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
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.
Authors
Daniel Moreno-Fernández Johannes Breidenbach Isabel Cañellas Gherardo Chirici Giovanni D’amico Marco Ferretti Francesca Giannetti Stefano Puliti Sebastian Schnell Ross Shackleton Mitja Skudnik Iciar AlberdiAbstract
Forest biodiversity is a multifaceted term encompassing tree and shrub diversity and the diversity of other life forms such as animals or fungi. Extensive forest monitoring networks such as National Forest Inventories or the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forest plots have implemented biodiversity-monitoring protocols to satisfy increasing information demands. However, these protocols often evaluate biodiversity through potential biodiversity indicators (e.g., stand structure and deadwood), which may not provide sufficient information on other aspects of the current forest biodiversity status. In this study, we present the forest biodiversity monitoring results and lessons from a cross-country study to support large-scale monitoring systems. We developed, evaluated, and discussed harmonized protocols, mainly focused on birds and mammals, which extend beyond the traditional features captured in large-scale forest inventories. We leverage information from 30 intensively monitored plots established in six European countries to achieve these goals. The protocols were helpful in recording data that could be used to reproduce biodiversity-related attributes such as measures of forest structure, regeneration, deadwood features, and bird and mammal diversity. Specifically, field data on trees was used to describe structural features of forests such as stand composition and forest complexity. In contrast, composition and regeneration data provided helpful information for other biodiversity indicators. Data gathering to monitor bird and mammal diversity requires revisiting the plots, which involves greater economic investment and human effort. Once the bird and mammal data have been collected, advanced algorithms could facilitate and enhance the efficiency of the analyses. To optimize the monitoring efficiency, we recommend including these two new biodiversity assessments in a subset of extensive survey plots. Furthermore, using standard guidelines for these new assessments across all countries would facilitate the comparison and reporting of statistical data.
Authors
Andreas Hagenbo Lise Dalsgaard Marius Hauglin Stephanie Eisner Line Tau Strand O. Janne KjønaasAbstract
Boreal forest soils are a critical terrestrial carbon (C) reservoir, with soil organic carbon (SOC) stocks playing a key role in global C cycling. In this study, we generated high-resolution (16 m) spatial predictions of SOC stocks in Norwegian forests for three depth intervals: (1) soil surface down to 100 cm depth, (2) forest floor (LFH layer), and (3) 0–30 cm into the mineral soil. Our predictions were based on legacy soil data collected between 1988 and 1992 from a subset (n = 1014) of National Forest Inventory plots. We used boosted regression tree models to generate SOC estimates, incorporating environmental predictors such as land cover, site moisture, climate, and remote sensing data. Based on the resulting maps, we estimate total SOC stocks of 1.57–1.87 Pg C down to 100 cm, with 0.55–0.66 Pg C stored in the LFH layer and 0.68–0.80 Pg C in the upper mineral soil. These correspond to average SOC densities of 15.3, 5.4, and 6.6 kg C m−2, respectively. We compared the predictive performance of these models with another set, supplemented by soil chemistry variables. These models showed higher predictive performance (R2 = 0.65–0.71) than those used for mapping (R2 = 0.44–0.58), suggesting that the mapping models did not fully capture environmental variability influencing SOC stock distributions. Within the spatial predictive models, Sentinel-2 Normalized Difference Vegetation Index, depth to water table, and slope contributed strongly, while soil nitrogen and manganese concentrations had major roles in models incorporating soil chemistry. Prediction uncertainties were related to soil depth, soil types, and geographical regions, and we compared the spatial prediction against external SOC data. The generated maps of this offer a valuable starting point for identifying forest areas in Norway where SOC may be vulnerable to climate warming and management-related disturbances, with implications for soil CO2 emissions.
Abstract
No abstract has been registered
Abstract
1. Field-based vegetation mapping is important for environmental assessments.Often, the area covered by a species is estimated visually within a reference frame.However, such assessments are prone to observer bias and a large variability. 2. We developed a deep learning pipeline relying on YOLOv8 models to segmentspecies and estimate the percentage cover (%) of Vaccinium myrtillus (blueberry)and Vaccinium vitis-idaea (lingonberry), two key understory species in borealforests. We used 138 nadir and downward-looking images of the forest floorcaptured in correspondence with 50 × 50 cm vegetation sub-plots assessedwithin National Forest Inventory (NFI) plots. First, we trained a bounding-boxframe detection model to crop the image to the same area assessed in the field.Second, we trained an instance segmentation model to classify species. Third,we flattened the class values into a semantic raster and estimated the species-specific cover by pixel counting. 3. We evaluated our method against an independent test set of 156 images andfound a root mean squared error (RMSE) of 8.82% for blueberry and 3.49% forlingonberry and no substantial systematic errors. An additional comparison withocular estimation by various field workers for the same plots showed that themodel estimates were within the range of estimates by field workers 8 out of 9times for blueberry and 7 out of 9 times for lingonberry. 4. The developed method shows promise in reducing observer bias and variabilityin vegetation surveys, thereby improving their consistency while significantlyreducing the time needed for species-specific coverage estimation. This isparticularly beneficial for repeated measurements and monitoring vegetationcover dynamics. However, as the method relies on RGB data, it is limited toestimating the percentage of visible species that are not obscured by others.Expanding the method to include a broader range of cover classes (e.g. grasses,rocks, logs) or species could automate the capture of crucial information