Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2026
Sammendrag
The top‐of‐atmosphere (TOA) albedo controls the amount of solar energy absorbed by Earth and is influenced by the reflectivity of both the atmosphere and surface. With considerable changes in land use over the past few decades it is reasonable to question whether a perturbed surface albedo has influenced TOA albedo over the corresponding period. Here, we identify regions for which surface albedo changes have been the dominant driver of TOA albedo trends from 2001 to 2020 and examine the degree to which this relates to changes in snow cover, surface soil moisture, and vegetation density and greenness. We show that land surface albedo changes have been the dominant driver of TOA albedo trends in 10.0% of the global land area, within which surface albedo decreases have led to increases in absorbed solar radiation of 0.737 ± 4.984 Wm −2 from 2001 to 2020. This corresponds to global change in absorbed solar radiation of 0.019 ± 0.812 Wm −2 , which is equivalent to approximately 7.0% of the radiative forcing from anthropogenic CO 2 emissions from 2011 to 2019 (IPCC, 2021, https://doi.org/10.1017/9781009157896.009 ). Net TOA darkening above tundra and deserts constitutes 38.6% and 21.4%, respectively, to the radiative feedback identified, whereas temperate biomes induced net TOA brightening, corresponding to 22.3%. Collectively, changes in snow cover, vegetation density and greenness, and surface soil moisture drive 68.5% of the surface albedo changes. The importance of surface albedo in explaining TOA albedo trends for parts of the globe highlights the relevance of land surface changes in understanding Earth's energy imbalance.
Sammendrag
Maximizing genetic response to selection while constraining inbreeding is a central challenge in breeding and conservation. Classic optimal contribution selection methods address this by managing average population coancestry. However, this often results in complex, nonlinear optimization problems that cannot be guaranteed to reach a global optimum. Furthermore, many applications require a stricter pairwise constraint to avoid immediate inbreeding in offspring. Here, we present a binary integer linear programming formulation to select an optimal subset of individuals under a strict maximum tolerable pairwise genomic relationship threshold. We construct a binary matrix indicating whether each pair exceeds this threshold. This reformulation transforms the problem from a complex nonlinear program into a binary integer linear program. While this formulation remains NP-hard, the linearity allows modern solvers to efficiently navigate the solution space and, when convergence is achieved within the imposed runtime and tolerance settings, certify global optimality, a key advantage over heuristic approaches. We demonstrate the method using two distinct datasets: a large Norway spruce breeding population and a conservation population of German Black Pied cattle. We explore the trade-offs between the selection response, the relationship threshold, and the maximum number of individuals that can be selected under the threshold. Although large, dense problem instances remain computationally demanding, our results show that typical applications can often be solved to proven global optimality in seconds, whereas denser instances may terminate with a remaining optimality gap. This method is a practical solution for breeders and conservation geneticists to select optimal subsets under a strict relationship threshold, enabling applications from maximizing gain in breeding populations to establishing genetic reserves for endangered species.
Sammendrag
Time and motion studies in forest operations benefit from video-based analysis, but manual annotation is time consuming. This pilot study aims to reduce analysis time by developing a deep-learning framework that classifies dashcam video into four work elements: crane out, cutting and processing, driving, and processing. Using a 3D ResNet-50 (PyTorchVideo) trained on manually annotated clips, the model achieved validation F1 = 0.88 and precision = 0.90, showing that spatiotemporal CNNs can capture rele-vant motion and appearance cues in forest environments. Overfitting indicates that more diverse data and better class balance are needed, but the approach shows clear potential to scale automated work-element monitoring and efficiency analysis.
Forfattere
Arne SteffenremSammendrag
Det er ikke registrert sammendrag
Forfattere
Stefano Puliti Binbin Xiang Maciej Wielgosz Eivind Handegard Nicolas Cattaneo Marta Vergarechea Terje Gobakken Juha Hyyppä Erik Næsset Mikko Vastaranta Tuomas Yrttimaa Rasmus AstrupSammendrag
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.
Sammendrag
Det er ikke registrert sammendrag
Forfattere
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 HyyppäSammendrag
Det er ikke registrert sammendrag
Sammendrag
Ny rapport viser hvordan norsk skog kan bidra til økt karbonopptak, klimatilpasning og bedre naturtilstand samtidig. Differensiert skogbruk, vern av gammel skog og ungskogpleie trekkes fram som viktige tiltak for klima og natur.
Sammendrag
På 1940- og 50-tallet ble det plantet mye ny granskog i Nord-Norge. Denne skogen er nå i ferd med å bli hogstmoden. Kan den brukes?
Forfattere
Marta Vergarechea C Antón-Fernández J.U Jepsen Ole Petter Laksforsmo Vindstad Nicolas Cattaneo J.J Camarero Rasmus AstrupSammendrag
resilience. In Norway, birch species (Betula pendula and Betula pubescens) dominate large areas of boreal forest, yet large-scale patterns of their age distribution and growth dynamics remain poorly quantified. Using increment core data from 2818 trees sampled across the Norwegian National Forest Inventory, spanning five vegetation zones (58–71◦N) and a broad productivity gradient, we analyzed the drivers of birch age structure and growth variation across age classes and historical cohorts. Intermediate-aged trees (35–80 years) dominated most regions, whereas older individuals were scarce, particularly on productive sites, reflecting the combined effects of forest management and the life-history strategy of fast-growing pioneer species. When compared at equivalent biological ages, younger trees consistently showed higher basal area increment (BAI) than older trees, with differences strongest during early development and on productive sites. Cohort analyses showed a pronounced long-term increase in juvenile growth: mean BAI during the first ten years after reaching breast height increased steadily across successive cohorts over the past 150 years. This increase became more pronounced after ~1960 and was consistent across vegetation zones and site productivity classes. Although sampling and survivor bias cannot be fully excluded, the consistency across environmental gradients points to broad-scale changes in early growth dynamics of birch forests in Norway. These results underscore the importance of considering both age structure and cohort-related variation when interpreting forest dynamics and planning future management.