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
Authors
Belachew Gizachew ZelekeAbstract
Tropical forests, despite their critical environmental and socio-economic roles, remain highly vulnerable to deforestation, forest degradation, and climate-related disturbances. There is a growing demand for robust and transparent forest monitoring systems, particularly under REDD+, the Paris Agreement’s Enhanced Transparency Framework (ETF), and emerging climate-finance mechanisms. Conventional approaches based on field inventories and traditional remote sensing are often constrained by limited or uneven field data, persistent cloud cover, complex forest conditions, and limited institutional and technical capacity. This review examines how artificial intelligence (AI) and machine learning (ML) are being integrated into remote sensing–based tropical forest monitoring to address these structural constraints. Using a semi-systematic synthesis of peer-reviewed studies, complemented by operational platforms and grey literature, the review assesses AI/ML approaches, remote sensing datasets, and applications relevant to national and large-scale monitoring. Evidence is synthesized across five analytical dimensions: AI/ML model families and workflows, multi-sensor datasets and training resources, operational monitoring platforms, application domains (including deforestation, degradation, and biomass/carbon estimation), and cross-cutting technical, institutional, and governance barriers. The review finds that AI/ML-enabled remote sensing, particularly those combining optical, radar, and LiDAR time series within cloud-based platforms, has substantially improved the automation, scalability, and speed of tropical forest monitoring. However, effective and equitable adoption remains constrained by limitations in training and validation data, dependence on proprietary platforms and data, uneven technical capacity, and unresolved governance and ethical challenges. Emerging solutions, including open and representative training datasets, platform-agnostic processing infrastructures, long-term capacity building, and inclusive data-governance frameworks, are identified as critical enablers of credible and nationally owned AI/ML-enabled forest-monitoring systems. The review highlights that AI/ML can play a transformative role in supporting climate mitigation, biodiversity conservation, and informed decision-making. This potential, however, depends on transparent data governance arrangements, long-term capacity building, and platform-agnostic infrastructures that support national ownership.
Authors
Karen Ane Frøyland Skjennum Jan Mulder Gijsbert Dirk Breedveld Thomas Hartnik Nicolas Estoppey Erlend Grenager SørmoAbstract
The present study investigates the long-term immobilization efficiency of biochar on target per- and polyfluoroalkyl substances (PFAS) and precursors in well-drained soils contaminated by aqueous film-forming foam (AFFF) (Ʃ27PFAS = 1624 ± 276 µg/kg) over 2 years. The total oxidizable precursor (TOP) assay revealed a large precursor reservoir in the soil. Fifteen outdoor field-scale columns were packed with contaminated soil (48 kg) without (control columns, triplicates) and with biochar amendments: Three sewage sludge-based biochars were homogeneously mixed into the soil at a 1% (w/w) dose in triplicate columns. One of the biochars was additionally applied as a barrier at the column base (1% w/w) in a separate set of columns. The best-performing biochar immobilized long-chain PFAS by 91.0 ± 35.0% and short-chain PFAS by 96.7 ± 32.9%, possibly due to a well-developed porosity. Compared to the control columns, the fluctuating PFAS leaching were negligible in columns amended with the best-performing biochar, but the immobilization efficiency of short-chain PFAS decreased after one year (from 97.8% to 74.2%). Applying biochar as a barrier was two times more effective than homogenous mixing, and the effect was most pronounced for long-chain PFAS. Our findings suggest that biochar may immobilize precursors, notably CF3-CF5 precursors, to the same extent or better than their typical target perfluoroalkyl acids transformation products. More research is, however, needed to confirm these trends. Going beyond simple lab experiments, this study suggests that biochar is a promising solution for PFAS remediation and brings the technology closer to field application.
