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.
2025
Abstract
The objective of this study was to demonstrate how height growth recalculated to periodic site index could be used to monitor and identify climatic drivers for growth variations. We used data from Norway’s National Forest Inventory (NFI), with attention to Norway spruce in the lowlands (<500 m a.s.l.) of southeastern Norway. We recalculated height growth to periodic site index and extracted a time series with annual values. We supplemented this with climatic data, i.e. monthly mean temperature, precipitation and deMartonne aridity index. The results showed that a characteristic two-peaked time series in volume growth in Norway 1994–2020 corresponded well to a time series of periodic site index for Norway spruce in the specific region mentioned above. Statistical analyses showed that for spruce, the periodic site index was higher in cold and moist summers than in warm and dry. Spruce mortality in this region tripled during 2012–22 when June temperature increased considerably, while periodic site index decreased. This corroborates warm and dry weather in June to be a main stress factor for spruce. In conclusion, periodic site index has a potential for being implemented for monitoring site productivity and for identification of climatic drivers.
Abstract
This study compared three data sources for estimating total and assortment timber volumes: airborne laser scanning (ALS), topographic indicators from digital terrain models, and forest stand data from management plans.
Authors
Johan Sjöström Ragni Fjellgaard Mikalsen Marius Hauglin Ellen Synnøve Skilbred Frida vermina Plathner Ana María De Lera Garrido Edvard Aamodt Kemal Sarp ArsavaAbstract
No abstract has been registered
Abstract
The year-to-year variation in the availability of lingonberries (Vaccinium vitis-idaea L.) is a challenge for commercial exploitation. There is also a need to identify the best locations for lingonberry harvesting. Here, we present research that utilized field observations from the Norwegian National Forest Inventory to model and map the association between lingonberry cover and stand characteristics. Additionally, a set of permanent sampling plots were established for annual recording of berry yields in different Norwegian regions, representing variations in slope and forest characteristics. Ultimately, the recorded information on yield from the temporary sample plots were combined with predictions from the cover model, as well as data from remote sensing and climatic data from nearby weather stations (for locations see Figure 1a) to derive: 1) a model for lingonberry yield, and 2) and a yield map covering all forest land in Norway. Variables included in the final berry yield model are main tree species, soil parent material, mean temperature June-August, stand basal area, latitude, slope and distance to coastline (Miina et al., 2024).
Authors
Johannes BreidenbachAbstract
Presentation
Authors
Johannes BreidenbachAbstract
No abstract has been registered
Authors
Maria Åsnes Moan Stefano Puliti Rasmus Astrup Ole Martin Bollandsås Terje Gobakken Maciej Wielgosz Hans Ole Ørka Lennart NoordermeerAbstract
The site index (SI) describes a site’s potential to produce wood volume. Accurate information on SI in young forests is essential for planning thinning operations and projecting future growth and yield. For tree species that form annual branch whorls, information on interwhorl distances along the stem may be used to determine the SI in young forests. Branch whorls, and consequently tree height growth trajectories, can be detected automatically using deep learning on very dense laser scanning data. In the current study, we demonstrate this approach in a case study in a young Norway spruce forest. We trained a pose estimation Convolutional Neural Network and detected branch whorls of 97 dominant trees in 54 plots scanned with mobile laser scanning data. We predicted SI determined from detected branch whorls in three different sections of each tree, selected in the stem height range between 2.5 and 8 m: all whorls, the lowest six whorls, and whorls selected with an automatic selection procedure. We compared the obtained SI to the SI determined from field-measured branch whorls. Obtained values of precision, recall, and F1 score for the branch whorl detection were 0.66, 0.58, and 0.62, respectively. Values of root mean square error and mean differences between reference and predicted SI ranged between 19.8%–20.9% and −3.6%–4.0%, respectively. Although the tested approach showed potential for SI determination in young forests, the obtained errors were large. This was due to detection errors and high sensitivity to small changes in height increment. These issues highlight the need for further research to improve branch whorl detection accuracy and address challenges associated with determining the SI in young forests.
Abstract
No abstract has been registered
Authors
Marte Ragnhild Owren Ingvild Byskov Britta Marian Hoem Julien Jabot Hans H. Kolhus Elise Grieg Jakob Sandven Kathrine Loe Bjønness Trude Melby Bothner Mona Irene Andersen Engedal Eirik Knutsen Lene Skyrudsmoen Berit Storbråten Kristina Vikesund Evan Hart Ana Aza Gry Alfredsen Johannes Breidenbach Lise Dalsgaard Rune Eriksen Katharina Hobrak Christian Wilhelm Mohr Christophe Moni Gunnhild SøgaardEditors
Ingeborg RønningAbstract
Norwegian greenhouse gas emission 1990-2023, report to the UN
Authors
Lennart Noordermeer Terje Gobakken Johannes Breidenbach Rune Eriksen Erik Næsset Hans Ole Ørka Ole Martin BollandsåsAbstract
Accurate field plot data on forest attributes are crucial in area-based forest inventories assisted by airborne laser scanning, providing an essential reference for calibrating predictive models. This study assessed how sample tree selection methods and plot data calculation methods affect the accuracy of field plot values of timber volume, Lorey’s mean height, and dominant height. We used data obtained from 12 420 circular sample plots of 250 m2, measured as part of the Norwegian national forest inventory and 45 local forest management inventories. We applied Monte Carlo simulations by which we tested various numbers of sample trees, methods to select sample trees, and methods to calculate plot-level values from tree-level measurements. Accuracies of plot values were statistically significantly affected by the number of sample trees, sample tree selection method, and calculation method. Obtained values of root mean square error ranged from 5% to 16% relative to the mean observed values, across the factors studied. Accuracy improved with increasing numbers of sample trees for all forest attributes. We obtained greatest accuracies by selecting sample trees with a probability proportional to basal area, and by retaining field-measured heights for sample trees and using heights predicted with a height-diameter model for non-sample trees. This study highlights the importance of appropriate sample tree selection methods and calculation methods in obtaining accurate field plot data in area-based forest inventories.