. Given that carrageenan composition and biochemical properties are species-specific, incorrect naming can affect reproducibility, product performance, and process optimization, and may also have implications for regulatory compliance, including food labeling and clean-label claims. This letter outlines the implications of taxonomic inaccuracies and draws attention to the importance of accurate species identification, and the use of taxonomic verification in applied research.
Abstract
Honey can be contaminated by various natural and anthropogenic substances, posing a health risk to consumers. Pyrrolizidine alkaloids (PAs) are naturally toxic compounds many plant species produce to protect against herbivores. Honey may become contaminated if bees collect nectar and pollen from PA-producing plants. Clopyralid is the active ingredient in some herbicides, including Matrigon 72 SG, approved for weed control in oilseed rape in several countries. As a systemic substance, its application before flowering may contaminate nectar, pollen, and honey. In 2023, 30 Norwegian honey samples were tested for the content of PAs and 22 other honey samples for clopyralid. Pyrrolizidine alkaloids were detected in 20 per cent of the samples, but predominantly at low levels (<12 μg kg−1). One sample contained a higher level (27.8 μg kg−1). Clopyralid was detected at levels exceeding the EU Maximum Residue Level (MRL) at the time (0.05 mg kg−1) and the current EU MRL (2024) (0.15 mg kg−1) in seven of 22 honey samples, including five honey samples produced close to clopyralid treated oilseed rape fields, one honey sample collected next to unsprayed fields, and in one sample received from a beekeeper. It was later clarified that beehives in proximity to unsprayed cropping areas with honey with a high clopyralid content also were close to conventional clopyralid-treated oilseed rape fields. The results indicate that a more extensive survey would be appropriate to evaluate whether PAs and clopyralid are a common problem in Norwegian honeybee products.
Authors
Stefano Puliti Binbin Xiang Maciej Wielgosz Eivind Handegard Nicolas Cattaneo Marta Vergarechea Terje Gobakken Juha Hyyppä Erik Næsset Mikko Vastaranta Tuomas Yrttimaa Rasmus AstrupAbstract
Accurately determining the age of individual trees is important for understanding forest dynamics, tree growth, site productivity and describing ecological processes. Traditional methods, such as dendrochronological coring, are invasive, labor-intensive, and costly. This study investigates the use of deep learning (DL) to predict tree age from high-density laser scanning data as a scalable, non-invasive alternative. The dataset includes approximately 1700 tree point clouds from approx. 1 K trees across Norway, Sweden, and Finland, encompassing Norway spruce (Picea abies) and Scots pine (Pinus sylvestris) and a broad range of tree age and developmental stages, from young seedlings (1 year) to old trees (∼350 years). Data were collected using terrestrial, mobile, and high-density airborne laser scanning platforms, enabling the development of sensor-agnostic models. We evaluated multiple modelling approaches, from linear regression to transformer architectures, using both training-from-scratch and fine-tuning strategies. Models fine-tuned starting from pre-trained weights from ForestFormer3D's U-Net as well as the transformer architecture (PointTransformerV3) trained from scratch, proved effective for age regression (RMSE ≤23 years). Although our analysis was limited to two tree species, we demonstrated that a single joint age-estimation model can be successfully trained for both species. We demonstrate that models trained on high-resolution data can generalize to lower-resolution, less costly inputs, provided that data augmentations that mimic reduced resolutions are included during training. This study presents a data-driven framework for estimating tree age without destructive sampling. The findings support the potential for AI-based methods to complement or replace traditional age estimation techniques in forest inventory and monitoring.
Authors
Josef Taher Eric Hyyppä Matti Hyyppä Klaara Salolahti Xiaowei Yu Leena Matikainen Antero Kukko Matti Lehtomäki Harri Kaartinen Sopitta Thurachen Paula Litkey Ville Luoma Markus Holopainen Gefei Kong Hongchao Fan Petri Rönnholm Matti Vaaja Antti Polvivaara Samuli Junttila Mikko Vastaranta Stefano Puliti Rasmus Astrup Joel Kostensalo Mari Myllymäki Maksymilian Kulicki Krzysztof Stereńczak Raul de Paula Pires Ruben Valbuena Juan Pedro Carbonell-Rivera Jesús Torralba Yi Chen Chen Lukas Winiwarter Markus Hollaus Gottfried Mandlburger Narges Takhtkeshha Fabio Remondino Maciej Lisiewicz Bartłomiej Kraszewski Xinlian Liang Jianchang Chen Eero Ahokas Kirsi Karila Eugeniu Vezeteu Petri Manninen Roope Näsi Heikki Hyyti Siiri Pyykkönen Peilun Hu Juha HyyppäAbstract
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Authors
Darius KviklysAbstract
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Darius KviklysAbstract
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Authors
Darius KviklysAbstract
No abstract has been registered
Authors
Darius KviklysAbstract
No abstract has been registered