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.
2023
Authors
Peter Zubkov Barry Gardiner Bjørn Egil Kringlebotn Nygaard Sigmund Guttu Svein Solberg Tron Haakon EidAbstract
Forest damage caused by heavy wet snow accumulation in the canopy is the second most important abiotic forest disturbance agent in Nordic conifer stands after wind. The extent and frequency of snow damage in the future climate in the Nordic region is a major uncertainty. Few mechanistic models of snow damage risk to trees exist that could support forest management scenario analysis and decision making. We propose a snow damage risk model consisting of a numerical weather prediction-based snow accumulation model for forest canopies and a mechanistic critical snow load model. Snow damage probability predictions were validated on snow breakage data from the winters of 2016 and 2018 covering 3.5 million individual trees in south-eastern Norway derived from pre- and post-damage aerial laser scanning campaigns. The proposed model demonstrated satisfactory damage and no-damage class separation with an AUC of 0.72 and 0.77 in Norway spruce and Scots pine, respectively, and an F1 score of 0.7 in conifers taller than 10 m that suffered moderate stem breakage. The model achieved a classification accuracy that is comparable to that of statistical models but is simpler and requires fewer inputs.
Abstract
We tested whether windthrow damage to Nordic conifer forest stands could be reliably detected as canopy height decrease between a pre-storm LiDAR (Light Detection and Ranging) digital surface model (DSM) and a photogrammetric DSM derived from a post-storm WorldView-3 stereo pair. The post-storm ground reference data consisted of field and unmanned aerial vehicle (UAV) observations of windthrow combined with no-damage areas collected by visual interpretation of the available very high resolution (VHR) satellite imagery. We trained and tested a thresholding model using canopy height change as the sole predictor. We undertook a two-step accuracy assessment by (1) running k-fold cross-validation on the ground reference dataset and examining the effect of the potential imperfections in the ground reference data, and (2) conducting rigorous accuracy assessment of the classified map of the study area using an extended set of VHR imagery. The thresholding model produced accurate windthrow maps in dense, productive forest stands with a sensitivity of 96%, specificity of 71%, and Matthews correlation coefficient (MCC) over 0.7. However, in sparse and high elevation stands, the classification accuracy was poor. Despite certain collection challenges during the winter months in the Nordic region, we consider VHR stereo satellite imagery to be a viable source of forest canopy height information and sufficiently accurate to map windthrow disturbance in forest stands of high to moderate density.
Abstract
Accurate estimation of site productivity is essential for forest projections and scenario modelling. We present and evaluate models to predict site index (SI) and whether a site is productive (potential total stem volume production ≥ 1 m3·ha−1·year−1) in a wall-to-wall high-resolution (16 m × 16 m) SI map for Norway. We investigate whether remotely sensed data improve predictions. We also study the advantages and disadvantages of using boosted regression trees (BRT), a machine-learning algorithm, to create high-accuracy SI maps. We use climatic and topographical data, soil parent material, a land resource map, and depth to water, together with Sentinel-2 satellite images and airborne laser scanning metrics, as predictor variables. We use the SI observed at more than 10 000 National Forest Inventory (NFI) sample plots throughout Norway to fit BRT models and validate the models using 5822 independent temporary plots from the NFI. We benchmark our results against SI estimates from forest monitoring inventories. We find that the SI from BRT has root mean squared error (RMSE) ranging from 2.3 m (hardwoods) to 3.6 m (spruce) when tested against independent validation data from the NFI temporary plots. These RMSEs are similar or marginally better than an evaluation of SI estimates from operational forest management plans where SI normally stems from manual photo interpretation.