Authors
Ulrika Jansson Asplund Damian Petkovic Karlsen Anne Krag Brysting Rune Halvorsen Håvard Kauserud O. Janne Kjønaas Johan AsplundAbstract
No abstract has been registered
Authors
Ming Yu Sebastian Kepfer-Rojas Yamina Micaela Rosas Teresa Gómez de la Bárcena Inger Kappel Schmidt Per Gundersen Ludovica D'Imperio Carsten W. Mueller Lars VesterdalAbstract
No abstract has been registered
Authors
L. Duncanson P. M. Montesano A. Neuenschwander A. Zarringhalam N. Thomas D. M. Minor M. A. Wulder J. C. White E. Guenther T. Feng V. Leitold S. Hancock J. Armston Stefano Puliti A. I. Mandel S. Shah C. Silva M. Purslow J. Bruening Johannes Breidenbach Erik Næsset Svetlana Saarela N. Hunka J. R. Kellner S. P. Healey D. Schepaschenko J. Wallerman C. S.R. Neigh N. Carvalhais R. DubayahAbstract
No abstract has been registered
Authors
José Luís Trevizan Chiomento Nícolas Alberton Mandelli Ana Júlia Schroeder Ely Matheus Welter Henrique Didó Maria Antônia Lacourt Oliveira Bruna Jirkowski Francisco Wilson Reichert Junior Mateus Possebon Bortoluzzi Rafael Rieder Thomas dos Santos Trentin Anita SønstebyAbstract
No abstract has been registered
Authors
Milica Fotirić Akšić Mirjana Pešić Ilinka Pećinar Mihajlo Jakanovski Danijel Milinčić Aleksandar Kostić Marko Kitanović Uroš Gašić Dragana Dabić Zagorac Dušanka Milojković Opsenica Mekjell MelandAbstract
No abstract has been registered
Authors
Chen‐Jui Yang Tomas Funda Rajiv Chaudhary Zhi‐Qiang Chen Maximiliano Estravis Barcala Harry WuAbstract
Genetic differentiation among populations often varies significantly across the genome due to factors such as selection and recombination, resulting in a heterogeneous genomic landscape. However, variation in low‐differentiation regions—genomic valleys—remains poorly understood. Moreover, most insights into plant genomic landscapes come from flowering plants, while comparable genome‐wide studies in other taxa, such as conifers, remain limited. We analyzed whole‐genome sequencing data from 100 individuals of three pine species— Pinus banksiana , Pinus contorta , and Pinus nigra . We found substantial genome‐wide variation in recombination rates, with intergenic regions exhibiting higher recombination than genic regions, and rates decreasing with increasing distance from genes. Recombination rate was negatively correlated with gene length, driven primarily by intron length, suggesting that long introns in conifers may promote the retention of exceptionally long genes by maintaining low recombination in these regions. Genomic scans further revealed that genomic valleys are maintained through either balancing, background, or parallel selection. Additionally, multiple forms of selection were strongly associated with local recombination rate variation, highlighting the significant role of recombination in shaping patterns of genomic differentiation. Our findings provide new insight into the evolution and maintenance of extremely long genes in conifers. Moreover, the results indicate that allopatric selection in regions of low recombination is a major force structuring genomic variation in these species.
Authors
Radosav Cerović Milica Fotirić Akšić Milena Ðorđević Marko Kitanović Anđelija Obradović Mekjell MelandAbstract
No abstract has been registered
Authors
Rajiv Chaudhary Maximiliano Estravis Barcala Irena Fundova Tomáš Funda Zhi-qiang Chen Harry X. WuAbstract
Background Drought intensity and frequency are increasing under global warming in the boreal forests, and breeding for drought resistance will facilitate adaptation of new planting material to changing climate conditions. We used a tree-ring dataset of 559 individuals to study Scots pine genetic variation and the efficiency of genomic selection of drought-response traits (drought resistance, recovery and resilience), for the first time. From genotyping-by-sequencing (GBS), 31,101 SNPs were generated and used for the study. Results Significant genetic variation was detected for drought-response and other growth, wood-anatomy and wood density traits. Heritability estimates for wood-anatomical traits were higher than those for drought-response and growth traits. Genetic correlations between drought-response and wood-anatomical traits were generally high but mostly nonsignificant. In contrast, drought resistance and recovery showed positive and significant correlations with basal area increment and height. We found that the predictive ability and accuracy for drought-response traits were lower than those for wood-anatomical traits, and were comparable between GBLUP and ABLUP. Greater genetic gain per year can be achieved through genomic selection relative to pedigree-based selection if the generation interval is reduced. Conclusions The positive genetic correlation between drought-response and growth traits will enable simultaneous selection for improved growth and increased drought resistant trees in Scots pine breeding through either pedigreed-based and genomic selection.