Abstract
No abstract has been registered
Abstract
Questions Observations in permanent forest vegetation plots in Norway and elsewhere indicate that complex changes have taken place over the period 1988–2020. These observations are summarised in the “climate-induced understorey change (CIUC)” hypothesis, i.e. that the understorey vegetation of old-growth boreal forests in Norway undergoes significant long-term changes and that these changes are consistent with the ongoing climate change as an important driver. Seven testable predictions were derived from the CIUC hypothesis. Location Norway. Methods Vegetation has been monitored in a total of 458 permanently marked plots, each 1 m2, in nine old-growth forest sites dominated by Picea abies at intervals of 5–8 years over the 32-year study period. For each of the 52 combinations of site and year, we obtained response variables for the abundance of single species, abundance and species density of taxonomic–ecological species groups and two size classes of cryptogams, and site species richness. All of these variables were subjected to linear regression modelling with site and year as predictors. Results Mean annual temperature, growing-season length and the number of days with precipitation were higher in the study period than in the preceding ca. 30-year period, resulting in increasingly favourable conditions for bryophyte growth. Site species richness decreased by 13% over the 32-year study period. On average, group abundance of vascular plants decreased by 24% (decrease in forbs: 38%). Patterns of group abundance change differed among cryptogam groups: although peat-moss abundance increased by 39%, the abundance of mosses, hepatics and lichens decreased by 13%, 49% and 67%, respectively. Group abundance of small cryptogams decreased by 61%, whereas a 13% increase was found for large cryptogams. Of 61 single species tested for abundance change, a significant decrease was found for 43 species, whereas a significant increase was found only for 6 species. Conclusions The major patterns of change in species richness, group species density and group abundance observed over the 32-year study period accord with most predictions from the CIUC hypothesis and are interpreted as direct and indirect responses to climate change, partly mediated through changes in the population dynamics of microtine rodents. The more favourable climate for bryophyte growth explains the observed increase for a few large bryophyte species, whereas the decrease observed for small mosses and hepatics is interpreted as an indirect amensalistic effect, brought about by shading and burial in mats of larger species and accelerated by reduced fine-scale disturbance by microtine rodents. Indirect effects of a thicker moss mat most likely drive the vascular plant decline although long-term effects of tree-stand dynamics and former logging cannot be completely ruled out. Our results suggest that the ongoing climate change has extensive, cascading effects on boreal forest ecosystems. The importance of long time-series of permanent vegetation plots for detecting and understanding the effects of climate change on boreal forests is emphasised.
Abstract
Information on tree height-growth dynamics is essential for optimizing forest management and wood procurement. Although methods to derive information on height-growth information from multi-temporal laser scanning data already exist, there is no method to derive such information from data acquired at a single point in time. Drone laser scanning data (unmanned aerial vehicles, UAV-LS) allows for the efficient collection of very dense point clouds, creating new opportunities to measure tree and branch architecture. In this study, we examine if it is possible to measure the vertical positions of branch whorls, which correspond to nodes, and thus can in turn be used to trace the height growth of individual trees. We propose a method to measure the vertical positions of whorls based on a single-acquisition of UAV-LS data coupled with deep-learning techniques. First, single-tree point clouds were converted into 2D image projections, and a YOLOv5 (you-only-look-once) convolutional neural network was trained to detect whorls based on a sample of manually annotated images. Second, the trained whorl detector was applied to a set of 39 trees that were destructively sampled after the UAV-LS data acquisition. The detected whorls were then used to estimate tree-, plot- and stand-level height-growth trajectories. The results indicated that 70 per cent (i.e. precision) of the measured whorls were correctly detected and that 63 per cent (i.e. recall) of the detected whorls were true whorls. These results translated into an overall root-mean-squared error and Bias of 8 and −5 cm for the estimated mean annual height increment. The method’s performance was consistent throughout the height of the trees and independent of tree size. As a use case, we demonstrate the possibility of developing a height-age curve, such as those that could be used for forecasting site productivity. Overall, this study provides proof of concept for new methods to analyse dense aerial point clouds based on image-based deep-learning techniques and demonstrates the potential for deriving useful analytics for forest management purposes at operationally-relevant spatial-scales.
Authors
Joyce Machado Nunes Romeiro Jostein Gohli Paal Krokene Tron Haakon Eid Clara Antón FernándezAbstract
Bark beetle (Ips typographus) outbreaks have the potential to damage large areas of spruce-dominated forests in Scandinavia. To define forest management strategies that will minimize the risk of bark beetle attacks, we need robust models that link forest structure and composition to the risk and potential damage of bark beetle attacks. Since data on bark beetle infestation rates and corresponding damages does not exist in Norway, we implement a previously published meta-model for estimating I. typographus damage probability and intensity. Using both current and projected climatic conditions we used the model to estimate damage inflicted by I. typographus in Norwegian spruce stands. The model produces feasible results for most of Norway’s climate and forest conditions, but a revised model tailored to Norway should be fitted to a dataset that includes older stands and lower temperatures. Based on current climate and forest conditions, the model predicts that approximately nine percent of productive forests within Norway’s main spruce-growing region will experience a loss ranging from 1.7 to 11 m3/ha of spruce over a span of five years. However, climate change is predicted to exacerbate the annual damage caused by I. typographus, potentially leading to a doubling of its detrimental effects.
Abstract
No abstract has been registered
Authors
O. Janne Kjønaas Ryan Bright Gro Hylen Teresa Gómez de la Bárcena Jørn-Frode Nordbakken Tonje Økland Sunil Mundra Håvard Kauserud Mette Hansen Morten Simonsen Carlo Leandro AallAbstract
No abstract has been registered
Abstract
No abstract has been registered